Systematic review of research on artificial intelligence applications in higher education – where are the educators?

Artificial intelligence (AI) applications in education are on the rise and have received a lot of attention in the last couple of years. AI and adaptive learning technologies are prominently featured as important developments in educational technology in the 2018 Horizon report (Educause, 2018), with a time to adoption of 2 or 3 years. According to the report, experts anticipate AI in education to grow by 43% in the period 2018–2022, although the Horizon Report 2019 Higher Education Edition (Educause, 2019) predicts that AI applications related to teaching and learning are projected to grow even more significantly than this. Contact North, a major Canadian non-profit online learning society, concludes that “there is little doubt that the [AI] technology is inexorably linked to the future of higher education” (Contact North, 2018, p. 5). With heavy investments by private companies such as Google, which acquired European AI start-up Deep Mind for $400 million, and also non-profit public-private partnerships such as the German Research Centre for Artificial IntelligenceFootnote 1 (DFKI), it is very likely that this wave of interest will soon have a significant impact on higher education institutions (Popenici & Kerr, 2017). The Technical University of Eindhoven in the Netherlands, for example, recently announced that they will launch an Artificial Intelligence Systems Institute with 50 new professorships for education and research in AI.Footnote 2

The application of AI in education (AIEd) has been the subject of research for about 30 years. The International AIEd Society (IAIED) was launched in 1997, and publishes the International Journal of AI in Education (IJAIED), with the 20th annual AIEd conference being organised this year. However, on a broader scale, educators have just started to explore the potential pedagogical opportunities that AI applications afford for supporting learners during the student life cycle.

Despite the enormous opportunities that AI might afford to support teaching and learning, new ethical implications and risks come in with the development of AI applications in higher education. For example, in times of budget cuts, it might be tempting for administrators to replace teaching by profitable automated AI solutions. Faculty members, teaching assistants, student counsellors, and administrative staff may fear that intelligent tutors, expert systems and chat bots will take their jobs. AI has the potential to advance the capabilities of learning analytics, but on the other hand, such systems require huge amounts of data, including confidential information about students and faculty, which raises serious issues of privacy and data protection. Some institutions have recently been established, such as the Institute for Ethical AI in EducationFootnote 3 in the UK, to produce a framework for ethical governance for AI in education, and the Analysis & Policy Observatory published a discussion paper in April 2019 to develop an AI ethics framework for Australia.Footnote 4

Russel and Norvig (2010) remind us in their leading textbook on artificial intelligence, “All AI researchers should be concerned with the ethical implications of their work” (p. 1020). Thus, we would like to explore what kind of fresh ethical implications and risks are reflected by the authors in the field of AI enhanced education. The aim of this article is to provide an overview for educators of research on AI applications in higher education. Given the dynamic development in recent years, and the growing interest of educators in this field, a review of the literature on AI in higher education is warranted.

Specifically, this paper addresses the following research questions in three areas, by means of a systematic review (see Gough, Oliver, & Thomas, 2017; Petticrew & Roberts, 2006):

• How have publications on AI in higher education developed over time, in which journals are they published, and where are they coming from in terms of geographical distribution and the author’s disciplinary affiliations?

• How is AI in education conceptualised and what kind of ethical implications, challenges and risks are considered?

• What is the nature and scope of AI applications in the context of higher education?

The field AI originates from computer science and engineering, but it is strongly influenced by other disciplines such as philosophy, cognitive science, neuroscience, and economics. Given the interdisciplinary nature of the field, there is little agreement among AI researchers on a common definition and understanding of AI – and intelligence in general (see Tegmark, 2018). With regard to the introduction of AI-based tools and services in higher education, Hinojo-Lucena, Aznar-Díaz, Cáceres-Reche, and Romero-Rodríguez (2019) note that “this technology [AI] is already being introduced in the field of higher education, although many teachers are unaware of its scope and, above all, of what it consists of” (p. 1). For the purpose of our analysis of artificial intelligence in higher education, it is desirable to clarify terminology. Thus, in the next section, we explore definitions of AI in education, and the elements and methods that AI applications might entail in higher education, before we proceed with the systematic review of the literature.