AI-Supported Analogy-Based Teaching Approach for Introductory Programming Education

This PhD project explores how real-world analogies, supported by artificial intelligence, can improve the teaching and learning of introductory programming. The research addresses the difficulty many beginners face with abstract programming concepts such as variables, control flow, data structures, and algorithms. The project will: use AI to help generate and refine analogy-based teaching materials develop an AI-enabled framework for teaching programming design, implement, and evaluate the approach in real classroom settings test the approach within two large first-year modules at Sheffield Hallam University use educational experiments, surveys, and focus groups to evaluate effectiveness The research aims to produce evidence-based guidance for educators and improve programming education for diverse learners.

Applicants should hold at least a 1st or 2:1 Honours degree in Computer Science, Software Engineering, Data Science, Educational Technology, or a related discipline. Interest in teaching, learning, and educational innovation is encouraged. English language proficiency with IELTS 7 (minimum 6.5 in all areas) is mandatory for non-native speakers. Applications from underrepresented groups are strongly encouraged.

Apply online for the Graduate Teaching Assistantship scholarship by submitting a personal statement (up to 2 pages), a CV (max 2 pages), two academic references dated within the last 2 years, highest degree certificate and transcript, IELTS results (if applicable), and passport copy (for non-UK applicants). List all GTA projects you are applying for and tailor personal statements accordingly. Application deadline is 07 May 2026.