User Interaction for Human–AI Interaction and Collaboration

doi:10.1017/9781009587877.003

2 - User Interaction for Human–AI Interaction and Collaboration

Published online by Cambridge University Press: aN Invalid Date NaN

Dan Wu and

Qingyue Guo

Edited by

Dan Wu and

Shaobo Liang

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Dan Wu: Affiliation:
Wuhan University, China
Shaobo Liang: Affiliation:
Wuhan University, China

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Summary

It is of great importance to integrate human-centered design concepts at the core of both algorithmic research and the implementation of applications. In order to do so, it is essential to gain an understanding of human–computer interaction and collaboration from the perspective of the user. To address this issue, this chapter initially presents a description of the process of human–AI interaction and collaboration, and subsequently proposes a theoretical framework for it. In accordance with this framework, the current research hotspots are identified in terms of interaction quality and interaction mode. Among these topics, user mental modeling, interpretable AI, trust, and anthropomorphism are currently the subject of academic interest with regard to interaction quality. The level of interaction mode encompasses a range of topics, including interaction paradigms, role assignment, interaction boundaries, and interaction ethics. To further advance the related research, this chapter identifies three areas for future exploration: cognitive frameworks about Human–AI Interaction, adaptive learning, and the complementary strengths of humans and AI.

Keywords

Artificial Intelligence Human–AI Interaction Human–AI Collaboration User Interaction

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Type: Chapter
Information: Human-AI Interaction and Collaboration , pp. 14 - 42

DOI: https://doi.org/10.1017/9781009587877.003 [Opens in a new window]

Publisher: Cambridge University Press

Print publication year: 2025

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Amershi, S., Weld, D., Vorvoreanu, M., Fourney, A., Nushi, B., Collisson, P., Suh, J., Iqbal, S., Bennett, P. N., Inkpen, K., Teevan, J., Kikin-Gil, R., & Horvitz, E. (2019). Guidelines for Human–AI Interaction. Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems, 1–13. https://doi.org/10.1145/3290605.3300233 CrossRef Google Scholar

Asan, O., Bayrak, A. E., & Choudhury, A. (2020). Artificial Intelligence and Human Trust in Healthcare: Focus on Clinicians. Journal of Medical Internet Research, 22(6), e15154. https://doi.org/10.2196/15154 Google Scholar PubMed

Bansal, G., Nushi, B., Kamar, E., Lasecki, W. S., Weld, D. S., & Horvitz, E. (2019). Beyond Accuracy: The Role of Mental Models in Human–AI Team Performance. Proceedings of the AAAI Conference on Human Computation and Crowdsourcing, 7, 2–11. https://doi.org/10.1609/hcomp.v7i1.5285 CrossRef Google Scholar

Barnes, W. H. F. (1944). The Nature of Explanation. Nature, 153(3890), 605–605. https://doi.org/10.1038/153605a0 CrossRef Google Scholar

Biondi, F., Alvarez, I., & Jeong, K.-A. (2019). Human–System Cooperation in Automated Driving. International Journal of Human–Computer Interaction, 35(11), 917–918. https://doi.org/10.1080/10447318.2018.1561793 CrossRef Google Scholar

Borman, L. (1996). SIGCHI: The Early Years. ACM SIGCHI Bulletin, 28(1), 4–6. https://doi.org/10.1145/249170.249172 Google Scholar

Buçinca, Z., Malaya, M. B., & Gajos, K. Z. (2021). To Trust or to Think: Cognitive Forcing Functions Can Reduce Overreliance on AI in AI-assisted Decision-making. Proceedings of the ACM on Human–Computer Interaction, 5 (CSCW1), 188:1–188:21. https://doi.org/10.1145/3449287 Google Scholar

Cabrera, Á. A., Perer, A., & Hong, J. I. (2023). Improving Human–AI Collaboration with Descriptions of AI Behavior. Proceedings of the ACM on Human–Computer Interaction, 7(CSCW1), 136:1–136:21. https://doi.org/10.1145/3579612 CrossRef Google Scholar

Cañas, J. J. (2022). AI and Ethics When Human Beings Collaborate with AI Agents. Frontiers in Psychology, 13. https://doi.org/10.3389/fpsyg.2022.836650 CrossRef Google Scholar

