Generative AI for Personalised Feedback in Computing Education

Project overview

Providing timely, individualised feedback is a persistent challenge in large-scale computing units.
This project investigates how Generative AI models can automatically produce pedagogically aligned, rubric-based feedback on student submissions.
A prototype system will interface with an LLM API (e.g., OpenAI GPT) and generate structured feedback, which will be evaluated for accuracy, usefulness, and tone against educator benchmarks.

Aim/outline

• Design a feedback-generation pipeline linking uploaded student work to AI output.
• Engineer prompts that ensure feedback relevance and academic integrity.
• Implement a lightweight web interface for students and educators.
• Evaluate generated feedback via qualitative and quantitative comparison.
• Provide design recommendations for ethical AI feedback systems in higher education.

Required knowledge

• Python (Flask or Streamlit preferred).
• Basic understanding of LLMs and prompt engineering.
• Familiarity with assessment rubrics and educational feedback principles.
• Interest in applied AI and human-centred evaluation.