Labor-Based Contract Grading
This course uses a labor-based grading contract, meaning final grades are determined by the completion of clearly defined labor rather than subjective judgments of quality. Students will document their work throughout the semester based on the terms of the contract. At the start of the semester, students indicate the grade they intend to earn. This choice may be revisited and renegotiated mid-semester if desired. I provide private, formative feedback on the quality of your work privately. The grade itself, however, is based on completing the agreed-upon labor to the best of your ability, reflecting sustained effort, self-direction, and engagement as a motivated learner. Labor in this course includes completing blog posts and written reflections; recording short voice reflections that explain your process and thinking; actively engaging with AI tools and documenting your choices; producing a polished, high-quality podcast; consistently developing, revising, and refining your portfolio over time; and delivering a final presentation of your portfolio that articulates your growth, decisions, and digital identity.
Why This Course Grades Learning Differently
In many courses, grades become the primary focus. Students learn to optimize for points, interpret rubrics strategically, and work toward what they think an instructor wants. While this can produce polished results, it often discourages risk-taking, experimentation, and honest reflection, especially in creative and exploratory work.
I take a different approach. In this course, grading is designed to support learning as a process rather than to judge talent or polish. I focus on sustained effort, engagement, and growth over time. My goal is to create an environment where you can try ideas, revise openly, ask real questions, and learn from missteps without fear that one imperfect outcome will define your performance.
This approach is sometimes called labor-based grading. In practice, it means your grade reflects the work you do: showing up consistently, engaging seriously with the process, completing assignments thoughtfully, and revising over time. It shifts attention away from guessing how work will be evaluated and toward doing the work, reflecting on it, and improving it.
Grading this way allows me to spend less time policing points and more time coaching. I can focus on giving meaningful feedback, having conversations about your ideas and decisions, and supporting your development across the semester. The emphasis stays on learning rather than negotiation.
This philosophy aligns closely with the goals of the course. Crafting a digital identity, working creatively with emerging technologies, and learning to communicate your work clearly all require experimentation, iteration, and judgment. A grading approach that values process and engagement supports those goals more honestly than one centered on isolated, high-stakes evaluations.
Learning Through Doing and Reflection
This course is structured around practice rather than performance. Learning happens by making things, testing ideas, revising work, and reflecting on what changes along the way. Instead of aiming for a single “perfect” outcome, you’ll engage in an ongoing process of experimentation and improvement.
Reflection is a central part of that process. You’ll regularly pause to consider what you tried, what worked, what didn’t, and why. These moments of reflection help make learning visible, strengthen judgment, and connect individual projects into a larger sense of growth over time.
By treating learning as something that unfolds through action and reflection, the course creates space for curiosity, uncertainty, and development. Progress matters more than polish, and learning is understood as something you build through sustained engagement rather than something you demonstrate once.
Building, Revising, and Presenting Work in a Community of Practice
Equally important is learning how to present and articulate your work, not just digitally, but in person. You’ll practice explaining what you made, why you made certain choices, what changed along the way, and what you learned through the process. This ability to talk clearly and confidently about your work is a core outcome of the course and a skill that carries well beyond it.
Because learning is social, peer engagement matters. You’ll regularly interact with one another’s work, offer feedback, learn from different approaches, and contribute to a shared learning environment where experimentation is supported. Knowledge isn’t delivered fully formed. It’s constructed through doing, reflection, dialogue, and revision.
This community-centered, studio-based approach allows learning to deepen over time. It creates space for curiosity, risk-taking, and growth, while grounding creative digital work in human conversation, shared practice, and thoughtful presentation.
Working with AI as a Thinking Partner
This course treats artificial intelligence as a tool for thinking and creativity, not as a shortcut and not as a replacement for your judgment or voice. AI supports exploration, drafting, revision, and experimentation, while responsibility for meaning, interpretation, and final decisions always remains with you.
You’ll learn to work with AI intentionally by deciding when it makes sense to delegate a task and when human judgment matters most. That includes learning how to describe your goals clearly, ask better questions, set meaningful constraints, and guide tools toward purposeful outcomes rather than generic output.
Equally important is learning how to evaluate what AI produces. You’ll practice pausing before accepting results, questioning sources and assumptions, comparing outputs across contexts, and recognizing limitations such as bias, overconfidence, or the flattening of individual voice. The goal is not to produce faster work, but to produce more thoughtful work.
AI is introduced as part of the same learning-by-doing approach used throughout the course. You’ll experiment with tools, reflect on how they influence your thinking, and document your choices and revisions along the way. What matters isn’t whether you use AI, but how you use it and what you learn from the process.
By treating AI as a thinking partner rather than an answer machine, the course emphasizes discernment, responsibility, and agency. These habits of mind are essential for working creatively and ethically in a digital environment where AI tools are increasingly common but never neutral.
From Philosophy to Practice
This page outlines the learning philosophy and values that shape the course: how learning is approached and evaluated, and why it’s structured as a studio-based community of practice. These principles guide the design of assignments, the pacing of the semester, and the role of reflection, collaboration, and feedback throughout.
The syllabus translates this philosophy into practice. It provides the applied structure of the course, including specific expectations, timelines, and requirements, as well as the labor-based framework that organizes how work is completed and assessed. Together, the philosophy and the syllabus work in tandem: one articulates purpose and values, and the other puts them into action for day-to-day learning.
By separating these two pieces, the approach remains both intentional and transparent. The emphasis stays on learning, growth, and meaningful engagement, while the syllabus offers a clear, practical guide for how that learning unfolds across the semester.