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Post 3

harmonypierce
1 Comment

Reducing Barriers to Support Learner Success

As I reflect on our AI misconceptions blueprint, Universal Design encourages me to look for limitations in my designs and to include multiple means of engagement, expression, and representation (Universal Designs, 2026). The myth of the “average learner” challenged me to identify possible barriers that could interfere with learners meeting the learning outcomes (Inclusive Learning Design, 2026). As it is mentioned throughout this course, we cannot see barriers if we do not actively look for them.

One potential barrier in my design appears in the fact-checking challenge. In this activity, learners are asked to analyze AI-generated responses, identify inaccuracies, and evaluate credibility using verification strategies. While this activity aligns with the learning outcomes, it must be considered whether the students have been provided with the proper background information. The assessment Triangle reminds me that cognition, observation, and interpretation must be aligned. If my cognition model focuses on critical evaluation of AI-generated information, the observation must require learners to show this work in a meaningful way. This ensures proper alignment between the cognition and observation models, which leads to an increased validity in the inferences drawn from the evidence (Alignment and the Assessment Triangle, 2026).

I am thinking about the role of formative assessment. Assessment should provide information about learner’s progress and misconceptions, not simply gathering marks (Assessing Learning, 2026). Including opportunities for feedback before summative tasks would allow me to address misconceptions early and support deeper learning. Additionally, it would allow learners to revise and refine their thinking, which supports ongoing progress towards learning outcomes and ensures a sustained understanding.

Overall, designing for inclusion requires identifying barriers, ensuring required skills are taught, and planning intentional feedback. Doing these things will ensure learning can be maximized for all students rather than the “average” student.

Photo by Julio Lopez on Unsplash

One Comment

  1. Hi Harmony,

    Thank you for such an interesting blog post! It was really interesting to learn that your group’s learning design focuses on AI misconceptions.

    I appreciated your reflection on how students might not always have enough background knowledge to determine whether AI-generated information is correct. As you mentioned, it’s easy to make assumptions about students when we have the “average leaner” in mind, but its important to actively look for potential barriers so we can remove them and better support all leaners.

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