Exploring Factors of the Willingness to Accept AI-Assisted Learning Environments: An Empirical Investigation Based on the UTAUT Model and Perceived Risk Theory

Abstract

Artificial intelligence (AI) technology has been widely applied in many fields. AI-assisted learning environments have been implemented in classrooms to facilitate the innovation of pedagogical models. However, college students' willingness to accept (WTA) AI-assisted learning environments has been ignored. Exploring the factors that influence college students' willingness to use AI can promote AI technology application in higher education. Based on the Unified Theory of Acceptance and Use of Technology (UTAUT) and the theory of perceived risk, this study identified six factors that influence students' willingness to use AI to analyze their relationships with WTA AI-assisted learning environments. A model including six hypotheses was constructed to test the factors affecting students' WTA. The results indicated that college students showed “weak rejection” of the construction of AI-assisted learning environments. Effort expectancy (EE), performance expectancy (PE), and social influence (SI) were all positively related to college students' WTA AI-assisted learning environments. Psychological risk (PR) significantly negatively influenced students' WTA. The findings of this study will be helpful for carrying out risk communication, which can promote the construction of AI-assisted learning environments.

AI Chatbots in Chinese higher education: adoption, perception, and influence among graduate students—an integrated analysis utilizing UTAUT and ECM models

“…Furthermore, Zhang et al (2023) provide compelling evidence of the causal relationship between social factors and users’ intention to adopt and utilize technology-mediated learning systems, thereby advancing our understanding of the intricate interplay between sociocultural elements and users’ BI. Wu et al (2022) posit a positive correlation between variables of PE, SI, and FC with the adoption of AI-assisted learning among university students. Similarly, Zhai et al (2022) proposes that FC indirectly impact the sustained usage intention of AI-assisted learning.…”

“… In AI, the correlation was found to be significantly positive between PE and BI to utilize AI in recruitment, with no significant impact discovered in terms of gender, age, experience, and education level ( Horodyski, 2023 ). In AI-assisted education, EE, PE, and SI were positively correlated with university students’ usage of AI-assisted learning, with Psychological Risk being a significant negative influence on the students’ BI ( Wu et al, 2022 ). In Web-based learning, PE, EE, Computer Self-efficacy, Achievement Value, Utility Value, and Intrinsic value were significant predictors of individuals’ intention to continue using web-based learning.…”

“…In AI-assisted education, EE, PE, and SI were positively correlated with university students’ usage of AI-assisted learning, with Psychological Risk being a significant negative influence on the students’ BI ( Wu et al, 2022 ).…”

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“…The Universal Theory of Adoption and Use of Technology (UTAUT) model suggests that mainstreaming AI into clinical practice is dependent on model performance (performance expectancy), user competency (effort expectancy), peer utilisation and societal values (social influence), and enabling processes and infrastructure (facilitating conditions). [ 13 ] A health-contextualised UTAUT model adapted to the Singapore healthcare system is shown in Table 1 .…”

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“…StatSheet was the first automated online sports writing tool introduced in 2007 in North Carolina. Then, in 2009, the Grammarly writing assistant was introduced, followed by other tools like WordSmith and Narrative Science's Quill (Wu et al, 2022). With the growing development and uptake of several AI tools today, scholars observe that examining the use of AI in academic writing courses involves drawing upon various theoretical perspectives to analyze its impact, effectiveness, and implications (Teng et al, 2022;Wilby, 2022).…”