Dr. Olgun Sadık

Indiana University Information Technology Services recently launched a forward-thinking pilot program to explore the potential of generative AI in higher education. The initiative invited 100 faculty members to test enterprise versions of several leading tools: OpenAI’s ChatGPT, Microsoft Copilot 365, Google’s Gemini 2.5 Pro, and NotebookLM.

After an initial orientation session, participants were asked to complete 25 challenges designed to explore AI’s applications across teaching, research, and service. But rather than approach these tasks as standalone activities, I took a little different route. 🙂

My Approach: A Real-World Integration

Instead of tackling the challenges individually, I decided to use this opportunity to redesign a real course I’m teaching in the upcoming fall: ENGR-E 299: Engineering Professionalization & Ethics (1 credit, 8 weeks). This is my second time teaching the course, and I wanted to enhance the learning experience by embedding AI tools directly into the course design process from the very beginning.

My goal is simulate what it would feel like to be assigned a new course with no existing materials and explore how generative AI can support the design of a meaningful and pedagogically sound course.

Before jumping into the assigned challenges, I set out to use these AI tools as instructional design collaborators—tools that would build an understanding of the course context over time, rather than just produce one-off responses.

Course Context: ENGR-E 299

ENGR-E 299 is designed for second-year engineering students. It introduces core topics in professionalism and ethics, with a focus on developing ethical reasoning skills, understanding professional responsibilities, and analyzing engineering-specific ethical dilemmas. Students are expected to have some prior exposure to ethics in engineering design but need more applied, reflective, and research-based experiences.

My First Prompt to the AI Tools

Here’s the first prompt I used with ChatGPT, Copilot, and Gemini to kick off the design process:

You are an instructional designer for higher-ed helping create meaningful course designs for your students in an engineering department. I will be teaching a course titled ENGR-E 299 Engineering Professionalization & Ethics. This course introduces topics in engineering related to professionalism and ethics designed to develop ethical reasoning skills, increase ethical awareness and professionalism, and to analyze ethical dilemmas, specific to engineering. Students will learn ethical principles that can be applied in research, design and development. This is a 1 credit eight-week course. This will be my first time teaching the course and I want you to help me design course objectives first. There are some details you may want to consider. This is a 2nd year course. Students have foundational knowledge in engineering with some previous experience about ethical decision making in the engineering design process. They need more hands-on experience reading, reflecting, and doing research in this important area. Let’s start our discussion to collaborate on this. We want to make it a meaningful learning experience for students.

AI Response Time (Anecdotal)

• Microsoft Copilot was the fastest (~1–2 seconds)
• ChatGPT followed (~5–10 seconds)
• Gemini took the longest (~10–15 seconds)

Note: These are estimated times and not formally measured.

General Observations from the Tools

All three tools responded with encouraging and thoughtful acknowledgments of the prompt. Here’s a brief overview of how each approached the task of writing course objectives:

ChatGPT

• Emphasized action-oriented, measurable objectives aligned with course length and student level.
• Used Bloom’s Taxonomy to guide the objectives toward higher-order thinking.
• Provided rationale for each objective.

Copilot

• Presented concise, applied objectives aligned with ABET criteria, which is crucial in engineering education.
• Highlighted cognitive appropriateness and flexibility for different teaching activities.
• Did not elaborate with rationale but was direct and efficient.

Gemini

• Similar to ChatGPT in structure with rationale, but also added value by asking reflective questions for continued collaboration.
• Encouraged feedback on whether the objectives aligned with course expectations and asked if any additional guidance was needed.

Which One Stood Out?

All three tools provided solid starting points, emphasizing the importance of foundational ethical knowledge. However, I found Gemini’s objectives most useful at this stage. They were:

• More comprehensive and explanatory
• Clearly measurable
• Supported with thoughtful rationale
• Positioned to help students move from theory to application

That said, I plan to continue working with all three tools, following their unique approaches to see how they evolve throughout the design process.

A Quick Note on NotebookLM

While Google’s NotebookLM was also included in the pilot, it appears to be designed for a different use case. Despite its name, it doesn’t quite function like a traditional notebook AI. I’ll share more detailed reflections on that tool in a future post.

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If you’re interested in how generative AI can support instructional design in higher education, especially in courses that tackle abstract yet essential topics like ethics and professionalism, stay tuned. In upcoming posts, I’ll:

• Share more prompts and responses
• Reflect on each tool’s strengths and limitations
• Offer examples of how I’m integrating their outputs into a real course

For now, if you’d like to explore or adapt my prompt for your own needs, feel free to do so. This is just the beginning of a longer journey into collaborative course design with AI. Please share this experience with your friends. 😉