@article{wu_ai_2024, title = {{AI} {Governance} in {Higher} {Education}: {Case} {Studies} of {Guidance} at {Big} {Ten} {Universities}}, volume = {16}, copyright = {http://creativecommons.org/licenses/by/3.0/}, issn = {1999-5903}, shorttitle = {{AI} {Governance} in {Higher} {Education}}, url = {https://www.mdpi.com/1999-5903/16/10/354}, doi = {10.3390/fi16100354}, abstract = {Generative AI has drawn significant attention from stakeholders in higher education. As it introduces new opportunities for personalized learning and tutoring support, it simultaneously poses challenges to academic integrity and leads to ethical issues. Consequently, governing responsible AI usage within higher education institutions (HEIs) becomes increasingly important. Leading universities have already published guidelines on Generative AI, with most attempting to embrace this technology responsibly. This study provides a new perspective by focusing on strategies for responsible AI governance as demonstrated in these guidelines. Through a case study of 14 prestigious universities in the United States, we identified the multi-unit governance of AI, the role-specific governance of AI, and the academic characteristics of AI governance from their AI guidelines. The strengths and potential limitations of these strategies and characteristics are discussed. The findings offer practical implications for guiding responsible AI usage in HEIs and beyond.}, language = {en}, number = {10}, urldate = {2025-12-06}, journal = {Future Internet}, publisher = {Multidisciplinary Digital Publishing Institute}, author = {Wu, Chuhao and Zhang, He and Carroll, John M.}, month = oct, year = {2024}, keywords = {AI policy, Generative AI, Artificial Intelligence, higher education}, pages = {354}, file = {Full Text PDF:C\:\\Users\\xh21556\\Zotero\\storage\\KJUDYADD\\Wu et al. - 2024 - AI Governance in Higher Education Case Studies of Guidance at Big Ten Universities.pdf:application/pdf}, } @article{atkinson-toal_generative_2024, title = {Generative {Artificial} {Intelligence} ({AI}) {Education} {Policies} of {UK} {Universities}}, volume = {2}, issn = {2755-9475}, url = {https://enhancingtandlinhe.org/index.php/1/article/view/20}, doi = {10.62512/etlhe.20}, abstract = {Generative artificial intelligence (AI) technologies are becoming integral to academic and professional landscapes, with universities rapidly developing policies that govern ethical and effective usage. Yet such efforts are fragmented across institutions, from outright blanket bans to bespoke frameworks supporting AI application. Seeking to offer evidence of this fragmented approach, this study conducts a systematic content analysis of AI policies of UK Russell Group universities, with specific focus on learning and teaching. The analysis reveals differences in policy comprehensiveness, enforcement mechanisms, and educational initiatives, demonstrating varied institutional priorities and approaches. This includes widespread methods of integrating the technology within the learning experience or academic integrity governance strategies. Findings also indicate that while some universities have robust frameworks promoting AI literacy and ethical usage, others provide minimal guidelines, reflecting disparate levels of readiness and commitment to integrating AI into the curriculum. This study underscores the importance of clear, comprehensive policies in fostering equal access and ethical use of AI among students whilst supporting AI literacy. Recommendations include adopting uniform policy elements across institutions to standardise AI usage norms and enhance student preparedness for an AI-driven future. This research contributes to the discourse on educational policy development, emphasising the need for adaptive and forward-thinking strategies in higher education to address AI learning requirements.}, language = {en}, urldate = {2025-12-06}, journal = {Enhancing Teaching and Learning in Higher Education}, author = {Atkinson-Toal, Aarron and Guo, Catherine}, month = nov, year = {2024}, keywords = {AI policy, AI in education, AI literacy, Curriculum development, Educational inequality, Higher education, Teaching innovation}, pages = {70--94}, file = {Full Text PDF:C\:\\Users\\xh21556\\Zotero\\storage\\TAGGJIJ9\\Atkinson-Toal and Guo - 2024 - Generative Artificial Intelligence (AI) Education Policies of UK Universities.pdf:application/pdf}, } @article{jiang_exploring_2025, title = {Exploring the {Effectiveness} of {Institutional} {Policies} and {Regulations} for {Generative} {AI} {Usage} in {Higher} {Education}}, volume = {79}, copyright = {© 2025 The Author(s). Higher Education Quarterly published by John Wiley \& Sons Ltd.}, issn = {1468-2273}, url = {https://onlinelibrary.wiley.com/doi/abs/10.1111/hequ.70054}, doi = {10.1111/hequ.70054}, abstract = {As generative artificial intelligence (Gen AI) becomes more integrated into higher education, institutions struggle to communicate clear and effective policies. This multiple-methods study examines survey and interview data collected with 124 undergraduates and seven faculty members from two U.S. R1 universities regarding their perceptions of institutional AI policies, supportive resources and challenges. Students reported that while classroom-level policies are recognised, institutional guidelines remain unclear. Similarly, faculty highlighted the implementation challenges they face due to practical constraints and insufficient training. Additional findings show that students aware of AI policies are less likely to use AI for writing and research; disciplinary and gender differences in institutional resources and policy awareness. Faculty interviews revealed themes such as AI use in research and varied colleague attitudes. Although this study is limited by the number of institutions and sample size, it highlights the need for ongoing research to support policy refinement.}, language = {en}, number = {4}, urldate = {2025-12-06}, journal = {Higher Education Quarterly}, author = {Jiang, Yingying and Xie, Lindai and Cao, Xiaoyu}, year = {2025}, note = {\_eprint: https://onlinelibrary.wiley.com/doi/pdf/10.1111/hequ.70054}, keywords = {AI policy, higher education, AI, faculty perception, student perception}, pages = {e70054}, annote = {e70054 HEQU-Feb-25-0108}, file = {Full Text PDF:C\:\\Users\\xh21556\\Zotero\\storage\\SS2EBT87\\Jiang et al. - 2025 - Exploring the Effectiveness of Institutional Policies and Regulations for Generative AI Usage in Hig.pdf:application/pdf;Snapshot:C\:\\Users\\xh21556\\Zotero\\storage\\KFBCU6QH\\hequ.html:text/html}, } @article{barus_shaping_2025, title = {Shaping generative {AI} governance in higher education: {Insights} from student perception}, volume = {8}, issn = {2666-3740}, shorttitle = {Shaping generative {AI} governance in higher education}, url = {https://www.sciencedirect.com/science/article/pii/S2666374025000184}, doi = {10.1016/j.ijedro.2025.100452}, abstract = {This study explores student perspectives on generative AI governance in higher education to ensure responsible and ethical AI integration. Employing a mixed-methods approach with an explanatory sequential design, the study gathered data from 111 undergraduate students at Universitas Pelita Harapan through an online survey and semi-structured interviews with 53 students. The study covered key aspects of AI governance, such as ethics, curriculum integration, misuse detection, and academic sanctions, which are crucial for ensuring responsible AI implementation. The findings reveal that students are highly aware of the potential benefits of Generative AI and support its integration into the curriculum. However, they also emphasize the need for clear guidelines, ethics training, and plagiarism detection mechanisms to prevent misuse and uphold academic integrity. These findings provide valuable insights for higher education institutions to develop a comprehensive AI governance framework that balances the potential of Generative AI with the ethical considerations of its use.}, urldate = {2025-12-06}, journal = {International Journal of Educational Research Open}, author = {Barus, Okky Putra and Hidayanto, Achmad Nizar and Handri, Eko Yon and Sensuse, Dana Indra and Yaiprasert, Chairote}, month = jun, year = {2025}, keywords = {Ethics, Generative AI, Higher education, Governance, Student perspective}, pages = {100452}, file = {ScienceDirect Snapshot:C\:\\Users\\xh21556\\Zotero\\storage\\5BCEU3TF\\S2666374025000184.html:text/html}, } @article{banh_generative_2023, title = {Generative artificial intelligence}, volume = {33}, doi = {10.1007/s12525-023-00680-1}, abstract = {Recent developments in the field of artificial intelligence (AI) have enabled new paradigms of machine processing, shifting from data-driven, discriminative AI tasks toward sophisticated, creative tasks through generative AI. Leveraging deep generative models, generative AI is capable of producing novel and realistic content across a broad spectrum (e.g., texts, images, or programming code) for various domains based on basic user prompts. In this article, we offer a comprehensive overview of the fundamentals of generative AI with its underpinning concepts and prospects. We provide a conceptual introduction to relevant terms and techniques, outline the inherent properties that constitute generative AI, and elaborate on the potentials and challenges. We underline the necessity for researchers and practitioners to comprehend the distinctive characteristics of generative artificial intelligence in order to harness its potential while mitigating its risks and to contribute to a principal understanding.}, journal = {Electronic Markets}, author = {Banh, Leonardo and Strobel, Gero}, month = dec, year = {2023}, file = {Full Text PDF:C\:\\Users\\xh21556\\Zotero\\storage\\RM7MNSKH\\Banh and Strobel - 2023 - Generative artificial intelligence.pdf:application/pdf}, } @article{kelly_what_2023, title = {What factors contribute to the acceptance of artificial intelligence? {A} systematic review}, volume = {77}, issn = {0736-5853}, shorttitle = {What factors contribute to the acceptance of artificial intelligence?}, url = {https://www.sciencedirect.com/science/article/pii/S0736585322001587}, doi = {10.1016/j.tele.2022.101925}, abstract = {Artificial Intelligence (AI) agents are predicted to infiltrate most industries within the next decade, creating a personal, industrial, and social shift towards the new technology. As a result, there has been a surge of interest and research towards user acceptance of AI technology in recent years. However, the existing research appears dispersed and lacks systematic synthesis, limiting our understanding of user acceptance of AI technologies. To address this gap in the literature, we conducted a systematic review following the Preferred Reporting Items for Systematic Reviews and meta-Analysis guidelines using five databases: EBSCO host, Embase, Inspec (Engineering Village host), Scopus, and Web of Science. Papers were required to focus on both user acceptance and AI technology. Acceptance was defined as the behavioural intention or willingness to use, buy, or try a good or service. A total of 7912 articles were identified in the database search. Sixty articles were included in the review. Most studies (n = 31) did not define AI in their papers, and 38 studies did not define AI for their participants. The extended Technology Acceptance Model (TAM) was the most frequently used theory to assess user acceptance of AI technologies. Perceived usefulness, performance expectancy, attitudes, trust, and effort expectancy significantly and positively predicted behavioural intention, willingness, and use behaviour of AI across multiple industries. However, in some cultural scenarios, it appears that the need for human contact cannot be replicated or replaced by AI, no matter the perceived usefulness or perceived ease of use. Given that most of the methodological approaches present in the literature have relied on self-reported data, further research using naturalistic methods is needed to validate the theoretical model/s that best predict the adoption of AI technologies.}, urldate = {2026-03-30}, journal = {Telematics and Informatics}, author = {Kelly, Sage and Kaye, Sherrie-Anne and Oviedo-Trespalacios, Oscar}, month = feb, year = {2023}, keywords = {Machine learning, AI, Human factors, Psychosocial models, Social robotics, User acceptance}, pages = {101925}, file = {ScienceDirect Snapshot:C\:\\Users\\xh21556\\Zotero\\storage\\SIIW6H82\\S0736585322001587.html:text/html}, } @misc{mioir_ai_2025, title = {The {AI} {Thesis} {Network}: {A} glimpse into gender dynamics of {UK} {STEMM} {PhD} theses}, shorttitle = {The {AI} {Thesis} {Network}}, url = {https://blogs.manchester.ac.uk/mioir/2025/09/19/the-ai-thesis-network-a-glimpse-into-gender-dynamics-of-uk-stemm-phd-theses/}, language = {en-GB}, urldate = {2026-03-30}, journal = {Manchester Institute of Innovation Research blog}, author = {{MIOIR}}, month = sep, year = {2025}, note = {Section: Emerging technologies}, file = {Snapshot:C\:\\Users\\xh21556\\Zotero\\storage\\9NYBQRHH\\the-ai-thesis-network-a-glimpse-into-gender-dynamics-of-uk-stemm-phd-theses.html:text/html}, } @inproceedings{patole_artificial_2024, title = {Artificial {Intelligence} ({AI}) and {Ethical} {Use} of {Data} in {HRD}}, abstract = {S.Patole\_Innovative Conceptual Model for AI and Ethical Use of Data in HRD\_AHRD 2024 Abstract The rapid advancements in artificial intelligence (AI) technologies and computing have important implications on the nature of work and the role of HRD research and practice (Scully-Russ \& Torraco, 2020). As technology converges, HRD is compelled to adapt and evolve, reimagining its methodologies and approaches to address the new paradigms of work. The digital age has ushered in a symbiotic relationship between technology and HRD, where the exploration of these tools has the potential to optimize workflows, necessitating reevaluation of the principles that underlie HRD practices. With the growing use of AI applications in the realm of HRD, the ethical issues pertaining to privacy and confidentiality gain greater significance (McWhorter \& Bennett, 2020). The question remains: how do you keep the employee information safe and how do you maintain the right ethical approach? HRD practitioners must anticipate the changes that advanced technology will bring to the workplace and understand the ethical challenges it poses for both the workforce and the organization. By prioritizing ethics at the core of AI integration, HRD practice can strike a balance between technological advancements and safeguarding employee privacy and rights. This study aims to provide valuable insights that can inform and guide HRD professionals in making informed decisions using ethical (teleological and deontological) theories to prioritize ethical considerations and safeguard the privacy and confidentiality of employee data within the context of AI applications in HRD. Keywords: responsible AI, ethics, digital leadership}, author = {Patole, Sanket and Carpenter, Rob and McWhorter, Rochell}, month = feb, year = {2024}, file = {Full Text PDF:C\:\\Users\\xh21556\\Zotero\\storage\\2FPJ3TBA\\Patole et al. - 2024 - Artificial Intelligence (AI) and Ethical Use of Data in HRD.pdf:application/pdf}, } @article{ulnicane_governance_2025, title = {Governance fix? {Power} and politics in controversies about governing generative {AI}}, volume = {44}, issn = {1449-4035}, shorttitle = {Governance fix?