Students' adoption of AI-based teacher-bots (T-bots) for learning in higher education

Abstract

Purpose The purpose of this paper is to investigate students' adoption intention (ADI) and actual usage (ATU) of artificial intelligence (AI)-based teacher bots (T-bots) for learning using technology adoption model (TAM) and context-specific variables. Design/methodology/approach A mixed-method design is used wherein the quantitative and qualitative approaches were used to explore the adoption of T-bots for learning. Overall, 45 principals/directors/deans/professors were interviewed and NVivo 8.0 was used for interview data analysis. Overall, 1,380 students of higher education institutes were surveyed, and the collected data was analyzed using the Partial Least Squares Structural Equation Modeling (PLS-SEM) technique. Findings The T-bot's ADI’s antecedents found were perceived ease of use, perceived usefulness, personalization, interactivity, perceived trust, anthropomorphism and perceived intelligence. The ADI influences the ATU of T-bots, and its relationship is negatively moderated by stickiness to learn from human teachers in the classroom. It comprehends the insights of senior authorities of the higher education institutions in India toward the adoption of T-bots. Practical implications The research provides distinctive insights for principals, directors and professors in higher education institutes to understand the factors affecting the students' behavioral intention and use of T-bots. The developers and designers of T-bots need to ensure that T-bots are more interacti…