Generative AI and Student Collaboration: A Scoping Review of Group Work Processes, Outcomes, and Risks

Synthesis: Wei and Perkins conducted a PRISMA-guided scoping review of 18 English-language studies (January 2023–March 2025) on how generative AI reshapes student group work in higher education, analyzing them using reflexive thematic analysis. Reported benefits include group knowledge development, idea generation, reflective thinking support, communication efficiency, task coordination, and feedback, while reported risks include reduced peer interaction and engagement under over-reliance, alongside privacy, transparency, and accuracy concerns. Outcomes appear to depend on task design, the degree of AI integration into group workflows, and student motivation and subject expertise.

Key Findings

1. 18 studies mapped across eight themes. The included studies spanned knowledge development, higher-order thinking, communication, team interaction, administrative efficiency, personalized feedback, data privacy/transparency, and bias/fairness/accuracy concerns.
2. GenAI reconfigures knowledge development. Learning shifts from an individual internal process toward a distributed, interactive one in which AI acts as facilitator, collaborator, and cognitive scaffold (e.g., within the zone of proximal development).
3. Higher-order thinking is both supported and constrained. AI can expand ideas, support reflection, and act as a "devil's advocate," but randomized evidence found AI produced more innovative suggestions without significantly boosting participants' overall innovativeness, and could homogenise ideas and invite cognitive offloading.
4. Communication becomes more efficient but can displace dialogue. AI reduced misunderstandings and coordination load, yet studies found group work became more efficient with reduced demand for communication, negotiation, and collective sensemaking (Lin et al., 2024).
5. Bias, fairness, and accuracy concerns are widespread. AI outputs can distort or over-generalize user ideas, hallucinate inaccurate information, and reinforce training-data biases that risk excluding marginalized voices.
6. The evidence base is uneven. Benefits (knowledge, communication efficiency, interaction) are more prominently and empirically supported; risks around privacy, transparency, bias, and accuracy are frequently acknowledged but largely discussed conceptually with limited empirical investigation.
7. Trade-offs are interconnected. Efficiency gains can reduce interaction, and AI-mediated communication can both enhance clarity and compress nuance—positioning GenAI as reshaping multiple dimensions of collaboration simultaneously rather than in isolation.
8. Key gaps remain. Much research is short-term or conceptual, with little longitudinal work on group dynamics, cognitive development, or the social-relational dimensions of group work (trust, community formation, peer support).

Implications

• Prioritize human agency. GenAI should augment rather than replace human effort; when used in group work, skill-building (AI literacy, content knowledge, self-regulation) should support critical engagement and evaluation of AI output quality.
• Redesign curriculum, assessments, and Pedagogies and Teaching Strategies. Conventional assessment poorly captures AI-enabled collaborative learning; institutions need frameworks that evaluate meta-skills (collaborative Problem Solving, AI literacy, critical thinking) and recognize hybrid human–AI outputs, shifting focus from product to process.
• Shift the educator role. Educators become facilitators, designers of learning experiences, and ethical stewards, fostering awareness of bias, authorship, and intellectual property while developing their own AI literacy and Learning Analytics interpretation skills.
• Balance AI-mediated and human communication. Preserve and prioritize authentic peer interaction, collective sensemaking, and social skills; embed principles of care and transparency in the systems students use.
• Cultivate learners' AI literacy so students treat GenAI as a fallible collaborator with inherent limitations—shaping how they evaluate and act on hybrid outputs—and demand longitudinal, empirical research on how sustained GenAI use shapes group dynamics and outcomes.

Connected Concepts

• Collaborative Learning
• Generative AI
• Higher Education
• Critical Thinking
• Self-Regulated Learning
• Human AI Collaboration
• Scaffolding
• Cognitive Offloading
• AI Literacy
• Assessment
• Equity
• Bias Mitigation

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Citation

Wei, Y., & Perkins, M. (2026). Generative AI and student collaboration: A scoping review of group work processes, outcomes, and risks. International Journal for Educational Integrity, 22(8).