Instructors integrating generative AI into writing-intensive courses should have students critically understand, access, prompt, vet and integrate AI-produced content¶

Strategy · All strategies
Evidence · no claims cited

Description¶

Drawing on Tate et al.'s study of an AI-facilitated writing tool in writing-intensive STEM courses, the review recommends enabling critical student use of AI. Students should be challenged "to understand, access, prompt, vet and integrate AI-produced content critically". The review also reports the study's best practices for instructors: setting clear learning goals, defining AI's role in the writing process, iterating curriculum design and reflecting on implementation outcomes.

Design Implications¶

Context¶

Requirements¶

• Instructors set clear learning goals and define AI's role in the writing process, per the best practices the review reports

Constraints¶

• The strategy is reported for writing-intensive STEM courses as studied by Tate et al.; the review attributes it rather than testing it directly

Target Learners¶

• students in writing-intensive STEM courses

Target Learning Goals¶

• critical evaluation and integration of AI-produced writing content

Related Strategies¶

• Integrate AI feedback as a supportive resource within process-oriented drafting cycles, with explicit guidance on critical evaluation
• Embed AI in guided accept/reject/justify workflows, teach digital critical thinking explicitly, and integrate institutional authorship standards
• Train teachers in prompt engineering, evaluating AI-generated responses, ethical and privacy considerations, and integrating AI outputs into pedagogical decision-making
• Scaffolded AI integration with teacher training and reflective tasks to reduce over-reliance
• Ground AI integration in higher education on core principles of human agency, academic freedom, transparency, ethics, inclusivity, and critical thinking

Examples¶

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Key Sources¶

• Kostopoulos, G., Gkamas, V., Rigou, M., & Kotsiantis, S. (2025). Agentic AI in Education: State of the Art and Future Directions. IEEE Access. https://doi.org/10.1109/ACCESS.2025.3620473