Exploring Shared Monitoring in Large Language Model (LLM)-Supported Online Collaborative Problem Solving for High-Cohesion and Low-Cohesion Groups
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| Title: | Exploring Shared Monitoring in Large Language Model (LLM)-Supported Online Collaborative Problem Solving for High-Cohesion and Low-Cohesion Groups |
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| Language: | English |
| Authors: | Xiaoyun Liu (ORCID |
| Source: | Journal of Computer Assisted Learning. 2026 42(3). |
| Availability: | Wiley. Available from: John Wiley & Sons, Inc. 111 River Street, Hoboken, NJ 07030. Tel: 800-835-6770; e-mail: cs-journals@wiley.com; Web site: https://www.wiley.com/en-us |
| Peer Reviewed: | Y |
| Page Count: | 21 |
| Publication Date: | 2026 |
| Document Type: | Journal Articles Reports - Research |
| Education Level: | Higher Education Postsecondary Education |
| Descriptors: | Artificial Intelligence, Intervention, Technology Uses in Education, Cooperative Learning, Problem Solving, College Students, Group Unity, Foreign Countries, Scaffolding (Teaching Technique), Student Behavior |
| Geographic Terms: | China |
| DOI: | 10.1002/jcal.70249 |
| ISSN: | 0266-4909 1365-2729 |
| 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. |
| Abstractor: | As Provided |
| Entry Date: | 2026 |
| Accession Number: | EJ1506852 |
| Database: | ERIC |
| 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. |
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| ISSN: | 0266-4909 1365-2729 |
| DOI: | 10.1002/jcal.70249 |
