Behavioral trends across three tasks showed high CT students maintained stable independent coding while low CT students' AICA use fluctuated¶

Claim · All claims
Evidence · 1 study · associational r1 · q2
Currency · newest study 2026 · not yet checked against its sources · not reviewed by a person

Subclaims¶

q2 i? Across three tasks, the high CT group's AICA-assisted coding proportion rose slightly in task 2 and fell in task 3, while the low CT group's proportion fell in task 2 and rebounded in task 3, which the authors interpret as answer-seeking rather than growing autonomy. → Shu Zhao 2026

Evidence¶

Shu Zhao 2026¶

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

q2 · i? · associational · r1

Descriptive analysis of behavior-category proportions across the three weekly tasks (Table 4 and Fig. 4), with interpretation supported by reflection-journal excerpts. The article reports the low CT group's proportion "decreased in the second task and rebounded in the third task"; no inferential test is printed.

"The proportion of AICA-assisted coding decreased in the second task and rebounded in the third task, showing an overall downward trend. Although this shift seems to reflect an increase in autonomy, it is more likely due to students focusing on task completion rather than a true understanding of coding logic."

Discussion¶

Related Claims¶

• High CT students used the AICA for code understanding and debugging while low CT students used it for direct answer retrieval — related
• ENA revealed high CT students had significantly more coherent SRL cognitive networks spanning planning, execution, and self-reflection than low CT students — related
• High and low CT groups showed comparable prior coding knowledge before the AICA intervention — related
• High CT students significantly outperformed low CT students on the post-test after AICA-assisted coding instruction — related
• High CT students significantly exceeded low CT students in task strategies, elaboration, and self-assessment during AICA interactions — related
• Low-level students showed stage-based fluctuation with a time-lag effect in which thinking improvement preceded writing improvement, while high-level students remained stable — related