Teaching & Learning
- Using generative AI in ways that misrepresents someone else’s work as the student’s own may be treated as academic dishonesty
- The university also provides instructional guidance indicating instructors should explicitly communicate expectations for AI use in the syllabus and assignments
- The university treats generative AI use in graded coursework as a matter governed by academic integrity rules and course-specific expectations; students are responsible for ensuring their work complies with instructor directions and academic integrity standards
Academic dishonesty includes but is not limited to: ""Cheating" (academic dishonesty) includes but is not limited to: (1) plagiarism; (2) fraudulence; (3) collusion; (4) falsification; and (5) any other actions that violate the standards of academic integrity."
"Plagiarism is the appropriation of another's work and the representation of it as one's own."
"The course syllabus will include ... course-specific policies (e.g., attendance; academic honesty; plagiarism; …)."
"Instructors should make expectations clear to students about whether, when, and how generative AI tools may be used in their courses and on assignments."
- Instructors are directed to clearly communicate whether and how AI tools may be used, which would apply to quizzes, tests, and other assessments when specified
- The university does not define a single campus-wide generative AI rule specific to exams in the provided sources; instead, exam and assessment rules fall under academic integrity expectations and instructor/course policies
"Cheating" (academic dishonesty) includes but is not limited to: (1) plagiarism; (2) fraudulence; (3) collusion; (4) falsification; and (5) any other actions that violate the standards of academic integrity."
"The course syllabus will include ... course-specific policies (e.g., attendance; academic honesty; plagiarism; …)."
"Instructors should make expectations clear to students about whether, when, and how generative AI tools may be used in their courses and on assignments."
- It does not establish a universal rule that permits or prohibits using AI for personal study, but frames responsible use around verifying outputs and following course/instructor expectations
- The university provides general guidance encouraging responsible use of generative AI as a tool that can support learning, while emphasizing accuracy limitations and the need for human judgment
"Generative AI tools can be useful for brainstorming, studying, and getting feedback, but they can also produce incorrect, biased, or fabricated information."
"You are responsible for any content you submit or share, including content generated with AI tools."
- The university does not define a programming-specific generative AI rule in the provided sources
- Use of AI code-generation tools in programming coursework is governed by academic integrity standards and by instructor/course-specific policies that should be communicated in the syllabus and assignment instructions
"The course syllabus will include ... course-specific policies (e.g., attendance; academic honesty; plagiarism; …)."
"Instructors should make expectations clear to students about whether, when, and how generative AI tools may be used in their courses and on assignments."
"Plagiarism is the appropriation of another's work and the representation of it as one's own."
Research
- The university does not define a research-manuscript-specific generative AI rule in the provided sources
- However, it emphasizes responsible use and that individuals are accountable for content they produce and share, and it separately maintains research misconduct standards that would apply if AI use contributed to fabrication, falsification, or plagiarism in research writing
"You are responsible for any content you submit or share, including content generated with AI tools."
"Research misconduct means fabrication, falsification, or plagiarism in proposing, performing, or reviewing research, or in reporting research results."
- Separately, university information security and confidentiality policies define obligations to safeguard confidential information and research data, which would constrain what research data may be put into AI tools
- The university's AI-related guidance focuses on responsible use and warns against entering sensitive information into unapproved tools; this has implications for research data and analysis workflows where protected data could be exposed
"Do not enter confidential or sensitive information into generative AI tools unless you are using an approved, UMass-provided tool and the data is allowed under university data classification rules."
"UMass Amherst data is categorized into levels based on risk and sensitivity."
"University employees must protect confidential information and research data from unauthorized access, use, disclosure, modification, loss, or theft."
- The university also emphasizes that individuals remain responsible for AI-assisted outputs, reinforcing accountability in research contexts
- The university applies established research misconduct standards (fabrication, falsification, plagiarism) to research activities, which would cover AI-assisted research if it results in those forms of misconduct
"Research misconduct means fabrication, falsification, or plagiarism in proposing, performing, or reviewing research, or in reporting research results."
