(Un)employment of AI in Higher Education

With the rapid adoption of technological advancements (e.g., artificial intelligence or AI) and changes within higher education, the (un)employment (herein referring to utilizing or firing AI as an educational employee) of AI will change and likely become more complex. There is a pressing need to explore the research trends of employing AI in higher education. We conduct a review to examine the research designs, areas of impact, and nature of benefits and disadvantages offered by (un)employing AI in higher education. We searched SCOPUS, Web of Science, and Google and identified a total of 584 studies for review (145 from WOS, 289 from SCOPUS, and 150 from Google). Following our inclusion criteria, the authors screened and assessed the studies for eligibility until a consensus was reached. A total of 47 studies were included in the review. For research design and theory, we find that the most emergent trend is qualitative research design with a mix of technological and social theories (N = 13). For impact areas and valence of AI on (un)employment in higher education, we find that the most emergent trend is the impact of AI on labor and employment is a mix of valences by having the highest count of a mix of positive and negative (N = 13) followed by being negative (N = 9) and positive (N = 5). For the benefits and disadvantages of AI on (un)employment in higher education, we find that the most emergent trend is a mix of both technological and social, and pedagogical benefits and disadvantages (N = 21). The contribution of this work is in recognizing and characterizing plausible shifts to AI (un)employment in higher education and mitigation strategies.