Evaluating generative artificial intelligence adoption in management education using AI–human augmentation and expectation confirmation theory

Abstract

Purpose This study aims to explore the effects of generative artificial intelligence (GAI) on management education and develops a framework for integrating GAI to enhance learning through AI–human augmentation. Design/methodology/approach This qualitative study involved in-depth interviews with 34 students and 16 faculty members from three business schools in India. The data was analyzed using thematic content analysis to explore the role of GAI in business education. Findings The study revealed that students benefit from GAI in productivity, tutorial assistance, engagement, personalization, industry readiness and communication, while expressing concerns about performance uncertainty, ethics, data privacy and skill gaps. Faculty valued GAI for designing assignments and assessments and enhancing communication but expressed concerns such as overreliance, plagiarism risks, lack of formal training and lack of clear institutional policies. Research limitations/implications The study provides early evidence of GAI adoption in management education, suggests ethical concerns and develops clear policy guidelines for its adoption in higher education. Future research should focus on developing GAI usage policies and training frameworks for students and faculty to use GAI responsibly in educational settings. Originality/value This study uniquely integrates expectation confirmation theory with AI–human augmentation in management education, contributing novel insights into human-machine collaboration in higher education.

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