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Evolutionary Trends in Decision Sciences Education Research from Simulation and Games to Big Data Analytics and
Ikpe Justice Akpan1, Rouzbeh Razavi1, Asuama A Akpan2
1Department of Information Systems and Business Analytics, Kent State University, Kent, OH, USA.
Decision sciences (DSC) has evolved significantly, with AI and data analytics becoming key skills. Future education must integrate generative AI (GenAI) while addressing ethical challenges.
Area of Science:
- Decision Sciences (DSC)
- Multidisciplinary research and education
Background:
- DSC aids informed choices in complex, uncertain systems.
- It integrates diverse methods for decision engineering and enhancement.
- This study examines 25 years of DSC education and research evolution.
Purpose of the Study:
- To analyze evolutionary trends and innovations in Decision Sciences education and research.
- To map and evaluate the thematic, intellectual, and social structures of DSC research over 25 years.
Main Methods:
- Bibliographic metadata analysis.
- Science mapping techniques.
- Text analytics for evaluating research structures.
Main Results:
- Key skills identified: knowledge management, decision support systems, data envelopment analysis, simulation, and artificial intelligence (AI).
- Emerging crucial areas: data analytics frameworks (big data, machine learning, business intelligence, data mining, visualization).
- Trends in education: virtual/online learning, computer simulation, and games; adoption of AI agents and generative AI (GenAI).
Conclusions:
- DSC education and research reflect practical advancements, emphasizing digital transformation.
- GenAI integration in DSC education is imminent, presenting both opportunities and challenges (academic integrity, ethics, legal issues).
- Future DSC education must proactively integrate GenAI and address associated challenges.
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