}, url = {https://doi.org/10.1093/polsoc/puae022}, doi = {10.1093/polsoc/puae022}, abstract = {The launch of ChatGPT in late 2022 led to major controversies about the governance of generative artificial intelligence (AI). This article examines the first international governance and policy initiatives dedicated specifically to generative AI: the G7 Hiroshima process, the Organisation for Economic Cooperation and Development reports, and the UK AI Safety Summit. This analysis is informed by policy framing and governance literature, in particular by the work on technology governance and Responsible Innovation. Emerging governance of generative AI exhibits characteristics of polycentric governance, where multiple and overlapping centers of decision-making are in collaborative relationships. However, it is dominated by a limited number of developed countries. The governance of generative AI is mostly framed in terms of the risk management, largely neglecting issues of purpose and direction of innovation, and assigning rather limited roles to the public. We can see a “paradox of generative AI governance” emerging, namely, that while this technology is being widely used by the public, its governance is rather narrow. This article coins the term “governance fix” to capture this rather narrow and technocratic approach to governing generative AI. As an alternative, it suggests embracing the politics of polycentric governance and Responsible Innovation that highlight democratic and participatory co-shaping of technology for social benefit. In the context of the highly unequal distribution of power in generative AI characterized by a high concentration of power in a small number of large tech companies, the government has a special role in reshaping the power imbalances by enabling wide-ranging public participation in the governance of generative AI.}, number = {1}, urldate = {2026-03-30}, journal = {Policy and Society}, author = {Ulnicane, Inga}, month = jan, year = {2025}, pages = {70--84}, file = {Full Text PDF:C\:\\Users\\xh21556\\Zotero\\storage\\TNHG2K2X\\Ulnicane - 2025 - Governance fix Power and politics in controversies about governing generative AI.pdf:application/pdf}, } @misc{ferguson_advancing_2023, title = {Advancing a {Model} of {Students}' {Intentional} {Persistence} in {Machine} {Learning} and {Artificial} {Intelligence}}, url = {https://arxiv.org/abs/2311.10744v1}, abstract = {Machine Learning (ML) and Artificial Intelligence (AI) are powering the applications we use, the decisions we make, and the decisions made about us. We have seen numerous examples of non-equitable outcomes, from facial recognition algorithms to recidivism algorithms, when they are designed without diversity in mind. Thus, we must take action to promote diversity among those in this field. A critical step in this work is understanding why some students who choose to study ML/AI later leave the field. While the persistence of diverse populations has been studied in engineering, there is a lack of research investigating factors that influence persistence in ML/AI. In this work, we present the advancement of a model of intentional persistence in ML/AI by surveying students in ML/AI courses. We examine persistence across demographic groups, such as gender, international student status, student loan status, and visible minority status. We investigate independent variables that distinguish ML/AI from other STEM fields, such as the varying emphasis on non-technical skills, the ambiguous ethical implications of the work, and the highly competitive and lucrative nature of the field. Our findings suggest that short-term intentional persistence is associated with academic enrollment factors such as major and level of study. Long-term intentional persistence is correlated with measures of professional role confidence. Unique to our study, we show that wanting your work to have a positive social benefit is a negative predictor of long-term intentional persistence, and women generally care more about this. We provide recommendations to educators to meaningfully discuss ML/AI ethics in classes and encourage the development of interpersonal skills to help increase diversity in the field.}, language = {en}, urldate = {2026-03-30}, journal = {arXiv.org}, author = {Ferguson, Sharon and Mao, Katherine and Magarian, James and Olechowski, Alison}, month = oct, year = {2023}, file = {Full Text PDF:C\:\\Users\\xh21556\\Zotero\\storage\\6DWEJBSY\\Ferguson et al. - 2023 - Advancing a Model of Students' Intentional Persistence in Machine Learning and Artificial Intelligen.pdf:application/pdf}, } @article{oliveira_exploring_2024, title = {Exploring the {Adoption} {Phenomenon} of {Artificial} {Intelligence} by {Doctoral} {Students} {Within} {Doctoral} {Education}}, volume = {36}, issn = {1939-4225}, url = {https://doi.org/10.1177/19394225241287032}, doi = {10.1177/19394225241287032}, abstract = {The adoption of artificial intelligence (AI) in academia is an emerging field of interest. However, there is scant literature that explores the phenomenon of AI adoption by graduate students in doctoral education. This study employs collaborative autoethnography to explore and better understand the nuances of how doctoral students experience AI technologies within academic pursuits. A critical analysis of data revealed that the collective researcher-participant experiences offered the primary overarching theme of adoption strategy, with four distinct subthemes: adoption fear, adoption resistance, adoption feasibility, and adoption ethics. The findings suggest a balanced approach to AI adoption depends on the development of comprehensive strategies that are informed by a deep understanding of both the technological capabilities and the human factors involved. We urge both doctoral students and educators involved in doctoral programs to think critically about these identified themes. For doctoral students, this analysis offers valuable insights into challenges associated with integrating AI technologies into formal learning environments, potentially enhancing a management strategy for their doctoral studies. Educators tasked with integrating and evaluating AI technologies for doctoral coursework may develop a deeper understanding of the challenges their students may encounter during the adoption process.}, number = {4}, urldate = {2026-03-30}, journal = {New Horizons in Adult Education and Human Resource Development}, publisher = {SAGE Publications}, author = {Oliveira, Joey and Murphy, Tim and Vaughn, Ginger and Elfahim, Salim and Carpenter, Rob E.}, month = dec, year = {2024}, pages = {248--262}, file = {Full Text PDF:C\:\\Users\\xh21556\\Zotero\\storage\\CZVMHEAY\\Oliveira et al. - 2024 - Exploring the Adoption Phenomenon of Artificial Intelligence by Doctoral Students Within Doctoral Ed.pdf:application/pdf}, } @article{kalim_barriers_2025, title = {Barriers to {AI} adoption for women in higher education: a systematic review of the {Asian} context}, volume = {12}, issn = {2196-7091}, shorttitle = {Barriers to {AI} adoption for women in higher education}, url = {https://doi.org/10.1186/s40561-025-00390-5}, doi = {10.1186/s40561-025-00390-5}, abstract = {Artificial Intelligence (AI) is transforming higher education rapidly by enabling personalized learning, enhancing administrative processes, and improving access to educational resources. However, disparities in AI adoption, particularly among women in the Asian context, raise concerns about equity, inclusivity, and access. This disparity could lead to a deficit in AI skills among women, affecting their ability to contribute as effectively as men in the future. Therefore, it is necessary to understand the current state of women's adoption of AI and the barriers they face in Asian higher education. The systematic review has been conducted using PRISMA guidelines. This review paper synthesizes the findings from the studies conducted in various contexts of Asia to present an overall picture of the state of AI adoption among women in Asia. A total of 17 studies were selected for this review, highlighting socio-cultural barriers, lack of trust, technological unawareness, biases in AI algorithms, and inadequate representation of women in AI policy formulation. Besides highlighting these barriers, the results also shed light on recommendations given by earlier studies that facilitate and encourage women to adopt AI in higher education. Based on the Asian perspective, the conclusion proposes specific recommendations for policymakers and practitioners to promote inclusive AI that empowers women in Asia to contribute more effectively to higher education.}, language = {en}, number = {1}, urldate = {2026-03-30}, journal = {Smart Learning Environments}, author = {Kalim, Usama and Kanwar, Asha and Sha, Jiena and Huang, Ronghuai}, month = jun, year = {2025}, keywords = {Higher education, Artificial intelligence(AI), Asian women, Educational technology}, pages = {38}, file = {Full Text PDF:C\:\\Users\\xh21556\\Zotero\\storage\\YAUTL4VP\\Kalim et al. - 2025 - Barriers to AI adoption for women in higher education a systematic review of the Asian context.pdf:application/pdf}, } @techreport{stanford_university_artificial_2023, title = {Artificial {Intelligence} {Index} {Report}: {Introduction} to the {AI} {Index} {Report} 2023 {GP}-003}, url = {https://hai.stanford.edu/ai-index/2023-ai-index-report}, abstract = {The AI Index is an independent initiative at the Stanford Institute for Human-Centered Artificial Intelligence (HAI), led by the AI Index Steering Committee, an interdisciplinary group of experts from across academia and industry.}, language = {en}, urldate = {2026-03-30}, author = {Stanford University}, year = {2023}, file = {Snapshot:C\:\\Users\\xh21556\\Zotero\\storage\\AJ9NICYR\\2023-ai-index-report.html:text/html}, } @article{frachtenberg_underrepresentation_2022, title = {Underrepresentation of women in computer systems research}, volume = {17}, issn = {1932-6203}, url = {https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0266439}, doi = {10.1371/journal.pone.0266439}, abstract = {The gender gap in computer science (CS) research is a well-studied problem, with an estimated ratio of 15\%–30\% women researchers. However, far less is known about gender representation in specific fields within CS. Here, we investigate the gender gap in one large field, computer systems. To this end, we collected data from 72 leading peer-reviewed CS conferences, totalling 6,949 accepted papers and 19,829 unique authors (2,946 women, 16,307 men, the rest unknown). We combined these data with external demographic and bibliometric data to evaluate the ratio of women authors and the factors that might affect this ratio. Our main findings are that women represent only about 10\% of systems researchers, and that this ratio is not associated with various conference factors such as size, prestige, double-blind reviewing, and inclusivity policies. Author research experience also does not significantly affect this ratio, although author country and work sector do. The 10\% ratio of women authors is significantly lower than the 16\% in the rest of CS. Our findings suggest that focusing on inclusivity policies alone cannot address this large gap. Increasing women’s participation in systems research will require addressing the systemic causes of their exclusion, which are even more pronounced in systems than in the rest of CS.}, language = {en}, number = {4}, urldate = {2026-03-30}, journal = {PLOS ONE}, publisher = {Public Library of Science}, author = {Frachtenberg, Eitan and Kaner, Rhody D.}, month = apr, year = {2022}, keywords = {Bibliometrics, Computer and information sciences, Computer engineering, Computing systems, Peer review, Species diversity, Test statistics, United States}, pages = {e0266439}, file = {Full Text PDF:C\:\\Users\\xh21556\\Zotero\\storage\\ILWSIZ7T\\Frachtenberg and Kaner - 2022 - Underrepresentation of women in computer systems research.pdf:application/pdf}, } @article{stathoulopoulos_gender_2019, title = {Gender {Diversity} in {AI} {Research}}, issn = {1556-5068}, url = {https://www.ssrn.com/abstract=3428240}, doi = {10.2139/ssrn.3428240}, abstract = {Lack of gender diversity in the Artificial Intelligence (AI) workforce is raising growing concerns, but the evidence base about this problem has until now been based on statistics about the workforce of large technology companies or submissions to a small number of prestigious conferences.}, language = {en}, urldate = {2026-03-30}, journal = {SSRN Electronic Journal}, author = {Stathoulopoulos, Konstantinos and Mateos-Garcia, Juan C}, year = {2019}, file = {PDF:C\:\\Users\\xh21556\\Zotero\\storage\\LVP7H85Q\\Stathoulopoulos and Mateos-Garcia - 2019 - Gender Diversity in AI Research.pdf:application/pdf}, } @inproceedings{ding_voices_2025, address = {Vienna, Austria}, title = {Voices of {Her}: {Analyzing} {Gender} {Differences} in the {AI} {Publication} {World}}, isbn = {978-1-959429-19-7}, shorttitle = {Voices of {Her}}, url = {https://aclanthology.org/2025.nlp4pi-1.17/}, doi = {10.18653/v1/2025.nlp4pi-1.17}, abstract = {While several previous studies have analyzed gender bias in research, we are still missing a comprehensive analysis of gender differences in the AI community, covering diverse topics and different development trends. Using the AI Scholar dataset of 78K researchers in the field of AI, we identify several gender differences: (1) Although female researchers tend to have fewer overall citations than males, this citation difference does not hold for all academic-age groups; (2) There exist large gender homophily in co-authorship on AI papers; (3) Female first-authored papers show distinct linguistic styles, such as longer text, more positive emotion words, and more catchy titles than male first-authored papers. Our analysis provides a window into the current demographic trends in our AI community, and encourages more gender equality and diversity in the future.