Teach prompting and AI literacy as epistemic practice with structured guidance¶

Strategy · All strategies
Evidence · 1 claim (1 for) · 1 study (1 review), q2 · 0 of 1 report an effect size · 1 claim rests on one study

Description¶

The review treats prompting in chemistry as an epistemic act, not merely a technical skill: a useful prompt specifies chemical constraints, relevant representations, assumptions, and adequacy criteria. It reports that "Preservice chemistry teachers initially relied heavily on copy-and-paste prompting and benefited from structured guidance", and that semester-long evidence suggests prompts become more purposeful with repeated instruction. AI literacy extends beyond tool operation to knowing when not to use GenAI, recognizing uncertainty, checking provenance, and understanding model- and version-dependent behavior.

Design Implications¶

Context¶

Requirements¶

• Structured guidance and repeated instruction across a course
• Ethical discussion tied to actual chemical tasks rather than generic policy topics

Constraints¶

• Teacher preparation is especially important because teachers must make these verification decisions for their own work and model them for students

Target Learners¶

• Preservice chemistry teachers
• University chemistry students

Target Learning Goals¶

• Purposeful prompting with chemical constraints
• AI literacy including provenance checking and uncertainty recognition

Affordances¶

• Verification Centered Johnstone 3P Genai Architecture

Claims¶

• Teacher-AI design collaboration faces recurring challenges including prompt literacy, curriculum misalignment, epistemic uncertainty, and explainability barriers [+W]

Related Strategies¶

• Counter-prompting: deliberately probing AI systems for their assumptions and using responses as data about the apparatus
• Strategies for integrating GenAI in STEAM teaching: outcome-anchored lesson planning, differentiation, inquiry and project variants, assessment redesign, and integrity as pedagogy
• Scenario A: designing and deconstructing AI prompts as an epistemic practice

Examples¶

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Key Sources¶

• Vega-Baudrit JR and Rivera Álvarez A (2026) Generative artificial intelligence in university chemistry education: a critical review using Johnstone’s chemistry triplet and Biggs’ 3P model. Front. Educ. 11:1918707. https://doi.org/10.3389/feduc.2026.1918707