AI versus tradition: shaping the future of higher education

Abstract

Purpose: This research aims to investigate the use of conversational artificial intelligence (CAI) in academic practice through the lens of activity theory, which emphasises the mediation of human actions by tools within a social context. Additionally, it seeks to determine if and how the results of qualitative analysis differ when using traditional qualitative analysis software tools compared to using artificial intelligence tools. Design/methodology/approach: A pragmatic approach to the research design was used. The data collection phase included a survey, with open- and closed-ended questions and was distributed to academics in four countries (South Africa, Hungary, Lebanon and Wales). The data analysis phase included a mixed-methods approach integrating and interpreting both types of data to leverage the strengths of both qualitative and quantitative insights. Furthermore, traditional qualitative analysis methods and artificial intelligence tools were used for the analysis phase, allowing for a comprehensive understanding of the interactions between academics and these tools. Findings: Younger academics used CAI more for research than teaching, with academics from the science faculty using it more for teaching, and business management lecturers using it more for research. While viewed positively, concerns arose about ethics and educational alignment. This research shows how CAI supports qualitative analysis by saving time and suggesting new directions. Originality/value: Using an “activity theory” theoretical lens, with a pragmatic approach, the research explores how CAI tools impact academic practices. The study enriches theoretical discourse and offers practical recommendations for education.