Enang, Imowo ORCID: https://orcid.org/0000-0003-1586-2175 and Omeihe, Kingsley Obi (2025) Real-time, adaptive AI-driven business simulation: design science research on a dynamic learning platform. [Working Paper]

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Abstract

This working paper presents a design science research (DSR) investigation into the development and evaluation of an innovative real-time, adaptive AI-driven business simulation platform. Traditional business simulations typically operate with static scenarios and predefined parameters that fail to capture the dynamic complexity of contemporary business environments. Using a rigorous DSR methodology spanning four design cycles over twenty-four months, we developed and refined a prototype system that integrates machine learning algorithms, natural language processing, and knowledge graph technologies to create dynamically evolving simulation scenarios. The platform was evaluated across diverse contexts including MBA education programmes, corporate strategy training, and entrepreneurial incubators, involving 287 participants across multiple evaluation phases. Our findings demonstrate the system's efficacy in enhancing strategic decision-making capabilities, improving knowledge transfer, and fostering adaptive reasoning skills among users. The paper lays the groundwork for next-generation business education and strategy testing environments that more authentically reflect the complex, evolving nature of real-world business ecosystems.

Item Type:	Working Paper
Uncontrolled Keywords:	adaptive AI, business solutions
Subjects:	H Social Sciences > H Social Sciences (General) H Social Sciences > HD Industries. Land use. Labor > HD28 Management. Industrial Management Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Faculty / School / Research Centre / Research Group:	Greenwich Business School Greenwich Business School > Greenwich Online School
Last Modified:	29 Sep 2026 12:06
URI:	https://gala.gre.ac.uk/id/eprint/54468

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