Learning outcomes are "measurable statements that articulate...what students should know, be able to do, or value as a result of taking a course" (Cornell University's Center for Teaching Innovation).
Usually, course learning outcomes are articulated in the course syllabus like in this example (adapted from UC San Diego's Muir College Writing Program (MCWP) 50:
By course completion, students will be able to:
● Develop a claim supported by appropriate evidence
● Develop questions to guide research
● Acknowledge different perspectives on a given topic or issue
● Distinguish among primary, secondary, and tertiary sources
● Identify scholarly resources and evaluate sources for credibility
● Navigate library resources
● Understand that contemporary issues are best understood with historical context
Ideally, learning outcomes are specific and should be achievable (at different levels of mastery, of course) given the situational factors of your course (for more on situational factors, see this article by D. Fink).
Students need to know what they are expected to achieve by taking a course. Not only do learning outcomes provide them with clear goals, but they also provide them with the WHY for the course. Why am I here? Why am I being asked to do A, B, and C? If the learning outcomes are articulated AND they are tied to the course assessments and activities, then this can enhance students' intrinsic motivations to learn (which reduces motivations to perform by cheating).
Unfortunately, many syllabi do not explicitly state the course learning outcomes. Rather, students are left to intuit the goals from a "course overview" paragraph like this one:
Language is a phenomenon in the natural world that can be approached scientifically, by studying language usage
patterns, human language apprehension and cognition, visual and auditory perception of language, and formal
mathematical models of the structure and interpretation of linguistic expressions. This course will examine the
theoretical foundations of the linguistic sciences. The most foundational question within philosophy of language
is: how can an object in the world have “meaning”? From there, we build outward: how do words have meanings?
How do sentences have meanings, and how are their meanings related towords’ meanings? How does communication
work? We will work through classic papers in philosophy of language on central topics of meaning, truth,
reference, names, descriptions, natural kind terms, conversational implicatures, presuppositions, and social and
political philosophy of language. (taken from UC San Diego PHIL 134 syllabus)
We recommend that all learning outcomes being clearly articulated and all course assessments are linked back to those learning outcomes.
Reason #1: Regular Revision is Good
Course learning outcomes should regularly be reviewed (and possibly revised) to keep them relevant in the face of changes that have occurred in the external world (like the launch of LLMs) or changes you've made in pedagogy, course content, or assessments.
Reason #2: Outcomes might be Outdated
Course learning outcomes written before the launch of ChatGPT might be irrelevant if a machine can accomplish them. For example, writing courses used to have "learn how to properly format references in the APA style" before tools starting doing that work for us.
Reason #3: Cognitive Offloading Could Amplify Learning
It could be that students should cognitively offload some tasks to GenAI. For example, is it still necessary for students to demonstrate that they can "identify scholarly resources" without tools or more important that they can critically analyze those resources?
Reason #4: Developing AI Skills Might be a New Outcome
Students will likely need to learn how to use AI critically and ethically within your field/discipline, so you may need to add a learning outcome related to that.
Review your course learning outcomes with the following questions in mind:
is it easy for students to fake achievement of the outcome with use of AI?
For example, if students were expected to be able to "define relevant vocabulary for international affairs discourse", AI could do this
are all of the learning outcomes still relevant in the age of AI?
For example, perhaps in a biology course, the learning outcome of "write a concise lab report" may no longer be relevant since machines can help us be more concise writers.
can you move any of the learning outcomes up Bloom's Taxonomy of Learning?
AI is particularly good at remembering and (at least feigning) understanding. Also, students tend to be less intrinsically motivated when learning outcomes are focused at these levels. So, instead of "define relevant vocabulary for international affairs discourse", perhaps the outcome could be "analyze international affairs discourse to compare and contrast persuasive versus unpersuasive arguments"
should you add a learning outcome related to ethically or responsibly using GenAI tools?
For example, perhaps a goal to "Navigate library resources" could be adapted to "navigate library, google scholar and research rabbit (AI) resources to find relevant source material"
Want some help? We suggest using a GenAI tool! Not only will doing so aid you in the task, but it gives you a good opportunity to build your own skills in using the tools. For a prompt template and instructions on how to use a GenAI tool to review and possibly revise your learning outcomes, click here.
For human-to-human assistance on reconsidering your learning outcomes, contact the teaching center on your campus. At UC San Diego, that is the Engaged Teaching Hub.