Book contents
- Human–AI Interaction and Collaboration
- Reviews
- Human–AI Interaction and Collaboration
- Copyright page
- Contents
- Contributors
- Preface
- 1 Introduction
- 2 User Interaction for Human–AI Interaction and Collaboration
- 3 Privacy Identification of Human–Generative AI Interaction
- 4 Credibility Assessment of Human–Generative AI Interaction
- 5 AI-supported Crowdsourcing for Knowledge Sharing
- 6 AI-supported Search Interaction for Enhancing Users’ Understanding
- 7 AI for Human and Misinformation Interactions
- 8 Effective Human–AI Collaborative Intelligence
- 9 Human–AI Collaboration for Identifying Health Information Wants
- Chapter 10 Human–AI Collaboration for Scientific Discovery
- 11 Challenges of Generative AI on Human–AI Interaction and Collaboration
- 12 Conclusion
- References
6 - AI-supported Search Interaction for Enhancing Users’ Understanding
Published online by Cambridge University Press: 19 September 2025
Book contents
- Human–AI Interaction and Collaboration
- Reviews
- Human–AI Interaction and Collaboration
- Copyright page
- Contents
- Contributors
- Preface
- 1 Introduction
- 2 User Interaction for Human–AI Interaction and Collaboration
- 3 Privacy Identification of Human–Generative AI Interaction
- 4 Credibility Assessment of Human–Generative AI Interaction
- 5 AI-supported Crowdsourcing for Knowledge Sharing
- 6 AI-supported Search Interaction for Enhancing Users’ Understanding
- 7 AI for Human and Misinformation Interactions
- 8 Effective Human–AI Collaborative Intelligence
- 9 Human–AI Collaboration for Identifying Health Information Wants
- Chapter 10 Human–AI Collaboration for Scientific Discovery
- 11 Challenges of Generative AI on Human–AI Interaction and Collaboration
- 12 Conclusion
- References
Summary
This chapter mainly investigates the role of Artificial Intelligence (AI) in augmenting search interactions to enhance users’ understanding across various domains. The chapter begins by examining the current limitations of traditional search interfaces in meeting diverse user needs and cognitive capacities. It then discusses how AI-driven enhancements can revolutionize search experiences by providing tailored, contextually relevant information and facilitating intuitive interactions. Through case studies and empirical analysis, the effectiveness of AI-supported search interaction in improving users’ understanding is evaluated in different scenarios. This chapter contributes to the literature on AI and human–computer interaction by highlighting the transformative potential of AI in optimizing search experiences for users, leading to enhanced comprehension and decision-making. It concludes with implications for research and practice, emphasizing the importance of human-centered design principles in developing AI-driven search systems.
Information
- Type
- Chapter
- Information
- Human-AI Interaction and Collaboration , pp. 135 - 152Publisher: Cambridge University PressPrint publication year: 2025
References
Ali, M. Y., Naeem, S. B., & Bhatti, R. (2020). Artificial Intelligence Tools and Perspectives of University Librarians: An Overview. Business Information Review, 37(3), 116–124.10.1177/0266382120952016CrossRefGoogle Scholar
Bender, E. M., Gebru, T., McMillan-Major, A., & Shmitchell, S. (2021). On the Dangers of Stochastic Parrots: Can Language Models Be Too Big? Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency (pp. 610–623).Google Scholar
Chang, Y., Lee, S., Wong, S. F., & Jeong, S. P. (2022). AI-powered Learning Application Use and Gratification: An Integrative Model. Information Technology & People, 35(7), 2115–2139.10.1108/ITP-09-2020-0632CrossRefGoogle Scholar
Cheng, Y., & Jiang, H. (2020). How Do AI-driven Chatbots Impact User Experience? Examining Gratifications, Perceived Privacy Risk, Satisfaction, Loyalty, and Continued Use. Journal of Broadcasting & Electronic Media, 64(4), 592–614.10.1080/08838151.2020.1834296CrossRefGoogle Scholar
Choi, T. R., & Drumwright, M. E. (2021). “OK, Google, Why Do I Use You?” Motivations, Post-consumption Evaluations, and Perceptions of Voice AI Assistants. Telematics and Informatics, 62, 101628.10.1016/j.tele.2021.101628CrossRefGoogle Scholar
Choung, H., David, P., & Ling, T. W. (2024). Acceptance of AI-powered Technology in Surveillance Scenarios: Role of Trust, Security, and Privacy Perceptions. Security, and Privacy Perceptions. http://dx.doi.org/10.2139/ssrn.4724446CrossRefGoogle Scholar
De Cremer, D., & Kasparov, G. (2022). The Ethics of Technology Innovation: A Double-edged Sword? AI and Ethics, 2(3), 533–537.10.1007/s43681-021-00103-xCrossRefGoogle Scholar
Falgoust, G., Winterlind, E., Moon, P., Parker, A., Zinzow, H., & Madathil, K. C. (2022). Applying the Uses and Gratifications Theory to Identify Motivational Factors behind Young Adult’s Participation in Viral Social Media Challenges on TikTok. Human Factors in Healthcare, 2, 100014.10.1016/j.hfh.2022.100014CrossRefGoogle Scholar
Gao, Y., & Liu, H. (2023). Artificial Intelligence-enabled Personalization in Interactive Marketing: A Customer Journey Perspective. Journal of Research in Interactive Marketing, 17(5), 663–680.10.1108/JRIM-01-2022-0023CrossRefGoogle Scholar
