Researchers at Carnegie Mellon University's Tepper School of Business are investigating how AI can strengthen human teamwork rather than replace team members. Professor Anita Williams Woolley and her colleagues developed COHUMAIN (Collective HUman-MAchine INtelligence), a framework for understanding where AI fits within organizational social psychology. The researchers argue AI should serve as a partner under human direction — facilitating connections and nudging collaboration — rather than acting as a manager or autonomous teammate. They note that while AI excels at tasks like summarizing meetings, it still can't replicate human abilities like reading the mood of a room.

Ph.D. student Allen Brown is exploring the tension between AI's collaborative potential and concerns around psychological safety and privacy. His research found that people feel more vulnerable when they believe an AI system is evaluating them, compared to being evaluated by another human, due to the loss of "mutual assumption of risk." Meanwhile, researcher Zhaohui (Zoey) Jiang is studying how AI transparency affects trust, finding that transparent AI benefits less-skilled users by helping them learn from the system's decision-making rules, while black box AI can actually be more effective for high-ability users, who tend to penalize AI mistakes more harshly when they understand the system's inner workings. Opacity, paradoxically, inflates perceptions of sophistication. The researchers suggest future AI systems may self-adapt their level of transparency to complement individual users' decision-making weaknesses.

This paper presents a large-scale dynamic experiment with over 800 participants across 40+ countries investigating how exposure to AI-generated ideas from ChatGPT affects human creativity, idea diversity, and the evolution of collective thought. Using a variant of the Alternate Uses Task (AUT), a standard divergent thinking measure, participants brainstormed creative uses for everyday objects after viewing example ideas that were either human-generated (from prior participants) or AI-generated. The researchers manipulated two key factors: AI exposure level (none, low, or high) and AI disclosure (whether AI-generated ideas were labeled as such). The experiment's dynamic design, inspired by the "multiple-worlds" paradigm, allowed ideas from earlier participants to feed forward as stimuli for later ones, simulating how AI content compounds through a cultural feedback loop.

The key finding is that high AI exposure increased collective idea diversity but did not improve individual creativity — in short, AI made ideas different, not better. High AI exposure also accelerated the rate of change in idea diversity over time, a compounding effect with potentially significant long-term implications. There were no main effects of disclosure (labeling ideas as AI-generated), though interesting moderating effects emerged: self-identified creative individuals were less influenced by AI disclosure, while participants appeared more likely to knowingly adopt AI ideas when the task was difficult. The study contributes a novel experimental framework for studying passive AI exposure — the increasingly common scenario where people encounter AI-generated content without actively soliciting it — and suggests that while AI integration may enrich the diversity of a collective idea pool, it does not necessarily elevate the quality of individual contributions.