Human–AI Interaction Research (2005–2025): A Bibliometric Mapping Based on Scopus Data
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Abstract
This study provides a comprehensive bibliometric analysis of Human–AI Interaction (HAI) research from 2005 to 2025 using data retrieved from the Scopus database. The objective is to map the intellectual structure, research trends, and thematic evolution of the field. A total of relevant publications were analyzed using bibliometric techniques, including co-authorship, co-citation, keyword co-occurrence, overlay, and density visualization, supported by VOSviewer. The results indicate a significant growth in HAI research, particularly after 2015, driven by rapid advancements in artificial intelligence technologies such as machine learning, deep learning, and large language models. The findings reveal that early research was predominantly technology-oriented, focusing on algorithm development and system performance, while more recent studies emphasize human-centered aspects such as trust, explainability, user experience, and ethical considerations. Network analysis shows that research collaboration is concentrated in developed regions, with strong contributions from North America, Europe, and East Asia, while participation from developing regions remains limited. Keyword analysis identifies major thematic clusters, including technical foundations, human-centered interaction, and application domains such as healthcare and decision-making. The study also highlights emerging topics such as generative AI and human–AI collaboration. Overall, this research provides a structured overview of the HAI research landscape and offers insights into future research directions, emphasizing the importance of interdisciplinary approaches and responsible AI development.
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