Affiliation:
1. The CVM University
2. The Assam Kaziranga University
Abstract
Abstract
This study investigated the impact of prior experience and education levels on user expectations in Artificial Intelligence (AI) based systems. The research aimed to determine whether these factors, individually or interactively, significantly influenced user expectations. Moreover, the effects of system interface, system feedback and system responsiveness on user comfort in AI-based systems were determined as well. The findings highlighted the importance of prior experience in shaping user expectations. It also suggests that educational level may have limited influence on user expectations. The choice of system interface and the responsiveness of the AI-based system significantly impact user comfort. The findings suggest for the creation of more user-friendly and comfortable interfaces. Understanding the various factors that influence user comfort and expectation, can aid the design and development of AI systems tailored to user backgrounds that better meet user needs and enhance their overall experience.
Publisher
Research Square Platform LLC
Reference22 articles.
1. AI-based Chatbots in Customer Service and their Effects on User Compliance;Adam M;Electron. Markets,2021
2. Alqahtani, T., Badreldin, H.A., Alrashed, M., Alshaya, A.I., Alghamdi, S.S., bin, Saleh, K., Albekairy, A.M.: The emergent role of artificial intelligence, natural learning processing, and large language models in higher education and research. Research in Social and Administrative Pharmacy, (2023). https://doi.org/10.1016/j.sapharm.2023.05.016
3. A consumer typology based on e-service quality and e-satisfaction;Bressolles G;J. Retailing Consumer Serv.,2014
4. Should AI-Based, conversational digital assistants employ social-or task-oriented interaction style? A task-competency and reciprocity perspective for older adults;Chattaraman V;Comput. Hum. Behav.,2019
5. Dang, H., Goller, S., Lehmann, F., Buschek, D.: Choice over control: How users write with large language models using diegetic and non-diegetic prompting. In: Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems, pp. 1–17. Association for Computing Machinery, New York, NY, USA (2023)
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2 articles.
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