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The emergence of artificial intelligence technologies, like ChatGPT, has taken the world by storm, particularly in the education sector. This study aims to adopt the unified theory of acceptance and use of technology to explore how effort expectancy (EEC) and performance expectancy (PEE) individually, jointly, congruently and incongruently affect higher education students’ intentions and actual uses of ChatGPT for their learning.

An advanced methodology – polynomial regression with response surface analysis – and a sample of 1,461 higher education students recruited in Vietnam through three-phase stratified random sampling approach were adopted to test developed hypotheses.

Both EEC and PEE were found to have a direct positive impact on the likelihood of higher education students’ intention to use ChatGPT, which in turn promotes them actually use this tool for learning purposes. Conversely, a large incongruence between EEC and PEE will lower the level of intentions and actual uses of ChatGPT for learning. However, when there is a growing incongruence between EEC and PEE, either in a positive or negative direction, the likelihood of students’ intentions to use ChatGPT for learning decreases.

Some practical implications are subsequently recommended to obtain advantages and address potential threats arising from the implementation of this novel technology in the education context.

This study shed the new light on the educational setting by testing how higher education students’ intentions to use ChatGPT and subsequent actual uses of ChatGPT are synthesized from the balance between high EEC and PEE.

This study aims to provide new insights into the relationship between individual characteristics, particularly personality traits and mature students' intention to use (ITU) mobile learning (m-learning).

The research model was constructed by integrating the Big Five personality traits into the unified theory of acceptance and use of technology (UTAUT) model. The data were collected from mature students at a university research center in Macau. Partial least squares structural equation modeling (PLS-SEM) was used to analyze the data and test the proposed hypotheses.

The results reveal that personality traits play a significant role in determining mature students' ITU m-learning technology. In particular, social influence (SI) mediates the relationship between agreeableness (AGB) and ITU.

This study examines how personality traits collectively influence mature students' receptiveness and intentions toward m-learning. As mature learners' motivations and preferences remain underexplored, insights into trait-technology links could address current gaps and optimize mobile educational support tailored to their distinct characteristics and needs.