Currently, Fort Lewis College does not have a single institution-wide policy governing artificial intelligence use. Instead, faculty are encouraged to develop course-specific policies that reflect disciplinary norms, learning goals, and expectations for student work.

These policies should clearly communicate when AI tools are permitted, restricted, or prohibited, and should be included in course syllabi and reinforced through classroom discussions. Transparent expectations help students make informed decisions about AI use.

Faculty approaches vary widely across disciplines. Some instructors encourage AI use for brainstorming, drafting, or coding support, while others restrict it to preserve specific learning outcomes such as writing fluency or problem-solving skills.

Professional organizations in fields like writing, computing, and education are developing guidance that emphasizes transparency, ethical use, and alignment with disciplinary practices. Reviewing statements from organizations in your field can help inform your own policies.

AI-detection tools are currently unreliable and can produce both false positives and false negatives. Research and guidance from teaching and learning organizations recommend against relying solely on detection tools for academic integrity decisions.

Instead, faculty are encouraged to design assignments that prioritize process, reflection, and original thinking, making inappropriate AI use more difficult and easier to identify through context.

When AI use violates a clearly stated course policy, it should be treated similarly to other academic integrity violations. Documentation should include the assignment, the suspected misuse, and how it conflicts with expectations set in the syllabus.

Follow existing institutional procedures for reporting violations, and, when appropriate, discuss the issue with the student as part of a learning-centered approach.

Students benefit from developing fluency with widely used AI tools such as text generators, research assistants, and discipline-specific platforms. More importantly, they should learn how to evaluate outputs, recognize limitations, and use tools ethically.

Critical thinking, prompt design, and responsible use are key transferable skills that will remain relevant even as specific tools change.

Citation practices for AI are evolving, but most guidelines emphasize transparency. Students should clearly indicate when AI tools contributed to their work, including the tool name, date, and nature of the assistance.

Different style guides (MLA, APA, Chicago) offer emerging recommendations, so faculty may wish to provide specific citation expectations within their courses.

Many AI tools collect user inputs to improve their systems, though policies vary by platform. Some tools allow users to opt out of data collection or provide enterprise-level privacy protections.

Faculty and students should review privacy policies carefully and avoid sharing sensitive or confidential information when using AI tools.

Users can reduce exposure to AI-generated content by adjusting search engine settings, using browser extensions that filter AI-generated results, or prioritizing trusted academic sources.

Developing strong information literacy practices—such as evaluating sources and cross-checking information—remains essential.