Show students why the work matters, especially for the career or life outside the classroom that brought them to your course. When purpose is visible and personally relevant, students are more likely to engage with the work itself instead of treating it as a hoop to jump through.
Below are examples from UO faculty.
Arifa Raza-Bayona (Indigenous, Race, and Ethnic Studies)

The purpose section doesn't just gesture at relevance. It ties the assignment to nine specific, named career-ready skills, checked off directly against the assignment's actual tasks. Professor Raza says, "By working collaboratively, you will gain important transferable skills used in various careers from policy and law to other advocacy-based professions."
Design Moves
- Explicitly marks which UO Core Ed Skills the assignment develops — Critical Thinking, Career & Self-Development, Communication, Teamwork, Leadership, Equity & Inclusion, Technology, and others — rather than a general statement.
- Purpose section separately answers "why this topic," "why groups," and "why present," giving students three distinct reasons rather than one blanket rationale.
- Builds on the Purpose/Task/Criteria structure from the AI-Aware Transparent Assignment Template, so purpose is stated before task details, not folded into instructions.
Tanya Gupta (Chemistry)

Professor Gupta explicitly links class content to students' majors and career paths, and models responsible AI use directly in her syllabus rather than just describing it. She says, "AI is a great tool and the more we can expose ourselves and our students to it in a positive way, the better."
Design Moves
- Syllabus includes real, ready-to-use sample prompts for students, e.g. "I'm a freshman in college. Provide detailed information about electrochemistry at the level of a first-year college introduction."
- Explicitly ties class examples to student majors and career paths.
- Asynchronous sections require handwritten, show-your-work submissions.
- In-person exams are preceded by full-class collaborative practice exams.
Damian Radcliffe (School of Journalism and Communication)

Professor Radcliffe ties assignments directly to the career readiness expectations students will face after graduation, framing AI fluency itself as a professional competency rather than a shortcut. He says, "A key focus of my classes is career readiness. I would always contend that when students graduate, there will be an expectation that they will be AI literate, that they will understand the pros and cons of different AI platforms and tools, and that they will be able to use them and know when not to use them."
Design Moves
- Students choose their own media company or content creator to research for the "Show Me the Money" project, which raises buy-in over an assigned topic.
- Research process is modeled and critically evaluated in class before students conduct their own, so process is demonstrated rather than just stated.
- Assessment weights accurate, relevant citations, tying the grading criteria back to the real industry skill the assignment is meant to build.
Have a practice like this to share? If you've redesigned an assignment or adapted a teaching practice in response to GenAI, let us know at tep@uoregon.edu.