Five design principles for responsible implementation of AI-generated course readings¶

Strategy · All strategies
Evidence · no claims cited

Description¶

The article proposes a replicable set of implementation principles for instructors using AI-generated readings: "treat generation as draft production; the instructor must review outputs and correct weak citations", maintain explicit norms requiring follow-up questions and verification, use applied activities (case analysis, simulations) as the quality-control layer, position the assistant as a fallible guide rather than an authoritative text, and build iterative refinement into the workflow so outputs improve across weeks.

Design Implications¶

Context¶

Requirements¶

• Requires instructor capacity for systematic review of generated outputs and explicit course norms for verification and follow-up questioning.

Constraints¶

• Documented in a single graduate course with a small sample; transferability to undergraduate or large-enrollment settings is untested.

Target Learners¶

• graduate professional students
• instructors in higher education

Target Learning Goals¶

• responsible use of AI-generated instructional text
• verification literacy and critical engagement with probabilistic text

Related Strategies¶

• Design PjBL projects so students must evaluate, critique, and improve AI-generated outputs rather than prohibiting AI use
• Train teachers in prompt engineering, evaluating AI-generated responses, ethical and privacy considerations, and integrating AI outputs into pedagogical decision-making
• Institutional governance should establish practical conditions supporting AI literacy, learner agency, and critical evaluation rather than relying on prohibition and detection

Examples¶

-

Key Sources¶

• Sidorkin, A. M. (2026). From One-Size Texts to Tailored Readings: Student Experiences with AI-Generated Course Materials. Open Praxis, 18(3), 506–522. https://doi.org/10.55982/openpraxis.18.3.1141