# Beyond the Machine: An Integrative Framework of Anthropomorphism in AI

## TL;DR

This paper proposes a new framework to understand how people perceive AI as human-like and how this affects their acceptance or resistance to it.

## Contribution

It introduces a mid-range conceptual framework integrating social perception dimensions to explain AI anthropomorphism effects.

## Key findings

- Anthropomorphism influences perceptions of AI through warmth and competence attributions.
- AI autonomy acts as a boundary condition for acceptance or resistance.
- The framework identifies pseudo-empathy and identity threat as potential outcomes of anthropomorphism.

## Abstract

AI-enabled technology (AI) has a transformational role in our modern society because it is increasingly used as an interaction partner, making anthropomorphism (tendency to ascribe human features to non-human agents) a central mechanism shaping how people evaluate, accept or resist AI systems. Existing technology acceptance models and anthropomorphism frameworks, however, offer limited guidance on how human-like attributes of AI translate into perceptions of usefulness, perceived control, perceived opportunity or threats, particularly across different levels of AI autonomy. Building on the theory of planned behavior, the technology acceptance model and threat rigidity model, this paper develops a mid-range conceptual framework of AI anthropomorphism grounded in universal social perception dimensions of warmth and competence. We integrate fragmented research to derive three core propositions and four corollaries that specify how warmth and competence attributions shape evaluative cognitions in relation to AI. The framework further identifies AI autonomy as a boundary condition under which anthropomorphic cues may either facilitate acceptance or trigger perceptions of pseudo-empathy, cognitive superiority and identity threat. By offering a parsimonious, theoretically informed model, this paper clarifies when anthropomorphism fosters acceptance versus resistance in human–AI interaction and provides a structured agenda for future empirical research and AI design aimed at fostering synergies and resilience in human–AI ecosystems.