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The rapid integration of generative artificial intelligence (AI) into educational settings prompts an urgent examination of its efficacy and the strategies that students employ to harness its potential. This study focuses on preservice chemistry teachers use of generative AI for chemistry-specific problem-solving and task completion. We found that there is a prevalent reliance on copy-pasting tactics in initial prompting approaches, and students need guidance to improve their prompting abilities. By implementing the "Five S" prompting framework, we explore the evolution of student prompts and the resultant satisfaction with AI-generated responses. Our findings indicate that, while students initially struggle with the nuances of effective prompting, the adoption of structured frameworks significantly enhances their perceived quality of AI-generated answers. This research sheds light on the current state of AI use among students but also underscores the importance of targeted educational frameworks to refine AI interaction in academic contexts. In particular, we suggest critical engagement and methodological prompt engineering strategies to maximize the educational benefits of generative AI technologies.
Sebastian Tassoti (2024) studied this question.