AI in the Classroom: AI as a Coding Partner

Avatar photo Dr. Aimee Schwab-McCoy

Embracing the Future of Computer Science Education

Computer science education is evolving rapidly as generative AI (GenAI) tools become increasingly accessible and user-friendly. GenAI brings challenges for educators, like academic integrity and rapid development. Students will graduate into a workforce where GenAI tools are commonplace, making it crucial that they learn to work effectively with these tools rather than despite them.

The Research Behind AI-Assisted Learning

GenAI can be a useful teaching assistant or coding partner for programming students. Recent research (https://onlinelibrary.wiley.com/doi/10.1111/dsji.12306) shows that GenAI tools like ChatGPT complete coding assignments at a similar level as mid-range college students. In another study (https://dl.acm.org/doi/10.1145/3544548.3580919), students with access to a GenAI tool showed improved code authoring performance with similar retention one week later.

GenAI tools can quickly identify syntax errors, suggest corrections, and explain common programming mistakes. This immediate feedback can help students understand error patterns and develop stronger debugging instincts.


Practical Applications in the Classroom

During class, instructors can use AI to show alternative approaches to solving problems. An instructor might ask students, “If I didn’t know what to do, what would I ask ChatGPT?” Working with students to craft an effective prompt during class gives a real-time demo of what to do, and what not to do.

This approach transforms the traditional lecture format into an interactive learning experience where students actively participate in problem-solving strategies that mirror real-world programming practices.


Guidelines for Effective GenAI Collaboration

How can students use GenAI as a coding partner? Here are some essential guidelines for writing effective coding prompts:

1. Be Specific

While GenAI can be impressive, it’s not magic. Vague requests like “make my code better” will yield generic responses. Instead, try: “Help me optimize this sorting algorithm for better time complexity,” or “Explain why my loop isn’t iterating through all array elements.”

2. Think About the Context Window

What other information does GenAI have? Provide relevant details about your programming environment, the language you’re using, any constraints or requirements, and what you’ve already tried. The more context you provide, the more targeted and useful the response will be.

3. Break Down Complex Operations into Simpler Steps

Instead of asking GenAI to solve an entire complex problem at once, decompose it into manageable components. This approach not only leads to better AI responses but also helps students develop critical problem-solving skills.


Beyond Coding: Developing Essential Skills

Working through a complex problem with GenAI can also help students practice written communication by writing specific prompts or project management by identifying smaller tasks within a larger problem. These meta-skills are invaluable in professional software development environments.

Students learn to articulate their problems clearly, think systematically about solutions, and communicate technical concepts effectively—all while leveraging AI as a collaborative tool rather than a replacement for critical thinking.


Preparing for the AI-Enhanced Workplace

As GenAI tools become standard in software development, students who learn to collaborate effectively with AI will have a significant advantage. They’ll enter the workforce already comfortable with AI-assisted development, understanding both the capabilities and limitations of these tools.

The goal isn’t to replace human creativity and problem-solving but to augment these skills with AI assistance, creating more efficient and effective programmers who can tackle increasingly complex challenges.


Want to learn more about integrating AI tools into your computer science curriculum? Explore zyBooks’ interactive learning platform and discover how our hands-on approach can complement AI-assisted learning in your classroom.

Avatar photo
Author Bio

Dr. Aimee Schwab-McCoy

Aimee Schwab-McCoy is the Senior Manager for Content Development in Data Science, Mathematics, and Statistics. She completed her PhD in Statistics at the University of Nebraska-Lincoln (2015). Before joining zyBooks in 2022, Dr. Schwab-McCoy was an Assistant Professor and Data Science Program Director at Creighton University, and a Lecturer at Institute of Technology Sligo. Dr. Schwab-McCoy has published several articles in statistics and data science education, and has received awards for teaching statistics in the health sciences.