Cao, S., Gomez, C., & Huang, C.-M. (2023). How Time Pressure in Different Phases of Decision-Making Influences Human–AI Collaboration. Proceedings of the ACM on Human–Computer Interaction, 7(CSCW2), 277:1–277:26. https://doi.org/10.1145/3610068 CrossRef Google Scholar

Chen, C., & Zheng, Y. (2023). When Consumers Need More Interpretability of Artificial Intelligence (AI) Recommendations? The Effect of Decision-making Domains. Behaviour & Information Technology. https://doi.org/10.1080/0144929X.2023.2279658 Google Scholar

Chen, C. Y., Lee, L., & Yap, A. J. (2016). Control Deprivation Motivates Acquisition of Utilitarian Products. Journal of Consumer Research, ucw068. https://doi.org/10.1093/jcr/ucw068 CrossRef Google Scholar

Chi, O. H., Denton, G., & Gursoy, D. (2020). Artificially Intelligent Device Use in Service Delivery: A Systematic Review, Synthesis, and Research Agenda. Journal of Hospitality Marketing & Management, 29(7), 757–786. https://doi.org/10.1080/19368623.2020.1721394 CrossRef Google Scholar

Deshpande, K. V., Pan, S., & Foulds, J. R. (2020). Mitigating Demographic Bias in AI-based Resume Filtering. Adjunct Publication of the 28th ACM Conference on User Modeling, Adaptation and Personalization, 268–275. https://doi.org/10.1145/3386392.3399569 Google Scholar

Desolda, G., Dimauro, G., Esposito, A., Lanzilotti, R., Matera, M., & Zancanaro, M. (2024). A human–AI Interaction Paradigm and Its Application to Rhinocytology. Artificial Intelligence in Medicine, 102933. https://doi.org/10.1016/j.artmed.2024.102933 CrossRef Google Scholar

van Diggelen, J., Barnhoorn, J., Post, R., Sijs, J., van der Stap, N., & van der Waa, J. (2021). Delegation in Human–Machine Teaming: Progress, Challenges and Prospects. In Russo, D., Ahram, T., Karwowski, W., Di Bucchianico, G., & Taiar, R. (eds.), Intelligent Human Systems Integration 2021 (pp. 10–16). Springer International Publishing. https://doi.org/10.1007/978-3-030-68017-6_2 Google Scholar

Donahue, K., Chouldechova, A., & Kenthapadi, K. (2022). Human–Algorithm Collaboration: Achieving Complementarity and Avoiding Unfairness. 2022 ACM Conference on Fairness, Accountability, and Transparency, 1639–1656. https://doi.org/10.1145/3531146.3533221 Google Scholar

Ebermann, C., Selisky, M., & Weibelzahl, S. (2023). Explainable AI: The Effect of Contradictory Decisions and Explanations on Users’ Acceptance of AI Systems. International Journal of Human–Computer Interaction. www.tandfonline.com/doi/abs/10.1080/10447318.2022.2126812 Google Scholar

Epley, N., Waytz, A., & Cacioppo, J. T. (2007). On Seeing Human: A Three-Factor Theory of Anthropomorphism. Psychological Review, 114(4), 864–886. https://doi.org/10.1037/0033-295X.114.4.864 CrossRef Google Scholar

Ethics of Artificial Intelligence | UNESCO. (n.d.). Retrieved July 30, 2024, from www.unesco.org/en/artificial-intelligence/recommendation-ethics Google Scholar

Fan, J., Tian, F., Dai, G., Du, Y., & Liu, Z. (2018). Thoughts on Human–Computer Interaction in the Age of Artificial Intelligence. SCIENTIA SINICA Informationis, 48(4), 361–375. https://doi.org/10.1360/N112017-00221 Google Scholar

Georganta, E., & Ulfert, A.-S. (2024). My Colleague Is an AI! Trust Differences between AI and Human Teammates. Team Performance Management: An International Journal, 30(1/2), 23–37. https://doi.org/10.1108/TPM-07-2023-0053 CrossRef Google Scholar

Google AI Principles. (n.d.). Google AI. Retrieved July 30, 2024, from https://ai.google/responsibility/principles/Google Scholar

