Computational Thinking Development in AI Agent Creation_A Mixed-Methods Study

• URL: http://arxiv.org/abs/2605.14330v1
• Date: Thu, 14 May 2026 03:48:08 GMT
• Title: Computational Thinking Development in AI Agent Creation_A Mixed-Methods Study
• Abstract summary: This study examined computational thinking development among 93 pre-high school students in a five-day AI agent creation workshop using CocoFlow.<n>Results revealed significant improvements in abstract thinking and algorithmic thinking.<n>Students with moderate initial CT levels demonstrated substantially greater gains than both high-CT and low-CT peers.
• Score: 7.73047406525975
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: This mixed-methods study examined computational thinking (CT) development among 93 pre-high school students in a five-day AI agent creation workshop using CocoFlow, a no-code platform. Integrating pre-post assessments, behavioral logs, and interviews, we investigated CT development and how initial CT levels shape learning trajectories. Results revealed significant improvements in abstract thinking (effect size d = 0.71) and algorithmic thinking (effect size d = 0.70). Hierarchical regression identified iterative testing engagement as a predictor of self-efficacy gains (beta = 0.20, p = 0.05). Notably, students with moderate initial CT levels demonstrated substantially greater gains than both high-CT and low-CT peers, revealing an Optimal Development Zone effect (eta squared = 0.55). Qualitative analysis showed moderate-CT students exhibited adaptive expertise, while high-CT students risked over-engineering and low-CT students struggled with task decomposition. These findings challenge linear learning assumptions and provide evidence for differentiated scaffolding in CT education.

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