Artificial Intelligence Agents in Computer-Supported Collaborative Learning: A Systematic Literature Review

Synthesis: This systematic literature review of 46 empirical studies (2014–2025) analyzes the roles and impacts of AI agents within computer-supported collaborative learning (CSCL). Guided by the community of inquiry model and learning engagement theory, it finds AI agents most frequently facilitate small-group collaboration and problem-solving via text-based online platforms, with functions spanning cognitive Scaffolding, social facilitation, and instructional orchestration. While cognitive gains are consistently reported, effects on behavioral, social, and emotional outcomes are context-dependent.

Key Findings

1. 46 studies, post-secondary emphasis. Most research is set in post-secondary settings, with mixed-methods designs most common; AI agents typically facilitate small-group collaboration and problem-solving through text-based online platforms.
2. Three agent functions. AI agents perform cognitive scaffolding, social facilitation, and instructional orchestration, with recent developments enabling more adaptive and participatory roles.
3. Domain-aligned effects. The alignment between agent functions and learning outcomes is strongest within the same domain, though important cross-domain influences are also evident.
4. Cognitive gains consistent, other outcomes context-dependent. Cognitive gains are consistently reported, but behavioral, social, and emotional outcomes appear context-dependent — highlighting the need for nuanced agent design.
5. Need for equity and context-sensitive deployment. The review underscores equitable access, expanded conceptual frameworks, and context-sensitive deployment for meaningful, responsible AI-agent integration in CSCL.

Implications

This review is the non-IBL companion paper, contributing to the wiki's agent and Collaborative Learning threads. It maps how AI agents (beyond chatbots) orchestrate and scaffold collaborative learning, connecting to pedagogical agents and the community-of-inquiry model. For designers, the domain-alignment finding implies agent functions should be matched to intended outcomes, and cognitive scaffolding functions can reliably boost learning while social/emotional effects need contextual tuning. It connects to Human AI Collaboration and equity concerns.

Connected Concepts

• Collaborative Learning
• Agentic AI
• Pedagogical Agent
• Scaffolding
• Human AI Collaboration
• Learning Gains
• Equity In AI Education
• Conversational AI

Connected Articles

• Conversational AI Agents Umbrella Review 2026 — Umbrella review of conversational AI agents in education
• Li Dbagent LLM Educational Agent CS 2026 — LLM-based educational agent in CS education
• GenAI Counter Learner Groupthink 2025 — GenAI agent counters groupthink in PBL

Citation

Ba, S., Shi, X., Wu, S., & Lu, G. (2026). Artificial intelligence agents in computer-supported collaborative learning: a systematic literature review. Computers and Education: Artificial Intelligence, 10, 100579.