High prior knowledge students engaged more in co-regulation behaviors while low prior knowledge students relied more on question-raising and idea-confirming sequences¶

Claim · All claims
Evidence · 1 study (2 entries) · associational r2 · q2
Currency · newest study 2025 · not yet checked against its sources · not reviewed by a person

Subclaims¶

q2 i? Compared with low prior knowledge students, high prior knowledge students showed higher proportions of initiating ideas (15.5% vs. 12.1%) and regulatory behaviors, while low prior knowledge students engaged more in question-raising (55.3% vs. 45.2%) and negotiating/confirming ideas (8.9% vs. 5.7%). → Hao 2025 q2 i? Group-level LSA showed monitoring and regulating (SB5) central for high prior knowledge students, while low prior knowledge students had more significant sequences involving negotiating and confirming behaviors (SB4). → Hao 2025 (2)

Evidence¶

Hao 2025¶

Hao, Z.; Cao, J.; Li, R.; Yu, J.; Liu, Z.; Zhang, Y. (2025). Mapping Student-AI Interaction Dynamics in Multi-Agent Learning Environments: Supporting Personalised Learning and Reducing Performance Gaps. https://osf.io/preprints/eduRxiv

q2 · i? · associational · r2

Behavioral proportion comparison between the top-30% (n = 33) and bottom-30% (n = 38) pre-test groups in the MAIC course. The article reports significantly more regulatory behaviors among high prior knowledge students and more question-raising among low prior knowledge students.

"students with higher prior knowledge demonstrated significantly more regulatory behaviors, including monitoring and regulating the learning process (12.9% vs. 11.2%), managing AI peers (7.5% vs. 4.5%), and sharing emotions (7.4% vs. 2.6%). In contrast, students with lower prior knowledge were more likely to engage in question-raising behaviors (55.3% vs. 45.2%)"

Hao 2025 (2)¶

Hao, Z.; Cao, J.; Li, R.; Yu, J.; Liu, Z.; Zhang, Y. (2025). Mapping Student-AI Interaction Dynamics in Multi-Agent Learning Environments: Supporting Personalised Learning and Reducing Performance Gaps. https://osf.io/preprints/eduRxiv

q2 · i? · associational · r2

Group-level LSA of behavioral transitions (Figures 7a and 7b) found more significant sequences for the high prior knowledge group, centered on SB5 regulation, whereas the low group's significant sequences centered on SB4 negotiating and confirming.

"the group with higher prior knowledge exhibited a greater number of significant behavioral sequences compared to the low prior knowledge group. Specifically, in the interactions between high prior knowledge students and AI agents, the monitoring and regulating process (SB5) plays a more central role"

Discussion¶

Related Claims¶

• High prior knowledge students sent significantly more messages and generated denser multi-turn interaction with AI agents than low prior knowledge students — related
• Student-AI interaction in a multi-agent learning environment is organized into two significant engagement patterns: co-construction of knowledge and co-regulation — related
• Epistemic network analysis shows high-learning groups integrate re-annotations with knowledge-confirming discourse more densely than low-learning groups — related
• Regulatory sequencing in synchronous CSCL: cognitive interactions initiate cycles, co-regulation iterates with task execution, and SSRL follows socio-emotional interactions — related