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Paper Details


Title
Unlocking teaching potential: exploring behavioral drivers of AI chatbots use in higher education teaching

Author
, Muhammad Khalilur Rahman,

Email

Abstract

This study aims to identify factors that determine the use of AI by lecturers, specifically the use of AI teaching chatbots. This study is grounded in the Unified Theory of Acceptance and Use of Technology (UTAUT) and Self-Determination Theory (SDT), and investigates how teaching chatbots are driven by behavioral intention and actual use of teaching chatbots by lecturers in the context of technological and organizational settings, as well as intrinsic motivation. Within the UTAUT framework, the study examines the roles of performance expectancy, effort expectancy, social influence, and facilitating conditions, while the intrinsic element of the SDT is used to capture lecturers’ intrinsic motivation to use AI. The analysis shows that the involvement of teaching chatbots is expected and valued by lecturers and is significantly facilitated by effort and the intrinsic element of enjoyment, while social influence is negligible. Also, the involvement of teaching chatbots is mediated by behavioral intention and is positively facilitated by the tech-savviness of lecturers. This study aims to understand the use of AI in the teaching sector by integrating UTAUT and SDT in its approach. It is evident that for the effective use of AI teaching chatbots by lecturers, the intrinsic motivation of the lecturers needs to be emphasized.


Keywords

Journal or Conference Name
Interactive Learning Environments

Publication Year
2026

Indexing
scopus