}, urldate = {2026-03-30}, booktitle = {Proceedings of the {Fourth} {Workshop} on {NLP} for {Positive} {Impact} ({NLP4PI})}, publisher = {Association for Computational Linguistics}, author = {Ding, Yiwen and Liu, Jiarui and Lyu, Zhiheng and Zhang, Kun and Schölkopf, Bernhard and Jin, Zhijing and Mihalcea, Rada}, editor = {Atwell, Katherine and Biester, Laura and Borah, Angana and Dementieva, Daryna and Ignat, Oana and Kotonya, Neema and Liu, Ziyi and Wan, Ruyuan and Wilson, Steven and Zhao, Jieyu}, month = jul, year = {2025}, pages = {196--214}, file = {Full Text PDF:C\:\\Users\\xh21556\\Zotero\\storage\\PPHY7IKG\\Ding et al. - 2025 - Voices of Her Analyzing Gender Differences in the AI Publication World.pdf:application/pdf}, } @article{young_mind_2023, title = {Mind the gender gap: {Inequalities} in the emergent professions of artificial intelligence ({AI}) and data science}, volume = {38}, copyright = {© 2023 The Authors. New Technology, Work and Employment published by Brian Towers (BRITOW) and John Wiley \& Sons Ltd.}, issn = {1468-005X}, shorttitle = {Mind the gender gap}, url = {https://onlinelibrary.wiley.com/doi/abs/10.1111/ntwe.12278}, doi = {10.1111/ntwe.12278}, abstract = {The emergence of new prestigious professions in data science and artificial intelligence (AI) provide a rare opportunity to explore the gendered dynamics of technical careers as they are being formed. In this paper, we contribute to the literature on gender inequality in digital work by curating and analysing a unique cross-country data set. We use innovative data science methodology to investigate the nature of work and skills in these under-researched fields. Our research finds persistent disparities in jobs, qualifications, seniority, industry, attrition and even self-confidence in these fields. We identify structural inequality in data and AI, with career trajectories of professionals differentiated by gender, reflecting the broader history of computing. Our work is original in illuminating gendering processes within elite high-tech jobs as they are being configured. Paying attention to these nascent fields is crucial if we are to ensure that women take their rightful place at forefront of technological innovation.}, language = {en}, number = {3}, urldate = {2026-03-30}, journal = {New Technology, Work and Employment}, author = {Young, Erin and Wajcman, Judy and Sprejer, Laila}, year = {2023}, note = {\_eprint: https://onlinelibrary.wiley.com/doi/pdf/10.1111/ntwe.12278}, keywords = {artificial intelligence, careers, data science, gender, inequalities, professionalisation}, pages = {391--414}, file = {Full Text PDF:C\:\\Users\\xh21556\\Zotero\\storage\\UHCU36V3\\Young et al. - 2023 - Mind the gender gap Inequalities in the emergent professions of artificial intelligence (AI) and da.pdf:application/pdf;Snapshot:C\:\\Users\\xh21556\\Zotero\\storage\\SBV6YRBE\\ntwe.html:text/html}, } @misc{kuhlman_no_2020, title = {No computation without representation: {Avoiding} data and algorithm biases through diversity}, shorttitle = {No computation without representation}, url = {http://arxiv.org/abs/2002.11836}, doi = {10.48550/arXiv.2002.11836}, abstract = {The emergence and growth of research on issues of ethics in AI, and in particular algorithmic fairness, has roots in an essential observation that structural inequalities in society are reflected in the data used to train predictive models and in the design of objective functions. While research aiming to mitigate these issues is inherently interdisciplinary, the design of unbiased algorithms and fair socio-technical systems are key desired outcomes which depend on practitioners from the fields of data science and computing. However, these computing fields broadly also suffer from the same under-representation issues that are found in the datasets we analyze. This disconnect affects the design of both the desired outcomes and metrics by which we measure success. If the ethical AI research community accepts this, we tacitly endorse the status quo and contradict the goals of non-discrimination and equity which work on algorithmic fairness, accountability, and transparency seeks to address. Therefore, we advocate in this work for diversifying computing as a core priority of the field and our efforts to achieve ethical AI practices. We draw connections between the lack of diversity within academic and professional computing fields and the type and breadth of the biases encountered in datasets, machine learning models, problem formulations, and interpretation of results. Examining the current fairness/ethics in AI literature, we highlight cases where this lack of diverse perspectives has been foundational to the inequity in treatment of underrepresented and protected group data. We also look to other professional communities, such as in law and health, where disparities have been reduced both in the educational diversity of trainees and among professional practices. We use these lessons to develop recommendations that provide concrete steps for the computing community to increase diversity.}, urldate = {2026-03-30}, publisher = {arXiv}, author = {Kuhlman, Caitlin and Jackson, Latifa and Chunara, Rumi}, month = feb, year = {2020}, note = {arXiv:2002.11836 [cs]}, keywords = {Computer Science - Computers and Society}, file = {Preprint PDF:C\:\\Users\\xh21556\\Zotero\\storage\\YC4IQHV5\\Kuhlman et al. - 2020 - No computation without representation Avoiding data and algorithm biases through diversity.pdf:application/pdf;Snapshot:C\:\\Users\\xh21556\\Zotero\\storage\\85UX5TCA\\2002.html:text/html}, } @article{roberts_review_2021, title = {Review of {Racially} {Equitable} {Admissions} {Practices} in {STEM} {Doctoral} {Programs}}, volume = {11}, copyright = {http://creativecommons.org/licenses/by/3.0/}, issn = {2227-7102}, url = {https://www.mdpi.com/2227-7102/11/6/270}, doi = {10.3390/educsci11060270}, abstract = {This study reviews literature on racially equitable admissions practices relevant to graduate programs in STEM. Graduate Record Exam (GRE) scores corr...}, language = {en}, number = {6}, urldate = {2026-03-30}, journal = {Education Sciences}, publisher = {Multidisciplinary Digital Publishing Institute}, author = {Roberts, Sonia F. and Pyfrom, Elana and Hoffman, Jacob A. and Pai, Christopher and Reagan, Erin K. and Light, Alysson E.}, month = may, year = {2021}, keywords = {admissions, composite, equity, GRE, personality, PhD, race, STEM, underrepresented}, file = {Full Text PDF:C\:\\Users\\xh21556\\Zotero\\storage\\53ELJNGF\\Roberts et al. - 2021 - Review of Racially Equitable Admissions Practices in STEM Doctoral Programs.pdf:application/pdf}, } @misc{low_predictors_2024, title = {Predictors and {Socio}-{Demographic} {Disparities} in {STEM} {Degree} {Outcomes}: {A} {UK} {Longitudinal} {Study} using {Hierarchical} {Logistic} {Regression}}, shorttitle = {Predictors and {Socio}-{Demographic} {Disparities} in {STEM} {Degree} {Outcomes}}, url = {http://arxiv.org/abs/2408.05853}, doi = {10.48550/arXiv.2408.05853}, abstract = {Socio-demographic disparities in STEM degree outcomes impact the diversity of the UK's future workforce, particularly in fields essential for innovation and growth. Despite the importance of institution-level, longitudinal analyses in understanding degree awarding gaps, detailed multivariate and hierarchical analyses remain limited within the UK context. This study addresses this gap by using a multivariate binary logistic model with random intercepts for STEM subjects to analyse predictors of first-class degree outcomes using a nine-year dataset (2014 to 2022) from a research-intensive Russell Group university. We find that prior academic attainment, ethnicity, gender, socioeconomic status, disability, age, and course duration are significant predictors of achieving a first-class degree, with Average Marginal Effects calculated to provide insight into probability differences across these groups. Key findings reveal that Black students face a significantly lower likelihood of achieving first-class degrees compared to White students, with an average 16 percent lower probability, while students graduating from 4-year degree programmes have an average 24 percent higher probability of achieving a first-class degree relative to those on 3-year programmes. Although male students received a higher proportion of first-class degrees overall, our multivariate hierarchical model shows higher odds for female students, underscoring the importance of model choice when quantifying awarding gaps. Baseline odds for first-class outcomes rose considerably from 2016, peaking in 2021, indicating possible grade inflation during the COVID-19 pandemic. Interaction effects between socio-demographic variables and graduation year indicate stability in ethnicity, disability, and socioeconomic awarding gaps but reveal a declining advantage for female students over time.}, urldate = {2026-03-30}, publisher = {arXiv}, author = {Low, Andrew M. and Kalender, Z. Yasemin}, month = nov, year = {2024}, note = {arXiv:2408.05853 [physics]}, keywords = {Physics - Physics Education}, annote = {Comment: 23 pages; new longitudinal analysis section added, additional tables, figures, and discussion}, file = {Preprint PDF:C\:\\Users\\xh21556\\Zotero\\storage\\VJ6YUBEQ\\Low and Kalender - 2024 - Predictors and Socio-Demographic Disparities in STEM Degree Outcomes A UK Longitudinal Study using.pdf:application/pdf;Snapshot:C\:\\Users\\xh21556\\Zotero\\storage\\VYHCV55E\\2408.html:text/html}, } @article{shams_ai_2025, title = {{AI} and the quest for diversity and inclusion: a systematic literature review}, volume = {5}, issn = {2730-5961}, shorttitle = {{AI} and the quest for diversity and inclusion}, url = {https://doi.org/10.1007/s43681-023-00362-w}, doi = {10.1007/s43681-023-00362-w}, abstract = {The pervasive presence and wide-ranging variety of artificial intelligence (AI) systems underscore the necessity for inclusivity and diversity in their design and implementation, to effectively address critical issues of fairness, trust, bias, and transparency. However, diversity and inclusion (D\&I) considerations are significantly neglected in AI systems design, development, and deployment. Ignoring D\&I in AI systems can cause digital redlining, discrimination, and algorithmic oppression, leading to AI systems being perceived as untrustworthy and unfair. Therefore, we conducted a systematic literature review (SLR) to identify the challenges and their corresponding solutions (guidelines/ strategies/ approaches/ practices) about D\&I in AI and about the applications of AI for D\&I practices. Through a rigorous search and selection, 48 relevant academic papers published from 2017 to 2022 were identified. By applying open coding on the extracted data from the selected papers, we identified 55 unique challenges and 33 unique solutions in addressing D\&I in AI. We also identified 24 unique challenges and 23 unique solutions for enhancing D\&I practices by AI. The result of our analysis and synthesis of the selected studies contributes to a deeper understanding of diversity and inclusion issues and considerations in the design, development and deployment of the AI ecosystem. The findings would play an important role in enhancing awareness and attracting the attention of researchers and practitioners in their quest to embed D\&I principles and practices in future AI systems. This study also identifies important gaps in the research literature that will inspire future direction for researchers.}, language = {en}, number = {1}, urldate = {2026-03-30}, journal = {AI and Ethics}, author = {Shams, Rifat Ara and Zowghi, Didar and Bano, Muneera}, month = feb, year = {2025}, keywords = {Artificial intelligence, Diversity, Inclusion, Systematic literature review}, pages = {411--438}, file = {Full Text PDF:C\:\\Users\\xh21556\\Zotero\\storage\\PCUWIL8L\\Shams et al. - 2025 - AI and the quest for diversity and inclusion a systematic literature review.pdf:application/pdf}, } @misc{vargas-solar_bridging_2025, title = {Bridging the {Divide}: {Gender}, {Diversity}, and {Inclusion} {Gaps} in {Data} {Science} and {Artificial} {Intelligence} {Across} {Academia} and {Industry} in the majority and minority worlds}, shorttitle = {Bridging the {Divide}}, url = {http://arxiv.org/abs/2511.18558}, doi = {10.48550/arXiv.2511.18558}, abstract = {As Artificial Intelligence (AI) and Data Science (DS) become pervasive, addressing gender disparities and diversity gaps in their workforce is urgent. These rapidly evolving fields have been further impacted by the COVID-19 pandemic, which disproportionately affected women and minorities, exposing deep-seated inequalities. Both academia and industry shape these disciplines, making it essential to map disparities across sectors, occupations, and skill levels. The dominance of men in AI and DS reinforces gender biases in machine learning systems, creating a feedback loop of inequality. This imbalance is a matter of social and economic justice and an ethical challenge, demanding value-driven diversity. Root causes include unequal access to education, disparities in academic programs, limited government investments, and underrepresented communities' perceptions of elite opportunities. This chapter examines the participation of women and minorities in AI and DS, focusing on their representation in both industry and academia. Analyzing the existing dynamics seeks to uncover the collective and individual impacts on the lives of women and minority groups within these fields. Additionally, the chapter aims to propose actionable strategies to promote equity, diversity, and inclusion (DEI), fostering a more representative and supportive environment for all.}, urldate = {2026-03-30}, publisher = {arXiv}, author = {Vargas-Solar, Genoveva}, month = nov, year = {2025}, note = {arXiv:2511.18558 [cs] version: 1}, keywords = {Computer Science - Computers and Society, Computer Science - Databases}, file = {Full Text PDF:C\:\\Users\\xh21556\\Zotero\\storage\\6FQEMGNK\\Vargas-Solar - 2025 - Bridging the Divide Gender, Diversity, and Inclusion Gaps in Data Science and Artificial Intelligen.pdf:application/pdf;Snapshot:C\:\\Users\\xh21556\\Zotero\\storage\\TGM694FQ\\2511.html:text/html}, } @misc{harehdasht_decoding_2025, title = {Decoding the {Gender} {Gap}: {Addressing} {Gender} {Stereotypes} and {Psychological} {Barriers} to {Empower} {Women} in {Technology}}, shorttitle = {Decoding the {Gender} {Gap}}, url = {http://arxiv.org/abs/2509.26332}, doi = {10.5281/zenodo.17226899}, abstract = {Recently, the unequal presence of women compared to men in technology has attracted the attention of researchers and practitioners across multiple fields. It is time to regard this problem as a global crisis that not only limits access to talent but also reduces the diversity of perspectives that shape technological innovation. This article examines the psychological and social barriers that influence this gap, as well as the interventions designed to reduce it. Using a structured review, the findings assemble evidence on the role of early gender stereotypes in the family and school and the continuation of this crisis in educational and career choices, through to the psychological challenges women face in professional settings, such as feelings of self-undervaluation, occupational anxiety, a heightened fear of technology, and structural limitations in educational environments. Special attention is paid to Germany, where the technology gap is particularly evident and where multiple national programs have been implemented to address it. The present review shows that effective solutions require more than anti-discrimination policies: they should include educational practices, organizational reforms, mentoring, and psychological support. The article concludes by outlining practical and research implications and introduces the NEURON project as a pilot interdisciplinary initiative aimed at accelerating current empowerment efforts and developing new programs for women in technology occupations.