Recent research emphasizes the importance of Artificial Intelligence applications as supporting tools for students in higher education. Simultaneously, an intensive exchange of views has started in the public debate in the international educational community. However, for a more proper use of these applications, it is necessary to investigate the factors that explain their intention and actual use in the future. With the Unified Theory of Acceptance and Use of Technology (UTAUT2) model, this work analyses the factors influencing students’ use and intention to use Artificial Intelligence technology. For this purpose, a sample of 197 Greek students at the School of Humanities and Social Sciences from the University of Patras participated in a survey. The findings highlight that expected performance, habit, and enjoyment of these Artificial Intelligence applications are key determinants influencing teachers’ intentions to use them. Moreover, behavioural intention, habit, and facilitating conditions explain the usage of these Artificial Intelligence applications. This study did not reveal any moderating effects. The limitations, practical implications, and proposed directions for future research based on these results are discussed.

An emerging area of research is the study of factors that influence the use of e-learning systems in higher education. Previous studies have mainly focused on the factors that influence the adoption of learning management systems (LMS) from the student’s point of view, and rarely from the point of view of the university lecturers. Moodle is an open-source LMS that has been used increasingly by the higher education community worldwide in the past few years. The purpose of this study is to investigate the factors that explain the acceptance of Moodle by academic personnel (faculty members) in the Greek higher education system. The convenience sample consists of 85 lecturers from different universities in Greece. All of them reported having used Moodle. Using the technology acceptance model, the correlations between the six latent variables (perceived ease of use, perceived usefulness, behavioral intention to use, perceived self-efficacy, subjective norms, and technology complexity) were examined. Five of the eight hypotheses were supported by variance base structural equation modelling. The total explained variance of the faculty members’ behavioral intention to use Moodle was estimated to be 68.3%. Perceived usefulness and perceived ease of use had a high overall effect. Subjective norms, self-efficacy, and technological complexity influenced the teachers’ intentions to adopt Moodle. This study recommends training as well as technical support for academic personnel. In addition, stakeholders should address these factors to increase usability, awareness of new opportunities for the educational community, accessibility, and the general dissemination of the benefits of learning management systems in education.

Recent research emphasises the importance of Artificial Intelligence applications as supporting tools for students in higher education. Simultaneously, an intensive exchange of views has started in the public debate in the international educational community. However, for a more proper use of these applications, it is necessary to investigate the factors that explain their intention and actual use in the future. With the Unified Theory of Acceptance and Use of Technology (UTAUT2) model, this work analyses the factors influencing students' use and intention to use artificial intelligence technology. For this purpose, a sample of 199 Greek students at the School of Humanities and Social Sciences from the University of Patras participated in a survey. The findings highlight that expected performance, habit, and enjoyment of these Artificial Intelligence applications are key determinants influencing teachers' intentions to use them. Moreover, behavioural intention, habit, and facilitating conditions explain the usage of these Artificial Intelligence applications. The study did not reveal any moderating effects. The limitations, practical implications, and proposed directions for future research based on these results are discussed.