Explainable Neuro-Symbolic AI for Novel Mathematical Sciences Pedagogy

Eligibility: UK/International (including EU) graduates with the required entry requirements

Duration: Full-Time – between three and three and a half years fixed term

Application deadline: 9 September 2026

Interview date: Will be confirmed to shortlisted candidates

Start date: January 2027

For further details contact: Prof. Matthew England or Prof. Bayaga

Introduction

This research program focuses on developing Artificial Intelligence (AI) tools for use in mathematics education. While AI offers personalized tuition potential, current development faces major hurdles: scarce, privacy-restricted, and context-specific data, alongside a reliance on opaque "black-box" deep learning models that clash with mathematics' need for transparent logical reasoning.

To overcome these challenges, the program will utilise neuro-symbolic AI, merging data-driven deep learning with symbolic, rule-based AI. This hybrid approach has the potential to reduce hallucinations, require smaller datasets, and provide greater trust and accountability for use in mathematics education.

This program is a dual degree project between Coventry University (UK) and Stellenbosch University (South Africa). The project is led by Stellenbosch, which is where the student is expected to start their degree, visiting Coventry for an extended period in the second year. The student will be supervised throughout by a joint Coventry-Stellenbosch team led by Prof. Anass Bayaga and Prof. Matthew England.

Project details

This project focuses on the development of new neuro-symbolic AI based tools which require less data for use in the area of STEM education. It will explore how best such tools can be designed, evaluated, and governed; and develop neuro-symbolic AI to overcome the data-scarcity problem directly by requiring less data.

Proposed research questions include:

How can AI models in mathematical sciences best make their reasoning pathways visible and open to critique?
How can models be developed responsibly where large, labelled datasets are not available?
To what extent can models trained in one mathematical or scientific domain generalise reasoning patterns across related domains?
How can deep learning be integrated with symbolic reasoning, formal rules, and proof-oriented validation?
What ethical safeguards are required to address bias, opacity and accountability in AI-supported STEM learning and research?

To evaluate explainability will require human participation: the project aims to engage consenting adult STEM students, lecturers, and researchers in both the UK and South Africa for this.

Funding

Tuition fees and bursary

Benefits

The successful candidate will receive comprehensive research training including technical, personal and professional skills. All researchers at Coventry University (from PhD to Professor) are part of the Doctoral and Researcher College, which provides support with high-quality training and career development activities.

Entry requirements

A minimum of a 2:1 first degree in a relevant discipline/subject area with a minimum 60% mark in the project element or equivalent with a minimum 60% overall module average.

PLUS

A master's degree in a relevant subject area.The master's must have been attained with minimum overall marks at merit level (60%)*. In addition, the dissertation or equivalent element in the master's must also have been attained with a minimum mark of merit level (60%).

The potential to engage in innovative research and to complete the PhD within 3.5 years.
A minimum of English language proficiency (IELTS academic overall minimum score of 6.5 with a minimum of 6.0 in each component).

Additional requirements

Candidates should have strong mathematics grades at high school and have undertaken an undergraduate degree with a mathematics component (e.g. mathematics, computer science, engineering). Candidates should have the proven ability to program in a high-level programming language such as Python. Experience of professional software development skills (e.g. version control, testing methodologies) is advantageous. Prior experience with machine learning and/or artificial intelligence development is advantageous. Prior research experience is highly advantageous. The application form has both a personal statement and a research proposal section: - Please use the personal statement to identify past experience on your CV that is relevant for application in this PhD project. - Please use the research proposal to identify some recent work in the scientific literature (e.g. 4-5 papers) related to the project topic that you summarise, critique, and offer ideas about how you could take further as part of this project.

How to apply

To find out more about the project, please contact Prof. Matthew England

or Prof Bayaga

All applications require full supporting documentation, a covering letter, plus a 2000-word supporting statement showing how the applicant’s expertise and interests are relevant to the project.

Apply to Coventry University