}, urldate = {2026-03-30}, author = {Harehdasht, Zahra Fakoor and Saki, Raziyeh}, month = sep, year = {2025}, note = {arXiv:2509.26332 [cs]}, keywords = {Computer Science - Computers and Society, Computer Science - Human-Computer Interaction}, file = {Preprint PDF:C\:\\Users\\xh21556\\Zotero\\storage\\ECT9J2YA\\Harehdasht and Saki - 2025 - Decoding the Gender Gap Addressing Gender Stereotypes and Psychological Barriers to Empower Women i.pdf:application/pdf;Snapshot:C\:\\Users\\xh21556\\Zotero\\storage\\5QMCHSMU\\2509.html:text/html}, } @misc{williams_women_2025, title = {Women upskilling or reskilling to an {ICT} career: {A} systematic review of drivers and barriers}, shorttitle = {Women upskilling or reskilling to an {ICT} career}, url = {http://arxiv.org/abs/2510.22508}, doi = {10.48550/arXiv.2510.22508}, abstract = {Demand for technology focused STEM professionals will increase globally over the coming decade, with many countries finding it difficult to meet growing demand. Compounding this are difficulties in attracting and retaining female technology-focused professionals. Research seeking to address this gender imbalance and workforce shortage focuses on increasing participation among school leavers. However, there is a paucity of research around the potential for females to upskill or reskill into an ICT career. As a starting point, this review asks the question: "What potential drivers and barriers have been identified that impact on female intentions or choices to reskill or upskill to a technology focused STEM career". Results indicate dissatisfaction in a first career, combined with positive computing experiences in the workplace can rouse interest in computing professions. Learning of job opportunities, especially from salient referents, is also a key driver. Results indicate women must overcome negative identity and academic beliefs, as well as self-doubt to make the switch. In summary, it is possible to increase and diversify the tech workforce by leveraging women's latent interest in computing. This review provides a roadmap for research to support educational institutions, employers, and women to benefit from upskilling or reskilling opportunities}, urldate = {2026-03-30}, publisher = {arXiv}, author = {Williams, Shondell and Blackmore, Karen and Berretta, Regina and Mansfield, Michelle}, month = oct, year = {2025}, note = {arXiv:2510.22508 [cs]}, keywords = {Computer Science - Computers and Society}, annote = {Comment: 31 pages, 3 figures, 2 tables}, file = {Full Text PDF:C\:\\Users\\xh21556\\Zotero\\storage\\9YI665MT\\Williams et al. - 2025 - Women upskilling or reskilling to an ICT career A systematic review of drivers and barriers.pdf:application/pdf;Snapshot:C\:\\Users\\xh21556\\Zotero\\storage\\N7ZPANF9\\2510.html:text/html}, } @inproceedings{long_what_2020, address = {New York, NY, USA}, series = {{CHI} '20}, title = {What is {AI} {Literacy}? {Competencies} and {Design} {Considerations}}, isbn = {978-1-4503-6708-0}, shorttitle = {What is {AI} {Literacy}?}, url = {https://dl.acm.org/doi/10.1145/3313831.3376727}, doi = {10.1145/3313831.3376727}, abstract = {Artificial intelligence (AI) is becoming increasingly integrated in user-facing technology, but public understanding of these technologies is often limited. There is a need for additional HCI research investigating a) what competencies users need in order to effectively interact with and critically evaluate AI and b) how to design learner-centered AI technologies that foster increased user understanding of AI. This paper takes a step towards realizing both of these goals by providing a concrete definition of AI literacy based on existing research. We synthesize a variety of interdisciplinary literature into a set of core competencies of AI literacy and suggest several design considerations to support AI developers and educators in creating learner-centered AI. These competencies and design considerations are organized in a conceptual framework thematically derived from the literature. This paper's contributions can be used to start a conversation about and guide future research on AI literacy within the HCI community.}, urldate = {2026-03-30}, booktitle = {Proceedings of the 2020 {CHI} {Conference} on {Human} {Factors} in {Computing} {Systems}}, publisher = {Association for Computing Machinery}, author = {Long, Duri and Magerko, Brian}, month = apr, year = {2020}, pages = {1--16}, file = {Full Text PDF:C\:\\Users\\xh21556\\Zotero\\storage\\QZAK3ZNK\\Long and Magerko - 2020 - What is AI Literacy Competencies and Design Considerations.pdf:application/pdf}, } @article{zhang_learning_2025, title = {Learning {About} {AI}: {A} {Systematic} {Review} of {Reviews} on {AI} {Literacy}}, volume = {63}, issn = {0735-6331}, shorttitle = {Learning {About} {AI}}, url = {https://doi.org/10.1177/07356331251342081}, doi = {10.1177/07356331251342081}, abstract = {Given the ubiquity of artificial intelligence (AI), it is essential to empower students to become creators, designers, and producers of AI technologies, rather than limiting them to the role of informed consumers. To achieve this, learners need to be equipped with AI knowledge and concepts and develop AI literacy. Paradoxically, it is largely unclear what AI literacy is and how we should learn and teach it. We address both of these questions through a systematic review of systematic reviews, also known as an umbrella review, to gain a comprehensive understanding of AI literacy. After searching the literature, we critically examine the results of 17 reviews focusing on AI literacy and the teaching and learning of AI concepts. Our analysis revealed several encouraging developments: a general consensus on the definition of AI literacy, the availability of teaching tools and materials that support AI learning without prior programming experience, and effective pedagogical approaches that have shown positive effects on students' understanding and engagement. In addition, we identified several areas needing attention in the field: an interdisciplinary pedagogical approach, integration of ethical considerations in AI education, discussions on AI policy, and standardized, content-validated, reliable assessments across educational levels and cultures.}, language = {EN}, number = {5}, urldate = {2026-03-30}, journal = {Journal of Educational Computing Research}, publisher = {SAGE Publications Inc}, author = {Zhang, Shan and Ganapathy Prasad, Priyadharshini and Schroeder, Noah L.}, month = sep, year = {2025}, pages = {1292--1322}, } @article{chen_exploring_2022, title = {Exploring the factors of students' intention to participate in {AI} software development}, volume = {42}, issn = {0737-8831}, url = {https://doi.org/10.1108/LHT-12-2021-0480}, doi = {10.1108/LHT-12-2021-0480}, abstract = {Although many universities have begun to provide artificial intelligence (AI)-related courses for students, the influence of the course on students' intention to participate in the development of AI-related products/services needs to be verified. In order to explore the factors that influence students' participation in AI services and system development, this study uses self-efficacy, AI literacy, and the theory of planned behaviour (TPB) to investigate students' intention to engage in AI software development.The questionnaire was distributed online to collect university students' responses in central Taiwan. The research model and eleven hypotheses are tested using 151 responses. The testing process adopted SmartPLS 3.3 and SPSS 26 software.AI programming self-efficacy, AI literacy, and course satisfaction directly affected the intention to participate in AI software development. Moreover, course playfulness significantly affected course satisfaction and AI literacy. However, course usefulness positively affected course satisfaction but did not significantly affect AI literacy and AI programming self-efficacy.The model improves our comprehension of the influence of AI literacy and AI programming self-efficacy on the intention. Moreover, the effects of AI course usefulness and playfulness on literacy and self-efficacy were verified. The findings and insights can help design the AI-related course and encourage university students to participate in AI software development. The study concludes with suggestions for course design for AI course instructors or related educators.}, number = {2}, urldate = {2026-03-30}, journal = {Library Hi Tech}, author = {Chen, Shih-Yeh and Su, Yu-Sheng and Ku, Ya-Yuan and Lai, Chin-Feng and Hsiao, Kuo-Lun}, month = jun, year = {2022}, pages = {392--408}, file = {Snapshot:C\:\\Users\\xh21556\\Zotero\\storage\\WBEBF99D\\LHT-12-2021-0480.html:text/html}, } @article{lu_how_2025, title = {How do artificial intelligence literacy constructs work—based on a survey of university non-expert students}, volume = {30}, issn = {1573-7608}, url = {https://doi.org/10.1007/s10639-025-13323-z}, doi = {10.1007/s10639-025-13323-z}, abstract = {Artificial intelligence is increasingly integrated into daily life, and modern educated individuals should have the ability to use AI tools correctly to improve work, study, and life efficiency. In this context, artificial intelligence literacy has been proposed. Due to the lack of consensus on the constructs of artificial intelligence literacy, this study used the scoping review to summarize the AI literacy constructs, including recognize AI, know AI, AI ethics, AI empowerment, AI self-competence and apply AI. In order to further explore the relationship between these six constructs, this study distributed an artificial intelligence literacy questionnaire to 276 non-expert university students (referring to students who have not received formal artificial intelligence education) and used structural equation modeling to verify the hypothesis. Research has found that recognize AI, know AI, AI ethics, AI empowerment, and AI self-competence all have significant positive predictive effects on apply AI. Know AI also has a significant positive predictive effect on AI ethics, AI empowerment, and AI self-competence. AI ethics, AI empowerment, and AI self-competence play a mediating role in the relationship between know AI and apply AI. The findings further improve the constructs exploration of artificial intelligence literacy in current research and provide some inspiration for teaching practice.}, language = {en}, number = {10}, urldate = {2026-03-30}, journal = {Education and Information Technologies}, author = {Lu, Weikang and Lin, Chenghua}, month = jul, year = {2025}, keywords = {Artificial intelligence literacy, Constructs, Non-expert university students, Structural equation modeling}, pages = {13779--13805}, } @article{zhang_modeling_2025, title = {Modeling the relationships between secondary school students’ {AI} learning attitude, {AI} literacy and {AI} career interest}, volume = {30}, issn = {1573-7608}, url = {https://doi.org/10.1007/s10639-025-13715-1}, doi = {10.1007/s10639-025-13715-1}, abstract = {The importance of artificial intelligence (AI) literacy has grown significantly, and there is a rapidly increasing demand for AI professionals. However, AI faces a talent shortage, and the widening gender gap exacerbates this issue. Given the crucial role of secondary schools in nurturing students’ interest in AI and shaping their career paths, this study sought to investigate the link between secondary school students’ attitude toward AI learning, their AI literacy, and their interest in AI careers while examining possible gender differences. We used the AI Learning Attitude Survey, AI Literacy Survey, and AI Career Interest Survey to collect data from 622 secondary school students who were selected with a stratified sampling method. The survey data were then analyzed using structural equation modeling. The findings revealed that: (1) female students demonstrate lower levels of AI learning attitude, AI literacy, and AI career interest compared to their male counterparts, whereas no gender differences were found in the model of learning attitude-AI literacy-career interest; (2) positive AI learning attitude and AI literacy predict higher AI career interest, with learning attitude positively influencing AI literacy; (3) AI literacy significantly mediates the relationship between AI learning attitude and AI career interest. The findings highlight the necessity for K-12 schools to introduce both formal and informal AI education initiatives at an early stage. By fostering inclusive and supportive environments, schools can inspire all students, especially girls, to boost their AI literacy and consider career opportunities in this field. Such efforts will play a vital role in advancing diversity, equity, and inclusion within the AI sector.}, language = {en}, number = {17}, urldate = {2026-03-30}, journal = {Education and Information Technologies}, author = {Zhang, Di and Yang, Hongwu and He, Yanshan and Guo, Weitong}, month = nov, year = {2025}, keywords = {AI literacy, AI career interest, AI education, AI learning attitude, Gender differences}, pages = {25223--25250}, file = {Full Text PDF:C\:\\Users\\xh21556\\Zotero\\storage\\EHARTTHE\\Zhang et al. - 2025 - Modeling the relationships between secondary school students’ AI learning attitude, AI literacy and.pdf:application/pdf}, } @techreport{elhussein_shaping_2024, title = {Shaping the {Future} of {Learning}: {The} {Role} of {AI} in {Education} 4.0}, shorttitle = {Shaping the {Future} of {Learning}}, url = {https://www.weforum.org/publications/shaping-the-future-of-learning-the-role-of-ai-in-education-4-0/}, abstract = {This report explores the potential for artificial intelligence to benefit educators, students and teachers.}, language = {en}, urldate = {2026-03-30}, author = {Elhussein, Genesis and Hasselaar, Elselot and Lutsyshyn, Ostap and Milberg, Tanya and Zahidi, Saadia}, year = {2024}, file = {Snapshot:C\:\\Users\\xh21556\\Zotero\\storage\\LK3KEBW8\\shaping-the-future-of-learning-the-role-of-ai-in-education-4-0.html:text/html}, } @misc{mendigutxia_role_2024, title = {The role of higher education in national artificial intelligence strategies: a comparative policy review - {UNESCO} {Digital} {Library}}, url = {https://unesdoc.unesco.org/ark:/48223/pf0000392047_eng}, urldate = {2026-03-30}, author = {Mendigutxia, Ana and Pedró, Francesc}, year = {2024}, file = {The role of higher education in national artificial intelligence strategies\: a comparative policy review - UNESCO Digital Library:C\:\\Users\\xh21556\\Zotero\\storage\\98533Y8E\\pf0000392047_eng.html:text/html}, } @techreport{the_digital_cooperation_organization_ai-real_2025, title = {{AI}-{REAL} {TOOLKIT} {AI} {READINESS} {TO} {EMPOWERMENT}, {ADOPTION}, {AND} {LEADERSHIP}}, url = {https://dco.org/dco-ai-adoption-playbook/}, abstract = {We’re working towards a world in which every country, business and person has a fair opportunity to prosper in the digital economy.