AI Museum Guides Acceptance for History Learning: Design Attributes, Dual Affective Pathways, and Largely Invariant Gender Effects

As AI-powered learning tools become more common in educational settings, understanding their acceptance mechanisms is increasingly important. This study examines how the design attributes of AI museum guides—anthropomorphism, interactivity, and personalization—are associated with the acceptance intention and perceived learning outcomes among Chinese high school students with prior museum experience. Using structural equation modeling with 324 participants, we test whether these features relate to acceptance through two affective pathways: perceived warmth and anxiety reduction. The results reveal distinct patterns: anthropomorphism shows an indirect-only association with anxiety reduction through perceived warmth; interactivity is associated with anxiety reduction through responsive feedback; and personalization serves dual functions, enhancing both pathways. Anxiety reduction shows strong positive associations with both acceptance intention and perceived learning outcomes. The multi-group analysis shows that most pathways function equivalently across genders, with one exception where anxiety reduction more strongly predicts learning outcomes for females than males. These findings reveal distinct psychological functions within the Chinese educational context: anthropomorphism influences anxiety reduction exclusively through perceived warmth, while personalization and interactivity provide both affective and cognitive support. The implications for AI museum guide design in similar contexts are discussed. The generalizability to other cultural contexts and populations, such as Western students or adult learners, requires further investigation.

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Published
2026-04-17
DOI
https://doi.org/10.3390/info17040376
Primary Topic
AI in Service Interactions
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article
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AI Museum Guides Acceptance for History Learning: Design Attributes, Dual Affective Pathways, and Largely Invariant Gender Effects

Xuezhen Wu, Chaohui Wang, Gang Ren, Yifan Zhuo et al.
Information
AI in Service Interactions
article

AI Museum Guides Acceptance for History Learning: Design Attributes, Dual Affective Pathways, and Largely Invariant Gender Effects

Xuezhen Wu, Chaohui Wang, Gang Ren, Yifan Zhuo, Li Wang
article en

Abstract

As AI-powered learning tools become more common in educational settings, understanding their acceptance mechanisms is increasingly important. This study examines how the design attributes of AI museum guides—anthropomorphism, interactivity, and personalization—are associated with the acceptance intention and perceived learning outcomes among Chinese high school students with prior museum experience. Using structural equation modeling with 324 participants, we test whether these features relate to acceptance through two affective pathways: perceived warmth and anxiety reduction. The results reveal distinct patterns: anthropomorphism shows an indirect-only association with anxiety reduction through perceived warmth; interactivity is associated with anxiety reduction through responsive feedback; and personalization serves dual functions, enhancing both pathways. Anxiety reduction shows strong positive associations with both acceptance intention and perceived learning outcomes. The multi-group analysis shows that most pathways function equivalently across genders, with one exception where anxiety reduction more strongly predicts learning outcomes for females than males. These findings reveal distinct psychological functions within the Chinese educational context: anthropomorphism influences anxiety reduction exclusively through perceived warmth, while personalization and interactivity provide both affective and cognitive support. The implications for AI museum guide design in similar contexts are discussed. The generalizability to other cultural contexts and populations, such as Western students or adult learners, requires further investigation.

InformationVol. 17(4)
Zhongyuan University of Technology (CN), Anhui Normal University (CN), Xiamen University of Technology (CN)
Quality Education, Gender equality
Openalex Percentile: Top 19%
AI in Service Interactions
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AI Museum Guides Acceptance for History Learning: Design Attributes, Dual Affective Pathways, and Largely Invariant Gender Effects — Xuezhen Wu, Chaohui Wang, et al. · Information (2026) | TGRS Research Map | TGRS