Gerlich, M. (2023). Perceptions and Acceptance of Artificial Intelligence: A Multi-dimensional Study. Social Sciences, 12(9), 502.10.3390/socsci12090502CrossRefGoogle Scholar
Hao, X., & Cukurova, M. (2023, June). Exploring the Effects of “AI-Generated” Discussion Summaries on Learners’ Engagement in Online Discussions. In International Conference on Artificial Intelligence in Education (pp. 155–161). Springer Nature Switzerland.Google Scholar
Hornbæk, K., & Oulasvirta, A. (2017, May). What Is Interaction? Proceedings of the 2017 CHI Conference on Human Factors in Computing Systems (pp. 5040–5052).10.1145/3025453.3025765CrossRefGoogle Scholar
Hsu, C. L., Lin, J. C. C., & Miao, Y. F. (2020). Why Are People Loyal to Live Stream Channels? The Perspectives of Uses and Gratifications and Nedia Richness Theories. Cyberpsychology, Behavior, and Social Networking, 23(5), 351–356.10.1089/cyber.2019.0547CrossRefGoogle Scholar
Huggins-Manley, A. C., Booth, B. M., & D’Mello, S. K. (2022). Toward Argument-based Fairness with an Application to AI-enhanced Educational Assessments. Journal of Educational Measurement, 59(3), 362–388.10.1111/jedm.12334CrossRefGoogle Scholar
Jiang, N., Liu, X., Liu, H., Lim, E. T. K., Tan, C. W., & Gu, J. (2023). Beyond AI-powered Context-aware Services: The Role of Human–AI Collaboration. Industrial Management & Data Systems, 123(11), 2771–2802.10.1108/IMDS-03-2022-0152CrossRefGoogle Scholar
Katz, E., Blumler, J. G., & Gurevitch, M. (1973). Uses and Gratifications Research. The Public Opinion Quarterly, 37(4), 509–523.10.1086/268109CrossRefGoogle Scholar
Loewenstein, G. (1994). The Psychology of Curiosity: A Review and Reinterpretation. Psychological Bulletin, 116(1), 75–98.10.1037/0033-2909.116.1.75CrossRefGoogle Scholar
Meng, C., Aliannejadi, M., & de Rijke, M. (2023, October). System Initiative Prediction for Multi-turn Conversational Information Seeking. Proceedings of the 32nd ACM International Conference on Information and Knowledge Management (pp. 1807–1817).10.1145/3583780.3615070CrossRefGoogle Scholar
Na, K., & Lee, J. (2016). When Two Heads Are Better Than One: Query Behavior, Cognitive Load, Search Time, and Task Type in Pairs versus Individuals. Aslib Journal of Information Management, 68(5), 545–565.10.1108/AJIM-04-2015-0057CrossRefGoogle Scholar
O’Brien, H. L., Arguello, J., & Capra, R. (2020). An Empirical Study of Interest, Task Complexity, and Search Behaviour on User Engagement. Information Processing & Management, 57(3), 102226.10.1016/j.ipm.2020.102226CrossRefGoogle Scholar
Pinzolits, R. (2024). AI in Academia: An Overview of Selected Tools and Their Areas of Application. MAP Education and Humanities, 4, 37–50.10.53880/2744-2373.2023.4.37CrossRefGoogle Scholar
Savolainen, R. (2013). Approaching the Motivators for Information Seeking: The Viewpoint of Attribution Theories. Library & Information Science Research, 35(1), 63–68.10.1016/j.lisr.2012.07.004CrossRefGoogle Scholar
Sundar, S. S., & Limperos, A. M. (2013). Uses and Grats 2.0: New Gratifications for New Media. Journal of Broadcasting & Electronic Media, 57(4), 504–525.10.1080/08838151.2013.845827CrossRefGoogle Scholar
Ullah, I., & Khusro, S. (2020). On the Search Behaviour of Users in the Context of Interactive Social Book Search. Behaviour & Information Technology, 39(4), 443–462.10.1080/0144929X.2019.1599069CrossRefGoogle Scholar
Venkatesh, V., & Bala, H. (2008). Technology Acceptance Model 3 and a Research Agenda on Interventions. Decision Sciences, 39(2), 273–315.10.1111/j.1540-5915.2008.00192.xCrossRefGoogle Scholar
Venkatesh, V., & Davis, F. D. (2000). A Theoretical Extension of the Technology Acceptance Model: Four Longitudinal Field Studies. Management Science, 46(2), 186–204.10.1287/mnsc.46.2.186.11926CrossRefGoogle Scholar
Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003). User Acceptance of Information Technology: Toward a Unified View. MIS Quarterly, 27(3), 425–478.10.2307/30036540CrossRefGoogle Scholar
Vuong, T., Saastamoinen, M., Jacucci, G., & Ruotsalo, T. (2019). Understanding User Behavior in Naturalistic Information Search Tasks. Journal of the Association for Information Science and Technology, 70(11), 1248–1261.10.1002/asi.24201CrossRefGoogle Scholar
Yiannakoulias, N. (2024). Spatial Intelligence and Contextual Relevance in AI-driven Health Information Retrieval. Applied Geography, 171, 103392.10.1016/j.apgeog.2024.103392CrossRefGoogle Scholar
Yue, G., & Peng, S. (2021, June). Application of Artificial Intelligence in the Academic Search Engine. International Conference on Applications and Techniques in Cyber Security and Intelligence (pp. 611–616). Springer International Publishing.Google Scholar
Zamani, H., Dumais, S., Craswell, N., Bennett, P., & Lueck, G. (2020, April). Generating Clarifying Questions for Information Retrieval. WWW ’20: Proceedings of the Web Conference 2020 (pp. 418–428). https://dl.acm.org/doi/abs/10.1145/3366423.3380126CrossRefGoogle Scholar
Zhao, W. X., Liu, J., Ren, R., & Wen, J. R. (2024). Dense Text Retrieval Based on Pretrained Language Models: A Survey. ACM Transactions on Information Systems, 42(4), 1–60.10.1145/3637870CrossRefGoogle Scholar
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