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

Abstract

Problem posing, a pedagogical practice that asks students to generate novel problems or meaningful variations to problems encountered, supports transfer of learning and strengthens problem-solving skills in physics. However, generating physics problems can be challenging, particularly for novice learners. This study investigates students' perceptions of an approach to facilitate their use of Generative AI in ways that maximize their benefits and limit risks. Students were introduced to Generative AI-assisted problem posing as a self-study technique. This study utilizes a phenomenological approach to investigate students' perceptions on how training shaped AI interactions, attitudes towards problem posing with Generative AI, and how students view its incorporation into personal study practices. Results of this study suggest that students perceived a positive change in their interactions with Generative AI after receiving training on prompt engineering techniques. This study also reveals that students hold generally positive views towards the problem-posing technique, with a smaller subset of students showing hesitations towards using Generative AI. These results lay the foundation to introduce and employ training methods for Generative AI more widely and to continue to incorporate Generative AI into structured study techniques, like problem posing.