Human-like conversational agents as social partners: a scoping review of socioaffective mechanisms, well-being outcomes, risks and governance in the post-Turing era.
Large language models have evolved from laboratory demonstrations into mass-market companion-style conversational agents that many users treat as social partners. As these systems produce increasingly human-like conversational behavior, users may attribute mind, form affective bonds, disclose sensitive information, and rely on agents for emotional support, creating both potential benefits and psychosocial risks.
We conducted a PRISMA-ScR-informed scoping review of socioaffective human-artificial intelligence interaction research published or posted from 2016 to 15 January 2026. Eligible sources addressed companion-style conversational agents and adjacent therapeutic, assistant-first, or governance sources. Evidence was stratified by system type and calibrated as stronger empirical support, moderate support, preliminary support, or proposed/normative synthesis.
The final evidence map included 58 sources. We synthesized mechanisms that make agents feel social, including anthropomorphism, social presence, mind perception, self-disclosure, parasocial attachment, and socioaffective alignment. Therapeutic chatbot studies provided the strongest evidence for short-term symptom reduction in selected contexts, whereas evidence for sustained loneliness reduction in open-domain companion systems remained emerging. Reported and hypothesized risks included dependency-like use, displacement of human interaction, maladaptive validation or sycophancy, persuasive manipulation, privacy harms, and risks to minors or vulnerable users.
We propose an integrative pathway linking model capabilities and product design cues to relational outcomes and outline a companion-specific relational safety stack as a synthesis-based evaluation agenda rather than a validated regulatory or clinical standard. The review identifies measurement priorities for evaluating companion agents and governance priorities for managing psychosocial impact as these systems scale.
We conducted a PRISMA-ScR-informed scoping review of socioaffective human-artificial intelligence interaction research published or posted from 2016 to 15 January 2026. Eligible sources addressed companion-style conversational agents and adjacent therapeutic, assistant-first, or governance sources. Evidence was stratified by system type and calibrated as stronger empirical support, moderate support, preliminary support, or proposed/normative synthesis.
The final evidence map included 58 sources. We synthesized mechanisms that make agents feel social, including anthropomorphism, social presence, mind perception, self-disclosure, parasocial attachment, and socioaffective alignment. Therapeutic chatbot studies provided the strongest evidence for short-term symptom reduction in selected contexts, whereas evidence for sustained loneliness reduction in open-domain companion systems remained emerging. Reported and hypothesized risks included dependency-like use, displacement of human interaction, maladaptive validation or sycophancy, persuasive manipulation, privacy harms, and risks to minors or vulnerable users.
We propose an integrative pathway linking model capabilities and product design cues to relational outcomes and outline a companion-specific relational safety stack as a synthesis-based evaluation agenda rather than a validated regulatory or clinical standard. The review identifies measurement priorities for evaluating companion agents and governance priorities for managing psychosocial impact as these systems scale.