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Generative AI Use in Education

Faculty, staff, and students: Review this page to understand the University’s guidelines for the responsible use of generative artificial intelligence (GenAI) in teaching and learning.

Topics on this page:

Purpose

In today’s rapidly evolving landscape of generative artificial intelligence (GenAI), the University of Rochester recognizes not only the significant potential these technologies hold for enhancing teaching, learning, and student services but also the associated challenges. Given the increasingly ubiquitous use of GenAI, the University also recognizes the need to prepare students to use AI effectively, safely, and ethically in their everyday lives for lifelong learning and for professional purposes. Consistent with the definition employed by the AI Research Committee, we consider GenAI to be systems capable of generating new content—including text, images, audio, video, and computer code—in response to user prompts, representing a subset of artificial intelligence systems that predict, recommend, or advance objectives traditionally requiring human input or judgment [1] [2]. This document establishes guiding principles for the responsible use of GenAI in teaching and learning at our university.

The impact of GenAI in education represents not only a technical advancement but a significant social shift. Potential GenAI uses for instructors include designing a course, developing lectures, workshops and discussion prompts, simulating environments or problems, providing feedback on student performance, developing adaptive learning pathways, conducting data analyses, and enhancing accessibility for diverse learning needs. Potential uses for students include using GenAI as a personal tutor, summarizing information, generating ideas, drafting documents, and self-testing for understanding [3] [4] [5] [6]. However, using GenAI in teaching and learning introduces important challenges that educators must carefully navigate. These challenges include potentially inaccurate or fabricated information, the potential for generating harmful or biased content, data privacy concerns, threats to information integrity and security, intellectual property violations, academic integrity violations, GenAI’s potential to not only enhance but also replace work, and GenAI’s role in marginalizing diverse voices [7] [8].

Our guidelines are rooted in our core Meliora values of equity, leadership, integrity, openness, respect, and accountability. We promote a human-centered approach to GenAI implementation. As such, we emphasize academic freedom and autonomy, academic honesty, responsibility and accountability, privacy, data ownership and authorship, transparency, and equitable access. We recognize that different disciplines and educational contexts may require varied approaches. We recommend that the university invest in necessary resources to balance innovation with responsible implementation and that it takes an agile approach to evaluating new tools and promoting GenAI literacy.

These guidelines are intended to accommodate various considerations while remaining adaptable to rapid advancements in GenAI technology. As the technology matures, risks may evolve, and new mitigation strategies may emerge, underscoring the need for regular review and updates to ensure our guidelines remain relevant and practical.

Guiding principles

This section presents the core principles that ground the guidelines in sections 3 (for instructors) and 4 (for students).

  • Student Learning: The primary purpose of GenAI in education is to support student success, both academically and holistically. All members of our educational community should prioritize learning and engagement over excessive reliance on GenAI.
  • Academic Freedom: Instructors, individually and collectively, may determine whether, when, and how GenAI should be integrated into their teaching activities. This principle recognizes the varied approaches needed across different disciplines, curricula, and individual educational contexts.
  • Accountability: All members of our educational community are responsible for the accuracy of all GenAI-assisted work. While GenAI can be a great help to teaching and learning, human oversight is essential. Users should review, question and evaluate all GenAI output in accordance with the norms and expectations of their respective fields.
  • Academic Integrity: The use of GenAI in education must adhere to the highest standards of academic integrity. Faculty, staff, peer educators, and students are responsible for ensuring that GenAI use does not compromise honesty or fairness. They should follow the University of Rochester academic honesty policy for their school.
  • Bias: Faculty staff, peer educators, and students should recognize that all GenAI tools are trained on large, unmoderated datasets. This can lead to biased outputs, which might include incomplete or incorrect information or limited representation of diverse views. Users should remain vigilant and avoid incorporating biases and inaccuracies into their work.
  • Privacy and Security: Those using GenAI should adhere to university principles of respect for intellectual property. Users should not upload to public GenAI platforms confidential and/or proprietary information, including moderate or high-risk data from the University of Rochester. Additionally, they should not share personal or sensitive data, including student information, with public GenAI tools and services.
  • Transparency: Faculty, staff, peer educators, and students should disclose when their work has been created, whole or in part, with a GenAI tool. Disclosures should specify how GenAI was used and, when appropriate, reflect on potential biases.
  • Equitable Access: Recognizing the importance of inclusivity and the potential barriers some students may face, the university should ensure equitable access to GenAI tools and resources.

Guidelines for instructors

(faculty, staff, and others in instructor roles)

This section provides GenAI teaching guidelines for instructors and covers GenAI use by both instructors and students.

