Decoding symmetric and asymmetric pathways in generative AI learning adoption: a multi-method study

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Original abstract

Abstract The rapid emergence of Generative Artificial Intelligence (Gen-AI), particularly ChatGPT, is transforming higher education. However, dominant technology adoption theories have largely been developed in high-resource contexts and primarily focus on cognitive evaluations of usefulness and ease of use. These theories offer limited explanations of how motivational design factors and contextual constraints shape sustained AI use in low-resource educational systems. This study addresses this theoretical and contextual gap by examining how cognitive–instrumental beliefs and motivational–affective experiences jointly influence students’ adoption of Gen-AI in Lesotho’s higher education sector, where infrastructural limitations and policy uncertainty remain significant. Guided by an integrated framework that combines the Unified Theory of Acceptance and Use of Technology (UTAUT3) and Keller’s ARCS Motivation Model, we argue that acceptance beliefs explain intention formation, while motivational perceptions explain continued engagement and actual use. Using data from 842 students, we analyzed causal, configurational, and predictive relationships. The results show that performance expectancy, effort expectancy, social influence, hedonic motivation, and habit significantly predict behavioral intention, whereas the ARCS motivation dimensions are stronger determinants of actual use. Configurational findings reveal multiple pathways to high adoption, with motivation, enjoyment, and habit serving as core conditions. Personal innovativeness and motivational moderation effects were weak, underscoring contextual sensitivity. This study advances theory by demonstrating that Gen-AI adoption follows a hybrid logic in which cognitive beliefs enable acceptance, motivational experiences sustain engagement, and habitual interaction normalizes use. It offers direction for motivation-centered design and context-responsive AI policies in higher education.

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