Course Design and AI
Course Design is one of the five domains in the University of Alberta Framework for Effective Teaching. As detailed in Appendix B of the Teaching, Learning and Evaluation Policy, the framework offers guidelines to enhance teaching quality and grounds the multifaceted evaluation of teaching and learning.
Effective course design aligns learning outcomes/objectives, course materials and activities, and assessment tasks. This section discusses how you can:
- Maintain this alignment and pedagogical integrity when incorporating or addressing (and mitigating the risk of) GenAI
- Thoughtfully incorporate GenAI while supporting diverse student backgrounds, learning goals, and perspectives
- Understand students’ varying experiences with AI, from extensive use to concerns about the tools
- Can guide course design to balance academic integrity with authentic learning
Learning Outcomes and Generative AI
Effective course design starts with clear learning outcomes (Wiggins & McTighe, 2005) that define what students should achieve and guide the development of activities, materials and assessments. Reflecting on technology use ensures GenAI is integrated responsibly and ethically, aligning with learning outcomes, which vary across disciplines, course levels, and learning contexts. When considering GenAI, critically review your course learning outcomes and program goals, asking yourself: What knowledge and skills must students demonstrate independent of technology?
- Which learning outcomes can be meaningfully enhanced by integrating GenAI tools?
- How do course-level outcomes align with broader program outcomes and professional competencies?
- What critical thinking and disciplinary practices remain essential regardless of technological tools?
For example, GenAI may aid brainstorming in a writing course but be unsuitable for developing core mathematical problem-solving or manual clinical skills.
Oregon State University’s Bloom’s Taxonomy Revisited (Version 2.0) guides course design regardless of an instructor's position on GenAI use. It offers a helpful framework for evaluating and adjusting course assessments and assignments given GenAI capabilities. It highlights the value of distinctive human qualities, human traits and qualities while exploring as well as the potential role of GenAI's potential as useful support for student learning. Considerations for each level of Bloom's Taxonomy guide course design regardless of an instructor's position on GenAI use.
Instructors integrating GenAI into their courses can use it to set criteria highlighting critical evaluation and revision of AI-generated content. In contrast, those restricting it can use it to redesign assessments emphasizing independent work and skill-building.
The examples below demonstrate Bloom’s Taxonomy can be used to write effective learning outcomes where GenAI can support student learning The examples provide opportunities for engagement with GenAI while preserving opportunities for students to demonstrate distinctive human skills and contributions.
Remembering
Learning Outcome: Define key theoretical frameworks used in developmental psychology.
GenAI Use: Students use GenAI to help construct basic timelines of theoretical developments and then focus on memorizing core concepts they'll need to access without technology.
Understanding
Learning Outcome: Explain how cultural factors influence business communication practices across different global markets.
GenAI Use: Students use Google Gemini Chat to get necessary background information and relevant business terms and concepts. They then develop their deeper understanding by analyzing particular cultural contexts and ethical implications.
Applying
Learning Outcome: Implement appropriate statistical methods to analyze real-world data sets.
GenAI Use: Students use GenAI to check their problem-solving steps and identify errors in their statistical calculations while maintaining responsibility for choosing appropriate methods and interpreting results.
Analyzing
Learning Outcome: Compare the environmental impacts of different urban transportation systems.
GenAI Use: Students use GenAI to help organize and visualize data trends but must critically evaluate the implications and propose context-specific recommendations.
Evaluating
Learning Outcome: Assess the quality of GenAI outputs for a series of specified tasks.
Process Assignment Template:
- Ask an AI to write an essay/write code/draw an image/create a script/design an experiment/draft a press release/propose a new business/analyze data.
- Evaluate the results.
- Make a list of errors or how this result could have been better. Adjust your prompt to improve the output.
- Which result was best and why? What was your strategy to improve the prompt? What worked best?
- Take the best output and make it even better with human editing.
- Describe for an employer what value you added to this process.
- Explain why your human work is better or improve the AI work.
(Bowen & Watson, 2024, p. 170)
GenAI Use: Students use GenAI in a series of scaffolded tasks (see image above) to evaluate the quality and accuracy of AI-generated content. Students must watch for signs of fabrication, bias, and harmful content and reflect on the ethical implications of using GenAI for such tasks.
Creating
Learning Outcome: Design an original research proposal addressing a current issue in the field.
GenAI Use: Students use Google Gemini Chat for initial brainstorming of research questions and methodology options but must integrate their expertise, creativity, and ethical judgment to develop an original proposal.
Generative AI Resistant Design
Learner-centered course design focuses on how students develop knowledge and skills over time. By emphasizing the learning process–how students understand, interpret, build, and apply competencies–you can design activities and assessments that showcase authentic learning and growth. Measuring learning across multiple points, rather than through single deliverables, ensures that assessment tasks better capture and reflect genuine student achievement of course learning outcomes.
