Learning Design: Experimental Learning

Jul 27, 2025

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My chosen approach to write about is Experimental Learning!

Overview

Experimental Learning is described as placing emphasis on hands-on, self-directed and experiential learning processes. Some key characteristics of this approach includes:

Experiential Engagement: Engaging with the tools or technologies directly, actively participating in prototyping and testing. Learners digests new concepts by doing instead of memorization.
Active Participation: Instead of following a series of defined steps, learners would actively devise solutions to problems or errors. And adapting based on feedback.
Self-Direction and Agency: Learners take ownership of their work, leading to more intrinsic motivation for deeper understanding.
Discovery-Driven Learning: Instead of memorizing, learners would discover insights throughout the solutioning process, by going through countless trials and errors.

Based on these definitions from the California Learning Resource Network (California Learning Resource Network. (n.d.)), I can provide some examples on Experimental Learning:

Learning a new programming language through porting your previous work into that new language.
Learning chemical formulas through conducting an experiment recreating that substance.
Learning a new cooking technique through attempting to recreate a dish using said technique.

Alignment with topic

Our chosen topic for our group is “Teaching better AI usage for students through asking better prompts and exploration of different AI models”.

Figure 1: AI black box theory (Monga, A., 2024)

While AI theories exists, this only explains the the building process of an AI chat-bot and the algorithms behind it. This leads to the “black box” nature of deep learning algorithms, you can only see the input and output, not the inner workings. We want to take a continuous learning and improvement approach through practical examples and experimentation, since it is impossible to create concrete theory from a “black box”. This leads to Experimental Learning aligning perfectly with out topic.

Figure 2: Kolb’s Experiential Learning Theory (Rahman, 2021)

An example approach that highlights this alignment is using Kolb’s Experiential Learning Theory (ELT). We can apply the framework Kolb created to our topic as following:

Concrete experience: Showcasing a wrongful response to a vague prompt, creating emotional engagement.
Reflective observation: Class discussions on why the prompt is vague causing wrongful response.
Abstract conceptualization: Introducing common prompting practices and how it would alleviate some of the issues shown.
Active experimentation: Let learners attempt to rewrite the previous prompt.

References

California Learning Resource Network. (n.d.). What is experimental learning? Retrieved July 27, 2025, from https://www.clrn.org/what-is-experimental-learning/
KOLB, D. A., BOYATZIS, R. E. & MAINEMELIS, C. 2001. Experiential learning theory: Previous research and new directions. Perspectives on thinking, learning, and cognitive styles, 1, 227-247.
Monga, A. (2024, May 19). The AI black box: Why cybersecurity professionals should care. Medium. https://medium.com/@ajay.monga73/the-ai-black-box-why-cybersecurity-professionals-should-care-4bec7ff32c7c
Rahman, M. (2021, November 28). Experiential learning theory: A practical approach to learning. HotCubator. https://hotcubator.com.au/higher-education/experiential-learning-theory-a-practical-approach-to-learning/
Stelzner, E. (2024, March 18). What is black box AI? Built In. https://builtin.com/articles/black-box-ai

Comments

3 Responses to “Learning Design: Experimental Learning”

shalanf

2025-07-28

Hello Brian,
I enjoyed reading your post about your preferred learning theory-Experimental. I do think this one could engage the learner more than others too. I do feel though in my line of working with grade 4’s it is rather tricky. Certain students would do amazing at this approach however in a classroom (public school specifically) you have students who are dealing with other situations such as mental health or intense behaviour. You have to make sure every task is achievable and with this theory you need independent learners. You also need to be able to support the learners as needed and in classes of 28 students providing time for all of the learners to ask questions and receive necessary guidance can be really tricky. I feel like this would benefit in a small student classroom with students motivated to learn about the specific topic. What do you think? -Shalan Gervais

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1. npham49
  
  2025-08-17
  
  Thanks for the feedback Shalan! It’s a great point you’ve brought up, especially with public web-based resources like our interactive learning resource. I gave this some thoughts and covered them in my latest blog post on how we added “scaffolds” and support to make sure all learners can achieve the lesson’s goal successfully! https://npham49.opened.ca/2025/08/16/inclusive-design-enhancing-interactive-learning-resource-and-web-based-accessibility/
  
  Reply
shwetanagdev

2025-08-17

Hey Brian, thanks for sharing this post! I really like how you connected Experiential Learning with the “black box” nature of AI. It makes a lot of sense that hands-on experimentation would be the best way for students to build their skills with prompting when there isn’t always a clear set of rules. Your breakdown of Kolb’s cycle into concrete examples (like showing a vague prompt and then having students reflect) is really practical.

How do you think instructors can keep students motivated during that trial-and-error phase? Sometimes too much error can feel discouraging. Do you think having a few example prompts as a starting point would still count as experiential learning, or would that make it less discovery-based?

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