Course systems
- This website contains the course overview, schedule, readings, project guide, and policies.
- Canvas is the official source of truth for deliverable due dates, submissions, and grading. If another source lists a different date or time, follow Canvas.
- Ed Discussion and Slack serve different purposes. Course announcements appear in the announcements thread on Ed Discussion, and course Q&A also takes place on Ed. Private Slack channels are reserved for project mentoring and will be created after four-person teams and mentors are assigned, by the end of Week 3. A deadline change is official only when it has also been reflected in Canvas.
Questions about an individual grade, an accommodation, health, or a team conflict should be discussed privately, not on Ed Discussion. See Help for communication norms.
Presence and participation
Class meetings combine discussion, critique, and hands-on project work that cannot be reproduced fully outside the room. Participation may include contributing to discussion, listening and responding thoughtfully, giving useful peer feedback, completing in-class checks, and engaging in workshop or project work.
Two drops, no routine makeups
Each class session has participation points. Your two lowest session scores are dropped automatically when we calculate the participation portion of your grade. An absence receives zero points for that session and will ordinarily become one of your drops. You do not need to explain or document either dropped absence, and there are no routine makeup assignments.
The passing threshold is calculated before drops. To pass the course, you must earn at least 50% of the raw participation points available across all class sessions. After we check that threshold, we drop your two lowest session scores to calculate the participation component of your course grade.
Please do not attend when you are ill. Students with approved attendance or participation accommodations should share their accommodation letter and discuss implementation with the teaching team.
Devices and course materials
Laptops are expected during build sessions. During discussions or guest conversations, we may ask everyone to close laptops or silence notifications so the room can focus. Selected lecture materials may be shared after class, but project critiques, guest conversations, and workshops ordinarily will not be recorded.
Deadlines and extensions
Deliverables are generally due at noon on the day of class; always confirm the exact date and time in Canvas.
- Each student has one no-questions-asked 48-hour extension for an individual deliverable.
- Each team has one shared no-questions-asked 48-hour extension that may be used for Gate 1 or Gate 2. Notify the teaching team before the deadline.
- The shared extension does not move Demo Day. If an emergency prevents a student or team from presenting, contact the teaching team as soon as possible to arrange an alternative.
- After an available extension is used, each additional late day incurs a 10% penalty.
These flexibilities cover ordinary disruptions. Disability-related accommodations and exceptional circumstances are handled separately.
Using AI in this course
AI tools are part of the subject and practice of MS&E 203. You may use them for coding, debugging, design, research synthesis, evaluation, and revision. Four expectations apply to every deliverable:
- Disclose material use. Briefly name the tools you used and what they contributed. You do not need to log every autocomplete.
- Verify outputs. You are responsible for the behavior of your system and for the accuracy of its code, claims, citations, and results.
- Understand your decisions. You must be able to explain and defend the work you claim, even when an AI wrote the first draft.
- Do not fabricate evidence. Do not present AI-generated interviews, users, ratings, citations, or evaluation results as observations from real people or systems.
Access to course tools
Stanford provides university access to OpenAI ChatGPT Edu and Anthropic Claude for Education. The standard Stanford-provided access is sufficient for required course work. Follow Stanford UIT’s access instructions to activate these tools through your Stanford account.
A higher-tier subscription is preferred for sustained build work and is available to Stanford students at a substantial discount, but it remains optional. Projects should work within the course-supported usage limits, and grades do not depend on buying more compute or paid services.
Responsible product development
The fact that a system can be built does not mean it is appropriate to deploy. Course projects should use low-risk data by default, even when a particular Stanford service permits more sensitive data.
- Never place API keys, passwords, access tokens, private interview data, student records, protected health information, or other sensitive information in a repository, on Ed Discussion, or in a private team Slack channel.
- Obtain informed permission before recording, interviewing, or testing with people. Make clear when a person is interacting with an AI system.
- Ask the teaching team before collecting non-public or sensitive data, operating in a consequential setting, scraping an external community, or connecting an agent to a real account.
- Agents that can send messages, make purchases, delete or modify data, moderate people, or take other consequential actions require advance approval and must begin with least privilege, spending or rate limits, and human confirmation.
- Respect platform terms, community rules, licenses, and the rights of people whose content or data you use.
If a credential or private dataset is exposed, disable access first and tell the teaching team promptly. Responsible disclosure will help us contain the problem and learn from it.
Privacy, recording, and public work
Do not audio- or video-record class meetings, guest speakers, user interviews, or project critiques without permission. Do not redistribute classmates’ Ed posts, team Slack messages, project ideas, prototypes, or feedback outside the course.
Demo Day is public. The expected audience is the class and invited guests, but anyone may attend. Present only information your team is authorized to disclose, and use synthetic or de-identified demonstration data when appropriate. Audience members may not record or redistribute a presentation without the team’s permission. Any official recording will be opt-in by team.
Project repositories are private by default. They must remain private during the quarter and be accessible to every team member and the teaching team. After the final submission, a team may make its repository public only with the unanimous agreement of all members. Before release, remove credentials and private data, confirm publication rights for code, data, and assets, and document external and AI-generated contributions. If any member prefers that the repository remain private, teammates may describe the project in portfolios but may not publish shared code or data without permission.
Academic integrity
All coursework is subject to the Stanford Honor Code. Collaboration within a project team and the responsible use of AI are permitted and encouraged. Misrepresenting another person’s work as your own, concealing material assistance, fabricating evidence, or claiming contributions you cannot explain is not permitted.
When in doubt about whether a form of collaboration, reuse, or AI assistance is allowed, ask before submitting.
Access and accommodations
Stanford University is committed to providing an inclusive and accessible educational environment for all students. Students seeking disability-related accommodations should contact the Office of Accessible Education (OAE) as early as possible to initiate the accommodation process. Once accommodations are approved, students should provide their accommodation letter to the instructor and discuss implementation within the course. Early communication supports timely access to course materials, instruction, and assessments. For more information, please visit: oae.stanford.edu.
Because participation, team work, and Demo Day are central to the course, please discuss implementation with us early. Students anticipating university-recognized obligations should also contact the teaching team as soon as possible.
Diversity statement
It is our intent that students from all backgrounds and perspectives be well served by this course, that students’ learning needs be addressed both in and out of class, and that the diversity that students bring to this class be viewed as a resource, strength, and benefit. We aim to present materials and conduct activities in ways that are respectful of this diversity. Your suggestions are encouraged and appreciated. Please let us know if you have ideas to improve the effectiveness of the course for you personally or for other students or student groups.