Teaching & Learning
- Hamilton College's LITS Generative AI Guidelines indicate that AI use in coursework and assignments is deferred to individual instructors
- Students must follow the AI use policy stated in each course syllabus, and unauthorized use of generative AI on assignments may constitute a violation of the Honor Code
Hamilton College's approach places responsibility on faculty to define acceptable AI use within their courses. Students are expected to adhere to instructor-specified policies regarding generative AI in assignments and coursework, and violations may be treated as academic integrity breaches under the Honor Code.
- Hamilton College's Honor Code and generative AI guidelines govern AI use in examinations and assessments
- Use of generative AI tools during assessed work is deferred to instructor-defined policies, and unauthorized use in assessed contexts is treated as an Honor Code violation
AI use in examinations and assessments is governed by the Honor Code. Instructors set the terms for permissible tool use; any unauthorized use of generative AI during assessments may be considered academic dishonesty.
- Hamilton College's LITS Tech Lab provides AI tools, tutorials, and services to support learning and study
- The college encourages informed and critical use of AI for academic assistance while directing students to LITS resources for guidance on responsible use
LITS offers curated AI tools, hands-on tutorials, and consultation services to help students use generative AI for learning. The library guide supplements these resources with research and study guidance.
- Hamilton College's LITS Generative AI Guidelines and Tech Lab resources treat AI coding tools as part of the broader generative AI framework
- Use of code generation tools in coursework is deferred to individual course policies set by instructors and remains subject to Honor Code compliance
AI tools, including those capable of code generation, are covered under LITS's generative AI framework. Use of such tools in programming assignments is governed by individual course policies set by instructors.
Research
- Hamilton College's AI editorial guidelines and LITS generative AI policies provide guidance on AI use in written content preparation
- Researchers and writers are advised to follow attribution and disclosure norms when using generative AI in manuscript or editorial preparation, consistent with academic integrity expectations
Hamilton has issued AI guidelines for editorial content that address appropriate use of generative AI in writing. These guidelines emphasize accuracy, transparency, and proper attribution when AI tools assist in content or manuscript preparation.
- Hamilton College's research misconduct policy and IRB framework provide the integrity standards governing research data and analysis
- While explicit AI-specific data analysis guidance appears limited, the research misconduct policy prohibits falsification or fabrication of data regardless of the tools used, so AI-assisted analysis is subject to those standards
Hamilton's research integrity framework requires that all data collection, analysis, and reporting meet standards of accuracy and honesty. AI tools used in data analysis are implicitly subject to these research misconduct provisions.
- Hamilton College's research misconduct policy and IRB procedures establish the ethical framework for research integrity, which extends to AI-assisted research
- The LITS generative AI guidelines reinforce that AI use in research must comply with honesty, accuracy, and ethical standards; misrepresentation of AI-generated content as original research may constitute research misconduct
Hamilton's research ethics policies require integrity in all stages of research. The generative AI guidelines extend these principles to AI use by requiring transparency and accuracy when AI tools are used in research contexts.
Academic Integrity
- Hamilton College's generative AI guidelines require disclosure of AI tool use when it is required by an instructor or where AI has meaningfully contributed to work
- Attribution expectations align with the Honor Code's commitment to academic honesty, and failure to disclose AI assistance when required may constitute a violation
Hamilton's AI guidelines emphasize transparency and honest attribution. Students are expected to acknowledge AI assistance in their work in accordance with instructor requirements and Honor Code standards; undisclosed AI use may be treated as academic dishonesty.
- Hamilton College enforces academic integrity violations, including unauthorized AI use, through its Honor Code procedures
- Suspected AI-related academic dishonesty is handled through the college's established academic integrity adjudication process and may result in disciplinary consequences
Violations of AI use policies, where defined by instructors or college policy, are adjudicated through Hamilton's Honor Code process. The college's academic integrity framework provides the enforcement mechanism for unauthorized or undisclosed AI use in academic work.
Institutional & Administrative
- Hamilton College's LITS Generative AI Guidelines provide faculty and staff with a framework for using AI tools in professional and pedagogical work
- Faculty are encouraged to engage critically with AI tools, define clear AI use policies in their syllabi, and consult LITS resources for support, while staff producing institutional content are guided by the editorial AI guidelines
Hamilton's LITS encourages faculty to develop course-specific AI policies and provides support through the Tech Lab. Staff are guided by editorial AI guidelines when using generative AI for institutional communications and content creation.
- Hamilton College's Appropriate Use of Information Technology Resources policy governs the use of institutional data with external tools, including AI platforms
- Community members are expected to protect sensitive institutional and personal data when using generative AI tools, and the LITS AI tools page curates recommended platforms to support compliance with data protection standards
Hamilton's IT appropriate use policy sets data protection expectations for all technology use. LITS provides a curated list of AI tools to guide community members toward platforms that meet institutional data privacy and security requirements.
- The college uses a distributed governance approach in which LITS provides institutional AI guidance while academic departments and individual faculty retain autonomy over course-level AI policies
- Hamilton College's AI governance is coordinated through LITS, which has published generative AI guidelines and established an AI Tech Lab with tools, tutorials, and services for the campus community
LITS serves as Hamilton's primary institutional body for AI governance, having developed generative AI guidelines and a Tech Lab dedicated to AI resources. The college's approach balances institutional guidance from LITS with faculty autonomy to set course-specific AI policies.
DocuMark: Responsible AI Use for Academic Integrity
Knowing your institution's AI policy is step one. DocuMark helps enforce it fairly by empowering universities to manage AI-generated content, prevent cheating, and support student writing through responsible AI use.
Common Questions About Hamilton College's AI Policies
Hamilton College has defined AI policies in 12 of 12 categories, with an overall coverage score of 100%.
Hamilton College's generative AI guidelines require disclosure of AI tool use when it is required by an instructor or where AI has meaningfully contributed to work. Attribution expectations align with the Honor Code's commitment to academic honesty, and failure to disclose AI assistance when required may constitute a violation.
Hamilton College enforces academic integrity violations, including unauthorized AI use, through its Honor Code procedures. Suspected AI-related academic dishonesty is handled through the college's established academic integrity adjudication process and may result in disciplinary consequences.
Hamilton College's Appropriate Use of Information Technology Resources policy governs the use of institutional data with external tools, including AI platforms. Community members are expected to protect sensitive institutional and personal data when using generative AI tools, and the LITS AI tools page curates recommended platforms to support compliance with data protection standards.