HomeAbstract of 16th-imeaconfEvaluating the Disruptive Impact of Generative AI in Business Education: A Diagnostic Study Using the Disruption Dynamics Maturity Model

Evaluating the Disruptive Impact of Generative AI in Business Education: A Diagnostic Study Using the Disruption Dynamics Maturity Model

  • Jul 12, 2026
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Abstract Book of the 16th International Conference on New Ideas in Management, Economics and Accounting

Year: 2026

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Evaluating the Disruptive Impact of Generative AI in Business Education: A Diagnostic Study Using the Disruption Dynamics Maturity Model

Dr. Dhrupad Mathur

ABSTRACT:

Generative Artificial Intelligence (GenAI), encompassing large language models such as GPT-4o, Claude 3.5, and Gemini 1.5 along with multimodal and code-generating AI systems, has emerged as a transformative force with far-reaching implications for business education globally. While prior literature documents individual impacts of GenAI on assessment integrity, faculty roles, and student learning behaviours, structured diagnostic frameworks capable of translating these observations into actionable management responses remain conspicuously absent. This paper applies the Disruption Dynamics Maturity Model (DDMM) – an empirical tool for business disruption diagnosis and intervention (Mathur, 2024) – to evaluate the specific disruptive impact of Generative AI on business education through an integrative literature review of publications from 2022 to 2026. The DDMM was applied empirically across its eight dimensions: Value Elements, Form Factors, Entities, Operating Model, Consumption Pattern, Interrelationships, Skill Gap, and Regulation. The analysis finds that six dimensions register at Very High maturity and two at High, far exceeding the DDMM threshold of four or more high-impact dimensions that signals imminent disruption. This confirms that business education is not approaching a disruption event – it is already in the midst of an active, systemic, and multi-dimensional disruption driven by GenAI’s content-generating capabilities. The paper concludes with ten actionable implications and recommendations for business education providers framed across short-term and long-term planning horizons, contributing both a novel domain-specific application of the DDMM and a structured basis for institutional decision-making in the GenAI era.

Keywords: Digital Disruption; Higher Education; DDMM; B-Schools; Gen AI in Education