Evidence map›Paper›PMID 42846343›Full record

ArticleFrontiers in artificial intelligence2026

Large language model attributes and perceived moral agency of artificial moral advisors.

Tze Wei Liew, Su-Mae Tan, Yi Yong Lee, Chin Lay Gan, Tak Jie Chan

Abstract read
In one paragraph

Article in Frontiers in artificial intelligence, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

What it found

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2 · The registry

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3 · Its place in the literature

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4 · The record

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5 · Who and what money

Authors and funding

5 authors.

Tze Wei LiewCentre for Interaction and Experience Design, CoE for Immersive Experience, Multimedia University, Persiaran Multimedia, Cyberjaya, Malaysia.
Su-Mae TanCentre for Interaction and Experience Design, CoE for Immersive Experience, Multimedia University, Persiaran Multimedia, Cyberjaya, Malaysia.
Yi Yong LeeCentre for Management and Marketing Innovation, CoE for Business Innovation and Communication, Multimedia University, Persiaran Multimedia, Cyberjaya, Malaysia.
Chin Lay GanCentre for Management and Marketing Innovation, CoE for Business Innovation and Communication, Multimedia University, Persiaran Multimedia, Cyberjaya, Malaysia.
Tak Jie ChanFaculty of Business, Information & Human Sciences, Kuala Lumpur University of Science and Technology, Kajang, Malaysia.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Introduction: Large Language Models (LLMs) are increasingly used as conversational artificial moral advisors capable of providing ethical guidance, moral reflection, and recommendations regarding complex moral dilemmas. Despite their ability to generate coherent and persuasive moral discourse, LLMs lack genuine moral understanding, intentionality, and reflective judgment, raising concerns that users may overattribute moral agency to these systems. Drawing on Theory of Mind, AI Moral Agency, and the Godspeed framework, this study investigates how perceived LLM attributes-namely anthropomorphism, animacy, likeability, and intelligence-are associated with users' perceptions of agency and morality in the context of artificial moral advisors. Methods: A cross-sectional survey was conducted with 758 valid respondents who were presented with vignette-based moral dilemmas relevant to young adults and instructed to imagine seeking advice from LLM-based conversational agents. Results: Perceived agency was positively associated with perceived morality, suggesting that users are more likely to regard LLM-based artificial moral advisors as morally capable when they perceive them as autonomous, intentional, and self-directed entities. Perceived LLM anthropomorphism and animacy were significantly and positively associated with perceived agency, with perceived agency fully mediating their associations with the perceived morality of artificial moral advisors. Perceived LLM likeability and intelligence were positively associated with perceived morality but were not significantly associated with perceived agency, suggesting that users may infer moral capability from social appeal and cognitive competence without necessarily attributing autonomy or intentionality to these systems. Discussion: These findings offer important theoretical and practical implications for the ethical design of artificial moral advisors, particularly regarding the need to prevent inflated perceptions of AI moral competence and authority.

Indexed as

artificial moral advisorsGodspeed indiceslarge language modelsperceived agencyperceived morality

Identifiers

PMID42846343
PMCPMC13642594

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.