Embed the ReACT reasoning-acting-observing-reflecting cycle in tutoring systems to mirror inquiry-based learning¶

Strategy · All strategies
Evidence · no claims cited

Description¶

The article recommends embedding the ReACT cycle — reasoning, acting, observing, reflecting — into intelligent tutoring systems or classroom simulations. As the article states, "the ReACT cycle mirrors effective inquiry-based learning": students hypothesize (reason), experiment (act), interpret evidence (observe), and engage in metacognition (reflect). An agent designed with ReACT principles could guide students through scientific experiments, prompting them to predict outcomes, test hypotheses, and reflect on discrepancies, aligning with constructivist pedagogy.

Design Implications¶

Context¶

Requirements¶

• An agent architecture supporting iterative reasoning-acting-observing-reflecting loops

Constraints¶

• The article presents this as a pedagogical implication rather than a tested intervention

Target Learners¶

• students in inquiry-based science learning
• learners using intelligent tutoring systems

Target Learning Goals¶

• iterative problem solving
• self-regulated learning
• scientific hypothesis testing

Related Strategies¶

• Simulation Learning
• Action Research
• Embed AI training for pre-service science teachers within science pedagogy and inquiry-based teaching rather than general tool operation

Examples¶

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Key Sources¶

• Kamalov, F., Santandreu Calonge, D., Smail, L., Azizov, D., Thadani, D. R., Kwong, T., Atif, A. (2025). Evolution of AI in Education: Agentic Workflows. https://github.com/AzizovDilshod/Multi-agent-System-for-Essay-Assessment/tree/main