Teaching Support and Innovation supports programs with new program development and curriculum (re)design.
Using AI in curriculum development and renewal
- Curriculum development and renewal is a multi-phase process that includes curriculum design, implementation, assessment, and review.
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To learn more: Explore this Teaching Support and Innovation page Curriculum design and renewal.
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- AI can assist to different degrees in different stages, but chats and initial outputs should not substitute for humans engaging in this dynamic process. The following sections outline recommendations for where AI can best help.
Gathering information
- Information gathering typically occurs early in the curriculum design phase, as well as iteratively throughout the entire process.
- AI can help with environmental scans of comparator programs, which is important to contextualizing both new and renewed programs.
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To learn more: Explore the New programs process pages from Academic Quality Enhancement (AQuE).
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Examples:
- A program is interested in getting a sense of whether other programs in North America require a portfolio for admission and how programs differ in cohort size and work-integrated learning requirements.
- Using GenAI to compare various aspects of existing programs. Use or adapt this prompt to produce a comparison table of institution and location, program and credential, admission requirements, cohort or enrolment, curriculum and key requirements, etc.
- AI can help with organizing large amounts of data. Always disclose in advance if you might use AI for survey analyses, and commit to anonymizing the data and to human review throughout the process. Avoid using AI if there is concern it will be a distraction for students completing the survey.
Important note: Review this section: Use of AI tools with University data on the AI tools page (IST).
Comparing draft outcomes to requirements
- Program learning outcomes (PLOs) must align with the degree level expectations (DLEs). While it is not recommended currently to use AI to generate PLOs, which undergird each phase of the curriculum development process, you can use AI to check alignment between drafts PLOs and DLEs.
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To learn more:
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Read Degree Level Expectations (DLEs) (Academic Quality Enhancement).
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Read Gathering Input (Teaching Support and Innovation).
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Example: Using GenAI to check the alignment of draft program learning outcomes (PLOs) to degree-level expectations (DLEs). Use or adapt this prompt to produce a table that shows which PLOs support which DLEs, including a gap analysis. Note: This prompt can be adapted to accreditation standards; see next point.
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Some programs have other requirements or graduate attributes they should consider in the development of their PLOs.
Example: For accredited programs, it is critical that the program meets the requirements of the accreditation body (e.g., Canadian Engineering Accreditation Board for Engineering programs, Council on Education for Public Health). Use the alignment prompt but substitute the DLEs for the necessary accreditation standards.
Brainstorming key curriculum experiences
- Capstone courses allow students to integrate their learning from across the curriculum, often including experiential learning.
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To learn more: Visit Capstone at Waterloo (Centre for Work-Integrated Learning)
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Example: Using GenAI to brainstorm ideas for capstone courses. Use or adapt these prompts for initial ideas, alignment with learning outcomes, and assessment strategies for capstone projects. Note: These prompts can be adapted to other common assignments embedded in the curriculum, such as signature assessments.
Integrating AI into the curriculum
Important note: Curriculum committees, or similar, typically collaborate on the curriculum development process. The following suggestions are best considered with other humans, but individual instructors might find these suggestions useful to reflect on the position of their own courses and related AI skills taught within the larger program.
- Identify where students have opportunities in the program to develop foundational skills before using AI tools. Students might not be prepared to engage effectively, responsibly, and ethically with AI if they have not developed a strong independent foundation.
- Discuss how expectations for AI use might evolve across courses and years of study and how this evolution might impact course content and intended learning outcomes.
Example: Coordinate with colleagues where possible to create a coherent progression of AI-related knowledge and skills across a program.
- Across the program, balance AI-integrated and AI-restricted learning opportunities so students can develop both independent expertise and responsible AI practices.
Example: Consider the advantages and disadvantages of developing “AI light” and “AI heavy” pathways through programs. Thoroughly evaluate the results of any curriculum changes involving a purposeful integration of AI, whether “light” or “heavy.”
- Integrate the topics of effective, responsible, and ethical uses of AI throughout the curriculum.
Examples:
- In capstone projects as a reflection on the limits and appropriate uses of AI within in the discipline.
- In foundational courses to teach students how use of AI meshes with their discipline.
- In required communication courses around how to use and when to avoid AI in writing, including the risks of made-up citations and homogenization of language and ideas.
References
Massachusetts Institute of Technology, Ad Hoc Committee on AI Use in Teaching, Learning, and Research Training. (2026, August 13). Report. MIT AI and Education. https://aiandeducation.mit.edu/report/