Papers for
knowledge worker teams
Papers whose findings have a practical use for this group, as judged from the abstract. Open a paper to read what it means in practice.
Human GenAI collaboration shows unique memory and trust patterns
The GenAI Who Knew Too Little: Revisiting Transactive Memory Systems in Human GenAI Collaboration
Abstract: Generative artificial intelligence (GenAI) is rapidly transforming knowledge work, creating new collaborative dynamics that challenge tradi-tional human-human interaction patterns. This study investigates how Transactive Memory Systems (TMS) manifest in human-GenAI collabora-tion. Through qualitative interpretive interviews with 14 knowledge work-ers and thematic analysis, we identified distinct TMS manifestations across its three main dimensions: (1) regarding specialization, expertise recogni-tion exhibited asymmetry. Humans developed awareness of GenAI capabili-ties while GenAI demonstrated only temporary recognition of human exper-tise; (2) in terms of credibility, humans formed ambivalent trust relation-ships with GenAI, simultaneously attributing expertise to the latter while implementing additional verification processes; and (3) concerning coor-dination, where humans primarily directed more passive GenAI systems lacking initiative, resulting in hierarchical interaction patterns and unidi-rectional information flow. Our findings reveal that TMS manifests differ-ently in human-GenAI compared to human-human collaboration, necessitat-ing theoretical adaptations including trust markers, acknowledging GenAI's absent metamemory, and identifying its temporary memory.
Ai boosts task scores but not users confidence calibration
Confident, Not Wiser: The Dunning-Kruger Effect in Human-AI Interaction
Abstract: AI assistance can improve performance without improving self-assessment. We report a study (N=366) comparing Human alone and Human+AI performance on reasoning tasks, for which the AI model is benchmarked on the same items. Participants estimated global and block performance and rated confidence in their answers. Human+AI achieved higher scores, but self-estimates tracked performance weakly. Average overestimation was similar across groups, covering individual errors. Across tasks, confidence distinguished correct from incorrect answers less accurately in the Human+AI group, while within-task differences remained uncertain. The Dunning-Kruger pattern was found in both groups, with a larger observed contrast in Human+AI. Controls for score noise reduced but did not eliminate the pattern, with the controlled group difference remaining inconclusive. An extended computational account describes global and block estimates. Our findings distinguish performance augmentation from metacognitive augmentation and motivate interfaces that support verification, communicate task-specific AI model performance, and help users evaluate the quality of their joint work rather than produce answers.