Modeling AI Tool Adoption in Higher Education: The Role of Authentic Learning and Prompting Competence

Adoption of Generative AI Tools In Higher Education: The Role of Attitude, Subjective Norms, and Perceived Behavioural Control

Artificial Intelligence (AI) technologies are increasingly transforming higher education by providing innovative tools that support students in learning, academic writing, and information processing. Despite the rapid adoption of generative AI tools such as ChatGPT, Grammarly, and QuillBot, the factors influencing undergraduate students’ intention to use these tools for learning remain insufficiently understood. This study aims to examine the determinants of students’ intention to use AI tools based on the Theory of Planned Behaviour (TPB). Specifically, the study investigates the influence of attitude, subjective norms, and perceived behavioural control on students’ behavioural intention to adopt AI technologies in academic contexts. This study adopts a quantitative approach, where data will be collected from approximately 300 undergraduate students from Universiti Teknologi MARA (UiTM), Malaysia, using a structured questionnaire. The measurement items are adapted from established literature and assessed using a five-point Likert scale. The collected data will be analysed using Partial Least Squares Structural Equation Modelling (PLS-SEM) through SPSS and SmartPLS software. The proposed framework suggests that students who perceive AI tools as useful and beneficial are more likely to develop positive attitudes toward their use. In addition, social influences from lecturers, peers, and institutions are expected to shape students’ perceptions and acceptance of AI tools. Furthermore, students’ confidence in their digital skills and access to technological resources may enhance their perceived behavioural control, which in turn strengthens their intention to use AI tools. This study contributes to the growing body of knowledge on AI adoption in higher education by applying TPB in the context of generative AI tools. The findings are expected to provide practical insights for universities in developing policies, training programs, and ethical guidelines to promote responsible and effective use of AI in academic learning environments.

A proposed model for predicting the intention to use the metaverse in computer programming education

The advent of new technologies has significantly increased digitalization in education by accelerating the shift to digital products and tools. In particular, notable innovations in artificial intelligence and Metaverse technologies hold promising potential to transform educational and training formats globally, demanding swift reforms in education systems. This study explored the factors influencing the adoption of Metaverse-based software among 640 students pursuing associate degrees in computer programming within the framework of the extended Technology Acceptance Model (TAM). While previous research has explored general factors influencing technology adoption, gaps remain regarding the specific role of perceived complexity, enjoyment, and student self-efficacy within technical education contexts. The model designed to explain students’ behavioral intentions toward Metaverse-based technologies was analyzed and validated using Partial Least Squares Structural Equation Modeling (PLS-SEM). The study findings indicated that students’ perceived ease of use (PEOU) and perceived usefulness (PU) significantly influenced their behavioral intentions (BI) regarding the Metaverse. Among the external factors included in the model, it was determined that social influence (SIE), peer influence (PIE), and personal innovativeness (PI) variables significantly affected PU. In contrast, the perceived complexity (PC) variable significantly impacted PEOU and PU. This article examines the opportunities and challenges that Metaverse technologies bring to learning in computer programming studies and discusses the proposed research model for explaining students’ behavioral intentions. The study aims to develop new approaches that can offer significant opportunities in this field by establishing a framework for future research on the applications of the Metaverse in education.

Teaching with Technology, Leading with Values: ELT Teachers and the Future of AI in Education

Teachers’ Adoption of ChatGPT in Higher Education: Integrating Technology Acceptance, Trust, and Perceived Risk

In recent years, artificial intelligence (AI) technologies have become increasingly prominent in education, with ChatGPT emerging as a notable tool that enhances learning experiences. This study explores teachers’ attitudes towards the adoption of ChatGPT as a learning technology, focusing on its potential benefits, limitations and implications for education. A quantitative approach was employed, involving an online survey targeting university educators. The survey assessed teachers’ perceptions of ChatGPT in higher education and identified barriers to its implementation. The conceptual framework integrates elements from the Technology Acceptance Model, the Unified Theory of Acceptance and Use of Technology and insights from risk perception theory. Data analysis was conducted using Partial Least Squares Structural Equation Modelling with SMART PLS software, ensuring reliability and discriminant validity. Findings indicate that most teachers view ChatGPT as an effective tool for enhancing student motivation, personalised learning and critical thinking. While teaching experience fosters positive perceptions and reduces perceived risks, it does not significantly influence challenges or trust in adoption decisions. Nonetheless, challenges persist, particularly regarding the reliability of information generated by ChatGPT. This study can assist policymakers, educators and technology developers in collaborating to implement practical and ethical AI-enabled tools like ChatGPT. Ultimately, while ChatGPT holds significant potential as an educational technology, its effectiveness hinges on addressing technical and pedagogical challenges.

Acceptance Towards A.I. Use among KPTM Bangi Students in an ESL Classroom

Generative AI Adoption in Commerce Education: Its Impact on Students' Learning Effectiveness, Critical Thinking, and Career Readiness

Generative Artificial Intelligence (GenAI) tools have proliferated rapidly in recent years and are now starting to revolutionize the way teaching and learning happens in higher education institutions. However, there still exists a dearth of empirical studies on the use of GenAI tools among commerce students in India. The current study focuses on evaluating the effects of GenAI adoption among commerce students on their learning effectiveness, critical thinking ability, and career-readiness preparedness, based on the Technology Acceptance Model, UTAUT2, and Bloom’s Taxonomy. Using a descriptive and survey-oriented research methodology, this study explores GenAI tool adoption patterns and their relationships with academic and professional aspects using correlations, regressions, and structural equation modeling. The results reveal that GenAI tool adoption has a significant positive effect on students' perceived learning effectiveness and career-readiness preparedness, and their effect on critical thinking ability is minimal and limited to lower order thinking skills only. Facilitating conditions make an impactful contribution to these relationships. Overall, the study proves that the value of GenAI tools in education is tied to systematic adoption and highlights the importance of designing a commerce curriculum that supports NEP 2020 goals.