}, language = {en-US}, urldate = {2026-03-30}, author = {The Digital Cooperation Organization}, year = {2025}, file = {Snapshot:C\:\\Users\\xh21556\\Zotero\\storage\\UPV8Y5FP\\dco-ai-adoption-playbook.html:text/html}, } @misc{noauthor_ai_nodate, title = {{AI} {Opportunities} {Action} {Plan}}, url = {https://www.gov.uk/government/publications/ai-opportunities-action-plan/ai-opportunities-action-plan}, language = {en}, urldate = {2026-03-30}, journal = {GOV.UK}, file = {Snapshot:C\:\\Users\\xh21556\\Zotero\\storage\\LU3HFFK7\\ai-opportunities-action-plan.html:text/html}, } @article{duong_ai_2026, title = {{AI} literacy and higher education students’ digital entrepreneurial intention: {A} moderated mediation model of {AI} self-efficacy and digital entrepreneurial self-efficacy}, volume = {40}, issn = {0950-4222}, shorttitle = {{AI} literacy and higher education students’ digital entrepreneurial intention}, url = {https://doi.org/10.1177/09504222251370089}, doi = {10.1177/09504222251370089}, abstract = {As digital entrepreneurship accelerates, aspiring founders increasingly rely on artificial intelligence (AI) to drive innovation. This study examines how AI literacy—defined as the ability to identify, use, and evaluate AI tools—influences digital entrepreneurial intention. Based on Social Cognitive Career Theory (SCCT), we propose a moderated mediation model, where digital entrepreneurial self-efficacy mediates the relationship between AI literacy and entrepreneurial intention, and AI self-efficacy moderates this pathway. Survey data from 1,061 Vietnamese university students were analyzed using the PROCESS macro. Results reveal that AI literacy positively predicts entrepreneurial intention both directly and indirectly via increased self-efficacy. Furthermore, AI self-efficacy strengthens the link between AI literacy and entrepreneurial self-efficacy, amplifying its mediated impact on intention. These findings extend SCCT to the AI-enabled entrepreneurship domain, emphasizing the joint role of competence and confidence. Practical implications highlight the need for entrepreneurship education to integrate AI skill development and foster technological self-belief among students.}, language = {EN}, number = {2}, urldate = {2026-03-30}, journal = {Industry and Higher Education}, publisher = {SAGE Publications Ltd}, author = {Duong, Cong Doanh}, month = apr, year = {2026}, pages = {242--255}, } @article{drydakis_artificial_2024, title = {Artificial intelligence capital and employment prospects}, volume = {76}, issn = {0030-7653}, url = {https://doi.org/10.1093/oep/gpae005}, doi = {10.1093/oep/gpae005}, abstract = {There is limited research assessing how AI knowledge affects employment prospects. The present study defines the term ‘AI capital’ as a vector of knowledge, skills, and capabilities related to AI technologies, which could boost individuals’ productivity, employment, and earnings. Subsequently, the study reports the outcomes of a genuine correspondence test in England. It was found that university graduates with AI capital, obtained through an AI business module, experienced more invitations for job interviews than graduates without AI capital. Moreover, graduates with AI capital were invited to interviews for jobs that offered higher wages than those without AI capital. Furthermore, it was found that large firms exhibited a preference for job applicants with AI capital, resulting in increased interview invitations and opportunities for higher-paying positions. The outcomes hold for both men and women. The study concludes that AI capital might be rewarded in terms of employment prospects, especially in large firms.}, number = {4}, urldate = {2026-03-30}, journal = {Oxford Economic Papers}, author = {Drydakis, Nick}, month = oct, year = {2024}, pages = {901--919}, } @article{drydakis_formation_2025, title = {The formation of {AI} {Capital} in higher education: {Enhancing} students’ academic performance and employment rates}, volume = {9}, issn = {2666-920X}, shorttitle = {The formation of {AI} {Capital} in higher education}, url = {https://www.sciencedirect.com/science/article/pii/S2666920X2500116X}, doi = {10.1016/j.caeai.2025.100476}, abstract = {The study evaluates the effectiveness of a 12-week AI module delivered to non-STEM university students in England, aimed at building students' AI Capital, encompassing AI-related knowledge, skills, and capabilities. An integral part of the process involved the development and validation of the AI Capital of Students scale, used to measure AI Capital before and after the educational intervention. The module was delivered on four occasions to final-year students between 2023 and 2024, with follow-up data collected on students' employment status. The findings indicate that AI learning enhances students' AI Capital across all three dimensions. Moreover, AI Capital is positively associated with academic performance in AI-related coursework. However, disparities persist. Although all demographic groups experienced progress, male students, White students, and those with stronger backgrounds in mathematics and empirical methods achieved higher levels of AI Capital and academic success. Furthermore, enhanced AI Capital is associated with higher employment rates six months after graduation. To provide a theoretical foundation for this pedagogical intervention, the study introduces and validates the AI Learning–Capital–Employment Transition model, which conceptualises the pathway from structured AI education to the development of AI Capital and, in turn, to improved employment outcomes. The model integrates pedagogical, empirical and equity-centred perspectives, offering a practical framework for curriculum design and digital inclusion. The study highlights the importance of targeted interventions, inclusive pedagogy, and the integration of AI across curricula, with support tailored to students’ prior academic experience.}, urldate = {2026-03-30}, journal = {Computers and Education: Artificial Intelligence}, author = {Drydakis, Nick}, month = dec, year = {2025}, keywords = {Artificial intelligence, AI literacy, Academic performance, AI capital, Employment rates, Grades, University students}, pages = {100476}, file = {ScienceDirect Full Text PDF:C\:\\Users\\xh21556\\Zotero\\storage\\QH48YSK7\\Drydakis - 2025 - The formation of AI Capital in higher education Enhancing students’ academic performance and employ.pdf:application/pdf;ScienceDirect Snapshot:C\:\\Users\\xh21556\\Zotero\\storage\\WDILIVI5\\S2666920X2500116X.html:text/html}, } @article{sarraju_leaky_2023, title = {The leaky pipeline of diverse race and ethnicity representation in academic science and technology training in the {United} {States}, 2003–2019}, volume = {18}, issn = {1932-6203}, url = {https://pmc.ncbi.nlm.nih.gov/articles/PMC10132634/}, doi = {10.1371/journal.pone.0284945}, abstract = {Introduction Diverse race and ethnicity representation remains lacking in science and technology (S\&T) careers in the United States (US). Due to systematic barriers across S\&T training stages, there may be sequential loss of diverse representation leading to low representation, often conceptualized as a leaky pipeline. We aimed to quantify the contemporary leaky pipeline of S\&T training in the US. Methods We analyzed US S\&T degree data, stratified by sex and then by race or ethnicity, obtained from survey data the National Science Foundation and the National Center for Science and Engineering Statistics. We assessed changes in race and ethnicity representation in 2019 at two major S\&T transition points: bachelor to doctorate degrees (2003–2019) and doctorate degrees to postdoctoral positions (2010–2019). We quantified representation changes at each point as the ratio of representation in the later stage to earlier stage (representation ratio [RR]). We assessed secular trends in the representation ratio through univariate linear regression. Results For 2019, the survey data included for bachelor degrees, 12,714,921 men and 10.612,879 women; for doctorate degrees 14,259 men and 12,860 women; and for postdoctoral data, 11,361 men and 8.672 women. In 2019, we observed that Black, Asian, and Hispanic women had comparable loss of representation among women in the bachelor to doctorate transition (RR 0.86, 95\% confidence interval [CI] 0.81–0.92; RR 0.85, 95\% CI 0.81–0.89; and RR 0.82, 95\% CI 0.77–0.87, respectively), while among men, Black and Asian men had the greatest loss of representation (Black men RR 0.72, 95\% CI 0.66–0.78; Asian men RR 0.73, 95\% CI 0.70–0.77)]. We observed that Black men (RR 0.60, 95\% CI 0.51–0.69) and Black women (RR 0.56, 95\% CI 0.49–0.63) experienced the greatest loss of representation among men and women, respectively, in the doctorate to postdoctoral transition. Black women had a statistically significant decrease in their representation ratio in the doctorate to postdoctoral transition from 2010 to 2019 (p-trend = 0.02). Conclusion We quantified diverse race and ethnicity representation in contemporary US S\&T training and found that Black men and women experienced the most consistent loss in representation across the S\&T training pipeline. Findings should spur efforts to mitigate the structural racism and systemic barriers underpinning such disparities.}, number = {4}, urldate = {2026-03-30}, journal = {PLOS ONE}, author = {Sarraju, Ashish and Ngo, Summer and Rodriguez, Fatima}, month = apr, year = {2023}, pages = {e0284945}, file = {Full Text PDF:C\:\\Users\\xh21556\\Zotero\\storage\\DBUYPF6Y\\Sarraju et al. - 2023 - The leaky pipeline of diverse race and ethnicity representation in academic science and technology t.pdf:application/pdf}, } @inproceedings{niousha_mapping_2024, title = {Mapping the {Pathways}: {A} {Comparative} {Analysis} of {AI}/{ML}/{DS} {Prerequisite} {Structures} in {R1} {Institutions} in the {United} {States}}, issn = {2377-634X}, shorttitle = {Mapping the {Pathways}}, url = {https://ieeexplore.ieee.org/abstract/document/10893290}, doi = {10.1109/FIE61694.2024.10893290}, abstract = {This Research Full paper focuses on the challenges in artificial intelligence, machine learning, and data science education—referred to as “artificial intelligence” courses here-after-often characterized by extensive prerequisites that limit student access. We analyze the course structures and prerequisites of these courses in computing departments at 50 Research-1 institutions in the United States, recognized for their “Very High Research Activity.” Our methodology involves analyzing course syllabi to examine the structure and prerequisites of these courses, using open coding to develop a unified codebook to identify prerequisites and determine the earliest exposure levels for students. A clustering analysis was also conducted to identify common and differing curriculum approaches among institutions. Results show that data science courses require less initial exposure, while artificial intelligence and machine learning courses require more prerequisites. Standard requirements for artificial intelligence courses include basic data structure (Computer Science 2) and algorithms, with machine learning courses requiring more mathematics preparation. Moreover, public institutions offer advanced courses with more prerequisites compared to private institutions. Overall, this study recognizes considerable diversity in curricular frameworks across Research-1 institutions and encourages institutions to revise curricula to broaden access to artificial intelligence education and increase participation in research.}, urldate = {2026-03-30}, booktitle = {2024 {IEEE} {Frontiers} in {Education} {Conference} ({FIE})}, author = {Niousha, Rose and Ahluwalia, Dev and Wu, Michael and Zhang, Lisa and Norouzi, Narges}, month = oct, year = {2024}, note = {ISSN: 2377-634X}, keywords = {Machine learning, Artificial Intelligence, Academic Retention, Curriculum Design, Data science, Data Science, Data structures, Education, Encoding, Focusing, Machine Learning, Machine learning algorithms, Mathematics, Prerequisites, Programming profession, Standards}, pages = {1--9}, file = {Snapshot:C\:\\Users\\xh21556\\Zotero\\storage\\5Y7M29BQ\\10893290.html:text/html}, } @misc{xia_comparative_2024, title = {Comparative {Analysis} {Vision} of {Worldwide} {AI} {Courses}}, url = {http://arxiv.org/abs/2407.16881}, doi = {10.48550/arXiv.2407.16881}, abstract = {This research investigates the curriculum structures of undergraduate Artificial Intelligence (AI) education across universities worldwide. By examining the curricula of leading universities, the research seeks to contribute to a deeper understanding of AI education on a global scale, facilitating the alignment of educational practices with the evolving needs of the AI landscape. This research delves into the diverse course structures of leading universities, exploring contemporary trends and priorities to reveal the nuanced approaches in AI education. It also investigates the core AI topics and learning contents frequently taught, comparing them with the CS2023 curriculum guidance to identify convergence and divergence. Additionally, it examines how universities across different countries approach AI education, analyzing educational objectives, priorities, potential careers, and methodologies to understand the global landscape and implications of AI pedagogy.