AI in the Classroom: Improving Course Development
AI as Your Teaching Assisstant
Course development has always been one of the most time-intensive aspects of teaching. GenAI tools can help instructors develop and improve their courses more efficiently, allowing more time to focus on teaching and research. For educators juggling course preparation, research responsibilities, and student support, AI can serve as a valuable collaborator in the course design process.
The key is approaching AI as a creative partner that can accelerate initial development while leaving the pedagogical expertise and final decisions to you. Rather than replacing the thoughtful design process that makes courses effective, AI can handle the time-consuming groundwork that often keeps instructors from implementing their best ideas.
Streamlining Syllabus and Schedule Design
Syllabus design represents one of the most impactful applications of AI in course development. Given a course description, target audience, and scheduling constraints, GenAI tools can produce a lecture schedule with topics for each class session. This initial framework saves hours of planning time and often surfaces logical topic progressions you might not have considered.
GenAI can also recommend learning objectives or revise existing learning objectives to align with specific frameworks, like Bloom’s taxonomy. This capability is particularly valuable when adapting courses for different audiences or ensuring alignment with program-level outcomes. The AI can suggest how to sequence objectives from lower-order to higher-order thinking skills, creating a more intentional learning progression.
For example, when designing a data structures course, AI might suggest starting with concrete implementations before moving to abstract analysis, or recommend specific lab timings that align with theoretical concepts. While you’ll still need to adjust for your specific context and teaching style, this foundation accelerates the design process significantly.
“The key is approaching AI as a creative partner that can accelerate initial development while leaving the pedagogical expertise and final decisions to you.“
Getting Fresh Content and Examples
New practice problems and examples often require the most creative energy from instructors. Updating lecture notes with new examples can be a time-consuming task, especially when trying to keep content current and relevant. GenAI can help you get past writers’ block by suggesting new examples for a given method or concept, offering fresh perspectives on familiar topics.
GenAI can also create additional variations of practice problems, giving students more opportunities to practice without the instructor needing to craft each problem from scratch. This is particularly valuable for concepts where repetition and variation help build mastery, such as algorithm analysis or debugging exercises.
Since GenAI tools are trained on a wide variety of data, GenAI may be able to suggest new case studies or real-world applications you may not have considered for a course. It might propose using social media algorithms to teach graph traversal, or suggest blockchain applications when covering hash functions. These connections can make abstract concepts more concrete and engaging for students.
Enhancing Assessment Development
Assessment development benefits significantly from AI assistance, particularly in the mechanical aspects of test creation. GenAI may be used to create rubrics for an assessment, ensuring consistent evaluation criteria and helping instructors articulate their expectations more clearly. This is especially helpful for subjective assessments like project presentations or code reviews.
GenAI excels at generating plausible distractors for multiple-choice questions, one of the most challenging aspects of creating effective assessments. Rather than spending time crafting incorrect but believable options, instructors can focus on ensuring questions assess the intended learning objectives. The AI can suggest distractors that target common misconceptions or typical student errors.
For programming assessments, AI can help generate test cases, suggest edge cases students should consider, or create variations of coding problems that test the same concepts in different contexts. This variety helps ensure that assessments measure understanding rather than memorization.
Approaching AI with Professional Caution
As instructors, we expect students to approach GenAI with caution, and we should do the same! GenAI output should always be considered a first draft, not the final product. The AI provides a starting point that requires your pedagogical expertise to refine, contextualize, and validate.
This means reviewing AI-generated content for accuracy, appropriateness for your student population, and alignment with your learning objectives. AI might suggest technically correct examples that don’t fit your course level, or propose assessment criteria that don’t match your grading philosophy. Your role is to curate and adapt these suggestions into materials that serve your students effectively.
Starting Small and Building Confidence
Start small: choose one area where you spend considerable time, such as quiz creation or content outlining, and experiment with GenAI tools for that task. This focused approach allows you to understand AI’s strengths and limitations in a specific context before expanding to other areas of course development.
Consider beginning with low-stakes applications like generating discussion questions or creating additional practice problems. As you become more comfortable with the AI’s output quality and develop effective prompting strategies, you can gradually incorporate AI assistance into more complex tasks like comprehensive assessment design or curriculum mapping.
Iterative and Multi-Modal Assessment Strategies
Iterative assignments where students respond to follow-up questions or make revisions are a good opportunity for students to start with a GenAI tool and use their knowledge to improve the output or identify errors. Using in-class assessment like quizzes, exams, or presentations alongside out-of-class assessment prevents students from becoming too dependent on GenAI.
This approach recognizes that AI can be a valuable starting point while ensuring that students develop the critical thinking skills necessary to evaluate, improve, and apply AI-generated content. Students learn to be critical consumers and collaborators with AI rather than passive recipients of its output.
Maintaining Educational Quality and Intent
The goal of using AI in course development isn’t to automate teaching but to amplify your effectiveness as an educator. AI can handle routine tasks and provide creative starting points, freeing you to focus on the uniquely human aspects of education: understanding your students’ needs, adapting to classroom dynamics, and making the pedagogical decisions that create meaningful learning experiences.
By thoughtfully integrating AI into your course development workflow, you can create richer, more varied learning experiences for your students while reclaiming time for the aspects of teaching that energize and fulfill you most.
Ready to streamline your course development process? Discover how zyBooks’ interactive content and customizable assignments can complement your AI-enhanced course design, creating engaging learning experiences that adapt to your teaching style and student needs.
