Incorporate AI-driven scaffolding strategies to support students' higher-order thinking skills¶

Strategy · All strategies
Evidence · no claims cited

Description¶

Among the article's implications for practice, "Course designers may need to incorporate AI-driven scaffolding strategies to support students' higher-order thinking skills." This responds directly to the finding that models struggle to distinguish facilitator and co-learner questions: where automated classification of higher-order inquiry is weak, designed scaffolding is needed so that AI-supported activities still push learners beyond factual questioning.

Design Implications¶

Context¶

Requirements¶

• Course designs that pair AI question analysis with scaffolding moves targeting facilitator and co-learner question types.

Constraints¶

• The article presents this as a need arising from models' difficulty with higher-order questions, not as a tested intervention.

Target Learners¶

• higher education students

Target Learning Goals¶

• higher-order thinking skills

Related Strategies¶

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