}, urldate = {2026-03-30}, publisher = {arXiv}, author = {Xia, Jianing and Li, Man and Li, Jianxin}, month = jun, year = {2024}, note = {arXiv:2407.16881 [cs]}, keywords = {Computer Science - Computers and Society, Computer Science - Artificial Intelligence}, annote = {Comment: 9 pages, 6 figures}, file = {Preprint PDF:C\:\\Users\\xh21556\\Zotero\\storage\\VRARSYTI\\Xia et al. - 2024 - Comparative Analysis Vision of Worldwide AI Courses.pdf:application/pdf;Snapshot:C\:\\Users\\xh21556\\Zotero\\storage\\AXQT8BAP\\2407.html:text/html}, } @article{noauthor_pdf_nodate, title = {({PDF}) {Factors} {Influencing} {University} {Students}' {AI} {Use} and {Knowledge} {Acquisition}}, url = {https://www.researchgate.net/publication/384962802_Factors_Influencing_University_Students'_AI_Use_and_Knowledge_Acquisition}, doi = {10.1002/pra2.1218}, abstract = {PDF {\textbar} Despite the growing emphasis on artificial intelligence (AI) education, there is relatively little research on the motivational factors that... {\textbar} Find, read and cite all the research you need on ResearchGate}, language = {en}, urldate = {2026-03-30}, journal = {ResearchGate}, file = {Snapshot:C\:\\Users\\xh21556\\Zotero\\storage\\NHUCAK6M\\384962802_Factors_Influencing_University_Students'_AI_Use_and_Knowledge_Acquisition.html:text/html}, } @inproceedings{yin_factors_2024, title = {Factors {Influencing} {University} {Students}' {AI} {Use} and {Knowledge} {Acquisition}}, volume = {61}, url = {https://www.researchgate.net/publication/384962802_Factors_Influencing_University_Students'_AI_Use_and_Knowledge_Acquisition}, doi = {10.1002/pra2.1218}, abstract = {PDF {\textbar} Despite the growing emphasis on artificial intelligence (AI) education, there is relatively little research on the motivational factors that... {\textbar} Find, read and cite all the research you need on ResearchGate}, language = {en}, number = {1}, urldate = {2026-03-30}, booktitle = {Proceedings of the {Association} for {Information} {Science} and {Technology}}, author = {Yin, Stella Xin and Goh, Dion}, year = {2024}, pages = {1162--1164}, file = {Snapshot:C\:\\Users\\xh21556\\Zotero\\storage\\E99YAJET\\384962802_Factors_Influencing_University_Students'_AI_Use_and_Knowledge_Acquisition.html:text/html}, } @article{chun_crisis_2023, title = {The {Crisis} of {Artificial} {Intelligence}: {A} {New} {Digital} {Humanities} {Curriculum} for {Human}-{Centred} {AI}}, volume = {17}, issn = {1753-8548}, shorttitle = {The {Crisis} of {Artificial} {Intelligence}}, url = {https://www.euppublishing.com/doi/full/10.3366/ijhac.2023.0310}, doi = {10.3366/ijhac.2023.0310}, abstract = {This article outlines what a successful artificial intelligence digital humanities (AI DH) curriculum entails and why it is so critical now. Artificial intelligence is rapidly reshaping our world a...}, number = {2}, urldate = {2026-03-30}, journal = {International Journal of Humanities and Arts Computing}, publisher = {Edinburgh University Press}, author = {Chun, Jon and Elkins, Katherine}, month = oct, year = {2023}, keywords = {artificial intelligence, AI alignment, AI curriculum, AI ethics, AI safety, computational digital humanities, digital humanities, generative AI, large language models}, pages = {147--167}, file = {EUP Snapshot:C\:\\Users\\xh21556\\Zotero\\storage\\9FGWVSFX\\ijhac.2023.html:text/html}, } @inproceedings{barretto_exploring_2021, address = {New York, NY, USA}, series = {{ITiCSE} '21}, title = {Exploring {Why} {Underrepresented} {Students} {Are} {Less} {Likely} to {Study} {Machine} {Learning} and {Artificial} {Intelligence}}, isbn = {978-1-4503-8214-4}, url = {https://dl.acm.org/doi/10.1145/3430665.3456332}, doi = {10.1145/3430665.3456332}, abstract = {There is little research on why underrepresented minorities are less likely to specifically study Machine Learning and Artificial Intelligence (ML/AI). We surveyed 159 undergraduate students about their interest in, exposure to, and personal views on ML/AI in order to explore variations in responses by self-reported gender and race/ethnicity groups. We found that students underrepresented by race/ethnicity are {\textasciitilde}6 times less likely to take a traditional ML/AI course than those not underrepresented by race/ethnicity, but no significant difference was found between gender representation. Additionally, students underrepresented by race/ethnicity are more likely to report interest in social, cultural, and political impacts of ML/AI rather than the more technical aspects of ML/AI itself, which is a prevalent interest of students not underrepresented by race/ethnicity. We explore potential reasoning for this difference through further analysis of their survey responses. Encouragingly, we find that regardless of representational status 72.0\% of students who report lack of interest in a traditional introductory course are interested in a ML/AI course that focuses more on the political, philosophical, and ethical issues raised by ML/AI and its impacts on society. Our findings suggest that a 'CS Principles" style introductory ML/AI course, emphasizing social and political impacts, could be an effective way to promote diversity in ML/AI.}, urldate = {2026-03-30}, booktitle = {Proceedings of the 26th {ACM} {Conference} on {Innovation} and {Technology} in {Computer} {Science} {Education} {V}. 1}, publisher = {Association for Computing Machinery}, author = {Barretto, Daphne and LaChance, Julienne and Burton, Emanuelle and Liao, Soohyun Nam}, month = jun, year = {2021}, pages = {457--463}, file = {Full Text PDF:C\:\\Users\\xh21556\\Zotero\\storage\\9V9KWFYJ\\Barretto et al. - 2021 - Exploring Why Underrepresented Students Are Less Likely to Study Machine Learning and Artificial Int.pdf:application/pdf}, } @article{beyer_why_2014, title = {Why are women underrepresented in {Computer} {Science}? {Gender} differences in stereotypes, self-efficacy, values, and interests and predictors of future {CS} course-taking and grades}, volume = {24}, issn = {0899-3408}, shorttitle = {Why are women underrepresented in {Computer} {Science}?}, url = {https://doi.org/10.1080/08993408.2014.963363}, doi = {10.1080/08993408.2014.963363}, abstract = {This study addresses why women are underrepresented in Computer Science (CS). Data from 1319 American first-year college students (872 female and 447 male) indicate that gender differences in computer self-efficacy, stereotypes, interests, values, interpersonal orientation, and personality exist. If students had had a positive experience in their first CS course, they had a stronger intention to take another CS course. A subset of 128 students (68 females and 60 males) took a CS course up to one year later. Students who were interested in CS, had high computer self-efficacy, were low in family orientation, low in conscientiousness, and low in openness to experiences were more likely to take CS courses. Furthermore, individuals who were highly conscientious and low in relational-interdependent self-construal earned the highest CS grades. Efforts to improve women’s representation in CS should bear these results in mind.}, number = {2-3}, urldate = {2026-03-30}, journal = {Computer Science Education}, publisher = {Routledge}, author = {Beyer, Sylvia}, month = jul, year = {2014}, note = {\_eprint: https://doi.org/10.1080/08993408.2014.963363}, keywords = {self-efficacy, STEM, recruitment of women, retention of women, stereotypes, underrepresentation of women}, pages = {153--192}, } @article{cheryan_stereotypical_2013, title = {The {Stereotypical} {Computer} {Scientist}: {Gendered} {Media} {Representations} as a {Barrier} to {Inclusion} for {Women}}, volume = {69}, issn = {1573-2762}, shorttitle = {The {Stereotypical} {Computer} {Scientist}}, url = {https://doi.org/10.1007/s11199-013-0296-x}, doi = {10.1007/s11199-013-0296-x}, abstract = {The present research examines undergraduates’ stereotypes of the people in computer science, and whether changing these stereotypes using the media can influence women’s interest in computer science. In Study 1, college students at two U.S. West Coast universities (N = 293) provided descriptions of computer science majors. Coding these descriptions revealed that computer scientists were perceived as having traits that are incompatible with the female gender role, such as lacking interpersonal skills and being singularly focused on computers. In Study 2, college students at two U.S. West Coast universities (N = 54) read fabricated newspaper articles about computer scientists that either described them as fitting the current stereotypes or no longer fitting these stereotypes. Women who read that computer scientists no longer fit the stereotypes expressed more interest in computer science than those who read that computer scientists fit the stereotypes. In contrast, men’s interest in computer science did not differ across articles. Taken together, these studies suggest that stereotypes of academic fields influence who chooses to participate in these fields, and that recruiting efforts to draw more women into computer science would benefit from media efforts that alter how computer scientists are depicted.}, language = {en}, number = {1}, urldate = {2026-03-30}, journal = {Sex Roles}, author = {Cheryan, Sapna and Plaut, Victoria C. and Handron, Caitlin and Hudson, Lauren}, month = jul, year = {2013}, keywords = {Computer science, Gender, Media, Stereotypes, Underrepresentation}, pages = {58--71}, } @inproceedings{lewis_i_2016, address = {New York, NY, USA}, series = {{ICER} '16}, title = {"{I} {Don}'t {Code} {All} {Day}": {Fitting} in {Computer} {Science} {When} the {Stereotypes} {Don}'t {Fit}}, isbn = {978-1-4503-4449-4}, shorttitle = {"{I} {Don}'t {Code} {All} {Day}"}, url = {https://dl.acm.org/doi/10.1145/2960310.2960332}, doi = {10.1145/2960310.2960332}, abstract = {Stereotypes of computer scientists are relevant to students' performance and feelings of belonging. While efforts exist to change these stereotypes, we argue that it may be possible to challenge a student's belief that stereotypes of computer scientists are relevant to whether they can become a computer scientist. In our previous work, we presented a model of five factors that influence students' decisions to major in computer science (CS). Data were collected from interviews with 31 students enrolled in introductory CS courses at two public universities in the United States. Here we elaborate on our grounded theory of one of these factors: how students assess their fit with CS. We describe how students measure their fit with CS in terms of the amount they see themselves as expressing the traits of singular focus, asocialness, competition, and maleness and how students make interpretations and decisions based upon these measurements. We found that students' interpretations were influenced by their attitudes toward the nature of stereotypes.}, urldate = {2026-03-30}, booktitle = {Proceedings of the 2016 {ACM} {Conference} on {International} {Computing} {Education} {Research}}, publisher = {Association for Computing Machinery}, author = {Lewis, Colleen M. and Anderson, Ruth E. and Yasuhara, Ken}, month = aug, year = {2016}, pages = {23--32}, file = {Full Text PDF:C\:\\Users\\xh21556\\Zotero\\storage\\AM7TJZ6Q\\Lewis et al. - 2016 - I Don't Code All Day Fitting in Computer Science When the Stereotypes Don't Fit.pdf:application/pdf}, } @inproceedings{cowit_student_2024, address = {New York, NY, USA}, series = {{SIGCSE} 2024}, title = {Student {Preconceptions} of {Artificial} {Intelligence}: {Results} from {Single} {Institution} {Survey}}, isbn = {979-8-4007-0424-6}, shorttitle = {Student {Preconceptions} of {Artificial} {Intelligence}}, url = {https://dl.acm.org/doi/10.1145/3626253.3635484}, doi = {10.1145/3626253.3635484}, abstract = {Artificial intelligence (AI) has become an increasingly critical component of not only the computing workforce but also society. It is essential for a diverse group of young people to contribute to this field. However, even within computing, AI is not taught to all post-secondary students. Students often must self-select into AI courses, meaning their reasons for choosing AI may be based on preconceptions of the discipline that may or may not be accurate. We extend the work of a small-n interview study of primarily Asian/Asian American undergraduate students, many of whom expressed perceptions of AI that paralleled identified computing stereotypes. Many of these stereotypes have the potential to discourage undergraduate computing students to take classes or specialize in AI, particularly those from underrepresented groups. Here we present a larger scale validation of those findings in the form of survey data conducted at a large public research institution in the USA. The survey largely confirmed the findings of the interview study at a larger scale, and we also found that gender did not significantly influence the results. Finally, we discuss strategies for AI integration into non-AI computing courses based on those previously used in responsible computing contexts, the goal being to counter harmful preconceptions before students specialize into computing subareas.AI has already made a great impact on a variety of computing and non-computing related disciplines, and is poised to play an increasing role across various areas in industry and society [1, 6, 7, 12, 13]. It is essential to educate young people to contribute to this field to ensure the development of a high-qualified workforce. This requires post-secondary computing students to sign on to learn about the discipline. However, within university computing departments AI is not always a part of the required undergraduate or graduate curriculum, meaning computing students must choose whether to take courses and further their education in AI based on their already existing opinions on the subject.One recent SIGCSE paper, ''Computing Specializations: Perceptions of AI and Cybersecurity among CS Students'' used interview methods to identify a variety of preconceptions related to AI: that AI is very difficult and time consuming ''intimidating'' ''rigorous''; AI requires advance meth skills ''I think all of AI/ML is essentially just math.''; AI is ''trending'' and ''cool''; AI requires an inherent brilliance ''they're really smart.''; AI will have a large societal impact (although not always for the better); and AI is a ''male-dominated'' discipline [11]. Many of these preconceptions were noted as matching preconceptions of computing disciplines more generally [9] and potentially having a discouraging impact on marginalized or historically excluded groups in computing environments, particularly women [8]. In this poster, we aim to validate the findings of Ojha et al. with quantitative data from a single institution survey of post-secondary computing students in the USA. To this end, we ask the following research questions.1.) To what degree are the preconceptions of AI identified in Ojha et al. (2023) confirmed by a larger sample of post-secondary computing students at a large public US university?2.) To what extent are there difference in preconceptions of AI based on gender?1}, urldate = {2026-03-30}, booktitle = {Proceedings of the 55th {ACM} {Technical} {Symposium} on {Computer} {Science} {Education} {V}. 