AI interactivity and human-technology engagement: psychological mechanisms underlying learners’ intention to use AI tools in language learning contexts
Year: 2026
Type: article
Study design: Observational Study
Abstract: BACKGROUND: The growing integration of Artificial Intelligence (AI) into education has transformed how learners interact with technology. Understanding the psychological mechanisms underlying this interaction is essential for optimizing human-technology engagement. Grounded in models of technology acceptance and human-AI interaction, this study examines how perceived AI interactivity influences learners’ behavioral intention to use AI tools, with perceived usefulness (PU) and perceived ease of use (PEOU) as mediators. METHODS: A total of 740 Chinese university EFL learners completed a validated questionnaire measuring four dimensions of AI interactivity (responsiveness, personalization, learner control, and learning engagement) along with PU, PEOU, and behavioral intention. Data were analyzed using structural equation modeling (SEM) with bootstrapping to test direct and indirect pathways. RESULTS: All four dimensions of AI interactivity significantly predicted behavioral intention both directly and indirectly through PU and PEOU. Responsiveness exerted the strongest direct effect, whereas, learning engagement showed the highest indirect influence via PU and PEOU. The final model demonstrated good fit (χ2/df = 2.216, RMSEA = 0.041, CFI = 0.964). The results reveal key psychological pathways through which human-AI interactivity shapes motivation and technology acceptance. CONCLUSIONS: AI interactivity plays a central role in shaping human-technology engagement. Immediate feedback and interactive design enhance motivation and perceived competence, thus offering practical implications for psychologically informed AI-based learning systems.
Source: BMC Psychology
Authors: Chenming Lyu, Shi Tang, Shaopeng Li
Institutions: Ludong University, Shanghai International Studies University
Language: English
Subfield: Information Systems
Field: Computer Science
Domain: Physical Sciences
SDG: Quality education
Open Access status: gold
Estimated APC paid: $1,990
OpenAlex ID: W7130925554