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.