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From Tool to Social Actor: A Systematic Review of the Psychological Mechanisms Through Which Conversational AI
Li Liu1, Ruijuan Zhang1, Xin Su2
1School of Management, China Women's University, Beijing 100101, China.
Abstract:
The rapid workplace diffusion of conversational artificial intelligence (CAI) introduces a new social actor, yet existing reviews often neglect the intrapsychic processes through which employees construe these systems. This systematic review synthesizes the emerging psychological mechanisms suggested by the literature through which CAI reshapes the employee experience. Following PRISMA guidelines, we searched the Web of Science database (up to February 2026) and analyzed 84 SSCI-indexed studies based on the sociotechnical systems theory, the Computers as Social Actors paradigm, and the Job Demands-Resources model; the corpus quality was appraised using the Mixed Methods Appraisal Tool. Findings reveal that while instrumental framing remains dominant, a meaningful theoretical turn has emerged among a minority of studies, where conversational artificial intelligence is increasingly conceptualized as a partner or supervisor fundamentally driven by anthropomorphic cues. The synthesis identifies a double-edged reshaping of work: cognitively, human intelligence augmentation competes with skill threat; emotionally, constant support contrasts with social fabric erosion; and career-wise, inclusion coexists with work alienation and generative AI loafing. These dimensions are psychologically coupled, with patterns indicating that cognitive threats co-occur with emotional anxiety and moral expediency. While limited by cross-sectional primary evidence, the review provides an integrated conceptual model illustrating proposed conceptual pathways that connect multi-layered antecedents to employee outcomes, which could be adopted to guide future longitudinal validation. By shifting the focus from productivity to psychological experience, this study offers theoretical foundations for human-AI collaboration and outlines a future research agenda on trust dynamics and algorithmic fairness.
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