ABSTRACT Background Recent research increasingly highlights the central role of interventions in enhancing shared monitoring during collaborative problem‐solving. However, traditional intervention approaches suffer from limitations in timeliness and adaptability. Large language model (LLM), equipped with deep semantic parsing and contextual perception, can dynamically detect latent challenges and provide targeted, timely, context‐sensitive feedback. Objectives This study examines how LLM‐supported interventions affect group shared monitoring during dynamic CPS processes. Methods This study designed a collaborative problem‐solving platform integrated with LLM, and 28 students from a university in China participated in CPS activities. Chi‐square tests, conditional random fields, linear mixed models and correlation analyses were adopted to examine the changes in both monitoring behaviour and equality of monitoring participation in high‐cohesion (HCGs) and low‐cohesion groups (LCGs) after LLM‐supported group metacognitive scaffolding (LLM‐GMS) intervention, as well as their effects on collaborative performance. Results and Conclusions The results show that (1) LLM‐GMS activated more socio‐cognitive and behavioural monitoring in HCGs, whereas LCGs mainly exhibited heightened behavioural monitoring. (2) Descriptive analyses revealed divergent trends in monitoring participation equality across group types, with HCGs showing increased equality and LCGs exhibiting a decline. (3) In HCGs, socio‐emotional monitoring was positively associated with collaborative performance, whereas participation equality and behavioural monitoring exhibited negative associations with collaborative performance. In contrast, among LCGs, behavioural monitoring was positively related to performance, whereas socio‐cognitive monitoring was unexpectedly negatively associated with performance. Implications These findings highlight that LLM‐GMS can be a valuable tool for supporting collaborative learning, but its effectiveness depends on group characteristics and its implementation approach.
Liu et al. (2026) studied this question.