Author ORCID Identifier
Tsai - https://orcid.org/0000-0001-9188-0362 Ashaduzzaman - https://orcid.org/0000-0002-3898-0089
Document Type
Conference Proceeding
Publication Date
6-12-2025
Publication Title
UMAP Adjunct '25: Adjunct Proceedings of the 33rd ACM Conference on User Modeling, Adaptation and Personalization
First Page
192
Last Page
201
DOI
https://doi.org/10.1145/3708319.373369
Abstract
Generative AI, particularly Large Language Models (LLMs), has revolutionized human-computer interaction by enabling the generation of nuanced, human-like text. This presents new opportunities, especially in enhancing explainability for AI systems like recommender systems, a crucial factor for fostering user trust and engagement. LLM-powered AI-Chatbots can be leveraged to provide personalized explanations for recommendations. Although users often find these chatbot explanations helpful, they may not fully comprehend the content. Our research focuses on assessing how well users comprehend these explanations and identifying gaps in understanding. We also explore the key behavioral differences between users who effectively understand AI-generated explanations and those who do not. We designed a three-phase user study with 17 participants to explore these dynamics. The findings indicate that the clarity and usefulness of the explanations are contingent on the user asking relevant follow-up questions and having a motivation to learn. Comprehension also varies significantly based on users’ educational backgrounds.
Recommended Citation
Ashaduzzaman, Md and Tsai, Chun-Hua, "Leveraging Generative AI to Improve Comprehensibility in Social Recommender Systems" (2025). Information Systems and Quantitative Analysis Faculty Publications. 172.
https://digitalcommons.unomaha.edu/isqafacpub/172
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.
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