Unlocking the True Potential of AI in Teamwork
As generative artificial intelligence (GenAI) becomes increasingly integrated into collaborative learning, educators and designers are reevaluating how AI can support the psychological and social processes that underpin effective teamwork.
Recent empirical studies, involving over 480 learners across higher education, suggest that the effectiveness of AI depends less on its capabilities than on how people work with it. These findings highlight the importance of designing AI for collaborative learning as a psychological challenge, rather than simply a technical one.
One key factor in determining the value of AI-supported collaboration is interpersonal trust. Research by Luo and colleagues (2025) found that students who perceived AI as intelligent developed stronger collective efficacy, a shared confidence in their group’s ability to succeed, which in turn promoted team creativity. However, AI also increased task conflict, creating disagreements that reduced creative performance. Trust proved to be the critical difference, as groups with stronger interpersonal trust were better able to turn AI-supported collaboration into creative outcomes.
Another critical aspect of successful collaboration with AI is critical engagement. Lehtinen and colleagues (2026) observed that higher-performing groups treated AI output as something to verify, not something to copy. They consistently corroborated AI responses before using them, rather than relying solely on AI-generated text. This approach enabled them to think critically and make informed decisions, leading to better outcomes.
The studies also highlight the importance of shared regulation in AI-supported collaboration. Gyasi and colleagues (2025) found that groups supported by both AI feedback and an AI chatbot demonstrated higher collaborative knowledge building, cognitive engagement, socially shared regulation, and overall group performance than those receiving either intervention independently or no AI support. This suggests that AI works best when its design reflects the integrated process of planning, monitoring, evaluating, and adapting group work.
Ultimately, designing AI means designing for collaboration. The evidence reviewed here suggests that the future of AI in education is unlikely to depend solely on building more capable systems. Instead, it may depend on designing AI that helps groups communicate more effectively, think more critically, and regulate their learning together.
In conclusion, while AI has the potential to significantly enhance teamwork, its effectiveness depends on how people work with it. By understanding the psychological and social processes that underpin effective collaboration, educators and designers can create AI systems that support the development of trust, critical engagement, and shared regulation, ultimately leading to better outcomes.
Researchers Rónan Fulton, Alana McCarthy, and Michael Hogan have been exploring these issues in their studies, providing valuable insights into the complex relationship between AI, teamwork, and learning outcomes.
