Artificial Intelligence Applications in Open and Distance Education: A Systematic Review of the Articles (2007-2021)

Abstract

With the urgent shift to distance learning due to Covid-19 measures, educational institutions around the world have started to adopt e-learning massively. According to many experts, artificial intelligence (AI) may provide both system-wide and pedagogical solutions to the problems which the administrators, educators, and students encounter during e-learning. The purpose of this study was to systematically review articles on AI applications in open and distance education between the years 2007 and 2021 in order to visualize the potential of intelligent educational systems. Thus, 171 selected articles regarding explicit inclusion and exclusion criteria, were analysed in terms of the year/ place of the paper, affiliation of the first author, the method applied, and the journals they published in. Then the research areas of AI applications in distance education were coded iteratively. The results indicated that most of the articles were published after 2018 mainly by Chinese, Spanish, Turkish, American, and Indian authors in computer science and STEM disciplines. Moreover, there is a sharp increase in publishing on this topic, especially during Covid-19 pandemics. Consequently, the analysis resulted in six areas of AI applications in open and distance education: 1. ITSs, 2. Adaptive systems and personalisation, 3. assessment and evaluation, 4. learner analytics, 5. affect recognition, and 6. Virtual learning environments, with 28 subcategories. In summary, our review results that intelligent systems can be effectively used for both system-wide solutions and pedagogical solutions to the problems such as the low level of adaptivity, collaboration, academic achievement, or engagement, and the high level of drop-out that we usually experience in distance learning via the use of adaptive expert systems, intelligent tutors or agents, chatbots, data analytics, and recommender systems.