Graefe, J., Rittger, L., Carollo, G., Engelhardt, D., & Bengler, K. (2023). Evaluating the Potential of Interactivity in Explanations for User-Adaptive In-Vehicle Systems: Insights from a Real-World Driving Study. In Duffy, V. G., Krömker, H., Streitz, N. A., & Konomi, S. (eds.), HCI International 2023: Late Breaking Papers (pp. 294–312). Springer Nature Switzerland. https://doi.org/10.1007/978-3-031-48047-8_19 Google Scholar

Green, B., & Chen, Y. (2019). The Principles and Limits of Algorithm-in-the-Loop Decision Making. Proceedings of the ACM on Human–Computer Interaction, 3(CSCW), 1–24. https://doi.org/10.1145/3359152 CrossRef Google Scholar

Grudin, J. (2018). From Tool to Partner: The Evolution of Human–Computer Interaction. Extended Abstracts of the 2018 CHI Conference on Human Factors in Computing Systems, 1–3. https://doi.org/10.1145/3170427.3170663 CrossRef Google Scholar

Gursoy, D., Chi, O. H., Lu, L., & Nunkoo, R. (2019). Consumers Acceptance of Artificially Intelligent (AI) Device Use in Service Delivery. International Journal of Information Management, 49, 157–169. https://doi.org/10.1016/j.ijinfomgt.2019.03.008 CrossRef Google Scholar

Harbarth, L., Gößwein, E., Bodemer, D., & Schnaubert, L. (2025). (Over)Trusting AI Recommendations: How System and Person Variables Affect Dimensions of Complacency. International Journal of Human–Computer Interaction, 41(1), 391–410. https://doi.org/10.1080/10447318.2023.2301250 CrossRef Google Scholar

Harrison, S., Sengers, P., & Tatar, D. (2011). Making Epistemological Trouble: Third-Paradigm HCI as Successor Science. Interacting with Computers, 23(5), 385–392. https://doi.org/10.1016/j.intcom.2011.03.005 CrossRef Google Scholar

Haupt, M., Freidank, J., & Haas, A. (2025). Consumer Responses to Human–AI Collaboration at Organizational Frontlines: Strategies to Escape Algorithm Aversion in Content Creation. Review of Managerial Science, 19, 377–413. https://doi.org/10.1007/s11846-024-00748-y CrossRef Google Scholar

Hornbæk, K., Mottelson, A., Knibbe, J., & Vogel, D. (2019). What Do We Mean by “Interaction”? An Analysis of 35 Years of CHI. ACM Transactions on Computer–Human Interaction, 26(4), 27:1–27:30. https://doi.org/10.1145/3325285 CrossRef Google Scholar

Hou, K., Hou, T., & Cai, L. (2023). Exploring Trust in Human–AI Collaboration in the Context of Multiplayer Online Games. Systems, 11(5), Article 5. https://doi.org/10.3390/systems11050217 CrossRef Google Scholar

Hou, T.-Y., Tseng, Y.-C., & Yuan, C. W. (Tina). (2024). Is This AI Sexist? The Effects of a Biased AI’s Anthropomorphic Appearance and Explainability on Users’ Bias Perceptions and Trust. International Journal of Information Management, 76, 102775. https://doi.org/10.1016/j.ijinfomgt.2024.102775 CrossRef Google Scholar

Hu, M., Zhang, G., Chong, L., Cagan, J., & Goucher-Lambert, K. (2025). How Being Outvoted by AI Teammates Impacts Human–AI Collaboration. International Journal of Human–Computer Interaction, 41, 4049–4066. https://doi.org/10.1080/10447318.2024.2345980 Google Scholar

Hu, P., Lu, Y., & Wang, B. (2022). Experiencing Power over AI: The Fit Effect of Perceived Power and Desire for Power on Consumers’ Choice for Voice Shopping. Computers in Human Behavior, 128, 107091. https://doi.org/10.1016/j.chb.2021.107091 Google Scholar

Hwang, A. H.-C., & Won, A. S. (2022). AI in Your Mind: Counterbalancing Perceived Agency and Experience in Human–AI Interaction. CHI Conference on Human Factors in Computing Systems Extended Abstracts, 1–10. https://doi.org/10.1145/3491101.3519833 Google Scholar

