Reinvigorate bottom-half AI×Ed research: AI literacy for reflection on learning, machine teaching, and agent-based models of learning¶

Strategy · All strategies
Evidence · no claims cited

Description¶

The authors articulate future directions to revive research in the bottom half of AI×Ed, where AI serves as an analogy to human intelligence. These include AI literacy work in which students learn about thinking by engaging with generative AI microworlds and teachable agents, machine teaching methods that could inform human teaching strategies, and agent-based modeling as a complex-systems method to bridge contemporary learning theories and computational modeling. They caution these are not necessarily the most productive routes but may help set a more expansive research agenda.

Design Implications¶

Context¶

Requirements¶

• A willingness of the AIED community to return to thinking about AI as an analogy to human intelligence
• Experimental work is needed to test the idea that learning about kinds of AI fosters metacognitive reflection on one's own ability to learn

Constraints¶

• Machine teaching techniques may suffer from using oversimplified learner models
• The authors do not claim these are necessarily the most productive routes

Target Learners¶

• AIED researchers; K-12 and higher-education students in AI literacy contexts; individual teachers

Target Learning Goals¶

• Metacognitive reflection on one's own learning; improved teaching strategies; understanding the complex nature of learning

Related Strategies¶

• Pursue symbolic, hybrid, and metaverse-based AI approaches to address gaps left by subsymbolic AI in collaborative learning

Examples¶

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Key Sources¶

• Sina Rismanchian & Shayan Doroudi. (2025). The Evolution of Research on AI and Education Across Four Decades: Insights from the AIxEd Framework. Int J Artif Intell Educ 35:2797–2820. https://doi.org/10.1007/s40593-025-00483-9