Qiannan Niu

Yimeng Sun, Haiyang Xin, Qiannan Niu et al.

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.

Yimeng Sun, Haiyang Xin, Shuang Li et al.

This study investigates teachers design behaviors and cognitive underpinnings when designing multi-agent instructional workflows. Analyzing behavioral logs (N=61), cluster and Markov analyses identified three archetypes: Systematic Optimizers iteratively refining complex architectures; Prolific Creators rapidly prototyping pragmatic tools via scaffolding; and Passive Observers exhibiting polarized expert-novice profiles. Subsequent artifact (n=15) and interview (n=12) analyses reveal AI-TPACK integration emerges from a dynamic interplay of systems thinking, pedagogical beliefs, and self-efficacy, not merely from the possession of discrete knowledge. These findings call for differentiated scaffolding responsive to teachers cognitive-behavioral diversity.

Haiyang Xin, Qiannan Niu, Shuang Li et al.

This two-cycle formative intervention study examined why teachers disengage from AI agent creation after professional development - a low engagement paradox - and tested whether systemic redesign could address it. Cycle 1 (N=218) revealed that despite completing comprehensive TPD, 87 percent of teachers ceased creating within three weeks, with behavioral tracking and interview analysis identifying systemic contradictions as the source of psychological need frustration rather than capacity deficits. Cycle 2 (N=26) implemented Cultural-Historical Activity Theory and Self-Determination Theory - driven redesign directly targeting diagnosed contradictions, achieving synchronized enhancement of both capacity and willingness. The findings reframe implementation failure as a rational response to need-thwarting systems and offer a replicable CHAT - SDT diagnostic framework for transformative professional development.

Shuang Li, Haiyang Xin, Yimeng Sun et al.

Effective collaboration requires Socially Shared Regulation (SSRL), but students often lack these skills. This study introduces the MIRACLE (Multi-Agent Intelligent Regulation to Advance Collaborative Learning Environment) system, which supports SSRL by orchestrating metacognitive regulation and proactively providing emotional and motivational support. We conducted a quasi-experimental study with 90 fifth-grade students. The experimental group (n=42) used a collaborative platform CocoNote equipped with MIRACLE, while the control group (n=48) used the same platform with a general GPT assistant. Quantitative results show the MIRACLE group achieved significant gains across SSRL phases (Planning, Monitoring, Reflection) and produced higher-quality collaborative artifacts compared to the control group. Qualitative findings indicate students perceived MIRACLE as an effective facilitator for cognitive, regulatory, and emotional support. This study demonstrates that specialized, orchestrated AI systems are more effective than generic AI in enhancing SSRL.