Use weekly oral code review interviews to incentivize students to understand AI-assisted code¶

Strategy · All strategies
Evidence · no claims cited

Description¶

This strategy replaces AI bans with formative oral assessments: after each weekly coding assignment, students explain their submitted code and justify implementation decisions to a trained TA in a 15-minute interview, then demonstrate functionality via integration test cases. The assessments are "designed to incentivize students to make an active attempt at understanding their submitted code, regardless of whether or not the code was generated by AI tools". Weighting the interview heavily in the grade makes understanding, not code production, the graded outcome.

Design Implications¶

Context¶

Requirements¶

• Trained TAs following a structured interview protocol targeting the assignment's learning goals
• A scheduling system and reserved time for weekly sessions
• Grade weight allocation (here 70% interview, 30% rubric) that makes the interview consequential

Constraints¶

• TA inconsistency in grading, question difficulty, and session length were the most common student complaints
• Students disliked the strict 48-hour scheduling window and zero grade for missed sessions

Target Learners¶

• University introductory computer science students

Target Learning Goals¶

• Explaining and justifying code decisions
• Avoiding over-reliance on AI-generated code

Related Strategies¶

• Cs1 Cr Course Structure
• Pair AI-permitted take-home assignments with supervised modifications, oral defense, prediction, and transfer tasks to verify understanding
• Replace detector-centred assessment with a four-stage process-oriented model: declared starting point, versioned drafting, process artefacts, and targeted oral verification
• Scaffolded critique-of-AI-output assignment sequence with paired verification of individual understanding
• Mini homework defenses: short oral one-on-one assessments of student solutions
• Two-instrument assessment: AI-free coding quizzes plus verification-gated oral defenses of comment-stripped AI-assisted work
• Require process transparency artefacts: process logs, prompt records, reflective commentaries and oral defences

Examples¶

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Key Sources¶

• Peter Fowles, Erik Falor, Sulove Bhattarai, John Edwards, and Seth Poulsen. (2026). Combating Harms of Generative AI in CS1 with Code Review Interviews and a Flipped Classroom. https://arxiv.org/abs/2605.21374