When GenAI use is not appropriate for learning, critically examine your course learning outcomes and assessment strategies:
- Review and Revise Learning Outcomes:
- Identify which outcomes might be easily achieved using GenAI alone
- Strengthen outcomes to emphasize higher-order thinking skills (Apply, Analyze, Evaluate, Create)
- Prioritize skills and mindsets requiring human engagement, like critical thinking, ethical judgment, and problem-solving
- Focus on learning as a process with formative steps and feedback opportunities
- Design Engaging Activities / Authentic Assessment Tasks:
- Create assignments revealing student thinking, not just final products
- Emphasize contextual understanding and real-world (authentic) applications
- Incorporate reflection, peer interaction, and iterative feedback
- Use assessment tasks as growth and learning opportunities
- Build multiple checkpoints to make student learning visible (process-oriented approach)
- Communicate Clear Rationale:
- Explain why specific tasks exclude AI assistance (not appropriate)
- Connect activities to professional competencies and disciplinary practices
- Emphasize how assessments align with learning outcomes
- Use a Statement of Expectations in your course syllabus to communicate the use of GenAI in your course
While designing fully “AI-proofing” assessments may be challenging, you can take steps to minimize inappropriate GenAI use. For strategies, refer to the Instructional Practices and Assessment Design sections.
Scoping Course Design
To help align AI use with learning outcomes, plan course components, set clear expectations, and evaluate GenAI integration through a pedagogical, responsible-use lens. Instructors, departments, and academic units should make decisions about whether, when, and how to use GenAI. By integrating GenAI appropriately, the goal is to enhance, not replace, authentic learning. Start with what fits best: a single session, a unit/module, or a complete course redesign. Establish clear expectations and guardrails.
For more on this topic please see GenAI and Course Design: A Brief Guide. Available as a downloadable PDF, this guide helps instructors thoughtfully integrate Gen AI into their courses. It provides a checklist for aligning AI use with learning outcomes, fostering student engagement, and promoting ethical practices, alongside practical examples and actionable strategies. This resource supports instructors whether they are making minor updates or planning a complete course redesign, ensuring GenAI enhances teaching and supports quality student learning.
Generative AI Enhanced Design
Thoughtful integration can enhance course design and engage student learning experiences while upholding academic standards. GenAI complements instructor expertise. Think about GenAI as a collaborative assistant that can help:
- Help brainstorm assessment ideas and generate diverse content, such as drafting syllabi and lecture outlines, creating case studies and developing practice problems
- Curate supplemental learning resources to enrich the student learning experience. GenAI can offer different perspectives on course materials. Thoughtful integration can enhance course design and engage student learning experiences that uphold academic standards
Learning design
GenAI can assist with refining course goals, organizing content, and creating engaging learning pathways. Use it to identify logical connections between topics and highlight necessary skills and knowledge.
Example: "Turn this simple statement, 'students will explain photosynthesis,' into measurable outcomes across cognitive levels."
Resource development
GenAI can generate lecture outlines, provide real-world examples, and develop discussion prompts to help foster critical thinking and student engagement. It can also help create study guides, concept maps, and interactive exercises to support diverse learning styles.
Example: "Suggest real-world applications of statistical concepts that resonate with first-year business students."
Assessment design
Use GenAI to develop both formative and summative assessments.
Examples:
- For a quick comprehension check, prompt Gemini Chat to "Create five ways to check understanding of Newton's Third Law during class."
- For larger assessments, try, for example, the prompt: "Suggest ideas for environmental science students to apply concepts to local sustainability issues."
These tools can also generate detailed grading rubrics (aligned with learning outcomes) and develop authentic assessments through real-world scenarios, case studies, and project prompts that mirror professional situations.
Remember, before sharing with students, you or teaching assistants must carefully review all AI-generated content for accuracy, appropriateness, and freedom from bias or other potentially harmful content. Transparency is vital: indicate when materials are AI-generated and provide proper attribution and acknowledgement to model and promote responsible and ethical use.
Explore more examples
Instructors can use a GenAI tool like Gemini Chat, Claude, or Google's NotebookLM to create a course FAQ to supplement and support student learning. For example, using Google's NotebookLM, an FAQ was developed based on this web resource's Statement of Expectations section.
Instructors can similarly create FAQs to answer commonly asked questions about course details such as objectives, course structure, activities, readings, assessments, and policies. The instructor should always review and refine the FAQ before sharing it with students.
Create concise, student-friendly summaries of complex topics or theories. Providing the GenAI tool with the source material or key concepts can generate clear, accessible explanations suitable for students’ level of understanding.
