Sample Policy on Academic Integrity

Academic integrity means taking pride in your work, accepting responsibility for your work, and meeting community standards for rigor, quality, and assigning credit (citing sources). Everything you submit in this class should meet these criteria.

Here’s why it matters:

1. Enhanced Learning: Taking ownership and responsibility for your work means if you don’t understand something, ask questions, read documentation, tweak things, and dive deeper. You’ll learn much more if you do!
2. Professional Preparation: Practicing good ethics now sets the foundation for professional integrity in your future careers.
3. Accurate Assessment: In order to teach effectively, I need to know how well everyone is doing in the course. Effective assessment requires honesty and transparency.

It’s ok to say “I don’t know” or “I couldn’t get this to work”! Knowing what you don’t know is a skill that takes practice. Asking questions is also a practiced skill. Practice them!

More information about RIT’s policies can be found here: https://www.rit.edu/ academicintegrity/, and information about the CS department’s specific policies can be found here: https://www.cs.rit.edu/SemesterConversion/common.html

Collaboration Policy

You are encouraged to discuss and share ideas. Verbal exchange of implementation details is acceptable, but direct copying or sharing of non-volatile information is against the rules. Learn by doing, not by copying!

This policy is an extension of the academic integrity policy above. Your work should be your own. This helps you learn.

Using Generative AI is a bit of a gray-area and will be handled on a case-by-case basis. Repeated misuse of Generative AI tools in ways that are "disallowed" by the stated course policy on generative AI may be treated as an academic integrity violation. When in doubt, ask!

Shared by Richard Lange from Computer Science.