Non-prescriptive GAI feedback via sequenced guiding questions calibrated to cognitive challenge¶

Strategy · All strategies
Evidence · no claims cited

Description¶

The article recommends that instead of offering direct answers or evaluation outcomes, GAI should pose guiding questions prompting deeper reflection so learners independently build their summative self-assessment frameworks. Feedback is to be "structured as thought-provoking, sequenced prompts, calibrated to an optimal level of cognitive challenge, designed to facilitate guided discovery, and promote deep reflective engagement". GAI acts as facilitator, assessor, and essentially collaborator in a guided discovery process, echoing knowledge-building and spiral collaborative knowledge improvement approaches.

Design Implications¶

Context¶

Requirements¶

• Feedback must be based on previous scoring assessments and avoid revealing GAI's evaluation or offering direct solutions

Constraints¶

• The article warns the approach presumes harmonious learner-GAI collaboration and that risks of perceived authority and externalization of judgment remain under-theorized

Target Learners¶

• learners in GAI-supported self-directed learning environments

Target Learning Goals¶

• reflective self-assessment
• independent framework construction
• deep reflective engagement

Related Strategies¶

• Question-based eLearning Design
• Questioning Techniques
• Guided Inquiry
• Combine GAI tools with traditional methods in a hybrid approach to balance efficient collaboration and critical evaluation

Examples¶

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Key Sources¶

• Qianrun Mao. (2024). Fostering Self-Directed Growth with Generative AI: Toward a New Learning Analytics Framework. https://www.google.com/search?q=Fostering+Self-Directed+Growth+with+Generative+AI+Toward+a+New+Learning+Analytics+Framework