I view learning and teaching as partnership between you and me. I firmly believe in a growth mindset and therefore set high expectations because I know that you can meet and exceed them — and I will support you in any way I can to help you achieve your goals. Academic integrity allows us to work together on these goals because it establishes a framework of mutual trust and honesty. At its core, academic integrity means to present only your own work as yours and to acknowledge any contributions made by others, including by generative AI. Academic integrity helps you trust your abilities, demonstrate your own accomplishments, and be responsible for your own learning. In practical terms, academic integrity means to make sure that all submitted assignments represent your own original work and to indicate clearly where you used materials other than those distributed through Canvas or the discussion forum. Please also review Brown University's Academic Code and contact me if you have any questions about how the academic code applies to this class.
Generative AI can be helpful in supporting your learning in this class, and I encourage you to explore ways in which this technology might benefit you. I believe that it is important to use AI in ways that help you understand the concepts, methods, and applications we cover in this course and not in ways that substitute or replace understanding. One of my goals for this course is to equip you with approaches and methods to design differential-equation models, analyze them, and interpret their solutions. AI can help you achieve these goals. Please note that you cannot use AI during the midterms, and it is therefore important that you are familiar with and understand the concepts, methods, model designs, and theoretical foundations from class. The second goal is to help you engage in critical thinking, develop analytical-reasoning skills, and acquire problem-solving strategies that are so valuable beyond the course and your time at Brown. Again, AI can help you with these goals, but it cannot replace your own deep and critical engagement with the problem sets and class material. My problem sets are designed to align with these two goals so that you can engage meaningfully with modeling and the mathematics behind ODEs, whilst also developing critical-thinking and problem-solving skills. Here is how you can use generative AI in this course:
Weekly assignments: You can use AI in all assignments. If you do use AI, you need to disclose its use and reflect on how you used and incorporated it into the solutions you submit. Since you will not be able to use AI for the midterms, I encourage you to tackle each assignment first yourself and utilize AI only to check or supplement the solutions you found.
Final group project: Your team can use AI to brainstorm ideas and get additional insight into how to approach and structure your project. You can also use AI to help with programming and with formatting equations for your report. Your team cannot use AI to help with the actual writing of the report.
You can, and are strongly encouraged to, collaborate on assignments and problem sets. All members of a study group should contribute to the solutions, and assignments must be written up separately and individually.
Late submissions of assignments can create unfair situations to others in the class and make it harder for my TAs and me to give timely feedback. Hence, we will generally not give credit for late work unless in the case of emergencies or illnesses. If you encounter circumstances that make it hard for you to complete assignments in time or keep up with the course material, please reach out to me as soon as possible, so that we can work together on a plan.
Please use the discussion forum (instead of email) to ask questions about the class (assignments, exams, lectures, logistics, ...) as this makes the answers available to everyone in the class, and allows everybody to add to our answers. Please read the rubrics and the posted solutions carefully before submitting regrade requests through Gradescope – thank you!