Journal of Chemical Education· 2026Q2
From Cheating to Coaching: How Chemistry Syllabi Frame Academic Integrity in the Age of Generative AI
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- 2026year
Short summary
Chemistry course documents (syllabi) rarely address generative AI, with only 11.1% (2 of 18) mentioning it, compared to 88.2% (15 of 17) of institutional policies, revealing a significant gap in translating AI governance into classroom practice.
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Key points
- Only 11.1% of chemistry course documents explicitly addressed generative AI, versus 88.2% of institutional policies.
- Chemistry course documents primarily frame academic integrity as student responsibility and consequences, with little explicit faculty guidance.
- Generative AI is often addressed through silence, ambiguity, or outright prohibition in course documents.
- A significant governance gap exists between institutional AI policies and their implementation in chemistry course syllabi.
AI-generated from the title and abstract; the full text is not read.
Abstract
Abstract Generative artificial intelligence (AI) is rapidly changing expectations for academic integrity in higher education, yet it remains unclear how institutional AI governance is translated into course-level guidance. This study examines how academic integrity and generative AI are framed in chemistry course documents and institutional policy documents. Using a hybrid inductive–deductive qualitative document analysis, I analyzed 18 publicly available chemistry course documents and 17 institutional policy documents from nine institutions across four countries. The analysis identified five higher-order themes in chemistry course documents: consequence-focused framing of academic integrity; academic integrity framed primarily as student responsibility; boilerplate policy language dominating over contextualized integrity guidance; generative AI addressed through silence, ambiguity, or prohibition; and faculty guidance and prevention rarely being explicit. A direct comparison revealed a substantial governance gap: 15 of 17 institutional policy documents (88.2%) explicitly mentioned generative AI, compared with only 2 of 18 chemistry course documents (11.1%). Institutional policies also more frequently addressed faculty guidance, research guidance, and AI governance than course documents. These findings suggest that institutional AI governance is developing more rapidly than its translation into chemistry course-level practice. Although policies and syllabi will continue to evolve as generative AI changes, the documented governance gap provides a baseline for examining how institution-level guidance is translated into instructional practice over time. The study concludes by proposing a coaching-oriented approach that shifts syllabus guidance from primarily punitive restrictions toward transparent, ethical, and pedagogically purposeful AI use.
The authors' abstract, as published at the source. Journal of Chemical Education, 2026 · DOI ↗
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