In my doctoral research at Central Michigan University, I am working on a model I call AI-Augmented Exploratory Learning (AAEL).
AAEL is a scaffolded, self-directed learning approach where professionals use AI tools to complete real-world tasks through iteration, prompting, and refinement. Unlike traditional training that often focuses on step-by-step instruction, AAEL emphasizes learning by doing with AI acting as both coach and co-creator.
Why does this matter?
Because it is not just about coding or real estate, which is my proof-of-concept domain. Every industry is facing the same challenge:
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Skills are evolving faster than traditional training can keep up.
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Professionals need to adapt quickly to unfamiliar workflows.
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AI can shorten the learning curve without removing the human from the loop.
In my proposed study, participants would complete both familiar and unfamiliar tasks using AI exclusively, with my role as facilitator rather than direct instructor. The same framework could apply in finance, education, marketing, healthcare administration, and small business operations.
The goal is simple: empower professionals to work smarter and adapt faster by making AI a partner in problem-solving, not just a tool.
I will be sharing more on how AAEL works and examples from different industries over the coming months.
Contact for Doctoral Research Inquiries
📧 forem1r@cmich.edu
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