According to various international reports, Artificial Intelligence in Education (AIEd) is one of the currently emerging fields in educational technology. Whilst it has been around for about 30 years, it is still unclear for educators how to make pedagogical advantage of it on a broader scale, and how it can actually impact meaningfully on teaching and learning in higher education. This paper seeks to provide an overview of research on AI applications in higher education through a systematic review. Out of 2656 initially identified publications for the period between 2007 and 2018, 146 articles were included for final synthesis, according to explicit inclusion and exclusion criteria. The descriptive results show that most of the disciplines involved in AIEd papers come from Computer Science and STEM, and that quantitative methods were the most frequently used in empirical studies. The synthesis of results presents four areas of AIEd applications in academic support services, and institutional and administrative services: 1. profiling and prediction, 2. assessment and evaluation, 3. adaptive systems and personalisation, and 4. intelligent tutoring systems. The conclusions reflect on the almost lack of critical reflection of challenges and risks of AIEd, the weak connection to theoretical pedagogical perspectives, and the need for further exploration of ethical and educational approaches in the application of AIEd in higher education.

Digital technology has become a central aspect of higher education, inherently affecting all aspects of the student experience. It has also been linked to an increase in behavioural, affective and cognitive student engagement, the facilitation of which is a central concern of educators. In order to delineate the complex nexus of technology and student engagement, this article systematically maps research from 243 studies published between 2007 and 2016. Research within the corpus was predominantly undertaken within the United States and the United Kingdom, with only limited research undertaken in the Global South, and largely focused on the fields of Arts & Humanities, Education, and Natural Sciences, Mathematics & Statistics. Studies most often used quantitative methods, followed by mixed methods, with little qualitative research methods employed. Few studies provided a definition of student engagement, and less than half were guided by a theoretical framework. The courses investigated used blended learning and text-based tools (e.g. discussion forums) most often, with undergraduate students as the primary target group. Stemming from the use of educational technology, behavioural engagement was by far the most often identified dimension, followed by affective and cognitive engagement. This mapping article provides the grounds for further exploration into discipline-specific use of technology to foster student engagement.

Although the field of Artificial Intelligence in Education (AIEd) has a substantial history as a research domain, never before has the rapid evolution of AI applications in education sparked such prominent public discourse. Given the already rapidly growing AIEd literature base in higher education, now is the time to ensure that the field has a solid research and conceptual grounding. This review of reviews is the first comprehensive meta review to explore the scope and nature of AIEd in higher education (AIHEd) research, by synthesising secondary research (e.g., systematic reviews), indexed in the Web of Science, Scopus, ERIC, EBSCOHost, IEEE Xplore, ScienceDirect and ACM Digital Library, or captured through snowballing in OpenAlex, ResearchGate and Google Scholar. Reviews were included if they synthesised applications of AI solely in formal higher or continuing education, were published in English between 2018 and July 2023, were journal articles or full conference papers, and if they had a method section 66 publications were included for data extraction and synthesis in EPPI Reviewer, which were predominantly systematic reviews (66.7%), published by authors from North America (27.3%), conducted in teams (89.4%) in mostly domestic-only collaborations (71.2%). Findings show that these reviews mostly focused on AIHEd generally (47.0%) or Profiling and Prediction (28.8%) as thematic foci, however key findings indicated a predominance of the use of Adaptive Systems and Personalisation in higher education. Research gaps identified suggest a need for greater ethical, methodological, and contextual considerations within future research, alongside interdisciplinary approaches to AIHEd application. Suggestions are provided to guide future primary and secondary research.

Digitalization in Higher Education (HE) institutions is an issue that concerns many educational stakeholders. ICT skills are becoming increasingly relevant in every context, especially in the workplace, therefore one of the prime objectives for universities has become preparing future professionals to be able to deal with problems and search for solutions, including digital competence as a vital skill set. Different policies, initiatives and strategies are currently being proposed in Germany, addressing educational technology innovations in HE. The University of Oldenburg is presented as an example, in an endeavour to gain an understanding of what is being proposed and what is actually happening in teaching and learning in German university classrooms. Two datasets were examined regarding the use and perceptions of students (n = 200) and teachers (n = 381) on the use of digital tools. Findings reveal that both teachers and students use a limited number of digital technology for predominantly assimilative tasks, with the Learning Management System being perceived as the most useful tool. In order to support the broader use of educational technology for teaching and learning purposes, strategies for HE institutions are suggested.

Due to the Covid-19 pandemic that spread globally in 2020, higher education courses were subsequently offered in fully remote, online formats. A plethora of primary studies began investigating a range of topics exploring teaching and learning in higher education, particularly during the initial semester. In order to provide an overview and initial understanding of this emerging research field, a systematic mapping review was conducted that collates and describes the characteristics of 282 primary empirical studies. Findings reveal that research was carried out mostly descriptively and cross-sectionally, focusing predominantly on undergraduate students and their perceptions of teaching and learning during the pandemic. Studies originate from a broad range of countries, are overwhelmingly published open access, and largely focused on the fields ofHealth & WelfareandNatural Sciences, Mathematics & Statistics. Educational technology used for emergency remote teaching are most oftensynchronous collaborative tools, used in combination withtext-based tools. The findings are discussed against pre-pandemic research on educational technology use in higher education teaching and learning, and perspectives for further research are provided.