Integrate AI-based question analysis tools to provide tailored feedback based on inquiry depth¶

Strategy · All strategies
Evidence · no claims cited

Description¶

The article's implication for practice states that "Instructors could enhance learner engagement by integrating AI-based question analysis tools to provide tailored feedback based on inquiry depth." The strategy uses automated classification of learner questions into depth categories so that feedback can be matched to whether a question is factual or higher-order, supporting personalised learning within the constructivist framing of the study.

Design Implications¶

Context¶

Requirements¶

• An AI question-classification model embedded in the instructor's workflow, with human oversight given models' difficulty with higher-order questions.

Constraints¶

• Model performance is weaker on facilitator and co-learner questions, so tailored feedback on higher-order inquiries needs human review.

Target Learners¶

• higher education students

Target Learning Goals¶

• learner engagement
• personalised feedback matched to inquiry depth

Related Strategies¶

• Incorporate AI-driven scaffolding strategies to support students' higher-order thinking skills
• Embed automated question categorisation in learning management systems and develop ethical guidelines for AI in formative assessment

Examples¶

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Key Sources¶

• Lee, H., Atif, A., & Kang, K. (2026). Analysing AI utilisation in education through learner question types: A constructivist approach. Australasian Journal of Educational Technology, 42(1), 79–96. https://doi.org/10.14742/ajet.10657