Redesign AI-era physics courses around an unaided written examination with mock exams, prepared tutorial discussion, and research-shaped tasks as bonus work with scaffolding¶

Strategy · All strategies
Evidence · no claims cited

Description¶

The article's forward-looking plan for the next KT1 course: a written examination determines the final grade while homework counts only toward bonus points; multiple mock examinations let students practice standard problems without assistance; traditional tutorial problems are given well in advance so students come prepared for group discussion; and parts of the research-shaped problems may remain as advanced or bonus work with AI available, accompanied by prerequisite preparation, worked examples, feedback, and explicit consolidation.

Design Implications¶

Context¶

Requirements¶

• Mock examinations for unassisted practice
• Tutorial problems disclosed in advance to enable prepared discussion
• Scaffolding (prerequisite preparation, worked examples, feedback, explicit consolidation) attached to any remaining research-shaped tasks

Constraints¶

• The article also recommends teaching AI literacy early, including verifying generated answers and using AI as a teacher rather than copying output

Target Learners¶

• undergraduate physics students in introductory nuclear and particle physics

Target Learning Goals¶

• unaided performance on standard calculations
• active participation in tutorial discussion
• AI literacy including verification of generated answers

Related Strategies¶

• Redesign structurally: grade the reasoning behind AI-assisted work and teach AI-use rules and AI literacy explicitly
• Redesign assessment toward supervised, oral, process-based and authentic formats to verify students' own reasoning under GenAI

Examples¶

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Key Sources¶

• M. Mikhasenko, M. Stahl, I. Segal, D. Parmar, A. Zimmer, and A. Kazatsky. (2026). AI in Particle Physics Education: Research Problems and Foundational Skills. https://arxiv.org/abs/2609.05719