Yesterday, I had the pleasure of supporting my colleague Kevin McGourty, PhD and students in our spring residency in a series of lectures and activities at Avila University Arizona.
Technology is no longer reserved for people who see themselves as programmers. While Dr. McGourty led a probability-based activity with students, I decided to test the practical power of AI-Augmented Exploratory Learning, or AAEL, in real time. I set a 30-minute timer and gave myself a simple challenge: build a blackjack advisor app that would let me enter the player’s hand, enter the dealer’s up card, and receive a recommendation on whether to hit or stand.
The first working iteration was generated, tested, and running in about five minutes. From there, the project evolved through live iteration. I refined the input flow, improved the hand logic, adjusted the Monte Carlo simulation, tested outputs, pasted the console results back into ChatGPT, and kept revising. Before the timer expired, the final version had progressed from a simple one-player console script into a much more advanced blackjack simulator with a dealer, NPC players, session tracking, and a Monte Carlo advisor that evaluated win rate, non-loss rate, and expected value.
From a Python and systems perspective, that is what makes this interesting. The real lesson was not just that AI can generate code. The lesson was that an idea can move from rough concept to functional prototype very quickly when the user is willing to think, test, inspect, and iterate. In this case, the LLM helped with structure, logic, debugging, and refinement, but the real progress came from collaboration between human judgment and AI-assisted development.
This is where AAEL becomes useful as more than a slogan. AAEL stands for AI-Augmented Exploratory Learning, a simple cycle of Ask → Adapt → Analyze. You begin by asking a focused question, adapt your approach as the tool gives feedback and new possibilities, and then analyze the outputs critically rather than accepting them blindly. That is exactly what happened here. The tool did not replace thinking. It accelerated exploration and lowered the barrier to building.
To me, that is the larger point. Non-technical people do not have to be afraid of technology anymore. They do not need to become expert software engineers overnight, but they can become capable builders, testers, and problem-solvers much faster than before. A passing idea during class became a viable working prototype in 28 minutes and 56 seconds. That is not just a story about AI. It is a story about access, iteration, and the growing ability of more people to create useful systems.
Robert Foreman
Doctoral Student, Educational Technology
Central Michigan University
Research Focus: AI-Augmented Exploratory Learning (AAEL)
Email: forem1r@cmich.edu
Phone: 480-415-0783
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