This paper aims to explore and investigate the potential factors influencing students' behavioral intentions to use the e-learning system. This paper proposes an extended technology acceptance model (TAM) that has been tested and examined through the use of both innovation diffusion theory (IDT) and integrating TAM. This paper was conducted on 1286 students utilizing systems of e-learning in Malaysia. The findings were obtained via a quantitative research method. The findings illustrate that six perceptions of innovation characteristics, in particular, have impacts on students' e-learning system behavioral intention. The influences of the relative advantages, observability, trialability, perceived compatibility, complexity, and perceived enjoyment on the perceived ease of use is noteworthy. Moreover, the effects of the relative advantages, complexity, trialability, observability, perceived compatibility, and perceived enjoyment on the perceived usefulness have a strong impact. Therefore, the empirical results provide strong backing to the integrative approach between TAM and IDT. The findings suggest an extended model of TAM with IDT for the acceptance of the e-learning system used to improve the students' learning performance, which can help decision makers in higher education, universities, as well as colleges to evaluate, plan and execute the use of e-learning systems.

This mixed-method study explores the acceptance of ChatGPT as a tool for Metacognitive Self-Regulated Learning (MSRL) among academics. Despite the growing attention towards ChatGPT as a metacognitive learning tool, there is a need for a comprehensive understanding of the factors influencing its acceptance in academic settings. Engaging 300 preservice teachers through a ChatGPT-based scenario learning activity and utilizing convenience sampling, this study administered a questionnaire based on the proposed Technology Acceptance Model at UTM University's School of Education. Structural equation modelling was applied to analyze participants' perspectives on ChatGPT, considering factors like MSRL's impact on usage intention. Post-reflection sessions, semi-structured interviews, and record analysis were conducted to gather results. Findings indicate a high acceptance of ChatGPT, significantly influenced by personal competency, social influence, perceived AI usefulness, enjoyment, trust, AI intelligence, positive attitude, and metacognitive self-regulated learning. Interviews and record analysis suggest that academics view ChatGPT positively as an educational tool, seeing it as a solution to challenges in teaching and learning processes. The study highlights ChatGPT's potential to enhance MSRL and holds implications for teacher education and AI integration in educational settings.

This research intends to investigate factors affecting students’ behavioural intentions to use a massive open online courses (MOOCs) system. Integrating the technology acceptance model (TAM) with the innovation diffusion theory (IDT), the present research proposes an extended technology acceptance model. Testing of data collected from 1148 students using (MOOCs) system in Malaysia achieved the research objective. The findings obtained via a quantitative research method, based on analysing the results using a Structure Equation Modeling (SEM). The outcomes reveal that six views of novelty features swayed students’ a (MOOCs) scheme behavioural objective. The properties of the relative advantages, intricacy, trialability, observability, compatibility, and perceived enjoyment on the perceived ease of use are significant. Also, the effects of the relative advantages, complexity, trialability, observability, compatibility, and perceived enjoyment on the usefulness have a substantial impact. Therefore, the practical outcomes offer robust backing for the integrative method between TAM and IDT. The results recommend a prolonged model of TAM with IDT for the taking of MOOCs) system used to improve students learning performance, which can help decision makers in higher education, universities and colleges to planning, evaluating and executing the use of (MOOCs) systems.

AI-powered chatbots hold great promise for enhancing learning experiences and outcomes in today's rapidly evolving education system. However, despite the increasing demand for such technologies, there remains a significant research gap regarding the factors influencing users' acceptance and adoption of AI-powered chatbots in educational contexts. This study aims to address this gap by investigating the factors that shape users' attitudes, intentions, and behaviors towards adopting ChatGPT for smart education systems. This research employed a quantitative research approach, data were collected from 458 of participants through a structured questionnaire designed to measure various constructs related to technology acceptance, including perceived ease of use, perceived usefulness, feedback quality, assessment quality, subject norms, attitude towards use, and behavioral intention to use ChatGPT. Structural model analysis (SEM) Statistical techniques were then utilized to examine the relationships between these constructs. The findings of the study revealed that Perceived ease of use and perceived usefulness emerged as significant predictors of users' attitudes towards ChatGPT for smart education. Additionally, feedback quality, assessment quality, and subject norms were found to positively influence users' behavioral intentions to use ChatGPT for smart educational purposes. Moreover, users' attitudes towards use and behavioral intentions were significantly proved for the actual adoption of ChatGPT. However, a few hypotheses, such as the relationship between trust in ChatGPT and perceived usefulness, were not supported by the data. This study contributes to the existing body information systems applications for the determining factor of technology acceptance in smart education context.

In higher education learning, e-learning systems have become renowned tools worldwide. The evident importance of e-learning in higher education has resulted in a prenominal increase in the number of e-learning systems delivering various forms of services, especially when traditional education (face-to-face) was suddenly forced to move online due to the COVID-19 outbreak. Accordingly, assessing e-learning systems is pivotal in the interest of effective use and successful implementation. By relying on the related literature review, an extensive model is developed by integrating the information system success model (ISSM) and the technology acceptance model (TAM) to illustrate key factors that influence the success of e-learning systems. Based on the proposed model, theory-based hypotheses are tested through structural equation modeling employing empirical data gathered through a survey questionnaire of 537 students from three private universities in Jordan. The findings demonstrate that quality factors, including instructor, technical system, support service, educational systems, and course content quality, have a direct positive influence on students’ satisfaction, perceived usefulness, and system use. Moreover, self-regulated learning negatively affects students’ satisfaction, perceived usefulness, and system use. Students’ satisfaction, perceived usefulness, and system use are key predictors of their academic performance. These findings provide e-learning stakeholders with important implications that guarantee the effective, successful use of e-learning that positively affects students’ learning.