In the last decade, artificial intelligence (AI), machine learning (ML) and learning data analytics have been introduced with great effect in the field of higher education. However, despite the potential benefits for higher education institutions (HIE´s) of these emerging technologies, most of them are still in the early stages of adoption of these technologies. Thus, a systematic literature review (SLR) on the literature published over the last 5 years on potential applications of machine learning in higher education is necessary. Following the PRISMA guidelines, out of the 1887 initially identified SCOPUS-indexed publications on the topic, 171 articles were selected for review. To screen the abstracts and titles of each citation, Rayyan QCRI was used. VOSViewer, a software tool for constructing and visualizing bibliometric networks, and Microsoft Excel were used to generate charts and figures. The findings show that the most widely researched application of ML in higher education is related to the prediction of academic performance and employability of students. The implications will be invaluable for researchers and practitioners to explore how ML and AI technologies ,in the era of ChatGPT, can be used in universities without jeopardizing academic integrity.
Few industries were more affected by the COVID-19 pandemic than tourism. One of Europe´s leading tourist destinations, Porto had undergone a major tourism boom until the start of pandemic. Mobile Augmented Reality (MAR) is one of the many emerging technologies that has great potential for tourist operators. Using this technology, they can create innovative tourism products that will help them recover from the present crisis. As a result, in this study, we will empirically test the latest version of the Unified Theory of Acceptance and Use of Technology (UTAUT) model to explore the factor leading to the adoption Mobile Augmented Reality in Tourism (MART) in Porto. In doing so, we aim to contribute to growing literature on the topic of Mobile Augmented Reality (MAR). The originality of this study lies in the use of an extended UTAUT model with greater predictive power and the exploration of the moderative role of gender, age and experience. To the data obtained from a random sample of 201 respondents who voluntarily answered an anonymous online questionnaire, we applied structural equational modeling and partial least squares (SEM-PLS) analysis to test the model. Our findings show that habit, hedonic motivations and facilitating conditions are the determinants of the use of MART.
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