Teaching in the Age of AI

For Rich Freeman, a longtime innovator in Georgian College’s Computer Studies department, integrating AI into teaching isn't about flashy transformations—it’s about purposeful productivity. Recently honoured with the 2025 Board of Governors’ Award of Distinction (Academic), Rich has spent his career piloting cutting-edge technologies, including web and mobile development frameworks, version control systems, coding helpers like GitHub Copilot, and cloud-based student tools.  

After completing his Masters in Education, Rich is applying new pedagogical insights directly into his classroom, experimenting in real time with what works—and sharing those successes with colleagues through CTL sessions and team collaboration.  

Innovating Pedagogy with GenAI

Rich has found AI to be an effective brainstorming companion. Whether he’s prepping a new class or updating old assignments, he turns to large language models like Mistral to help kick-start the creative process. For example, he’ll prompt the AI with a simple setup: “I’m teaching topic X to college students—give me five practice exercises.” From there, he filters and refines the suggestions, using only what resonates with his learning goals.

This collaborative approach with AI isn’t limited to exercises. Rich uses it to develop assignment concepts, grading rubrics and even sample test problems. On a recent assignment, he used AI to generate a list of unique topics, then randomly assigned each one to a different student to help promote academic integrity. The key, he says, is treating the AI like a colleague throwing out ideas in a brainstorm—not as a source of polished, ready-to-use materials.  A sample prompt:

create a react programming assignment for a college course. It should include react-router-dom and the state and effect hooks. include a grading rubric. the app should also use bootstrap for styling

While the overall response was effective, the rubric simply assigned points to each section, without providing a breakdown and explanation of the various achievement levels.  Rich took the provided rubric then added his own detailed explanation for each section and achievement level. 

AI in Course Design and Assessment

One particularly successful experiment came at the end of a recent term, when Rich asked Anthropic’s Claude (via duck.ai so his prompts aren’t stored and added to the LLM) to generate a practice test aligned with specific learning outcomes for a final exam review. He described the goals and format of the test, and the AI returned a usable draft that he shared with his students. The result? A quick and helpful resource that gave students a clear sense of what to expect.

Redesigning Assessment for an AI-Era

As AI tools become more capable, Rich has started to rethink what kinds of assessments truly measure student learning. He’s candid about the reality: “AI has largely made discussion posts and essays irrelevant unless they’re done in class.”  In the world of coding, it’s also increasingly hard to detect AI-generated submissions.

Students need to understand AI use without subject knowledge has no value.  AI is designed to enhance knowledge rather than serve as a substitute for knowledge.

This has pushed Rich—and many educators like him—to revisit not just the how of assessment, but the why. What does meaningful demonstration of learning look like in an AI-rich environment? The answers, he suggests, is the opportunity to help students develop skills and knowledge experientially.  We need to provide students with more than what AI can.  How faculty grapple with these new tools will likely shape higher education for years to come. 

Each level on this scale specifically explains how the students can (or cannot) use GenAI for their assessment work.  

Productivity Gains, with Caution

Rich is quick to clarify that AI is no silver bullet. “It doesn’t replace anything for me,” he says. But it does enhance his productivity, especially during the early stages of course planning or when teaching unfamiliar technologies. Tools like GitHub Copilot have even helped him debug code—an invaluable aid when learning alongside his students.

Still, AI has its limitations. Sometimes the output lacks sufficient context or nuance. Rich is also mindful of data privacy, avoiding prompts that could inadvertently expose sensitive or proprietary information.  See Exploring privacy issues in the age of AI for tips.

Ethical and Transparent AI Use

Rich’s attitude toward AI is one of cautious optimism. He recognizes the urgency of using these tools responsibly, particularly as broader concerns around privacy, security, copyright, and energy consumption continue to grow. His advice to fellow faculty is pragmatic: learn the tools, understand their strengths and weaknesses, and model mindful use for students.

Lessons Learned and Future Directions

Looking ahead, Rich sees AI not as a disruptor, but as a workplace reality. “Workers won’t be replaced by AI,” he reflects, “but by those who can use it well.” His experience underscores a key message: AI can free up educators to do more of what matters—like teaching, mentoring, and innovating.  

For Rich, the journey with AI is ongoing. He encourages his peers to explore, experiment, and adapt, always with an eye toward maintaining academic integrity and student ownership of their learning. 

Written by: Rich Freeman, Kelly Fox, Amy Goruk and Ashley Priest. Inspiration provided by ChatGPT.

Ready to explore AI in your teaching practice?

The Centre for Teaching and Learning offers workshops, resources, and 1:1 consultations to help you confidently integrate AI into your classroom.

📧 Contact us at ctl@georgiancollege.ca to get started.
🖥️ Visit our Artificial Intelligence in Teaching page for guides, sample activities, and more.

Online Self-paced course: Generative AI Foundations for Faculty **New Winter 2025**