Reframing Learning Outcomes for the Generative AI Era

Drafting and writing learning outcomes is an ongoing process. With each course iteration, you may wish to revisit, revise, and refine your learning outcomes by embedding UDL-informed strategies and inclusive practices. In addition, you may consider explicitly reframing your learning outcomes in light of ongoing advancements in generative AI capabilities.

Generative AI-informed reframing might involve strategically integrating generative AI tools as an explicit supplement to the learning process or deliberately emphasizing human-centred skills that generative AI technologies cannot easily replicate. By strategically reframing learning outcomes in these ways, you can create opportunities for students to develop meaningful learning experiences that prioritize critical thinking, creativity, and authentic skill development.

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Reframing Learning Outcomes with Bloom's Cognitive Taxonomy Revisited (2024)

Earlier, you explored Bloom's Revised Cognitive Taxonomy (2001) as a basis for developing action-oriented verbs for the six cognitive domain levels (remember, understand, apply, analyze, evaluate, and create) to concretize the learning outcomes for students. More recently, Oregon State University developed Bloom's Cognitive Taxonomy Revisited, Version 2.0 (2024): a revised explication of Bloom's Taxonomy that instructors may use to reconsider the way that they communicate course outcomes and student learning for the generative AI era.

Since students at the University of Toronto are permitted to use generative AI as learning aids, it is valuable to reframe your course-level learning outcomes to explicitly prioritize human-centred skills, thereby mitigating potential overreliance on generative AI. Bloom's Cognitive Taxonomy Revisited, Version 2.0 can help you clarify what skills and actions you wish to emphasize and evaluate at each cognitive process. Each cognitive process invites opportunities for you to integrate or limit student use of generative AI in assessments and learning activities.

Refer to the following table for potential reframing of your learning outcomes, considering aligned course assessments and activities that either emphasize human-centred skills or integrate generative AI tools as a supplement to the learning process. Not all learning outcomes necessarily require explicit reframing in response to generative AI. By reflecting on this table, you can develop a plan to determine if and how you will implement specific changes to your course-level learning outcomes.

Bloom's Cognitive Taxonomy Revisited
Cognitive Process	Human-Centred Skills	How Generative AI can Supplement Learning Processes
Remember	Recall information without technological assistance	Retrieve, organize, and synthesize factual information across timelines and definitions
Understand	Contextualize answers within emotional, moral, or ethical considerations; select relevant information; explain significance	Accurately describe a concept in different words; recognize a related example; translate
Apply	Implement, experiment, and test in the real world; apply human creativity and imagination to solutions	Apply systematic problem-solving methods and help diagnose solution errors
Analyze	Critically analyze across cognitive and affective domains with clear justification	Compare and contrast, and analyze data trends and relate findings to real-world applications
Evaluate	Metacognitively reflect on ethical implications and broader contexts	Evaluate potential strategies and develop assessment criteria
Create	Generate original solutions by integrating human experiences, social-emotional insights, and intuitive judgment	Support creative exploration by suggesting alternatives and developing tangible outcomes

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Reframing Sample Learning Outcomes with Generative AI in Mind

Compare the second and third iterations of the learning outcomes provided below. What changes were made to reframe the learning outcomes so that they emphasize human-centred skills that mitigate overreliance on generative AI tools?

English

Learning outcome (second iteration): Students will analyze the relationship between the language of satire and literary form through the close examination of three eighteenth-century texts.

Learning outcome (third iteration with generative AI in mind): Students will critically analyze and interpret the relationship between satirical language and literary form, developing original interpretations through close textual examination.

History of Education

Learning outcome (second iteration): By analyzing case studies, students will be able to connect core philosophical frameworks to key historical events and pedagogical examples.

Learning outcome (third iteration with generative AI in mind): Students will synthesize core philosophical frameworks with key historical events and pedagogical examples, developing original insights through in-depth case study analysis.

Pharmacy

Learning outcome (second iteration): Students will apply the principles underpinning the use of molecular graphics in the design of drugs to illustrate general and specific cases.

Learning outcome (third iteration with generative AI in mind): Students will apply and critically evaluate the principles underpinning the use of molecular graphics principles in drug design to illustrate general and specific cases.

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Pause for Workbook Activity: Reframing Your Learning Outcomes for the Generative AI Era

As generative AI technologies continue to reshape the educational landscape, it is valuable to reframe your learning outcomes with generative AI in mind.

Let's take a moment to open your Course Design Foundations Workbook docx and refer back to your draft learning outcomes. Consider the following prompts for reframing your learning outcomes for the generative AI era:

Which cognitive process does your learning outcome align with? Consult the "Bloom's Cognitive Taxonomy Revisited" table above for reference.
What human-centred skills does this outcome highlight or develop?
To what extent could generative AI tools be used to support or enhance this learning outcome?
How can you reframe this outcome to emphasize human-centred skills that generative AI technologies cannot easily replicate?

Add your reframed learning outcomes with generative AI in mind to the three-column table. Compare the changes you made and how your reframed learning outcomes address the challenges and opportunities presented by generative AI technologies.

Course Design Three-column Table
Learning Outcomes	Assessments	Learning Activities
Draft learning outcomes (from earlier) Reframed learning outcomes (with generative AI in mind)	To complete in module 5	To complete in module 6

As you progress through the modules, the three-column table is a helpful tool to ensure the alignment of your learning outcomes, assessments, and learning activities and instruction.

Supporting Student GenAI Literacy

As you reframe your learning outcomes with generative AI in mind, consider how you will support students in developing the literacy and critical thinking skills needed to use these tools ethically and effectively.

U of T offers Coursework and GenAI: A Practical Guide for Students, an open educational resource designed to enhance students' literacy around generative AI. The resource includes customizable modules that instructors can flexibly adapt and integrate into their courses across disciplines. Instructors may also choose to link to an adapted version of this resource in their syllabus to support students' understanding of generative AI tools and expectations.

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