Phase-aligned teaching applications for LLM-supported learning across five phases¶

Strategy · All strategies
Evidence · no claims cited

Description¶

Table 3 outlines classroom activities aligned with each phase of the instructional model, designed to build technical fluency with LLMs while supporting ethical awareness, critical reflection, and learner agency. Activities include AI bias exploration, prompt remix workshops, AI plus human draft comparison, revision logs, and personal AI use policies. The authors state these strategies position students as co-constructors of knowledge exploring LLMs through creation, iteration, and inquiry.

Design Implications¶

Context¶

Requirements¶

• Activities are rooted in constructionist principles and aligned to the phase's instructional goal

Constraints¶

• The applications are sample teaching strategies proposed conceptually, not empirically evaluated in this article

Target Learners¶

• higher education students

Target Learning Goals¶

• technical fluency with LLMs
• ethical awareness
• critical reflection
• learner agency

Related Strategies¶

• Instructional Model Human Centered Generative Ai Engagement
• Assign a language function to each 5E phase (describe, compare and contrast, classify, classify with justification) and align language objectives with science objectives
• Educators gamify learning materials or create learning games using generative AI with iterative, phased prompting

Examples¶

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Key Sources¶

• April Joy Miles, Paige Haber-Curran, Khalid Arar. (2026). Prompts to Practice: A Pedagogical Framework for Human-Centered AI Engagement. Open Praxis, 18(3), 481–494. https://doi.org/10.55982/openpraxis.18.3.1191