Artificial Intelligence and the Disruption of Higher Education: Strategies for Integrations across Disciplines

Artificial intelligence (AI) and its impact on society have received a great deal of attention in the past five years since the first Stanford AI100 report. AI already globally impacts individuals in critical and personal ways, and many industries will continue to experience disruptions as the full algorithmic effects are understood. Higher education is one of the industries that will be greatly impacted; consequently, many institutions have begun accelerating its adoption across disciplines to address the fast-approaching market shift. Recent advances with the technology are especially promising for its potential to create and scale personalized learning for students, to optimize strategies for learning outcomes, and to increase access to a more diverse populations. In the US alone, colleges are predicted to witness a 48% growth in AI market between 2018-2022. Research has confirmed that the current use of AI in education (AIEd) leads to positive outcomes, including improved learning outcomes for students, along with increased access, increased retention, lower cost of education, and decreased time to completion. Future uses of AI will include the following: enabling engaging and interactive education anytime and anywhere; personalized AI mentors that will help students identify and reach their goals; and mass-personalization that will allow AI to be tailored to each student’s learning style, level, and needs. Yet with all the potential benefits that AI and machine learning (ML) may provide students, there remains a general reticence to adopt this technology because of misconceptions and perceptions that faculty will need to retool since their current teaching strategies will be outmoded. This study provides an overview for those in higher education of what AI is and is not, and how it may be used in various disciplines. Considerations of becoming an AI institution include the following: 1) curricular planning and oversight from academic affairs to identify appropriate use cases for AI in various disciplines, and 2) coordination with IT and technology infrastructure to develop ML to support student services in general