As artificial intelligence tools become an integral part of software development, a new skill is emerging: prompt engineering. This isn’t just a trendy buzzword—it’s a critical competency that enables developers to work effectively with AI systems like ChatGPT and GitHub Copilot. 

For educators, this presents an exciting opportunity to prepare students for the realities of modern coding. At the same time, it’s understandable to feel skeptical or overwhelmed by the idea of reshaping courses to include such a disruptive (in a neutral sense) development in the educational landscape. After all, integrating new technologies requires careful planning to ensure they complement rather than complicate learning.

How do you teach prompt engineering in a way that’s meaningful, practical, and aligned with your curriculum? Let’s break it down.

What Is Prompt Engineering?

In simple terms, prompt engineering involves crafting inputs for AI tools to get the desired outputs. Think of it as a conversation—your students need to ask the right questions in the right way to make AI tools work for them.

For example, a vague prompt like “Write code for a game” will produce basic results. But a well-engineered prompt like “Write a Python script for a two-player tic-tac-toe game, ensuring clear separation of game logic and user interface” generates specific, actionable outputs.

Why Prompt Engineering Matters

AI tools are already reshaping industries, and software development is no exception. Whether it’s automating code generation, debugging, or enhancing workflows, AI has become a powerful ally (though this depends on who you ask!).

By teaching prompt engineering, you:

• Help students understand how to guide AI rather than rely on it blindly.
• Encourage critical thinking and iterative problem-solving.
• Bridge the gap between traditional coding skills and real-world AI usage.

Challenges in the Classroom

Incorporating prompt engineering into your course may feel daunting. How do you balance teaching foundational coding concepts with this new skill? And how do you prevent students from becoming overly reliant on AI?

Here are some key challenges educators may face:

• Application Over Expertise
  Prompt engineering doesn’t require students to become AI experts. The goal is to teach them how to apply AI effectively to solve coding problems, much like using a debugging tool or an API. This shift in focus can feel unfamiliar but aligns well with real-world needs. As Brian Brady, Associate Professor of Mechanical Engineering Technology, explains in his work with engineering students, the emphasis is on problem-solving rather than perfecting programming skills. You can learn more about his approach in Teaching Engineers to Program with AI.
• Practical Learning Over Theory
  While theoretical resources on prompt engineering abound, the real learning happens when students practice and receive feedback. Creating hands-on exercises and assignments ensures students gain meaningful experience rather than relying on abstract concepts.
• Monitoring the Use of AI
  Banning AI tools outright is rarely effective—students are likely to use them anyway, often without guidance. Instead, consider incorporating AI into your curriculum and learning management system. This way, you can monitor how students interact with these tools, provide oversight, and ensure ethical and effective usage.
• Balancing AI with Foundational Skills
  There’s a risk of students leaning too heavily on AI tools, potentially neglecting the foundational coding skills they need. Assignments should strike a balance, requiring students to analyze, refine, and understand AI-generated outputs rather than accepting them at face value.
• Ethical and Practical Concerns
  AI isn’t perfect, and neither are the prompts students create. Educators must address issues like bias in AI outputs, intellectual property questions, and when it’s appropriate to rely on AI versus building solutions from scratch.

Strategies for Teaching Prompt Engineering

To effectively teach prompt engineering, it’s important to combine practical exercises with opportunities for reflection. Here’s how you can do it:

• Design Meaningful Assessments
  Assessments should go beyond theoretical tasks to provide hands-on experience. Create exercises where students refine AI outputs, troubleshoot errors, or test how varying prompts affect results. These tasks emphasize iteration and deeper understanding over surface-level outcomes.
  
  
• Incorporate Small, Targeted Prompts
  Introduce quick exercises focusing on single skills, like crafting concise instructions or testing prompt variations. Highlight the importance of being economical—students should learn to achieve clarity and precision with minimal input.
  
  
• Assign Real-World AI Projects
  Longer assignments should simulate complex scenarios where students interact with AI iteratively. For example, projects could involve developing a solution through multiple AI interactions, teaching students to engage with AI conversationally and improve outcomes over time.
  
  
• Provide Clear Rubrics
  Use rubrics to help students evaluate their prompts and AI outputs. Include criteria like clarity, adaptability, and effectiveness, giving students a clear understanding of what makes a prompt successful.
  
  
• Encourage Reflection
  Reflection is key to learning. Ask students to document their prompt engineering process, detailing what worked, what didn’t, and why. This helps them internalize lessons and refine their approach for future tasks.
  
  

By blending targeted exercises with real-world applications and structured feedback, you can teach students to approach AI tools strategically and confidently.

Tools and Resources for Educators

Educators often face time pressures to adapt courses to keep up with emerging trends in the educational landscape. The good news? You don’t have to completely redesign your curriculum to integrate AI. Instead, consider adding supplementary modules or tools to complement your teaching.

For example, AI Engineering Labs can be a valuable addition to your course. This pre-built, flexible module is designed to introduce students to prompt engineering without requiring a full course overhaul. By incorporating such labs, you can ensure your students gain hands-on experience with AI tools while keeping your core lessons intact.

Looking Ahead

Prompt engineering isn’t just about using AI; it’s about teaching students to approach problems thoughtfully and strategically. Much like the evolution from calculators to coding, AI tools are simply the next step in modern education.

By integrating prompt engineering into your curriculum today, you’re preparing students for a world where coding and AI work hand in hand.

What strategies have worked for you in teaching AI tools? We’d love to hear your ideas!