Exploring the Impact of Avatar Representations in AI Chatbot Tutors on Learning Experiences

<p dir="ltr">Despite the growing prominence of Artificial Intelligence (AI) chatbots used in education, there remains a significant gap in our understanding of how interface design elements, particularly avatar representations, influence learning experiences. This paper explores the impact of different AI chatbot avatar representations on students’ learning experiences through a mixed-methods within-subjects study, where participants interacted with three distinct types of AI chatbot interfaces with a common large language model (LLM) over a 14-week university course. Our findings reveal that preferences vary according to factors such as learning habits and learning activities. Avatar design also exhibits affordances for specific prompting behaviors, while the perceived human touch influenced learning experiences in nuanced ways. Additionally, real-world relationships with the individuals behind deepfakes influence these experiences. These insights suggest that the thoughtful integration of diverse avatar representations in AI chatbot systems for different learners and settings can greatly enhance learning experiences.

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Publication Details

Published
2025-04-24
DOI
https://doi.org/10.1145/3706598.3713456
Citations
6
Primary Topic
AI in Service Interactions
Type
article
Field-Weighted Citation Impact
14.89
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article

Exploring the Impact of Avatar Representations in AI Chatbot Tutors on Learning Experiences

Chek Tien Tan, Songjia Shen, Indriyati Atmosukarto, Steven Wong et al.
6 citations
AI in Service Interactions
14.89
article

Exploring the Impact of Avatar Representations in AI Chatbot Tutors on Learning Experiences

Chek Tien Tan, Songjia Shen, Indriyati Atmosukarto, Steven Wong, Budianto Tandianus
article en
6 citations

Abstract

<p dir="ltr">Despite the growing prominence of Artificial Intelligence (AI) chatbots used in education, there remains a significant gap in our understanding of how interface design elements, particularly avatar representations, influence learning experiences. This paper explores the impact of different AI chatbot avatar representations on students’ learning experiences through a mixed-methods within-subjects study, where participants interacted with three distinct types of AI chatbot interfaces with a common large language model (LLM) over a 14-week university course. Our findings reveal that preferences vary according to factors such as learning habits and learning activities. Avatar design also exhibits affordances for specific prompting behaviors, while the perceived human touch influenced learning experiences in nuanced ways. Additionally, real-world relationships with the individuals behind deepfakes influence these experiences. These insights suggest that the thoughtful integration of diverse avatar representations in AI chatbot systems for different learners and settings can greatly enhance learning experiences.

Singapore Institute of Technology (SG)
Openalex Percentile: Top 1%
AI in Service Interactions
14.89
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