For example, an instructor teaching organizational behaviour could input a description of Maslow's Hierarchy of Needs. GenAI could summarize the core principles, applications, and common critiques.
Example Prompt: Summarize the key principles, applications, and critiques of Maslow's Hierarchy of Needs in a way that is accessible to undergraduate business students. The summary should be concise (around 200-300 words) and use clear, straightforward language. Avoid jargon, but ensure the explanation accurately reflects the theory and includes examples of its real-world application.
The instructor should review and refine the output to ensure accuracy and relevance. This can save valuable instructor time and offer students an engaging way to grasp challenging material.
*Alternatively, instructors could ask students (individuals or groups) to create and share Study Summaries with their classmates. The evaluation and validation of AI-generated output is necessary for this type of activity.
Leverage GenAI to streamline the creation of presentations. For instance, Gamma generates quality presentations based on users' input on a topic or outline. The AI then crafts a structured slide deck with design elements and content suggestions, which instructors can customize to align with teaching goals.
This example is a presentation based on the Students and AI section of this web resource. The only input provided to Gamma was text content and colour palette suggestions.
By integrating tools like Gamma into workflows, instructors can save time developing presentation materials, enabling them to focus more on connecting with students and delivering effective instruction.
GenAI can generate various question types, create plausible distractors for multiple-choice questions, and develop scenario-based questions aligned with different levels of Bloom’s taxonomy.
For example: “Generate M-CQs about photosynthesis at varying levels, including application and analysis type questions.”
Alternatively, tools like the multi-agent Assessment Partner, developed at McMaster University, allow instructors to generate targeted multiple-choice questions based on:
- Discipline and study level
- Specific topic or skill to focus on
- Learning taxonomy or target level
- Difficulty level
The Assessment Partner also hosts a repository of instructor-generated multiple-choice questions. Example: Biomechanical Engineering: Application of Principles.
Remember: Always review and verify AI-generated output for accuracy.
Resources
U of A resources for instructors:
GenAI and Course Design Checklist
GenAI and Course (Re)Design: A Brief Guide
This resource supports instructors, ensuring GenAI enhances teaching and supports student learning
GenAI Use: Acknowledgement and Reflection
Process documentation and reflection form. See Student Dialogue for more information
GenAI Tool Use: Responsibility Statement
AI use responsibility form for students. See Student Dialogue for more information
Suggested supplementary resources for instructors:
AI Pedagogy Project (metaLab (at) Harvard)
The AI Pedagogy Project helps educators engage students in conversations about the capabilities and limitations of GenAI
GenAI Curriculum Design: Design, Refine, Create
Leon Furze explains a three-part framework – Design-Refine-Create – for course design using GenAI technologies
GenAI Quickstart: Foundations for Faculty (McGill University)
Responsible Use Considerations Part four in online modules exploring GenAI and teaching and learning
Potential Uses in Teaching Part five in online modules exploring GenAI and teaching and learning
Potential Uses in Learning Part six in online modules exploring GenAI and teaching and learning
One Useful Thing (Ethan Mollick)
Ethan Mollick. A GenAI Resource and Prompt Library to assist instructors with preparation and generating activity/assessment ideas
Prompts-for-edu/Educators
Lists examples for instructors who want to create engaging lessons, offer timely feedback and generate creative learning activities and resources
Teaching and Generative AI: Pedagogical Possibilities and Productive Tensions (Utah State University)
Provides practical pedagogical resources for navigating the possibilities and challenges of teaching in an AI era
Teaching with AI (José Antonio Bowen & C. Edward Watson)
Available through the U of A Library (in ebook and audiobook versions)
Sources
Bita, N. (2023, January 27). AI is about to Transform Education: Are We Ready? The Australian (Online)
Bowen, J. A., Watson, E.. (2024). Teaching with AI (C. E. Watson & A. B. Wehrlen (Eds.); [First edition].). Ascent Audio
Bowen, R. (2017). Understanding by design. Vanderbilt University Center for Teaching
Fang, B., Broussard, K. (2024). Augmented Course Design: Using AI to Boost Efficiency and Expand Capacity. [Blogpost] Educause
Furze, L. (2024, August 19). Design, refine, create: A framework for GenAI curriculum design
Genone, J., & Hughes, S. (2023). Integrating Artificial Intelligence [White paper]. Minerva Project and the Edmond de Rothschild Bridge for Higher Education and Employment
Oregon State University Ecampus. (2024) Bloom’s Taxonomy Revisited
Richardson, W. (2023). Assessing the Learning Process, not the Product. Modern Learners
Centre for Teaching and Learning. (n.d.). Framework for effective teaching. University of Alberta
Wiggins, & Jay McTighe. (2005). Understanding by Design: Vol. Expanded 2nd ed. ASCD