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FLEX your MBA: Empowering learning efficiency in the digital classroom using an AI‐based application.

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Τίτλος: FLEX your MBA: Empowering learning efficiency in the digital classroom using an AI‐based application.
Συγγραφείς: Shah, Piyush1 (AUTHOR) pshah@fgcu.edu, Mishra, Irita2 (AUTHOR), Amarasuriya, Senali3 (AUTHOR)
Πηγή: Decision Sciences Journal of Innovative Education. Jul2026, Vol. 24 Issue 3, p1-15. 15p.
Θεματικοί όροι: Individualized instruction, Formative evaluation, Virtual classrooms, Reflective learning, Intelligent tutoring systems, Active learning, Educational technology, Online education
Εταιρία/Οντότητα: OpenAI Inc.
Περίληψη: The rapid expansion of online MBA programs has improved flexibility and accessibility but continues to face challenges related to limited personalization, reduced interactivity, and summative feedback. To mitigate these issues, we introduce FLEX (Feedback‐Linked Education for Excellence), an AI‐powered educational platform designed to enhance learning efficiency through dynamic, iterative feedback and active engagement. Developed using R Shiny and integrated with OpenAI's API, FLEX provides a modular, user‐friendly scaffolding tool that supports reflective learning, active learning, personalized learning, and formative feedback. Utilizing the Apple Transportation Case, a logistics‐focused assignment, FLEX guided MBA students at a large US university through critical decision‐making tasks while offering tailored feedback. Results indicated significant improvements in reflective learning, active engagement, personalized learning and formative feedback. This article also discusses FLEX's potential applications across disciplines and its implications for the future of online education. [ABSTRACT FROM AUTHOR]
Copyright of Decision Sciences Journal of Innovative Education is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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  Data: The rapid expansion of online MBA programs has improved flexibility and accessibility but continues to face challenges related to limited personalization, reduced interactivity, and summative feedback. To mitigate these issues, we introduce FLEX (Feedback‐Linked Education for Excellence), an AI‐powered educational platform designed to enhance learning efficiency through dynamic, iterative feedback and active engagement. Developed using R Shiny and integrated with OpenAI's API, FLEX provides a modular, user‐friendly scaffolding tool that supports reflective learning, active learning, personalized learning, and formative feedback. Utilizing the Apple Transportation Case, a logistics‐focused assignment, FLEX guided MBA students at a large US university through critical decision‐making tasks while offering tailored feedback. Results indicated significant improvements in reflective learning, active engagement, personalized learning and formative feedback. This article also discusses FLEX's potential applications across disciplines and its implications for the future of online education. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of Decision Sciences Journal of Innovative Education is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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              Text: Jul2026
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