"You are responsible for any content you submit or share, including content generated with AI tools."
Academic Integrity
- It indicates instructors should clearly define expectations for AI use, and students are responsible for what they submit, supporting disclosure requirements when required by the instructor or course
- The university does not set a single universal disclosure/citation format for generative AI use in student work in the provided sources, but it frames AI use under academic integrity and instructor-defined course policies
"Instructors should make expectations clear to students about whether, when, and how generative AI tools may be used in their courses and on assignments."
"You are responsible for any content you submit or share, including content generated with AI tools."
"The course syllabus will include ... course-specific policies (e.g., attendance; academic honesty; plagiarism; …)."
- The provided sources do not define a campus-wide position on the use of AI detection tools, but they do define adjudication procedures and potential penalties for violations
- The university enforces academic integrity through defined procedures and sanctions for academic dishonesty, which can apply to improper or undisclosed AI use when it constitutes cheating or plagiarism under the policy
""Cheating" (academic dishonesty) includes but is not limited to: (1) plagiarism; (2) fraudulence; (3) collusion; (4) falsification; and (5) any other actions that violate the standards of academic integrity."
"Plagiarism is the appropriation of another's work and the representation of it as one's own."
"A faculty member who believes that a student has committed an academic integrity violation shall make a reasonable effort to meet with the student…"
"If the faculty member concludes that an academic integrity violation has occurred, the faculty member may assign an academic penalty…"
Institutional & Administrative
- The provided sources do not define a universal rule for faculty/staff using AI for grading, feedback, or administrative work, but do emphasize responsible use and accountability for AI-generated outputs
- The university provides guidance for instructors to proactively set and communicate rules for generative AI use in their courses and assignments, implying faculty responsibility for clarifying boundaries and expectations
"Instructors should make expectations clear to students about whether, when, and how generative AI tools may be used in their courses and on assignments."
"You are responsible for any content you submit or share, including content generated with AI tools."
- The university restricts what institutional data may be entered into generative AI tools, tying permissions to data classification rules and approved UMass-provided platforms
- It provides an institutional GenAI platform and privacy guidance, and it maintains data classification levels and confidentiality requirements that govern the handling of sensitive or restricted data
"Do not enter confidential or sensitive information into generative AI tools unless you are using an approved, UMass-provided tool and the data is allowed under university data classification rules."
"UMass Amherst data is categorized into levels based on risk and sensitivity."
"University employees must protect confidential information and research data from unauthorized access, use, disclosure, modification, loss, or theft."
- The university has established governance activity around generative AI in academics via a joint task force and related university pages that organize guidance and resources
- The provided sources indicate an institutional effort to coordinate recommendations and guidance for academic uses of generative AI, though they do not present a single consolidated, binding university-wide AI strategy document within the sources listed
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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 University of Massachusetts Amherst's AI Policies
University of Massachusetts Amherst has defined AI policies in 12 of 12 categories, with an overall coverage score of 100%.
The university does not set a single universal disclosure/citation format for generative AI use in student work in the provided sources, but it frames AI use under academic integrity and instructor-defined course policies. It indicates instructors should clearly define expectations for AI use, and students are responsible for what they submit, supporting disclosure requirements when required by the instructor or course.
The university enforces academic integrity through defined procedures and sanctions for academic dishonesty, which can apply to improper or undisclosed AI use when it constitutes cheating or plagiarism under the policy. The provided sources do not define a campus-wide position on the use of AI detection tools, but they do define adjudication procedures and potential penalties for violations.
The university restricts what institutional data may be entered into generative AI tools, tying permissions to data classification rules and approved UMass-provided platforms. It provides an institutional GenAI platform and privacy guidance, and it maintains data classification levels and confidentiality requirements that govern the handling of sensitive or restricted data.