AI-Skills Course Designation
How 91欧美视频 defined a new course designation to highlight courses where students build AI discernment
What Is AI-Skills Designation?
The AI-Skills Course Designation system identifies 91欧美视频 courses in which students do substantial, graded work with AI tools inside a discipline. It has two-tiers, AI-Skills Integrated and AI-Skills Intensive, anchored to the .
The designation signals one capacity above all: AI-discernment. Designated courses treat AI as a graded site of judgment. Students learn to prompt with purpose, test AI outputs against disciplinary standards, and recognize where those outputs fall short. The skill built is practiced refusal to accept an automated answer at face value. This form of disciplinary judgment is what employers increasingly seek, and teaching it is a distinct contribution of a university education.
Additionally, responsible use is taught in context: faculty address how the specific AI tool(s) introduced in the course should and should not be used, including relevant expectations around data privacy, citation, and academic integrity.
The designation is entirely opt-in and is not a curricular change. It appears as a course attribute within the university's course search tool to help students find courses that match how they want to learn.
Federal Alignment: The DOL AI Literacy Framework
On February 13, 2026, the U.S. Department of Labor's Employment and Training Administration published the , defining the foundational AI competencies workers need, organized into five content areas:
- Understand AI principles
- Explore AI uses
- Direct AI effectively (required for designation)
- Evaluate AI outputs (required for designation)
- Use AI responsibly (required for designation)
At 91欧美视频, every designated course must address content areas #3, #4, and #5. Requiring these three ensures that students build hands on AI evaluation and usage skills rather than stopping at tool exposure or abstract discussion.
The Two-Tier Framework
Tier 1: AI-Skills Integrated
In AI-Skills Integrated courses, 15-49% of the course grade comes from work that covers any subject matter or assignment format, but within that work, critical use of AI tools is a required and evaluated component.
- For example, an in-class presentation on labor market trends worth 15% of the final course grade qualifies if students are required to use and critically evaluate AI tool(s) as part of completing the presentation.
Tier 2: AI-Skills Intensive
In AI-Skills Intensive courses, 50% or more of the course grade comes from work that covers any subject matter or assignment format, but within that work, critical use of AI tool(s) is a required and evaluated component.
- For example, a multi-stage research project on labor market trends worth 50% of the final course grade qualifies if students are required to use and critically evaluate AI tool(s) as part of completing the project.
| AI-Skills Integrated | AI-Skills Intensive | |
|---|---|---|
| Coursework involving AI-Skills |
15–49% of course grade | 50% or more of course grade |
| Required tools |
At least one AI tool, used critically in at least one graded component | At least one AI tool, used critically in at least one graded component |
| DOL content areas |
#3 Direct, #4 Evaluate, #5 Use Responsibly | #3 Direct, #4 Evaluate, #5 Use Responsibly |
| Scope |
Any course, any discipline | Any course, any discipline |
The Designation Process
Faculty apply during one of two annual windows aligned with registration, submitting a syllabus and the assignment(s) that make up the declared grade percentage. An in-house AI tool checks each submission against the rubric, and a Faculty Group on AI Attributes led by Dr. Monica Liu reviews each application, so the designation carries the same meaning university wide.
Approved courses receive the corresponding designation through the Registrar's existing workflows. Designations are granted per instructor, per section, per semester.
Leadership
Founding Faculty Lead
Associate Professor of Sociology, Department of Justice and Society Studies. Monica’s teaching and research dive into topics like artificial intelligence, workplace transformation and higher education reform. She focuses on the human side: how culture, incentives and trust shape whether AI succeeds in practice.
Founding Faculty Co-Lead
Associate Vice President, Academic Technology, AI Enablement and Innovation – Chief Academic Technology Officer. Jonathan leads the university's AI enablement work across governance, policy, curriculum, staff training, and faculty development. His focus is on moving AI from experiment to practice, building the structures that let faculty and students use these tools in ways that deepen learning rather than shortcut it. Jonathan also teaches in the MAIL and MACI graduate programs.
Origins & Launch
Across her courses, Monica Liu observed consistent student demand for AI skills alongside a gap: no institutional mechanism existed to identify which courses actually taught them. Students had no way to find those courses, and faculty had no shared standard for what counted as meaningful AI instruction. In summer 2025, Liu proposed a course labeling framework to close both gaps.
Before developing the idea further, she set out to establish whether the demand was real. Jonathan Keiser, Associate Vice President for AI Enablement and Innovation, joined as co-lead, and together they administered a campus wide survey of faculty and students. Of the 646 students who completed the survey, 79% wanted course listings to indicate whether a course teaches AI, 9% did not, and 12% were unsure.
With demand established, Liu and Keiser sought internal funding to build out the designation framework. They received a 2026 Faculty & Staff Innovation Fellowship from the Provost's Office and in Spring 2026 convened an eight-member committee spanning six colleges (see below) to design and test the rubric.
Framework Development Committee
- — Assistant Professor, Computer and Information Sciences
- — Director of Academic Affairs and Registrar, School of Law
- — Clinical Faculty, Susan S. Morrison School of Nursing
- — Professor, Educational Leadership
- — Associate Professor, Philosophy; Director, M.A. in AI Leadership
- — Associate Professor, Software Engineering & Data Science
- — Professor and Chair, Modern and Classical Languages
- — Distinguished Service Professor and AI Faculty Lead, Opus College of Business
For Peer Institutions
Colleges and universities exploring a similar designation are welcome to be in touch. We are happy to share the full rubric, the reasoning behind our design choices, and lessons from our launch.
Contact: Dr. Monica Liu mliu@stthomas.edu
How to cite this framework: Liu, M., & Keiser, J. (2026). AI-Skills Course Designation Framework. 91欧美视频 Minnesota. /ai-for-common-good-institute/ai-skills-course-designation/index.html
What Is the AI-Free Attribute?
While the AI-Skills designation recognizes courses that build fluency with these tools, 91欧美视频 also recognizes that some learning is best done without them. Our university offers a separate attribute to signal that a course is intentionally designed as a space for students to build and demonstrate skills (critical thinking, discernment, original analysis) without the aid of artificial intelligence tools. Faculty apply this where the learning happens precisely in the struggle: working through a problem, drafting an argument, or forming a judgment entirely on one's own strengths and skills that AI assistance can quietly erode if relied upon too early or too often. This attribute frames the absence of AI not as a restriction, but as the course’s distinct contribution to students’ growth as thinkers.
Note: The AI-Free attribute is automatically granted upon faculty request and is not part of the AI-Skills Course Designation Framework.