Paper Type
ERF
Abstract
Conversational AI tools are increasingly embedded in collaborative problem‑solving settings, yet we still know little about how they shape the distribution of cognitive work, coordination, and shared understanding within teams. Using a distributed‑cognition lens, we investigate how dyads engage in hybrid human–AI problem solving during a structured analytical task. A five‑step activity design (individual attempt, partner comparison, AI consultation, reconciliation, and reflection) enables us to trace how conversational AI contributes to representational repair, clarification, and coordination. Early observations show that AI’s collaborative role depends on whether teams begin with consistent or inconsistent initial attempts: in consistent cases, AI primarily validates and clarifies shared representations, whereas in inconsistent cases, AI provides a broader range of mediation functions—such as recomputation, strategy support, clarification, and reconciliation—to stabilize and align understanding. This work provides early insight into how conversational AI functions as a collaborative partner, informing future research and design.
Paper Number
1822
Recommended Citation
Sun, Jun; Islam, Md Rafiqul; and Wang, Ying, "Conversational AI as a Collaborative Partner: A Distributed Cognition Lens on Hybrid Human–AI Problem Solving" (2026). AMCIS 2026 Proceedings. 19.
https://aisel.aisnet.org/amcis2026/sig_hci/sig_hci/19
Conversational AI as a Collaborative Partner: A Distributed Cognition Lens on Hybrid Human–AI Problem Solving
Conversational AI tools are increasingly embedded in collaborative problem‑solving settings, yet we still know little about how they shape the distribution of cognitive work, coordination, and shared understanding within teams. Using a distributed‑cognition lens, we investigate how dyads engage in hybrid human–AI problem solving during a structured analytical task. A five‑step activity design (individual attempt, partner comparison, AI consultation, reconciliation, and reflection) enables us to trace how conversational AI contributes to representational repair, clarification, and coordination. Early observations show that AI’s collaborative role depends on whether teams begin with consistent or inconsistent initial attempts: in consistent cases, AI primarily validates and clarifies shared representations, whereas in inconsistent cases, AI provides a broader range of mediation functions—such as recomputation, strategy support, clarification, and reconciliation—to stabilize and align understanding. This work provides early insight into how conversational AI functions as a collaborative partner, informing future research and design.
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