Ai journal makes students use of ai visible in coding classes
Making the Invisible Visible: A Framework for Reflective AI Use in Software Engineering Education
Software EngineeringComputers and SocietyHuman-Computer Interaction
Summary
It can be hard for teachers to understand how students use AI tools when making software projects since they mostly see only the finished work. The authors designed the AI Journal, a way for students to record their questions, checks, changes, and thoughts while using AI in coding classes. Using this journal helped teachers see how students tested and changed AI suggestions and learned from the process, not just the final result. This method aims to help students become responsible and thoughtful users of AI in their work.
What this means in practice
- •For software engineering instructors: Capture and review students’ AI interactions during coding projects to better assess their learning and decision-making processes.
- •For online course providers: Integrate reflective AI use tracking to enhance monitoring of learners’ engagement with AI tools and promote responsible AI practices.
Authors
Ali Shakiba, Thomas Chaffey
Abstract
Generative AI (GenAI) is increasingly embedded in software engineering education, supporting activities such as requirements development, design exploration, documentation, and prototyping. However, educators often have visibility only into final artefacts, with limited insight into how students evaluate, verify, and refine AI-generated outputs during the learning process. This creates challenges for assessing evaluative judgement and responsible AI-assisted practice. This paper introduces the AI Journal, a structured reflection framework designed to make student-GenAI interaction visible in first-year software engineering education. The framework combines execution tracking, which records prompts, outputs, intent, and interaction context, with cognitive auditing, which captures verification strategies, intervention decisions, confidence judgements, critical learning moments, and reflections on AI-supported work. Deployed in a first-semester software engineering course, the AI Journal enabled visibility into aspects of student learning not observable through artefact-based assessments alone. Preliminary observations suggested variation in verification practices, intervention strategies, and perceptions of AI-supported work. Critical learning moments frequently occurred when students evaluated contextual suitability, feasibility, and requirements alignment rather than identifying obvious errors. The AI Journal demonstrates a practical, lightweight, and model-agnostic approach for making AI-assisted learning processes visible. By foregrounding verification, intervention, and reflection, it shifts attention from product-focused assessment toward evaluative judgement and responsible AI-assisted practice.