Jain, R., Garg, N., & Khera, S. N. (2022). Effective Human–AI Work Design for Collaborative Decision-making. Kybernetes, 52(11), 5017–5040. https://doi.org/10.1108/K-04-2022-0548 CrossRef Google Scholar

Jeon, Y., Jin, S., Shih, P. C., & Han, K. (2021). FashionQ: An AI-Driven Creativity Support Tool for Facilitating Ideation in Fashion Design. Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems, 1–18. https://doi.org/10.1145/3411764.3445093 CrossRef Google Scholar

Jiang, T., Sun, Z., Fu, S., & Lv, Y. (2024). Human–AI Interaction Research Agenda: A User-centered Perspective. Data and Information Management, 100078. https://doi.org/10.1016/j.dim.2024.100078 CrossRef Google Scholar

Kahn, L. H., Savas, O., Morrison, A., Shaffer, K. A., & Zapata, L. (2020). Modelling Hybrid Human–Artificial Intelligence Cooperation: A Call Center Customer Service Case Study. 2020 IEEE International Conference on Big Data (Big Data), 3072–3075. https://doi.org/10.1109/BigData50022.2020.9377747 CrossRef Google Scholar

Kelly, M., Kumar, A., Smyth, P., & Steyvers, M. (2023). Capturing Humans’ Mental Models of AI: An Item Response Theory Approach. 2023 ACM Conference on Fairness, Accountability, and Transparency, 1723–1734. https://doi.org/10.1145/3593013.3594111 CrossRef Google Scholar

Kim, A., Cho, M., Ahn, J., & Sung, Y. (2019). Effects of Gender and Relationship Type on the Response to Artificial Intelligence. Cyberpsychology, Behavior, and Social Networking, 22(4), 249–253. https://doi.org/10.1089/cyber.2018.0581 CrossRef Google Scholar PubMed

Kim, S. (1995). Interdisciplinary Cooperation. In Baecker, R. M., Grudin, J., Buxton, W. A. S., & Greenberg, S. (eds.), Readings in Human–Computer Interaction (pp. 304–311). Morgan Kaufmann. https://doi.org/10.1016/B978-0-08-051574-8.50033-9 Google Scholar

Kim, S. S. Y., Watkins, E. A., Russakovsky, O., Fong, R., & Monroy-Hernández, A. (2023). “Help Me Help the AI”: Understanding How Explainability Can Support Human–AI Interaction. Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems, 1–17. https://doi.org/10.1145/3544548.3581001 Google Scholar

Kitchin, J., & Baber, C. (2016). A Comparison of Shared and Distributed Situation Awareness in Teams through the Use of Agent-based Modelling. Theoretical Issues in Ergonomics Science, 17(1), 8–41. https://www.tandfonline.com/doi/abs/10.1080/1463922X.2015.1106616 CrossRef Google Scholar

Lee, C. T., Pan, L.-Y., & Hsieh, S. H. (2021). Artificial Intelligent Chatbots as Brand Promoters: A Two-stage Structural Equation Modeling–Artificial Neural Network Approach. Internet Research, 32(4), 1329–1356. https://doi.org/10.1108/INTR-01-2021-0030 CrossRef Google Scholar

Li, X., & Sung, Y. (2021). Anthropomorphism Brings Us Closer: The Mediating Role of Psychological Distance in User–AI Assistant Interactions. Computers in Human Behavior, 118, 106680. https://doi.org/10.1016/j.chb.2021.106680 CrossRef Google Scholar

Lindvall, M., Lundström, C., & Löwgren, J. (2021). Rapid Assisted Visual Search: Supporting Digital Pathologists with Imperfect AI. 26th International Conference on Intelligent User Interfaces, 504–513. https://doi.org/10.1145/3397481.3450681 CrossRef Google Scholar

Lobo, I., Koch, J., Renoux, J., Batina, I., & Prada, R. (2024). When Should I Lead or Follow: Understanding Initiative Levels in Human–AI Collaborative Gameplay. Designing Interactive Systems Conference, 2037–2056. https://doi.org/10.1145/3643834.3661583 CrossRef Google Scholar

