Universal Design for Learning
Darci Spangler
Adapting an OER Activity
Universal Design for Learning (UDL) is a well-established pedagogical framework that enhances educational outcomes by addressing the diverse needs of a broad spectrum of learners. By intentionally designing course materials to provide multiple means of engagement, representation, and expression, instructors facilitate more inclusive access to the learning process. Integrating UDL principles into existing Open Educational Resources (OER) constitutes an equitable practice that promotes accessibility and learner success.
However, redesigning course materials to incorporate multiple modalities can be a time-intensive endeavor, often requiring significant investment from instructors. Utilizing AI tools to analyze existing OER and generate UDL-informed adaptations can dramatically reduce this workload, producing comprehensive strategies within seconds that might otherwise require hours of manual development. Crucially, subsequent review and refinement by subject matter experts are necessary to ensure that the adapted content maintains accuracy and aligns effectively with intended learning outcomes.
Example: Offering Multiple Means for a Sleep Strategies Activity
Goal
The overall goal is to transform a traditional discussion into an interactive, student-centered learning experience that fosters deeper understanding and higher-order thinking. By incorporating creativity, engagement, social collaboration, and metacognitive reflection, the redesigned activity aims to motivate students to actively apply concepts to real-life scenarios, collaborate with peers, and critically evaluate their own learning progress. This approach seeks to enhance both comprehension and retention by making learning more relevant, dynamic, and reflective.
How It Was Done
For this example, an OER activity was selected that effectively aligned with the key concepts and learning objectives of a sleep strategies module within a Lifetime Health and Fitness course. While the structure of the activity was solid (it included class discussion, sleep log evaluation, self-reflection, and strategy implementation), I was looking for a version that was more engaging, creative, and fully executable within a single class period.
Relying solely on traditional group discussion of these questions may not facilitate meaningful learning or promote deeper cognitive engagement. Some students may disengage due to lack of interest, others might focus on identifying the “correct” answer without engaging in higher-order thinking, and some may find the material difficult to comprehend when presented abstractly rather than within relatable, real-world contexts. To address these challenges and support diverse learners, the activity was redesigned following Universal Design for Learning (UDL) principles, aiming to provide multiple means of engagement, representation, and expression. The objective was to create an inclusive learning experience incorporating the following qualities:
- Creativity: Giving students a chance to practice synthesis by employing what we’re learning, coming up with their own responses to actual examples
- Engagement: By both gamifying the learning activity and ensuring students draw from their own prior knowledge and experiences, the activity can trigger intrinsic motivation
- Social Learning: Students learn better when they learn collaboratively
- Metacognition: When student have a chance to practice and produce, they’re better able to see their own learning progress, identifying where their strengths and needs lie
To explore alternatives, I copied the OER activity into Copilot and asked for revised activity ideas. Copilot generated six suggestions, categorizing each according to the original assignment’s components: class discussion, sleep log evaluation, self-reflection, and strategy implementation.
Results
Copilot’s response offered thoughtful variations aligned with the original activity structure. For instance, as an alternative to the written self-reflection essay, it proposed a “Creative Self-Reflection” exercise where students visually represent their personal sleep insights. While these ideas added a creative dimension, most required work outside of class, which did not meet my criteria for a fully in-class activity.
After refining my prompts, I was able to identify a more suitable activity that aligned with the goals of the original assignment and fit within a single class session. I then asked Copilot to analyze how this revised activity supported the key learning outcomes of the original, ensuring that instructional intent and academic rigor were preserved in the adapted version.
Potential issues
- While AI tools like Copilot can offer creative ideas, there is a risk of over-relying on them without critical pedagogical evaluation. AI-generated suggestions may not always align with evidence-based instructional practices or student learning needs.
- This exercise highlights the need to refine prompts repeatedly to get usable results. This trial-and-error process can be time-consuming and may present a barrier for faculty who are less experienced with AI tools or who lack the time to engage in iterative refinement.
- Careful attention needs to be given to existing open educational resources to ensure that the resource has the appropriate Creative Commons license. Instructors unfamiliar with open licenses may inadvertently violate terms of use if not properly guided.
- Human intervention was essential to ensure that the original learning objectives were being met.
Best Tools for the Job
- Although Copilot was used in this example, the exercise is simple enough that any large language model can perform effectively.