An integrated framework for Gen AI-assisted management learning: Insights from Kolb's learning cycle theory and knowledge types perspectives

Abstract

Generative Artificial Intelligence (Gen AI), particularly through advanced models such as ChatGPT developed on the foundation of sophisticated Large Language Models (LLMs), has shown the potential to revolutionize management education. Nevertheless, a comprehensive framework for employing Gen AI in this context remains to be developed. This study proposes a theoretical framework utilizing Gen AI, with a specific focus on ChatGPT, based on Kolb's learning cycle theory and the knowledge type perspective to facilitate systematic integration into management learning.

Analyzing data from 348 business students through structural equation modeling, the study demonstrates that the Gen AI -assisted learning process enhances the acquisition of diverse knowledge types. The findings also highlight that teacher support partially strengthens the effectiveness of the Gen AI -assisted learning process in knowledge acquisition.

The study contributes to the academic discourse by developing an integrated framework and practical guidelines for integrating Gen AI into management learning, thereby addressing an existing gap in current research.