Malandri, L., Mercorio, F., Mezzanzanica, M., & Nobani, N. (2023). ConvXAI: A System for Multimodal Interaction with Any Black-box Explainer. Cognitive Computation, 15(2), 613–644. https://doi.org/10.1007/s12559-022-10067-7 CrossRef Google Scholar

Merry, M., Riddle, P., & Warren, J. (2021). A Mental Models Approach for Defining Explainable Artificial Intelligence. BMC Medical Informatics and Decision Making, 21(1), 344. https://doi.org/10.1186/s12911-021-01703-7 CrossRef Google Scholar PubMed

Myers, B., Hollan, J., Cruz, I., Bryson, S., Bulterman, D., Catarci, T., Citrin, W., Glinert, E., Grudin, J., & Ioannidis, Y. (1996). Strategic Directions in Human–Computer Interaction. ACM Computing Surveys, 28(4), 794–809. https://doi.org/10.1145/242223.246855 Google Scholar

Nagao, K. (2019). Symbiosis between Humans and Artificial Intelligence. In Nagao, K. (ed.), Artificial Intelligence Accelerates Human Learning: Discussion Data Analytics (pp. 135–151). Springer. https://doi.org/10.1007/978-981-13-6175-3_6 CrossRef Google Scholar

Newn, J., Singh, R., Velloso, E., & Vetere, F. (2019). Combining Implicit Gaze and AI for Real-time Intention Projection. Adjunct Proceedings of the 2019 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2019 ACM International Symposium on Wearable Computers, 324–327. https://doi.org/10.1145/3341162.3343786 CrossRef Google Scholar

Oulasvirta, A., & Hornbæk, K. (2016). HCI Research as Problem-Solving. Proceedings of the 2016 CHI Conference on Human Factors in Computing Systems, 4956–4967. https://doi.org/10.1145/2858036.2858283 CrossRef Google Scholar

Pargman, D. S., Eriksson, E., Bates, O., Kirman, B., Comber, R., Hedman, A., & van den Broeck, M. (2019). The Future of Computing and Wisdom: Insights from Human–Computer Interaction. Futures, 113, 102434. https://doi.org/10.1016/j.futures.2019.06.006 CrossRef Google Scholar

Pataranutaporn, P., Liu, R., Finn, E., & Maes, P. (2023). Influencing Human–AI Interaction by Priming Beliefs about AI Can Increase Perceived Trustworthiness, Empathy and Effectiveness. Nature Machine Intelligence, 5(10), 1076–1086. https://doi.org/10.1038/s42256-023-00720-7 CrossRef Google Scholar

Paul, S. K., Nicolescu, M., & Nicolescu, M. (2023). Enhancing Human–Robot Collaboration through a Multi-Module Interaction Framework with Sensor Fusion: Object Recognition, Verbal Communication, User of Interest Detection, Gesture and Gaze Recognition. Sensors, 23(13), Article 13. https://doi.org/10.3390/s23135798 CrossRef Google Scholar PubMed

Rastogi, C., Zhang, Y., Wei, D., Varshney, K. R., Dhurandhar, A., & Tomsett, R. (2022). Deciding Fast and Slow: The Role of Cognitive Biases in AI-assisted Decision-making. Proceedings of the ACM on Human–Computer Interactions, 6(CSCW1), 83:1–83:22. https://doi.org/10.1145/3512930 Google Scholar

Ribera, M., & Lapedriza, À. (2019, March 2). Can We Do Better Explanations? A Proposal of User-centered Explainable AI. IUI Workshops. www.semanticscholar.org/paper/Can-we-do-better-explanations-A-proposal-of-AI-Ribera-Lapedriza/60758a8b15d843ed4f731b4eaa7832be8a7a7e13 Google Scholar

Rosenfeld, A., & Richardson, A. (2019). Explainability in Human–Agent Systems. Autonomous Agents and Multi-Agent Systems, 33(6), 673–705. https://doi.org/10.1007/s10458-019-09408-y CrossRef Google Scholar

Sadeghian, S., & Hassenzahl, M. (2022). The “Artificial” Colleague: Evaluation of Work Satisfaction in Collaboration with Non-human Coworkers. 27th International Conference on Intelligent User Interfaces, 27–35. https://doi.org/10.1145/3490099.3511128 CrossRef Google Scholar

