HMAN 0900V: Family Studies

Professor Jessaca Leinaweaver

Students are expected not to use GenAI tools for any work in this course, apart from those tools which are so embedded in the applications or systems they use (such as spellcheck or google search) that they cannot be avoided. Optional embedded AI Assistants should not be used for core learning activities and assignments (e.g., revising documents, summarizing readings or conducting literature searches).

ENGN 1860: Advanced Fluid Mechanics

Professor Kenny Breuer

Using AI is not discouraged, but should be used with caution. There are a few concerns:  

1. Using AI as an additional source to help you understand the material or to help you prepare for an exam is great, but sometimes (often) AI gets it wrong, so make sure that you check the AI for accuracy.
2. Using AI to proof-read or to format a document is fine, but using AI to generate content for an assignment is terrible for your learning, and is strictly prohibited.
3. If you use AI, you must include either a specific citation (as you would cite a paper or textbook) or document your usage (prompt history etc) with a footnote or appendix for more extensive uses.

IMPORTANT: Be careful that you understand what the AI tells you - learning requires doing, and relying on the AI can remove the “doing” part. Make sure you maintain the “human-in-the-loop”

You are personally responsible for everything you submit.  If asked, you must be able to defend and explain your work in a verbal face-to-face conversation.  People may be called on in class or in private to present and/or explain their submissions.

LANG 2900: Seminar in Language Pedagogy and Second Language Acquisition

Associate Teaching Professor Jeremy Lehnen and Assistant Teaching Professor Katherine Kang

Many students are exploring ways to enhance, supplement, or speed up their learning with GenAI tools. However, inappropriate use of GenAI can undermine the learning process and limit opportunities for meaningful feedback and assessment. In this course, permitted GenAI use will vary by assignment. While GenAI may enhance learning when used appropriately at specific stages of the learning process, its use should always support, not replace, your own intellectual work. Assignment-specific guidelines will be provided by the professor or developed in dialogue with students, who may also propose appropriate uses of GenAI. Representing AI-generated content as your own original work is prohibited under Brown’s Academic Code. If you are unsure whether a particular use of GenAI is permitted in accordance with university or course policy, please consult the professor.

DSIO 2000: Technical Foundation for Data Science Success

Associate Teaching Professor Linda Clark

The emergence of AI tools presents both exciting opportunities and important considerations for academic work. Our approach to AI usage in this course is aligned with the online Data Science Program AI Philosophy, and is designed both to promote academic integrity and to foster critical thinking. All AI usage in this course must align with the policies defined in the academic code and the student code.

By enrolling in this course you have established the goal of learning the basic foundational skills of data science. Given this intention, LLMs can do the math and coding assignments, but instead of you learning the material, the LLM will be doing the material. Additionally, in the frequent situations where the LLM code does not work, you will need to figure out how to debug your code. If you have used a code generator to generate your code and do not understand how to code, you will not learn the skills necessary to know how to debug your code.

If you ask for coding help and you cannot explain your code, the course staff will not be able to assist you.  In other words, course staff will not debug LLM code. 

Therefore, depending on what you want to get out of this course, you may choose to use the assistance of LLMs or not. Note that you must attribute the authorship of the work to the AI tool if you choose to use it. Situations where there is no attribution may result in conduct-related offenses of Academic Integrity. For assistance in citation, please see the Brown University Library guide. 

A clear AI policy is a strong start to helping students understand your expectations. Communicating that policy is the next critical step. Students are receiving many messages from each of their instructors. They pay the most attention to those messages instructors communicate in multiple modalities and when instructors explain why they matter. In the fall 2025 GAITL community feedback form, many Brown faculty reported discussing their AI policy on the first day of class and including it in their assignment instructions. Some also emphasized the importance of discussing their policy throughout the term.

When students hear instructors’ AI policies, some wonder if their instructors really understand how they are using AI tools or whether the policy is based on the instructor’s personal stance toward AI and not considering their own positions. The underlying question many students are asking is why are certain or all AI uses prohibited, why are some allowed? When instructors explain their policy in terms of the kinds of learning they are trying to promote and assess in their courses or the values that guide them as instructors, students are focused on what really matters and also better able to understand why policies vary from one course to another. While your “why” may not appear in the syllabus statement, communicating the “why” when discussing your AI policy and specific assignments is a powerful way to build trust with students in your course.

The AI Policy Explorer is a basic web tool and does not utilize GenAI tools. However, GenAI tools were used to create the tool. Gemini 3.6 (Thinking mode) was used to generate 114 course-level learning outcomes for undergraduate and graduate level courses in the arts, humanities, languages, life sciences, physical sciences and social sciences with additional focus on writing intensive, advanced reading, problem-solving, and quantitative reasoning courses. After reviewing outcomes and making revisions to generalize some statements, Gemini was directed to generate a limited list of prohibited uses of GenAI tools based on the rationale that the uses would undermine student learning or make it impossible to assess student learning. After the committee reviewed the 15 prohibitions generated using Gemini, three additional prohibitions were added including a simple “No AI” policy. Gemini was then directed to review the learning outcomes again and generate a list of GenAI uses that could support student learning and enhance accessibility for a diverse range of learners. This resulted in 19 allowed use cases. For design and programming, Claude Code was used to create the web tool through an iterative process.