Differentiate AICA support by CT level: open-ended exploration for high CT students, structured SRL scripts for low CT students¶

Strategy · All strategies
Evidence · no claims cited

Description¶

The article proposes two differentiated instructional strategies grounded in its findings. For high CT students, "unrestricted access to AICA should be granted" with encouragement to explore multiple code-implementation methods and engage in metacognitive reflection. For low CT students, structured SRL scripts should guide them through "goal setting – strategy selection – monitoring – evaluation" to internalize external prompts into an autonomous regulation cycle, transforming the AICA from an answer source into a cognitive partner.

Design Implications¶

Context¶

Requirements¶

• Requires knowing students' CT levels, e.g. via a validated CT assessment, to assign the appropriate support type

Constraints¶

• The article states reflection journals are inherently subjective and may not fully capture actual cognitive states; novelty effects on behavior and performance could not be completely eliminated

Target Learners¶

• eighth-grade students (aged 13–15) in K–12 coding education

Target Learning Goals¶

• Python coding knowledge and skills
• self-regulated learning skills
• computational thinking

Related Strategies¶

• Differentiate CT scaffolding by initial CT level: open-ended challenges for moderate-CT, constraint-based tasks for high-CT, structured pathways for low-CT learners
• Use CT profiles and behavioural metrics to differentiate pre-service teacher instruction and design a learning analytics dashboard

Examples¶

-

Key Sources¶

• Shu Zhao, Ziqi Wang, Tingting Chen, Chunchen Kang, Yu-Ju Lan. (2026). Exploring the effect of computational thinking levels on students' learning performance, cognition, and behavior when using AI coding assistants. Education Tech Research Dev. https://doi.org/10.1007/s11423-026-10663-6