Exploring Students' Perceptions of Using Generative AI-Assisted Problem Posing

Synthesis: Dawson and Rebello (2026) investigate students' perceptions of generative AI-assisted problem posing, a pedagogical practice in which learners generate novel problems or variations to strengthen transfer and problem-solving in physics. Using a phenomenological approach, they found that students perceived a positive change in their AI interactions after receiving prompt-engineering training, and held generally positive views of problem posing as a self-study technique, though a smaller subset showed hesitations about using AI. The study suggests structured training can help students use Generative AI productively in physics while mitigating risks, positioning problem posing as a Self Regulated Learning strategy.

Problem Posing as a Learning Practice

Problem posing — asking students to generate novel problems or meaningful variations — supports Transfer Of Learning and strengthens problem-solving in physics. It is challenging for novices, who benefit from structured support. This study positions GenAI as a tool to facilitate problem posing in ways that maximize benefits and limit risks.

Training Shapes AI Interactions

Students were introduced to GenAI-assisted problem posing as a self-study technique and received training on Prompt Engineering techniques. A phenomenological analysis found students perceived a positive change in their interactions with GenAI after training, and positive attitudes toward the problem-posing technique itself, with a subset expressing hesitation about GenAI. This suggests that how students are trained to interact with AI shapes both the quality of interaction and their confidence.

Toward Structured Study Techniques

The findings lay a foundation for broader Prompt Engineering training and for incorporating GenAI into structured study practices like problem posing, connecting to Self Regulated Learning, Motivation, and Student Experience in Physics Education. It complements work on AI-generated solutions by emphasizing learner-generation of problems rather than consumption of answers.

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Citation

Dawson, L., & Rebello, N. S. (2026). Exploring students' perceptions of using generative AI-assisted problem posing. arXiv:2608.12523.