2}, publisher = {Association for Computing Machinery}, author = {Cowit, Noah Q. and Fiesler, Casey}, month = mar, year = {2024}, pages = {1610--1611}, file = {Full Text PDF:C\:\\Users\\xh21556\\Zotero\\storage\\RGB3L7ST\\Cowit and Fiesler - 2024 - Student Preconceptions of Artificial Intelligence Results from Single Institution Survey.pdf:application/pdf}, } @article{parker_comparative_2025, title = {Comparative analysis of artificial intelligence policies in universities across five countries}, volume = {28}, issn = {2948-2992}, url = {https://doi.org/10.1007/s10791-025-09745-5}, doi = {10.1007/s10791-025-09745-5}, abstract = {The rapid integration of artificial intelligence (AI) in higher education has led to significant gaps in policy frameworks across universities worldwide. This study analyzes AI policies at 343 leading universities in Australia, Canada, China, the U.K., and the U.S., selected from the top 1500 institutions globally, as ranked by Times Higher Education for excellence in education across 2024/2025. Using a comparative analysis approach, we examined how these institutions govern the use of AI tools in teaching and learning contexts. Data were gathered from publicly available policy documents, student handbooks, and faculty guidelines, and analyzed using qualitative thematic coding supported by descriptive statistics. Our findings reveal diverse approaches, ranging from outright prohibitions to policies granting instructor's discretion, with notable regional differences influenced by cultural and regulatory factors. Quantitatively, for example, nearly half of universities adopted discretionary policies, while fewer than one in five issued outright bans. This study fills a gap in the literature by providing the first cross-regional analysis of AI policies in higher education, highlighting the absence of a universal framework. These insights offer valuable guidance for institutions to develop flexible, adaptive AI governance models that reflect their unique needs and values.}, language = {en}, number = {1}, urldate = {2026-03-30}, journal = {Discover Computing}, author = {Parker, Luke and Loper, A. Jane and Hayes, Josh and Karakas, Alice and White, Steven and Hallman, Heidi}, month = nov, year = {2025}, keywords = {AI governance, AI guidelines, AI policies in universities, AI policy, AI regulation in universities, AI usage in education, Artificial intelligence in education, ChatGPT, Generative AI}, pages = {267}, file = {Full Text PDF:C\:\\Users\\xh21556\\Zotero\\storage\\NM3Y6KZU\\Parker et al. - 2025 - Comparative analysis of artificial intelligence policies in universities across five countries.pdf:application/pdf}, } @misc{university_of_bristol_equality_nodate, title = {Equality, {Diversity} and {Inclusion} {\textbar} {Centres} for {Doctoral} {Training} {\textbar} {University} of {Bristol}}, url = {https://www.bristol.ac.uk/cdt/interactive-ai/about-the-centre/equality-diversity-and-inclusion/?utm_source=chatgpt.com}, abstract = {Equality, diversity and inclusion strategy in the Interactive AI CDT}, language = {English}, urldate = {2026-03-30}, journal = {UKRI Centre for Doctoral Training in Interactive Artificial Intelligence}, author = {University of Bristol}, file = {Snapshot:C\:\\Users\\xh21556\\Zotero\\storage\\8NI5B7L7\\equality-diversity-and-inclusion.html:text/html}, } @techreport{lindner_barriers_2020, address = {UCL Doctoral Shool}, title = {Barriers to doctoral education: {Equality}, {Diversity} and {Inclusion} for {Postgraduate} {Research} {Students} at {UCL}}, url = {https://www.ucl.ac.uk/doctoral-school/sites/doctoral_school/files/barriers-to-doctoral-education_0.pdf?utm_source=chatgpt.com}, urldate = {2026-01-06}, institution = {UCL}, author = {Lindner, Rebecca}, month = jul, year = {2020}, } @techreport{smith_pgr_2023, title = {{PGR} {Finance}}, url = {https://drive.google.com/drive/u/0/home}, urldate = {2026-01-06}, institution = {University of Bristol}, author = {Smith, Phil and Khan, Russell Azad and Glowacki, Maciej and Ayesha, Irfan and Samiullah, Zulekha and Tucker, Matthew Ryan and Jezierska, Adrianna}, year = {2023}, } @misc{university_of_bristol_september_2024, title = {September: {AI} {Awards} {\textbar} {News} and features {\textbar} {University} of {Bristol}}, shorttitle = {September}, url = {https://www.bristol.ac.uk/news/2024/september/ai-awards-2024.html}, abstract = {University of Bristol has been crowned ‘AI University of The Year’ at the National AI Awards solidifying the University’s reputation as a leader in artificial intelligence research and education.}, language = {English}, urldate = {2026-03-30}, author = {University of Bristol}, month = sep, year = {2024}, file = {Snapshot:C\:\\Users\\xh21556\\Zotero\\storage\\5QQR5F35\\ai-awards-2024.html:text/html}, } @book{gillespie_pragmatism_2024, edition = {1}, title = {Pragmatism and {Methodology}: {Doing} {Research} {That} {Matters} with {Mixed} {Methods}}, copyright = {https://www.cambridge.org/core/terms}, isbn = {978-1-009-03106-6 978-1-316-51614-0 978-1-009-01378-9}, shorttitle = {Pragmatism and {Methodology}}, url = {https://www.cambridge.org/core/product/identifier/9781009031066/type/book}, doi = {10.1017/9781009031066}, abstract = {Taking a pragmatist approach to methods and methodology that fosters meaningful, impactful, and ethical research, this book rises to the challenge of today's data revolution. It shows how pragmatism can turn challenges, such as the abundance and accumulation of big qualitative data, into opportunities. The authors summarize the pragmatist approach to different aspects of research, from epistemology, theory, and questions to ethics, as well as data collection and analysis. The chapters outline and document a new type of mixed methods design called 'multi-resolution research,” which serves to overcome old divides between quantitative and qualitative methods. It is the ideal resource for students and researchers within the social and behavioural sciences seeking new ways to analyze large sets of qualitative data. This book is also available as Open Access on Cambridge Core.}, language = {en}, urldate = {2026-03-30}, publisher = {Cambridge University Press}, author = {Gillespie, Alex and Glăveanu, Vlad and De Saint Laurent, Constance}, month = jan, year = {2024}, file = {PDF:C\:\\Users\\xh21556\\Zotero\\storage\\QD48ZJLG\\Gillespie et al. - 2024 - Pragmatism and Methodology Doing Research That Matters with Mixed Methods.pdf:application/pdf}, } @article{molyneaux_diversity_2022, title = {{DIVERSITY} {AND} {INCLUSION} {SURVEY} ({DAISY}) {QUESTION} {GUIDANCE}}, url = {https://edisgroup.org/wp-content/uploads/2022/05/DAISY-guidance-current-upated-May-2022-V2.pdf}, language = {en}, urldate = {2026-03-03}, author = {Molyneaux, Dr Emma and Hunt, Dr Lilian}, year = {2022}, file = {PDF:C\:\\Users\\xh21556\\Zotero\\storage\\U8PGMAZD\\Molyneaux and Hunt - DIVERSITY AND INCLUSION SURVEY (DAISY) QUESTION GUIDANCE - WORKING DRAFT (V2).pdf:application/pdf}, } @misc{ons_census_2021, title = {Census 2021 paper questionnaires}, url = {https://www.ons.gov.uk/census/censustransformationprogramme/questiondevelopment/census2021paperquestionnaires}, urldate = {2026-03-09}, author = {ONS, Office for National Statistics}, year = {2021}, file = {Census 2021 paper questionnaires - Office for National Statistics:C\:\\Users\\xh21556\\Zotero\\storage\\J3T6MG4D\\census2021paperquestionnaires.html:text/html}, } @article{clarke_thematic_2017, title = {Thematic analysis}, volume = {12}, issn = {1743-9760}, url = {https://doi.org/10.1080/17439760.2016.1262613}, doi = {10.1080/17439760.2016.1262613}, number = {3}, urldate = {2026-03-30}, journal = {The Journal of Positive Psychology}, publisher = {Routledge}, author = {Clarke, Victoria and Braun, Virginia}, month = may, year = {2017}, note = {\_eprint: https://doi.org/10.1080/17439760.2016.1262613}, pages = {297--298}, file = {Full Text PDF:C\:\\Users\\xh21556\\Zotero\\storage\\ZEA37IRG\\Clarke and Braun - 2017 - Thematic analysis.pdf:application/pdf}, } @article{fife_deductive_2024, title = {Deductive {Qualitative} {Analysis}: {Evaluating}, {Expanding}, and {Refining} {Theory}}, volume = {23}, issn = {1609-4069}, shorttitle = {Deductive {Qualitative} {Analysis}}, url = {https://doi.org/10.1177/16094069241244856}, doi = {10.1177/16094069241244856}, abstract = {Although qualitative research is often equated with inductive analysis, researchers may also use deductive qualitative approaches for certain types of research questions and purposes. Deductive qualitative research allows researchers to use existing theory to examine meanings, processes, and narratives of interpersonal and intrapersonal phenomena. Deductive qualitative analysis (DQA; Gilgun, 2005, 2019) is one form of deductive qualitative research that is suited to theory application, testing, and refinement. Within DQA, researchers combine deductive and inductive analysis to examine supporting, contradicting, refining, and expanding evidence for the theory or conceptual model being examined, resulting in a theory that better fits the present sample and accounts for increased diversity in the phenomenon being studied. This paper acts as a primer on DQA and presents two worked examples of DQA studies. Our discussion focuses on the five primary components of DQA: selecting a research question and guiding theory, operationalizing theory, collecting a purposive sample, coding and analyzing data, and theorizing. We highlight different ways of operationalizing theory as sensitizing constructs or as working hypotheses and discuss common pitfalls in theory operationalization. We divide the coding and analyzing process into two sections for parsimony: early analysis, focused on familiarity with the data, code generation, and identification of negative cases, and middle analysis, focused on developing a thorough understanding of evidence related to the guiding theory and negative cases that depart from the guiding theory. Theorizing occurs throughout as researchers consider ways in which the theory being examined is supported, refuted, refined, or expanded. We also discuss strengths and limitations of DQA and potential difficulties researchers may experience when utilizing this methodology.}, language = {EN}, urldate = {2026-03-30}, journal = {International Journal of Qualitative Methods}, publisher = {SAGE Publications Inc}, author = {Fife, Stephen T. and Gossner, Jacob D.}, month = may, year = {2024}, pages = {16094069241244856}, file = {SAGE PDF Full Text:C\:\\Users\\xh21556\\Zotero\\storage\\8YJQFN8Y\\Fife and Gossner - 2024 - Deductive Qualitative Analysis Evaluating, Expanding, and Refining Theory.pdf:application/pdf}, } @inproceedings{humble_content_2022, title = {Content analysis or thematic analysis: {Similarities}, differences and applications in qualitative research}, volume = {21}, isbn = {2049-0976}, number = {1}, author = {Humble, Niklas and Mozelius, Peter}, year = {2022}, pages = {76--81}, } @inproceedings{agrawal_large_2022, address = {Abu Dhabi, United Arab Emirates}, title = {Large language models are few-shot clinical information extractors}, url = {https://aclanthology.org/2022.emnlp-main.130/}, doi = {10.18653/v1/2022.emnlp-main.130}, abstract = {A long-running goal of the clinical NLP community is the extraction of important variables trapped in clinical notes. However, roadblocks have included dataset shift from the general domain and a lack of public clinical corpora and annotations. In this work, we show that large language models, such as InstructGPT (Ouyang et al., 2022), perform well at zero- and few-shot information extraction from clinical text despite not being trained specifically for the clinical domain. Whereas text classification and generation performance have already been studied extensively in such models, here we additionally demonstrate how to leverage them to tackle a diverse set of NLP tasks which require more structured outputs, including span identification, token-level sequence classification, and relation extraction. Further, due to the dearth of available data to evaluate these systems, we introduce new datasets for benchmarking few-shot clinical information extraction based on a manual re-annotation of the CASI dataset (Moon et al., 2014) for new tasks. On the clinical extraction tasks we studied, the GPT-3 systems significantly outperform existing zero- and few-shot baselines.}, urldate = {2026-03-30}, booktitle = {Proceedings of the 2022 {Conference} on {Empirical} {Methods} in {Natural} {Language} {Processing}}, publisher = {Association for Computational Linguistics}, author = {Agrawal, Monica and Hegselmann, Stefan and Lang, Hunter and Kim, Yoon and Sontag, David}, editor = {Goldberg, Yoav and Kozareva, Zornitsa and Zhang, Yue}, month = dec, year = {2022}, pages = {1998--2022}, file = {Full Text PDF:C\:\\Users\\xh21556\\Zotero\\storage\\B7U7EYDE\\Agrawal et al. - 2022 - Large language models are few-shot clinical information extractors.pdf:application/pdf}, } @article{van_de_werfhorst_gender_2017, title = {Gender {Segregation} across {Fields} of {Study} in {Post}-{Secondary} {Education}: {Trends} and {Social} {Differentials}}, volume = {33}, issn = {0266-7215}, shorttitle = {Gender {Segregation} across {Fields} of {Study} in {Post}-{Secondary} {Education}}, url = {https://www.jstor.org/stable/44507724}, abstract = {This article examines whether gender segregation across fields of study in higher education varies between children coming from different socio-economic groups, and changed across time. A possible intersectionality between gender and socio-economic background has hardly been addressed thus far. Using Dutch survey data covering cohorts born between the 1930s and 1980s, I study trends in gender segregation across seven broad fields in post-secondary education, and examine whether gender segregation is different across parental educational levels. Segregation is found to diminish over time, although the trend has stalled. Segregation is, in some fields, less strong among children of higher social origins, both because higher-socio-economic status (SES) daughters are more likely to enrol in the science, technology, engineering, and math fields, and because higher-SES sons are more likely to enrol in health than their lower-SES counterparts. Tentative explanations for these findings are presented that relate to stronger gender-typical socialization in lower-SES families, and potential differential abilities in mathematics and languages across SES groups.}, number = {3}, urldate = {2026-03-30}, journal = {European Sociological Review}, publisher = {Oxford University Press}, author = {van de Werfhorst, Herman G.}, year = {2017}, pages = {449--464}, file = {JSTOR Full Text PDF:C\:\\Users\\xh21556\\Zotero\\storage\\25MMRMRY\\van de Werfhorst - 2017 - Gender Segregation across Fields of Study in Post-Secondary Education Trends and Social Differentia.pdf:application/pdf}, } @misc{ukcge_equity_2024, title = {Equity, {Diversity}, and {Inclusion} in {Postgraduate} {Study} 2022/23}, url = {https://ukcge.ac.uk/resources/resource-library/equity-diversity-and-inclusion-in-postgraduate-study-2022-23}, language = {en}, urldate = {2026-03-30}, journal = {UK Council for Graduate Education}, author = {UKCGE}, month = nov, year = {2024}, file = {Snapshot:C\:\\Users\\xh21556\\Zotero\\storage\\F2WHWAYC\\equity-diversity-and-inclusion-in-postgraduate-study-2022-23.html:text/html}, } @techreport{universities_uk_international_annual_2024, address = {London}, title = {Annual report 2023–24.}, url = {https://www.universitiesuk.ac.uk/sites/default/files/field/downloads/2024-10/Universities UK International Annual Report 23-24.pdf}, urldate = {2025-12-17}, author = {Universities UK International}, year = {2024}, } @misc{new_jersey_department_of_education_new_2025, title = {New {Jersey} {Department} of {Education} {Announces} {Grant} {Awards} to {Support} {Artificial} {Intelligence} in {Education}}, urldate = {2026-01-12}, author = {New Jersey Department of Education}, year = {2025}, } @techreport{dsit_ai_2025, title = {{AI} {Opportunities} {Action} {Plan}.