Embracing AI in the Computer Science Classroom

The impact of widespread generative AI use is clear in our programming courses. Teachers are scrambling to adapt assignments, assessments, and classroom activities that have been impacted by generative AI. This fall, I had the invaluable opportunity to participate in the CSTA Knowledge Exchange Community: Why Teach Programming with GenAI? Led by Beth Simon from the University of California, San Diego (UCSD), this course presented cutting-edge strategies for integrating generative AI into higher education programming instruction and, crucially, discussed how these approaches could be adapted for the AP Computer Science A (CSA) curriculum.

The common discourse surrounding AI in education often centers on fear: Will it replace programming instruction altogether? This knowledge exchange, however, dug into a more creative and constructive approach. It focused not merely on AI’s ability to generate code, but on how different types of prompts and assignments could actively support skill development, leading students to become more confident and prepared programmers while still ensuring they are fully ready for the AP exam.

The Value of Relevant, Tool-Agnostic PD

As the participants were all CSTA members currently teaching CS A, the material was immediately and directly applicable to our daily classroom needs. Like the best professional development, the exchange was entirely tool-agnostic, presenting general pedagogical techniques that could be utilized with any generative AI platform. Each topic was delivered with clear, practical classroom applications, and participants were encouraged to test the new material with their students immediately.

For instance, one session focused on teaching debugging with generative AI. We explored several collaborative approaches that help students understand errors in their code. The session provided example student assignments, strategic AI prompts, and essential follow-up activities. Tasked with exploring these materials using our own AI tool of choice, I not only gained a new way to discuss GenAI with my students but also modified course materials for an upcoming lesson, incorporating the insights I gained. 

The result was an activity my students genuinely enjoyed, one that helped them build confidence in their debugging skills and provided a productive, research-backed way to use generative AI while learning programming. We’ve referenced this assignment several times since. When students get stuck on a bug, I now frequently remind them that they have generative AI as a powerful tool to help them identify their next step.

An Innovative Model for Professional Learning

In addition to timely, research-based content, the Knowledge Exchange piloted an innovative format for professional learning. The cohort was designed around a model that respects the busy lives of educators: it was primarily run as an asynchronous online course, bracketed by only two hour-long synchronous meetings (a kickoff and a wrap-up).

This format proved to be a great solution to the problem of teacher overload. The initial meeting introduced us to the course structure and to each other, fostering a sense of community. Throughout the course, we engaged in peer grading, which allowed us to see how other cohort members were planning to use and adapt the new materials. Participants benefited not only from the core content but from the diverse contributions and insights of their peers. The concluding meeting was a valuable chance to share our final takeaways and reflect on our classroom experiences.

Suggestions for Future Cohorts: The Power of a Study Group

I deeply appreciate CSTA’s commitment to providing timely, relevant PD and its willingness to innovate formats to work around teacher schedules. I loved the flexibility of the asynchronous structure.

However, a minor drawback to this highly flexible format was the minimal interaction outside of the synchronous meetings. With every cohort member working at their own pace, I missed the chance for regular check-ins or quick feedback on in-progress work.

My primary advice to future Knowledge Exchange participants would be to either sign up with a few colleagues or use the initial meeting to quickly recruit a small study group. A smaller, self-directed group that regularly checks in and keeps pace with each other would be the perfect complement to the course’s open scheduling.

For example, a group could agree to share draft lesson plans by a certain date, offer formative feedback, report back on student reaction, and then write final reflections for the official course assignments. This arrangement maintains the Knowledge Exchange’s open scheduling flexibility while ensuring participants receive the formative, real-time feedback that accelerates learning.

Thank You and a Call to Action

A huge thank you to CSTA and Beth Simon for providing this outstanding PD opportunity. As AI and CS education rapidly evolve, it is vital for teachers to have access to research-backed best practices and low-overhead ways to collaborate. I am thrilled to see CSTA continuing to modify its approach to professional learning to make it more accessible and relevant. I encourage all CSTA members to take advantage of this innovative and high-value opportunity.

[AI use statement: Everyone reading this article is thankful that I used Grammarly to correct my spelling and typing errors 🙂 I also asked Gemini for formatting assistance and help identifying and correcting run-on sentences, and reducing my use of the word ‘really’, which I really seem to use a real lot.]

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