EPSRC Funded Research Project

Exploring Barriers to
AI Doctoral Studies

An interdisciplinary study by the University of Bristol uncovering the socio-economic, literacy, and structural factors shaping the future of AI research.

Project Overview Browse Findings

What We Did

Our research utilized a robust mixed-methods approach to map the transition from undergraduate to doctoral AI studies. We developed a custom Natural Language Processing (NLP) pipeline to analyze over 2,600 PhD recruitment listings, identifying linguistic and structural barriers in funding and eligibility.

Parallel to this data-driven analysis, we collaborated with the Jean Golding Institute (JGI) to deliver "Getting Started in AI" training sessions. These workshops served as an intervention for students from non-technical backgrounds, allowing us to track shifts in self-efficacy and study intentions.

  • 01

    NLP Listing Analysis

    2,643 PhD opportunities scraped and analyzed for structural exclusion.

  • 02

    Training Interventions

    JGI-led workshops designed to demystify AI for underrepresented groups.

  • 03

    Depth Interviews

    45-60 minute sessions exploring student trust and perceived barriers.

Why It Matters

Addressing the fundamental challenges in AI doctoral accessibility and institutional reform.

Economic Innovation

The AI talent shortage is a bottleneck for global innovation. By expanding the pipeline, we ensure the most capable minds shape the next era of technology.

Algorithmic Justice

Inclusion is critical for preventing bias. Diverse researchers bring the perspectives necessary to build systems that are fair and representative of all society.

Institutional Reform

Our findings reveal systemic "False Hope" barriers. We provide evidence-based recommendations to help universities reform their recruitment pipelines.

Project Navigation Hub

Explore each research section directly.

Literature Review

Theoretical framework and identified barriers.

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Methodology

NLP pipeline and research design details.

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Findings

Results from data analysis and interviews.

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Resources Hub

Access datasets, reports, and toolkits.

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Research Team

Meet the interdisciplinary researchers.

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Literature Dimensions

01

Socio-economic

Investigating how gender, race, and financial background form "invisible gates" in the PhD application pipeline.

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02

AI Literacy

Utilising the AILIT-S framework to distinguish technical ability from the psychological confidence gap.

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03

Structural

Auditing institutional policy misalignment between advertised inclusivity and restrictive funding caps.

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Read Full Literature Review

Methodology

Our robust sequential mixed-methods design moves from theoretical scoping to deep analytical interventions.

  • 1. Literature Review

    Identified core dimensions of exclusion: socio-economic, literacy, and structural.

  • 2. Data Scraping

    Collected 2,643 live PhD listings from FindAPhD.com for analysis.

  • 3. Training Intervention

    JGI-led "Getting Started in AI" workshops for non-technical students.

  • 4. Quantitative Surveys

    Longitudinal Pre/Post tracking using the AILIT-S framework.

  • 5. Interviews

    Semi-structured sessions probing personal narratives and trust.

  • 6. Data Analysis

    Triangulation of quantitative stats and qualitative reflexive thematic analysis.

View Full Pipeline Details
01 Literature Review
02 Data Scraping
03 Training Intervention
04 Quantitative Surveys
05 Interviews
06 Data Analysis

Findings Preview

01

Global Ambition vs. Restriction

"While 79.2% of listings claim global openness, recruitment caps create a 'False Hope' barrier for international talent."

02

Structural Confidence Gap

"Lower socio-economic students report lower self-efficacy despite academic parity with their peers."

ACCESS FULL RESULTS

Resources Hub

Open science repository featuring all research outputs and pedagogical tools.

Reports

Final project outputs and executive summaries.

Curricula

Syllabi and slides from our training workshops.

Toolkit

AILIT-S survey items and interview guides.

Code

Python NLP scripts and data metadata.

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Research Team

Principal Investigator

Prof. Flavia De Luca

Project Lead

Postgraduate Researchers

Jana Alloush
Erin Brady
Rosie Castiglione
Weilin He
Sven Hollowell
Adrianna Jezierska

Training & Coordination

Pau Erola
Emily Wride
FULL TEAM BIOS & PROFILES