Use structured exercises requiring students to compare, critique, and justify against AI-generated code¶

Strategy · All strategies
Evidence · no claims cited

Description¶

A concrete integration recipe in which instructors design ChatGPT-supported activities that keep students in an evaluative, reasoning role rather than a consumption role. The chapter recommends that "Instructors can use ChatGPT to design structured exercises that require students to compare AI-generated code with their own, identify logic errors, and justify algorithmic choices." This positions the tool as an object of analysis that encourages analysis and deeper comprehension of programming structures, and it aligns with the chapter's broader call for active engagement and computational reasoning.

Design Implications¶

Context¶

Requirements¶

• Instructors need competence in AI literacy, prompt construction, and evaluation of AI-supported outputs

Constraints¶

• Aimed at programming courses; the chapter frames unstructured use as risking dependency and reduced persistence

Target Learners¶

• novice and intermediate programming students in higher education

Target Learning Goals¶

• computational reasoning
• debugging and logic-error identification
• algorithmic justification
• deeper comprehension of programming structures

Related Strategies¶

• Engage students in active learning assignments using generative AI with structured reflection and critique of AI outputs
• Integrate AI into university physics with instructional structure, task alignment, and critical evaluation of AI outputs
• Classroom exercise cycle: select a task, formulate a baseline prompt, submit it to an LLM, then critically analyse the output before revising instructions
• Calibrate GenAI feedback through calibrated prompts and pedagogical (re-)framing
• Scenario B: constructing, critiquing, and reframing AI-mediated arguments

Examples¶

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Key Sources¶

• Grume, J. C., Miranda, J. P. P., De Leon, A. P., Salenga, J. L., Hernandez, H. E., Castro, M. A., Maniago, V. G. M., Canlas, J. D., & Quiambao, J. B. (2026). Pedagogical Promise and Peril of AI: A Text Mining Analysis of ChatGPT Research Discussions in Programming Education. In M. B. Garcia (Ed.), Pedagogical Innovations in Computer Science Education (pp. 305-330). IGI Global Scientific Publishing. https://doi.org/10.4018/979-8-3373-6546-6.ch010