}, url = {https://www.gov.uk/government/publications/ai-opportunities-action-plan/ai-opportunities-action-plan}, urldate = {2026-01-12}, institution = {Department for Science, Innovation and Technology}, author = {DSIT}, year = {2025}, } @article{maslej_artificial_2025, title = {Artificial {Intelligence} {Index} {Report} 2025}, url = {https://hai.stanford.edu/assets/files/hai_ai_index_report_2025.pdf}, language = {en}, urldate = {2026-02-11}, journal = {Artificial Intelligence}, author = {Maslej, Nestor}, year = {2025}, file = {PDF:C\:\\Users\\xh21556\\Zotero\\storage\\43JSCY54\\Maslej - 2025 - Artificial Intelligence Index Report 2025.pdf:application/pdf}, } @article{oskotsky_nurturing_2022, title = {Nurturing diversity and inclusion in {AI} in {Biomedicine} through a virtual summer program for high school students}, volume = {18}, issn = {1553-7358}, url = {https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1009719}, doi = {10.1371/journal.pcbi.1009719}, abstract = {Artificial Intelligence (AI) has the power to improve our lives through a wide variety of applications, many of which fall into the healthcare space; however, a lack of diversity is contributing to limitations in how broadly AI can help people. The UCSF AI4ALL program was established in 2019 to address this issue by targeting high school students from underrepresented backgrounds in AI, giving them a chance to learn about AI with a focus on biomedicine, and promoting diversity and inclusion. In 2020, the UCSF AI4ALL three-week program was held entirely online due to the COVID-19 pandemic. Thus, students participated virtually to gain experience with AI, interact with diverse role models in AI, and learn about advancing health through AI. Specifically, they attended lectures in coding and AI, received an in-depth research experience through hands-on projects exploring COVID-19, and engaged in mentoring and personal development sessions with faculty, researchers, industry professionals, and undergraduate and graduate students, many of whom were women and from underrepresented racial and ethnic backgrounds. At the conclusion of the program, the students presented the results of their research projects at the final symposium. Comparison of pre- and post-program survey responses from students demonstrated that after the program, significantly more students were familiar with how to work with data and to evaluate and apply machine learning algorithms. There were also nominally significant increases in the students’ knowing people in AI from historically underrepresented groups, feeling confident in discussing AI, and being aware of careers in AI. We found that we were able to engage young students in AI via our online training program and nurture greater diversity in AI. This work can guide AI training programs aspiring to engage and educate students entirely online, and motivate people in AI to strive towards increasing diversity and inclusion in this field.}, language = {en}, number = {1}, urldate = {2026-03-30}, journal = {PLOS Computational Biology}, publisher = {Public Library of Science}, author = {Oskotsky, Tomiko and Bajaj, Ruchika and Burchard, Jillian and Cavazos, Taylor and Chen, Ina and Connell, William T. and Eaneff, Stephanie and Grant, Tianna and Kanungo, Ishan and Lindquist, Karla and Myers-Turnbull, Douglas and Naing, Zun Zar Chi and Tang, Alice and Vora, Bianca and Wang, Jon and Karim, Isha and Swadling, Claire and Yang, Janice and Cohort 2020, AI4ALL Student and Lindstaedt, Bill and Sirota, Marina}, month = jan, year = {2022}, keywords = {Artificial intelligence, Biodiversity, COVID 19, Lectures, Machine learning, Medicine and health sciences, Schools, Surveys}, pages = {e1009719}, file = {Full Text PDF:C\:\\Users\\xh21556\\Zotero\\storage\\84USMMLF\\Oskotsky et al. - 2022 - Nurturing diversity and inclusion in AI in Biomedicine through a virtual summer program for high sch.pdf:application/pdf}, } @article{hopson_enhancing_2025, title = {Enhancing {AI} literacy in undergraduate pre-medical education through student associations: an educational intervention}, volume = {25}, issn = {1472-6920}, shorttitle = {Enhancing {AI} literacy in undergraduate pre-medical education through student associations}, doi = {10.1186/s12909-025-07556-2}, abstract = {BACKGROUND: The integration of artificial intelligence (AI) into healthcare is rapidly advancing, with profound implications for medical practice. However, a gap exists in formal AI education for pre-medical students. This study evaluates the effectiveness of the AI in Medicine Association (AIM), an extracurricular program designed to equip pre-medical students with foundational AI knowledge. METHODS: A quasi-experimental pretest-posttest control group design was employed, comparing knowledge acquisition between students participating in the AIM program (cohort group) and a control group of students not participating. The intervention spanned four weeks and included hands-on AI training, ethical considerations, data preprocessing, and model evaluation. Pretest and posttest assessments measured AI knowledge and pathology-related skills. RESULTS: Participants in the AIM program demonstrated significant improvements in both AI knowledge and pathology-related scores. The cohort group showed a large effect size across all measured domains, particularly in pathology, with Cohen's d values ranging from 1.83 to 4.74. Statistical analysis confirmed robust, significant improvements in test scores (t-test and Mann-Whitney U test, p {\textless} 0.001). There was no significant correlation between previous AI experience or attitudes toward AI and overall score improvement. CONCLUSIONS: The AIM program effectively improved pre-medical students' understanding of AI and its application in medicine, particularly in pathology. This study highlights the potential of extracurricular programs to address the need for AI education in medical curricula, especially in the pre-medical phase, and suggests that such initiatives could serve as a model for other institutions seeking to integrate AI education into healthcare training.}, language = {eng}, number = {1}, journal = {BMC medical education}, author = {Hopson, Spencer and Mildon, Carson and Hassard, Kyle and Kubalek, Corbyn and Laverty, Lauren and Urie, Paul and Corte, Dennis Della}, month = jul, year = {2025}, keywords = {Adult, AI in medicine, Artificial Intelligence, Artificial intelligence education, Curriculum, Education, Medical, Undergraduate, Education, Premedical, Educational Measurement, Female, Humans, Male, Medical AI curriculum, Pathology AI training, Pre-medical education, Students, Medical, Young Adult}, pages = {999}, } @article{laupichler_development_2023, title = {Development of the “{Scale} for the assessment of non-experts’ {AI} literacy” – {An} exploratory factor analysis}, volume = {12}, issn = {2451-9588}, url = {https://www.sciencedirect.com/science/article/pii/S2451958823000714}, doi = {10.1016/j.chbr.2023.100338}, abstract = {Artificial Intelligence competencies will become increasingly important in the near future. Therefore, it is essential that the AI literacy of individuals can be assessed in a valid and reliable way. This study presents the development of the “Scale for the assessment of non-experts' AI literacy” (SNAIL). An existing AI literacy item set was distributed as an online questionnaire to a heterogeneous group of non-experts (i.e., individuals without a formal AI or computer science education). Based on the data collected, an exploratory factor analysis was conducted to investigate the underlying latent factor structure. The results indicated that a three-factor model had the best model fit. The individual factors reflected AI competencies in the areas of “Technical Understanding”, “Critical Appraisal”, and “Practical Application”. In addition, eight items from the original questionnaire were deleted based on high intercorrelations and low communalities to reduce the length of the questionnaire. The final SNAIL-questionnaire consists of 31 items that can be used to assess the AI literacy of individual non-experts or specific groups and is also designed to enable the evaluation of AI literacy courses’ teaching effectiveness.}, urldate = {2026-03-30}, journal = {Computers in Human Behavior Reports}, author = {Laupichler, Matthias Carl and Aster, Alexandra and Haverkamp, Nicolas and Raupach, Tobias}, month = dec, year = {2023}, keywords = {AI competencies, AI literacy, AI literacy questionnaire, AI literacy scale, Assessment, Exploratory factor analysis}, pages = {100338}, file = {ScienceDirect Full Text PDF:C\:\\Users\\xh21556\\Zotero\\storage\\RYFGTMBI\\Laupichler et al. - 2023 - Development of the “Scale for the assessment of non-experts’ AI literacy” – An exploratory factor an.pdf:application/pdf;ScienceDirect Snapshot:C\:\\Users\\xh21556\\Zotero\\storage\\ZCBIFPEW\\S2451958823000714.html:text/html}, } @article{hornberger_development_2025, title = {Development and validation of a short {AI} literacy test ({AILIT}-{S}) for university students}, volume = {5}, issn = {2949-8821}, url = {https://www.sciencedirect.com/science/article/pii/S294988212500060X}, doi = {10.1016/j.chbah.2025.100176}, abstract = {Fostering AI literacy is an important goal in higher education in many disciplines. Assessing AI literacy can inform researchers and educators on current AI literacy levels and provide insights into the effectiveness of learning and teaching in the field of AI. It can also inform decision-makers and policymakers about the successes and gaps with respect to AI literacy within certain institutions, populations, or countries, for example. However, most of the available AI literacy tests are quite long and time-consuming. A short test of AI literacy would instead enable efficient measurement and facilitate better research and understanding. In this study, we develop and validate a short version of an existing validated AI literacy test. Based on a sample of 1,465 university students across three Western countries (Germany, UK, US), we select a subset of items according to content validity, coverage of different difficulty levels, and ability to discriminate between participants. The resulting short version, AILIT-S, consists of 10 items and can be used to assess AI literacy in under 5 minutes. While the shortened test is less reliable than the long version, it maintains high construct validity and has high congruent validity. We offer recommendations for researchers and practitioners on when to use the long or short version.}, urldate = {2026-03-30}, journal = {Computers in Human Behavior: Artificial Humans}, author = {Hornberger, Marie and Bewersdorff, Arne and Schiff, Daniel S. and Nerdel, Claudia}, month = aug, year = {2025}, keywords = {AI education, AI literacy, Artificial intelligence, Higher education, Item response theory}, pages = {100176}, file = {ScienceDirect Full Text PDF:C\:\\Users\\xh21556\\Zotero\\storage\\F2NRLD9W\\Hornberger et al. - 2025 - Development and validation of a short AI literacy test (AILIT-S) for university students.pdf:application/pdf;ScienceDirect Snapshot:C\:\\Users\\xh21556\\Zotero\\storage\\KWIZMKVK\\S294988212500060X.html:text/html}, } @misc{noauthor_student_nodate, title = {Student {Experiences} of {Hybrid} and {Online} {Engineering} {Labs} in a {Logic} {Control} {Course} - {Zhang} - 2025 - {Computer} {Applications} in {Engineering} {Education} - {Wiley} {Online} {Library}}, url = {https://onlinelibrary-wiley-com.bris.idm.oclc.org/doi/10.1002/cae.70032?msockid=3b589b324f956a9f0b178fce4eb26b9a}, urldate = {2026-03-30}, file = {Student Experiences of Hybrid and Online Engineering Labs in a Logic Control Course - Zhang - 2025 - Computer Applications in Engineering Education - Wiley Online Library:C\:\\Users\\xh21556\\Zotero\\storage\\5KKKSPSA\\cae.html:text/html}, } @article{zhang_student_2025, title = {Student {Experiences} of {Hybrid} and {Online} {Engineering} {Labs} in a {Logic} {Control} {Course}}, volume = {33}, copyright = {© 2025 The Author(s). Computer Applications in Engineering Education published by Wiley Periodicals LLC.}, issn = {1099-0542}, url = {https://onlinelibrary.wiley.com/doi/abs/10.1002/cae.70032}, doi = {10.1002/cae.70032}, abstract = {In the rapidly evolving landscape of engineering education, the shift toward online and hybrid lab formats requires a critical examination of their impact on students' learning experiences. This study investigates the experiences of 82 students in a logic control course with campus, remote, and simulation labs, through the lens of the Community of Inquiry framework. Although our qualitative thematic analysis confirms students' general preference for campus labs, we extend this observation through nuanced insights into the cognitive, teaching, and social elements of students' perceptions of their learning experiences in the different lab formats. Students appreciate the increased accessibility and flexibility of remote options, while also identifying challenges and limitations for cognitive engagement, instructional support, and social connection. Our results suggest that with targeted improvements, online and hybrid labs can enhance students' learning experience considerably, particularly if integrated purposefully with campus labs. We discuss theoretical and practical key implications for designing blended lab environments.}, language = {en}, number = {3}, urldate = {2026-03-30}, journal = {Computer Applications in Engineering Education}, author = {Zhang, Yihua and Stöhr, Christian and Jämsvi, Susanne Strömberg and Kabo, Jens and Malmqvist, Johan}, year = {2025}, note = {\_eprint: https://onlinelibrary.wiley.com/doi/pdf/10.1002/cae.70032}, keywords = {Community of Inquiry, engineering education, hybrid learning, lab training, online learning}, pages = {e70032}, file = {Full Text PDF:C\:\\Users\\xh21556\\Zotero\\storage\\79UBXG2N\\Zhang et al. - 2025 - Student Experiences of Hybrid and Online Engineering Labs in a Logic Control Course.pdf:application/pdf;Snapshot:C\:\\Users\\xh21556\\Zotero\\storage\\VBLL6TN8\\cae.html:text/html}, } @misc{unesco_prep-ai_2025, title = {Prep-{AI}: {Preparing} policy-makers and educators for {AI} and inclusive}, shorttitle = {Prep-{AI}}, url = {https://www.unesco.org/en/digital-education/artificial-intelligence/prep-ai}, abstract = {The Prep-AI project aims to enhance the capacity of education leaders and decision-makers to steer digital transformation in ways that uphold fundamental rights, equity and sustainability, and to}, language = {en}, urldate = {2026-03-30}, author = {UNESCO}, month = dec, year = {2025}, file = {Snapshot:C\:\\Users\\xh21556\\Zotero\\storage\\9J5ND5GD\\prep-ai.html:text/html}, }