Santoni de Sio, F., & van den Hoven, J. (2018). Meaningful Human Control over Autonomous Systems: A Philosophical Account. Frontiers in Robotics and AI, 5. https://doi.org/10.3389/frobt.2018.00015 CrossRef Google Scholar PubMed

Sarter, N. B., & Woods, D. D. (1995). How in the World Did We Ever Get into That Mode? Mode Error and Awareness in Supervisory Control. Human Factors: The Journal of the Human Factors and Ergonomics Society, 37(1), 5–19. https://doi.org/10.1518/001872095779049516 CrossRef Google Scholar

Scholtz, J. (2003). Theory and Evaluation of Human Robot Interactions. Proceedings of The 36th Annual Hawaii International Conference on System Sciences, 2003, 10 pp. https://doi.org/10.1109/HICSS.2003.1174284 CrossRef Google Scholar

Seeber, I., Bittner, E., Briggs, R. O., de Vreede, T., de Vreede, G.-J., Elkins, A., Maier, R., Merz, A. B., Oeste-Reiß, S., Randrup, N., Schwabe, G., & Söllner, M. (2020). Machines as Teammates: A Research Agenda on AI in Team Collaboration. Information & Management, 57(2), 103174. https://doi.org/10.1016/j.im.2019.103174 CrossRef Google Scholar

Sekmen, A., & Challa, P. (2013). Assessment of Adaptive Human–Robot Interactions. Knowledge-Based Systems, 42, 49–59. https://doi.org/10.1016/j.knosys.2013.01.003 CrossRef Google Scholar

Shin, J.-G., Choi, G.-Y., Hwang, H.-J., & Kim, S.-H. (2021). Evaluation of Emotional Satisfaction Using Questionnaires in Voice-Based Human–AI Interaction. Applied Sciences, 11(4), Article 4. https://doi.org/10.3390/app11041920 Google Scholar

Spatola, N., Marchesi, S., & Wykowska, A. (2022). Different Models of Anthropomorphism across Cultures and Ontological Limits in Current Frameworks: The Integrative Framework of Anthropomorphism. Frontiers in Robotics and AI, 9. https://doi.org/10.3389/frobt.2022.863319 CrossRef Google Scholar PubMed

Stanford GDPi. (2018, July 9). Human-Centered AI: Building Trust, Democracy and Human Rights by Design. Stanford’s GDPi. https://medium.com/stanfords-gdpi/human-centered-ai-building-trust-democracy-and-human-rights-by-design-2fc14a0b48af Google Scholar

Steyvers, M., & Kumar, A. (2023). Three Challenges for AI-Assisted Decision-Making. Perspectives on Psychological Science, 19(5), 17456916231181102. https://doi.org/10.1177/17456916231181102 Google Scholar PubMed

Sun, X., Zhang, Y., Qin, J., Li, J., & Wang, S. (2020). Review on Human–Intelligent System Collaboration. Packaging Engineering, 41(18), 1–11. https://doi.org/10.19554/j.cnki.1001-3563.2020.18.001 Google Scholar

Tutul, A. A., Chaspari, T., Levitan, S. I., & Hirschberg, J. (2023). Human–AI Collaboration for the Detection of Deceptive Speech. 2023 11th International Conference on Affective Computing and Intelligent Interaction Workshops and Demos (ACIIW), 1–4. https://doi.org/10.1109/ACIIW59127.2023.10388114 CrossRef Google Scholar

Villareale, J., Harteveld, C., & Zhu, J. (2022). “I Want To See How Smart This AI Really Is”: Player Mental Model Development of an Adversarial AI Player. Proceedings of the ACM on Human–Computer Interactions, 6(CHI PLAY), 219:1–219:26. https://doi.org/10.1145/3549482 Google Scholar

Wang, B., Yuan, T., & Rau, P.-L. P. (2024). Effects of Explanation Strategy and Autonomy of Explainable AI on Human–AI Collaborative Decision-making. International Journal of Social Robotics, 16(4), 791–810. https://doi.org/10.1007/s12369-024-01132-2 CrossRef Google Scholar

