CS-31 | Tufts University | Fall 2026 · Tu · Th 12:00 PM – 1:15 PM
Tufts and CS-31 strive to create a learning environment that welcomes students of all backgrounds and abilities. It is expected that everyone in the course is treated with dignity and respect. We realize everyone comes from a different background with different experiences and abilities. Our knowledge will always be used to better everyone in the class.
If you feel uncomfortable or unwelcome for any reason, please talk to your instructor or a TA so we can work to make things better. If you feel uncomfortable talking to members of the teaching staff, consider reaching out to your academic advisor, the department chair, or your dean.
We recognize that in some circumstances, it is not possible to meet an assignment deadline. In these circumstances, you can utilize the late token system.
You have 9 late tokens for this course, which can be used on labs and project milestones. A late token grants you a 24-hour extension on an assignment, and requires no action on your part; we will automatically deduct late tokens based on the date and time of your submission.
If you feel that you need more tokens, or feel that you are falling behind, you must send a request to the instructor (either in person, by email, or on Piazza) so we can have a longer conversation.
Our ultimate goal is for each student to fully understand the course material.
For quizzes, all work must be done individually, with no collaboration with others whatsoever.
For labs, we have the following policy for student work.
You are allowed to work in groups for the lab. You must write anything that will be turned in -- all code and all written solutions -- on your own.
Within your groups, we encourage a high amount of interaction. For understanding code, draw things on whiteboards, pass notes and code snippets to each other, whatever helps you understand! However remember, YOU must understand the code YOU turn in. For written and reflection questions, your answers and the words you write should be your own.
Outside of your group, you can interact at a high level. You may verbally discuss assignments with others in the class, but you cannot share solutions or code. You may work out solutions together on whiteboards, laptops, or other media, but you are not allowed to take away any written or electronic information from joint work sessions with others. No notes, no diagrams, and no code. Emails, text messages, and other forms of virtual communication also constitute "notes" and should not be used for preparing solutions.
When preparing your solutions, you may always consult textbooks, materials on the course website, or existing content on the web for general background knowledge. However, you cannot ask for answers through any question answering websites such as (but not limited to) Quora, StackOverflow, etc. If you see any material having the same problem and providing a solution, you cannot check or copy the solution provided. If general-purpose material was helpful to you, you must cite it in your Collaboration Statement (see below).
We are not barring the use of Generative AI tools, but we advise caution in using them. While they can be helpful in some respects, these resources are susceptible to "hallucinations" and subtle errors in reasoning and calculation and may mislead you given their authoritative tone.
Much like discovering existing content using the web, you can NOT use generative AI tools to ask for answers. You are NOT allowed to upload any of the course materials (PDFs, videos, etc) into an online service unless it is explicitly stated that you are allowed to do so.
For the labs, unless stated otherwise, any code that you submit must be directly authored by you (no "vibe coding"). You should write every word of any free-response questions yourself (no AI-assisted writing). If you use a Generative AI tool to help you, clarify content, or ask questions - even if you don't use any of its output in your answers - you must disclose all steps that involve AI assistance.
See the Final Project page for rules on Generative AI for the final project.
Good:
Understanding a general method, algorithm, or approach.
Student: "In bayes nets, why does conditioning on a common effect make the parents dependent? That seems backwards."
Agent: [full explanation, worked toy example, follow up question to test understanding]
Understanding error messages.
Student: "PuLP returns
Infeasible. What does that mean?"Agent: "
Infeasibleis the result of the solver, not an error. It means..."
Improving the readability of correct code that you've already written. This is for your own understanding, submit your original code for your lab submission.
Student: "This code I wrote solved the problem, passed test cases, and I finished the lab. Any tips for next time on how I could write this simpler next time I do something like this?"
Agent: "Sure. You may want to decompose this function..."
Use Caution:
These types of questions begin to overlap with the goals of the course. If the LLM starts to give solution code, or give you actual concrete steps on what to do, you should stop interacting with it and start a fresh chat.
