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.

Text summarization is a subtask of natural language processing referring to the automatic creation of a concise and fluent summary that captures the main ideas and topics from one or multiple documents. Earlier literature surveys focus on extractive approaches, which rank the top-n most important sentences in the input document and then combine them to form a summary. As argued in the literature, the summaries of these approaches do not have the same lexical flow or coherence as summaries that are manually produced by humans. Newer surveys elaborate abstractive approaches, which generate a summary with potentially new phrases and sentences compared to the input document. Generally speaking, contrary to the extractive approaches, the abstractive ones create summaries that are more similar to those produced by humans. However, these approaches still lack the contextual representation needed to form fluent summaries. Recent advancements in deep learning and pretrained language models led to the improvement of many natural language processing tasks, including abstractive summarization. Overall, these surveys do not present a comprehensive evaluation framework that assesses the aforementioned approaches. Taking the above into account, the contribution of this survey is fourfold: (i) we provide a comprehensive survey of the state-of-the-art approaches in text summarization; (ii) we conduct a comparative evaluation of these approaches, using well-known datasets from the related literature, as well as popular evaluation scores such as ROUGE-1, ROUGE-2, ROUGE-L, ROUGE-LSUM, BLEU-1, BLEU-2 and SACREBLEU; (iii) we report on insights gained on various aspects of the text summarization process, including existing approaches, datasets and evaluation methods, and we outline a set of open issues and future research directions; (iv) we upload the datasets and the code used in our experiments in a public repository, aiming to increase the reproducibility of this work and facilitate future research in the field.

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.

The main purpose of this article is to provide an up-to-date understanding of the utilization and deployment of emerging technologies in the public sector, as this is reflected through 19 recently funded Horizon 2020 research projects. For the needs of this study, we have adopted a well-known literature review method that enables a concept-centric analysis of the accumulated knowledge in the field under consideration, and accordingly proposed a conceptual framework that facilitates such an analysis. Through a detailed consideration of these projects and their pilot case implementations, a series of insights about recent research development and applications in the public sector are extracted and discussed. To the best of our knowledge, this is the first attempt to gain such insights from a research projects perspective, which may reveal useful information about the utilization and deployment of these technologies in real-life pilots. The findings of this study are also justified or challenged by referring to recent review articles that investigate the use of emerging technologies in the public sector.