Wang, D., Weisz, J. D., Muller, M., Ram, P., Geyer, W., Dugan, C., Tausczik, Y., Samulowitz, H., & Gray, A. (2019). Human–AI Collaboration in Data Science: Exploring Data Scientists’ Perceptions of Automated AI. Proceedings of the ACM on Human–Computer Interactions, 3(CSCW), 211:1–211:24. https://doi.org/10.1145/3359313 Google Scholar

Westphal, M., Vössing, M., Satzger, G., Yom-Tov, G. B., & Rafaeli, A. (2023). Decision Control and Explanations in Human–AI Collaboration: Improving User Perceptions and Compliance. Computers in Human Behavior, 144, 107714. https://doi.org/10.1016/j.chb.2023.107714 Google Scholar

Xie, Y., Zhao, S., Zhou, P., Lu, L., Liang, C., & Jiang, L. (2024). Estimating the Impact of “Humanizing” AI Assistants. International Journal of Human–Computer Interaction, 40(24), 8876–8889. https://doi.org/10.1080/10447318.2023.2291614 CrossRef Google Scholar

Xie, Y., Zhu, K., Zhou, P., & Liang, C. (2023). How Does Anthropomorphism Improve Human–AI Interaction Satisfaction: A Dual-Path Model. Computers in Human Behavior, 148, 107878. https://doi.org/10.1016/j.chb.2023.107878 CrossRef Google Scholar

Xing, Y., Lv, C., Cao, D., & Hang, P. (2021). Toward Human–Vehicle Collaboration: Review and Perspectives on Human-centered Collaborative Automated Driving. Transportation Research Part C: Emerging Technologies, 128, 103199. https://doi.org/10.1016/j.trc.2021.103199 CrossRef Google Scholar

Xu, W. (2019). Toward Human-centered AI: A Perspective from Human–Computer Interaction. Interactions, 26(4), 42–46. https://doi.org/10.1145/3328485 CrossRef Google Scholar

Xu, W., & Gao, Z. (2024). Applying HCAI in Developing Effective Human–AI Teaming: A Perspective from Human–AI Joint Cognitive Systems. Interactions, 31(1), 32–37. https://doi.org/10.1145/3635116 CrossRef Google Scholar

Yang, K. B., Lawrence, L., Echeverria, V., Guo, B., Rummel, N., & Aleven, V. (2021). Surveying Teachers’ Preferences and Boundaries Regarding Human–AI Control in Dynamic Pairing of Students for Collaborative Learning. In De Laet, T., Klemke, R., Alario-Hoyos, C., Hilliger, I., & Ortega-Arranz, A. (eds.), Technology-Enhanced Learning for a Free, Safe, and Sustainable World (pp. 260–274). Springer International Publishing. https://doi.org/10.1007/978-3-030-86436-1_20 CrossRef Google Scholar

Zhang, G., Raina, A., Brownell, E., & Cagan, J. (2022). The Impact of a Strategy of Deception about the Identity of an Artificial Intelligence Teammate on Human Designers. Volume 3B: 48th Design Automation Conference (DAC), V03BT03A017. https://doi.org/10.1115/DETC2022-88535 CrossRef Google Scholar

Zhang, M., Gursoy, D., Zhu, Z., & Shi, S. (2021). Impact of Anthropomorphic Features of Artificially Intelligent Service Robots on Consumer Acceptance: Moderating Role of Sense of Humor. International Journal of Contemporary Hospitality Management, 33(11), 3883–3905. https://doi.org/10.1108/IJCHM-11-2020-1256 Google Scholar

Zhao, Y., & Bao, X. (2023). Narratron: Collaborative Writing and Shadow-playing of Children Stories with Large Language Models. Adjunct Proceedings of the 36th Annual ACM Symposium on User Interface Software and Technology, 1–6. https://doi.org/10.1145/3586182.3625120 CrossRef Google Scholar

Zou, M., & Huang, L. (2024). The Impact of ChatGPT on L2 Writing and Expected Responses: Voice from Doctoral Students. Education and Information Technologies, 29, 13201–13219. https://doi.org/10.1007/s10639-023-12397-x CrossRef Google Scholar

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