Provide high-level guidance on problem formulation.
Student: "How could I write an objective function that makes the distribution more egalitarian?"
Agent: "Two questions instead: what does 'egalitarian' mean as a number you could maximize or minimize? And if you wrote it down, which single quantity across all the regions would the solver be pushing on?"
Student: "How do I say 'each nurse works at most one shift per day' in CP-SAT?"
Agent: CP-SAT's constraint methods live under
CpModel— scan the reference for constraints that take a list of boolean variables and bound how many can be true. Which one looks like it matches 'at most one'?"
Bad:
Asking an LLM to write code for you.
Student: "Solve this. [Pasted question]"
Agent: "[Solved problem with code]"
Asking an LLM to fix code for you.
Student: "My code isn't working. Help! [Pasted broken code]"
Agent: "You have an error on line 12. Here's the full fix..."
Generating the interpretation of results.
Student: "Here are the outputs of the solver. What does this mean? [Pasted output of solver]"
Agent: "Interpreting this output, the output seems to imply that..."
TL;DR - If you ever get the feeling "I think this is giving away the solution to the problem", stop interacting with the model and start a new chat. When in doubt, contact the course staff, post on Piazza, or go to office hours.
Remember: The goal is to learn by doing, not by watching an AI generate solutions! You are responsible for everything that you hand in. You should understand it and be able to answer questions about it, if asked. We will not hesitate to refer cases we suspect violate this policy to the appropriate disciplinary authorities at Tufts.
Along with all submitted work, you will fill out a short form declaring the names of any others you got help from (including TAs), and in what way you worked with them (discussed ideas, debugged math). Turning in this form will certify your compliance with this policy.
This course will strictly follow the Academic Integrity Policy of Tufts University. Students are expected to finish course work independently when instructed, and to acknowledge all collaborators appropriately when group work is allowed. Submitted work should truthfully represent the time and effort applied.
Please refer to the Academic Integrity Policy at the following URL: https://students.tufts.edu/community-standards/get-help/academic-integrity
Tufts is committed to providing equal access and support to all qualified students through the provision of reasonable accommodations. If you have a disability that requires reasonable accommodations, contact the StAAR Center at StaarCenter@tufts.edu or 617-627-4539. Please be aware that accommodations cannot be enacted retroactively, making timeliness a critical aspect for their provision.
Please see the detailed accessibility policy at the following URL: https://students.tufts.edu/health-wellness/health-service/our-services/support-services-and-policies
Tufts University faculty, staff, and administration highly value and acknowledge the religious diversity of its student body. Students seeking religious accommodations related to their holy days are encouraged to collaborate with faculty to make arrangements during the first week of each semester. Consult the Multifaith Calendar for upcoming holidays, the University Religious Accommodations Policy, and members of the University Chaplaincy who are available to respond to questions on religious observances.
The StAAR Center offers a variety of FREE resources to all students. Students may make an appointment to work on any writing-related project or assignment, attend subject tutoring in a variety of disciplines, or meet with an academic coach to hone skills like time management and navigating procrastination. Students can make an appointment for any of these services by visiting https://students.tufts.edu/staar-center.
As a student, there may be times when personal stressors or difficulties interfere with your academic performance or well-being. The Dean of Student Affairs Office offers support and care to undergraduates and graduate students who are experiencing difficulties, and can also aid faculty in their work with students.
In addition, through Tufts' Counseling and Mental Health Service (CMHS) students can access mental health support 24/7, and they can provide information on additional resources. CMHS also provides confidential consultation, brief counseling, and urgent care at no cost for all Tufts undergraduates as well as for graduate students who have paid the student health fee.
To make an appointment, call 617-627-3360.
Please visit the CMHS website: http://go.tufts.edu/Counseling to learn more about their services and resources.
Tufts' anonymous and confidential student-run hotline. Open every night 7pm-7am. Call: 617-627-3888. Text: ears4peers.up.railway.app