AI-Powered Human-Computer Interaction: A Bibliometric Study

Öz

This study presents a thorough analysis of the Artificial Intelligence (AI) and Human-Computer Interaction (HCI) intersection, with the aim of identifying important trends, themes, and influential research in this rapidly changing field. The integration of AI into HCI has resulted in significant advancements across various domains, such as healthcare, education, and user experience design. Although there is a growing interest in this area, the number of studies is still limited, and the research is gradually increasing. This study aims to fill the gap by providing a comprehensive overview of the current literature, focusing on the gaps, emerging trends, and future directions in AI-driven HCI. The research methodology adheres to the PRISMA protocol, which guarantees a systematic and clear review process. A total of 84 peer-reviewed publications from the Scopus database, spanning a 30-year period from 1994 to 2025, were examined. The research corpus was subjected to bibliometric analysis, Social Network Analysis (SNA), and text mining techniques to map the landscape of AI and HCI research. The study also recognized key authors, influential countries, and significant academic sources contributing to this field. The results of the analysis identified five primary thematic groups: Explainable AI (XAI), Human-Computer Interaction (HCI) and AI in Education and Training, Human-Robot Interaction (HRI), and AI and User Experience (UX). These themes emphasize the wide-ranging applications of AI in HCI, such as enhancing diagnostic precision in healthcare, personalizing educational content, and enhancing user experience through adaptive and emotionally intelligent interfaces. However, the study also revealed significant gaps in the existing literature, particularly regarding ethical considerations, transparency, and user control. The analysis indicates that ethical issues are not adequately emphasized in current research, suggesting a crucial area for future investigation. The study suggests that while AI has considerable potential to transform HCI, its successful incorporation will depend on addressing these gaps and ensuring that AI-driven systems prioritize human-centered design principles. The results also highlight the prominent role of countries like the People's Republic of China (PRC) in advancing this field, and emphasize the need for broader international cooperation. This research provides a deeper understanding of the evolving landscape of AI and HCI and serves as a foundation for future studies.

Anahtar Kelimeler

• Artificial intelligence
• Human-computer Interaction
• Explainable artificial intelligence
• Human-centered design
• Human-robot interaction
• Social network analysis

Kaynakça

1. Aggarwal, C. C. (2015). Mining text data. Springer International Publishing.
2. Alkatheiri, M. S. (2022). Artificial intelligence assisted improved human-computer interactions for computer systems. Computers and Electrical Engineering, 101, Article 107950. https://doi.org/10.1016/j.compeleceng.2022.107950
3. Dix, A. (2017). Human–computer interaction, foundations and new paradigms. Journal of Visual Languages & Computing, 42, 122–134. https://doi.org/10.1016/j.jvlc.2016.04.001
4. Donthu, N., Kumar, S., Mukherjee, D., Pandey, N., & Lim, W. M. (2021). How to conduct a bibliometric analysis: An overview and guidelines. Journal of Business Research, 133, 285–296.
5. Govindaraju, V., & Thangam, D. (2024). Leveraging emotional AI for improved human-computer interactions: An interdisciplinary perspective. In Harnessing artificial emotional intelligence for improved human-computer interactions (pp. 66–81). IGI Global.
6. Huang, Y. (2023, October). The future of generative ai: How genai would change human-computer co-creation in the next 10 to 15 years. In Companion Proceedings of the Annual Symposium on Computer-Human Interaction in Play (pp. 322-325).
7. Kosch, T., Welsch, R., Chuang, L., & Schmidt, A. (2023). The placebo effect of artificial intelligence in human–computer interaction. ACM Transactions on Computer-Human Interaction, 29(6), 1–32.
8. Lisetti, C. L., & Schiano, D. J. (2000). Automatic facial expression interpretation: Where human-computer interaction, artificial intelligence and cognitive science intersect. Pragmatics & cognition, 8(1), 185-235.

9. Maruyama, Y. (2022). Human-computer interaction and coevolution in science AI robotics. In Proceedings of the International Conference on Human-Computer Interaction (pp. 523–531). Springer Nature Switzerland.
10. Myers, B. A. (1998). A brief history of human-computer interaction technology. Interactions, 5(2), 44–54. https://doi.org/10.1145/274430.274436
11. Nazar, M., Alam, M. M., Yafi, E., & Su’ud, M. M. (2021). A systematic review of human–computer interaction and explainable artificial intelligence in healthcare with artificial intelligence techniques. IEEE Access, 9, 153316–153348.
12. Nicolescu, L., & Tudorache, M. T. (2022). Human-computer interaction in customer service: The experience with AI chatbots—A systematic literature review. Electronics, 11(10), Article 1579. https://doi.org/10.3390/electronics11101579
13. Okpala, B. (2024). Examining the Impact of Generative AI on UX/UI Design. UI Design (November 30, 2024).
14. Page, M. J., McKenzie, J. E., Bossuyt, P. M., Boutron, I., Hoffmann, T. C., Mulrow, C. D., et al. (2021). The PRISMA 2020 statement: An updated guideline for reporting systematic reviews. BMJ, 372.
15. Peláez, C. A., Solano, A., Núñez V, J. M., Castro, D., Cardona, J. J., Duque, J. S., ... & Prieta, F. D. L. (2024, June). Designing User Experience in the Context of Human-Centered AI and Generative Artificial Intelligence: A Systematic Review. In International Symposium on Distributed Computing and Artificial Intelligence (pp. 201-209). Cham: Springer Nature Switzerland.
16. Serrat, O. (2017). Social network analysis. In Knowledge solutions: Tools, methods, and approaches to drive organizational performance (pp. 39–43). Springer.
17. Stige, Å., Zamani, E. D., Mikalef, P., & Zhu, Y. (2024). Artificial intelligence (AI) for user experience (UX) design: a systematic literature review and future research agenda. Information Technology & People, 37(6), 2324-2352.
18. Šumak, B., Brdnik, S., & Pušnik, M. (2021). Sensors and artificial intelligence methods and algorithms for human–computer intelligent interaction: A systematic mapping study. Sensors, 22(1), Article 20.
19. Tabassum, S., Pereira, F. S., Fernandes, S., & Gama, J. (2018). Social network analysis: An overview. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 8(5), Article e1256.
20. Usmani, U. A., Happonen, A., & Watada, J. (2024). The digital age: Exploring the intersection of AI/CI and human cognition and social interactions. Procedia Computer Science, 239, 1044–1052.
21. Virvou, M. (2023). Artificial Intelligence and User Experience in reciprocity: Contributions and state of the art. Intelligent Decision Technologies, 17(1), 73-125.
22. Wang, D., Han, L., Cong, L., Zhu, H., & Liu, Y. (2023). Practical evaluation of human–computer interaction and artificial intelligence deep learning algorithm in innovation and entrepreneurship teaching evaluation. International Journal of Human–Computer Interaction, 1–9.
23. Xu, W. (2019). Toward human-centered AI: A perspective from human-computer interaction. Interactions, 26(4), 42–46.
24. Xu, W., Dainoff, M. J., Ge, L., & Gao, Z. (2023). Transitioning to human interaction with AI systems: New challenges and opportunities for HCI professionals to enable human-centered AI. International Journal of Human–Computer Interaction, 39(3), 494–518.
25. Zhou, D. (2022). Human-computer interaction interface design in intelligent medical system under the background of artificial intelligence. In 2022 International Conference on Artificial Intelligence and Autonomous Robot Systems (AIARS) (pp. 10-13). IEEE.