Computational Thinking Development in AI Agent Creation: A Mixed-Methods Study Sun et al. (2026) โ Multiple institutions. arXiv cs.CY. ๐ Full text (arXiv)
Summary
This mixed-methods study examines how 93 pre-high school students develop computational-thinking skills through a five-day AI agent creation workshop using CocoFlow, a no-code platform. The study integrates pre-post assessments, behavioral logs, and interviews to trace learning trajectories.
Key quantitative findings:
- Abstract thinking: significant improvement (d = 0.71)
- Algorithmic thinking: significant improvement (d = 0.70)
- Iterative testing engagement predicted self-efficacy gains (ฮฒ = 0.20, p = 0.05)
The Optimal Development Zone effect (ฮทยฒ = 0.55): Students with moderate initial CT levels showed substantially greater gains than both high-CT and low-CT peers. Qualitative analysis revealed why:
- Moderate-CT students exhibited adaptive expertise โ flexible, effective problem-solving
- High-CT students risked over-engineering โ creating unnecessarily complex solutions
- Low-CT students struggled with task decomposition โ breaking problems into manageable parts
These findings challenge linear learning assumptions in k-12-ai-education and provide direct evidence for differentiated scaffolding in CT education. The Optimal Development Zone concept parallels ai-metacognition-stem-review findings on tailoring AI support to student readiness. The study also contributes to the ai-literacy evidence base by showing that no-code AI agent creation platforms can effectively develop CT in young learners, complementing work on agentic-education-coding with older students using coding assistants.
Related Pages
- k-12-ai-education โ AI education in K-12 settings
- ai-literacy โ AI literacy frameworks and competencies
- stem-education โ STEM education and AI integration
- scaffolding โ Adaptive instructional support
- personalized-learning โ Personalized learning approaches
- ai-metacognition-stem-review โ AI tools scaffolding metacognition in STEM
- agentic-education-coding โ Agentic education with AI coding assistants
- understanding-student-effort-response-time โ Student effort modeling