A Scoping Review of the Ethical Perspectives on Anthropomorphising Large Language Model-Based Conversational Agents

2026-01-14

AI Summary

This study addresses the ethical risks—such as deception and overreliance—arising from anthropomorphism in large language model (LLM) conversational agents, an area lacking systematic synthesis and a unified evaluation framework. Employing a scoping review methodology, the research integrates multi-source literature from five databases and three preprint platforms, combining bibliometric and content analyses to map the interdisciplinary ethical landscape for the first time. Findings reveal that while scholars commonly adopt attribution-based definitions of anthropomorphism, operationalizations remain highly heterogeneous. Moreover, ethical discourse is predominantly risk-oriented and lacks robust empirical grounding, limiting its utility for governance. In response, this work proposes an integrative research agenda that bridges interaction effects with practical governance mechanisms.

Technology Category

• Philosophy and Ethics of AI: Artificial General Intelligence
• Natural Language Processing: Ethics — Bias, Fairness, Transparency & Privacy
• Humans and AI: Learning Human Values and Preferences

Application Category

• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation
• Economics, Online Markets and Human Computation: Humans versus LLMs for data annotation and labeling

Abstract

Anthropomorphisation -- the phenomenon whereby non-human entities are ascribed human-like qualities -- has become increasingly salient with the rise of large language model (LLM)-based conversational agents (CAs). Unlike earlier chatbots, LLM-based CAs routinely generate interactional and linguistic cues, such as first-person self-reference, epistemic and affective expressions that empirical work shows can increase engagement. On the other hand, anthropomorphisation raises ethical concerns, including deception, overreliance, and exploitative relationship framing, while some authors argue that anthropomorphic interaction may support autonomy, well-being, and inclusion. Despite increasing interest in the phenomenon, literature remains fragmented across domains and varies substantially in how it defines, operationalizes, and normatively evaluates anthropomorphisation. This scoping review maps ethically oriented work on anthropomorphising LLM-based CAs across five databases and three preprint repositories. We synthesize (1) conceptual foundations, (2) ethical challenges and opportunities, and (3) methodological approaches. We find convergence on attribution-based definitions but substantial divergence in operationalization, a predominantly risk-forward normative framing, and limited empirical work that links observed interaction effects to actionable governance guidance. We conclude with a research agenda and design/governance recommendations for ethically deploying anthropomorphic cues in LLM-based conversational agents.

Problem

• anthropomorphisation
• large language models
• conversational agents
• ethical concerns
• scoping review

Innovation

• anthropomorphisation
• large language models
• conversational agents
• ethical AI
• scoping review