Design and Implementation of a General Chemistry Course that Promotes AI Use

As generative AI tools become widely accessible, instructors may face uncertainty about how to respond. This review describes how a large-enrollment general chemistry course was adapted to promote AI use through intentional instructional design. The course featured early framing of AI use, in-class modeling of prompting strategies, AI-compatible practice resources, and regular reflection. These elements were designed to promote intentional, ethical, and metacognitive engagement with AI. Student interviews conducted at the end of the semester offer illustrative insights into how learners responded to the course structure, highlighting shifts from transactional to generative AI use. This grounded example offers a model for promoting AI use in STEM classrooms and provides practical strategies and instructional resources for instructors seeking to integrate AI in pedagogically meaningful ways.

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Publication Details

Journal
Journal of Chemical Education
Published
2025-08-15
DOI
https://doi.org/10.1021/acs.jchemed.5c00728
Citations
2
Primary Topic
Machine Learning in Materials Science
Type
article
Field-Weighted Citation Impact
1.04
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article

Design and Implementation of a General Chemistry Course that Promotes AI Use

Ted M. Clark, Nicolas Tafini
2 citations
Journal of Chemical Education
Machine Learning in Materials Science
1.04
article

Design and Implementation of a General Chemistry Course that Promotes AI Use

Ted M. Clark, Nicolas Tafini
article en
2 citations

Abstract

As generative AI tools become widely accessible, instructors may face uncertainty about how to respond. This review describes how a large-enrollment general chemistry course was adapted to promote AI use through intentional instructional design. The course featured early framing of AI use, in-class modeling of prompting strategies, AI-compatible practice resources, and regular reflection. These elements were designed to promote intentional, ethical, and metacognitive engagement with AI. Student interviews conducted at the end of the semester offer illustrative insights into how learners responded to the course structure, highlighting shifts from transactional to generative AI use. This grounded example offers a model for promoting AI use in STEM classrooms and provides practical strategies and instructional resources for instructors seeking to integrate AI in pedagogically meaningful ways.

Journal of Chemical EducationVol. 102(9)
The Ohio State University (US)
Quality Education
Openalex Percentile: Top 23%
Machine Learning in Materials Science
1.04
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