Dr. Olgun Sadık

If you missed my first post (you can catch up [here]), let me briefly recap: As part of Indiana University’s Information Technology Services generative AI project, I’ve been testing the enterprise editions of leading generative AI tools—ChatGPT, Microsoft Copilot, and Google Gemini—to help me design my Professionalization and Ethics course from scratch.

The first task I gave each tool was to generate course learning objectives. All three did a solid job by focusing on objectives that were both meaningful and measurable—a good starting point for any curriculum design.

But I wanted to test how well these tools could handle real-world complexity, particularly as it relates to scalability. In my first year teaching this course, I had just over 30 students. This year, however, enrollment has doubled to over 60 students. That growth introduces significant challenges for both instructional design and classroom management.

So I followed up with this prompt to all three tools:

“There are 60 students in the class. Do you recommend any changes in this plan?”

Here’s how each tool responded—and how I evaluated their suggestions.

ChatGPT: Focused on Scalability & Classroom Management

ChatGPT acknowledged the challenge head-on: “With 60 students in the class and the constraints of a 1-credit, 8-week course, we should make some strategic adjustments.”

What stood out in ChatGPT’s response was its strategic shift in course objectives. It recommended removing objectives focused on individual project development, and instead emphasized group work and peer review. The reasoning? These strategies scale better in larger classes while preserving engagement.

Key suggestions included:

• Small group activities
• Online discussion forums
• Low-stakes assessments
• Reframing learning outcomes to be collaborative and scalable

I appreciated that ChatGPT provided two summary tables—one for scalable learning activities, and another for assessment strategies. These visuals are helpful if you’re looking for quick takeaways without diving into long explanations.

Google Gemini: Prioritized Pedagogy and Participation

Gemini took a slightly different approach, stating: “A class size of 60 students is a significant factor, and it absolutely requires us to think strategically about the course design.”

Rather than altering the course objectives, Gemini proposed structural and pedagogical changes:

• Forming permanent small groups (which it creatively named Ethics Circles)
• In-class active learning strategies like think-pair-share
• Asynchronous participation through collaborative discussion forums

The idea of Ethics Circles was a unique and thoughtful touch, adding a community-building layer to the course—something that’s often hard to foster in large classes. Gemini leaned more into fostering engagement through design rather than changing content.

Microsoft Copilot: Practical Implementation Strategies

Copilot echoed the others: “With a class size of 60 students, your original plan is strong, but a few thoughtful adjustments can help ensure the course remains interactive, manageable, and meaningful.”

Copilot retained the original learning objectives but offered practical suggestions for how to implement them at scale:

• Small group discussions around ethical case studies
• Reflection papers to assess personal values
• Mini-projects for group exploration of ethical topics
• Peer reviews and low-stakes assessments
• Rubrics to ensure grading consistency (though it didn’t provide specifics)

I noticed Copilot’s language was more direct and concise than the others. While this might appeal to users looking for brevity, I personally prefer responses that include a rationale—especially as an experienced educator who values pedagogical justification.

Common Themes and Final Thoughts

Despite differences in tone and detail, all three tools converged on similar themes for adapting the course to a larger class size:

• Use of group work and collaborative learning
• Implementation of peer review mechanisms
• Emphasis on low-stakes assessments
• Strategies for maintaining student engagement at scale

Each tool brought something unique:

• ChatGPT stood out with its organizational clarity and tables for quick reference.
• Gemini offered the most creative idea with its Ethics Circles.
• Copilot provided the most succinct recommendations, which may be ideal for users looking for efficient responses without too much explanation.

As an educator, I value thoughtful rationale and creativity in course design. While Copilot’s brevity might work for some, Gemini and ChatGPT better aligned with my needs for pedagogical depth and practical implementation ideas.

What’s Next?

In my next post, I’ll continue putting these tools to the test as I move into designing class activities and assessments. Stay tuned as I explore how each AI supports deeper instructional planning.

Have you used generative AI to help design a course? I’d love to hear how your experience compares!