I completed a final paper for EDU 842 Mobile Learning on how students use mobile AI tools during moments of uncertainty or quick clarification. What stood out was how often these informal learning moments happen outside structured coursework and how unprepared most institutions are to support them.
This is where the AAEL workflow comes in.
AAEL stands for Ask, Adapt, Analyze, a learning process I developed to guide responsible, transparent, and iterative use of AI.
Ask: learners begin by asking clarifying questions or probing the problem.
Adapt: they refine prompts, approaches, or strategies as their understanding evolves.
Analyze: they verify AI outputs, compare alternatives, and apply human judgment before moving forward.
Mobile learning research aligns closely with this pattern. Students naturally cycle through these stages as they explore new concepts, solve problems, or test ideas. That alignment is a major reason I am now writing a book chapter introducing AAEL as a practical framework for teaching ethical AI use across disciplines, including support for neurodivergent learners.
As my dissertation continues, AAEL sits at the center of my work. It provides a structured way to teach students how to think with AI, not just use it. It also gives educators and institutions a path toward policy that supports autonomy, verification, and responsible learning.
You can find the paper here:
edu_842_final_paper_foreman
If you’re an educator, school leader, or EdTech professional interested in ethical AI workflows, I welcome conversations and collaboration.
Contact Information
Robert Foreman
Doctoral Student, Educational Technology – Central Michigan University
Email: forem1r@cmich.edu
NhanceData: https://nhancedata.com