Provide explicit instruction in AI prompting and information verification alongside AI integration in laboratory courses¶

Strategy · All strategies
Evidence · 3 claims (2 for, 1 mixed) · 3 studies (2 associational, 1 qualitative), q2 · 0 of 3 report an effect size · 3 claims rest on one study

Description¶

The article recommends that curricula incorporate explicit instruction in effective AI prompting and information verification to help students recognize both advantages and risks of AI-generated content. Students found that AI generated clear instructions "only when provided with sufficiently detailed input," underscoring the importance of prompting techniques. Verification skills are needed because AI content may contain inaccuracies in calculations and practical details.

Design Implications¶

Context¶

Requirements¶

• Introductory workshop on AI tools and institutional usage principles
• Guidance on ethical AI use and citation practices
• Instruction on prompting strategies for experimental design contexts

Constraints¶

• AI tools evolve rapidly, requiring ongoing curriculum updates
• Students may copy AI content without understanding if safeguards are absent

Target Learners¶

• undergraduate chemistry students

Target Learning Goals¶

• effective AI use
• information verification
• academic integrity
• critical evaluation of AI-generated content

Claims¶

• Students showed bounded trust and uneven verification behavior: 23 of 837 student turns (about 2.7%) contained explicit risk-aware moves [+W]
• Students' top two concerns about educational LLM use are output quality (fabricated or misleading content, 53 mentions) and the verification burden (34 mentions) [+W]
• Limited output accuracy and over-reliance are the most pressing challenges Chinese HSS students report in GenAI use, alongside strong ethical concern [~W]

Related Strategies¶

• Design authentic, scaffolded activities that convert students' intuitive GenAI concerns into explicit ethical competencies
• Embed GenAI literacy and ethical use directly in nursing curricula rather than prohibiting it
• Scaffolded AI integration with teacher training and reflective tasks to reduce over-reliance
• Train teachers in prompt engineering, evaluating AI-generated responses, ethical and privacy considerations, and integrating AI outputs into pedagogical decision-making
• Frame GenAI labs explicitly as verification and repair practice alongside prompting
• Raise student and colleague awareness of AI-related privacy risks through research-ethics curricula

Examples¶

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Key Sources¶

• Yim, K.-H.; Lui, M. Y. (2026). Supporting Undergraduate Students' Learning in Practical Chemistry Courses through AI-Supported Experimental Design. Journal of Chemical Education. https://doi.org/10.1021/acs.jchemed.5c01311