Unlocking future learning: Exploring higher education students' intention to adopt meta-education

Al-Adwan, Ahmad Samed; Alsoud, Malek; Li, Na; Majali, Tha'er; Smedley, Jo; Habibi, Akhmad

doi:10.1016/j.heliyon.2024.e29544

Cited by 11 publications

(1 citation statement)

References 71 publications

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“…Attitude towards use (ATT) is a person’s perception of technology, which is a psychological feedback of liking, enjoying, and being happy with technology 58 , usability, which has a positive impact on the practical use of m-learning systems 82 . In the previous research, there is a higher education students to adopt the meta-educational intention of the factors 83 . and studies on users’ sustained intention towards e-learning 84 have both concluded that both PU and PEOU affect a person’s ability to use the system’s ATT.…”

Section: Methodsmentioning

confidence: 99%

An explanatory study of factors influencing engagement in AI education at the K-12 Level: an extension of the classic TAM model

Li,

Zhang,

et al. 2024

Sci Rep

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Artificial intelligence (AI) holds immense promise for K-12 education, yet understanding the factors influencing students’ engagement with AI courses remains a challenge. This study addresses this gap by extending the technology acceptance model (TAM) to incorporate cognitive factors such as AI intrinsic motivation (AIIM), AI readiness (AIRD), AI confidence (AICF), and AI anxiety (AIAX), alongside human–computer interaction (HCI) elements like user interface (UI), content (C), and learner-interface interactivity (LINT) in the context of using generative AI (GenAI) tools. By including these factors, an expanded model is presented to capture the complexity of student engagement with AI education. To validate the model, 210 Chinese students spanning grades K7 to K9 participated in a 1 month artificial intelligence course. Survey data and structural equation modeling reveal significant relationships between cognitive and HCI factors and perceived usefulness (PU) and ease of use (PEOU). Specifically, AIIM, AIRD, AICF, UI, C, and LINT positively influence PU and PEOU, while AIAX negatively affects both. Furthermore, PU and PEOU significantly predict students’ attitudes toward AI curriculum learning. These findings underscore the importance of considering cognitive and HCI factors in the design and implementation of AI education initiatives. By providing a theoretical foundation and practical insights, this study informs curriculum development and aids educational institutions and businesses in evaluating and optimizing AI4K12 curriculum design and implementation strategies.

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