draft syllabus statement on code, plagiarism, and generative AI

I’m spending a chunk of today starting on revisions to my Intro to Data Science course for my unit’s LIS and ICT graduate prograrms. I’d expected to spend most of the time shuffling around the content and assessment for particular weeks, but I quickly realized that I was going to need to update what I had to say in the syllabus about plagiarism and academic offenses. Last year’s offering of the course involved a case of potential plagiarism, so I wanted to include more explicit instruction that encourages students to borrow code while making it clear that there are right and wrong ways of doing so. Likewise, my colleague Josh Rosenberg posted earlier today about ChatGPT’s Code Interpreter, and while I don’t know that my students will even know that’s an option, I wanted to get out ahead of that possibility, too.

I need to get back to the shuffling around of content and assessment, so I don’t know that what I’ve written is what my Fall 2023 students will see, but I thought I’d put my draft statement out there for anyone who wants to give feedback on it or take inspiration from it:

It is common practice in data science and programming communities to borrow code from other, more knowledgeable programmers. Indeed, many of the weekly activities in this class will explicitly involve copying or adapting code from our textbook, and you might find online or other sources helpful for figuring out how to complete a specific task for your class projects. When done properly, this is not plagiarism—in fact, it is good practice in data science.

Nonetheless, you are ultimately responsible for completing assessments, and plagiarism remains a serious concern for this course. Thus, these rules related to academic offenses are also in effect for this course, and I will not tolerate their violation. If you consult other sources, please ensure that they support (rather than replace) your personal work, effort, initiative, and understanding. It is your responsibility to ensure that you understand what plagiarism is and how to avoid it; when in doubt, reach out to me with your questions.

Along these lines, I strongly discourage you from using any generative AI tool to write code or text for you. AI-generated output can include errors, and as a general rule, if you know enough to catch those errors, you know enough to generate that output yourself; conversely, generating that output yourself will help you further develop your knowledge more than relying on a tool. If you do use any generative AI in completing your work, you must explicitly acknowledge it in your submission—and you will assume responsibility for any errors the tool makes.

• macro
• Work
• programming
• plagiarism
• academic offenses
• generative AI
• data science
• ICT 661
• Josh Rosenberg
• ChatGPT