Focus on Student Learning

In teaching and learning, GenAI use should facilitate holistic student development and student success in meeting course learning outcomes. Instructors should use course learning outcomes to determine when students must, may, or cannot use GenAI. Likewise, instructors should use course learning outcomes to evaluate their own use of GenAI in their teaching. Instructors should avoid use of GenAI, whether by instructor or students, when it might undermine learning outcomes or misrepresent students’ abilities.

Respect Academic Freedom

In keeping with the principles of academic freedom, instructors play a pivotal role, individually and collectively (through programs, schools, etc.), in determining whether, when, and how GenAI is integrated into their courses and their work. Instructors bear primary responsibility for teaching their classes, communicating about their courses, and building student relationships; as such, when instructors use GenAI tools, these tools should complement their work and decision-making. Instructors should refrain from delegating core course responsibilities to GenAI.

Be Transparent About GenAI Use in Teaching

When instructors use GenAI tools to develop teaching materials, they should disclose and document their GenAI use to model professionalism, transparency, and academic honesty within the classroom. Instructors are responsible and accountable for all materials created. As such, they should thoroughly review all materials for accuracy and bias and revise as necessary before use with students and course staff.

Uphold Academic Honesty

Instructors should make sure that their course GenAI policy and approach to policy violations align with the University of Rochester academic honesty policy for their school. They are also encouraged to discuss with students both broad academic honesty values and issues as well as those specifically relevant to GenAI.

Develop Clear Course GenAI Policies

Instructors should create and communicate student GenAI course policies. For each assignment, the policy should state when students must, may, or cannot use GenAI and how they should verify, disclose, document, and attribute any GenAI use. Instructors should communicate their GenAI policies in writing and discuss them with students.

Ensure Equitable Access

If a course requires students to use GenAI, instructors should ensure equitable access to GenAI for all students. When possible, instructors should select GenAI tools that are vetted and supported by the university due to concerns such as student privacy, data protection, and access to technical support. If an instructor requires students to use a GenAI tool not provided through the university to which a student objects based on student privacy or data protection, the instructor should provide an alternative for the student that would meet the same learning outcomes.

Evaluate Accuracy of GenAI Output

When instructors require students to use a GenAI learning tool or interface, they must inform the students that the tool may not always provide accurate information. Instructors should educate students on the tool’s strengths, limitations, and proper use through both written guidelines and in-class discussions (if applicable). These guidelines should include instructions for verifying the tool’s output and instructor expectations for its use. Instructors should consider that students are by definition learners for whom verifying the accuracy of AI outputs is more challenging and time-consuming than it is for experts. Students will not be held responsible for errors generated by the tool, provided they use it as directed by the instructor and have followed the instructor’s recommendations on how to verify the veracity of the tools’ output. Instructors should select or develop GenAI tools that preserve usage data. They retain responsibility for monitoring the tool’s outputs and accuracy to ensure the learning environment remains fair and effective.

Foster GenAI Literacy and Ethical Use

In all teaching and learning contexts, instructors should not assume GenAI literacy but should provide GenAI literacy instruction to students and course staff (such as peer educators and lab managers). Such instruction should include modeling responsible, discipline-specific use and discussing the strengths and limitations of GenAI, paying particular attention to inaccuracies, bias, and ethics. Instructors and students should recognize that GenAI outputs carry inherent biases due to the data used to train them and should take steps to avoid incorporating these biases into their work.

Protect Data Privacy and Security

GenAI tools can infringe on instructor and student intellectual property rights. Non-public or sensitive University information should never be uploaded into public AI tools—whether free or paid—unless there is a university agreement with the vendor approved by one of the various AI governance groups. Thus, to safeguard student privacy and intellectual property, instructors should only input student data, assignments, or work into university-approved GenAI tools for medium and high-risk data.

Maintain Human Oversight in Grading and Feedback

Instructors and course staff should maintain oversight of grading and feedback for student work. Grading and feedback are core teaching responsibilities that require human oversight. When contemplating using GenAI in grading and feedback processes, instructors should pay particular attention to GenAI inaccuracies and biases (linguistic, neurodiverse, etc.).

Provide Guidance for Course Staff

If a course has staff such as peer educators, lab managers, etc., the instructor should create and communicate in writing the GenAI work policies that course staff are required to follow. If course staff is permitted or required to use GenAI in their work, the instructor should provide access to and training on the GenAI tool, including GenAI strengths and limitations and potential intersection with state and federal laws and university policies.

Approach GenAI Detection Tools Cautiously

GenAI detection tools are unreliable, biased, easily defeated, and unable to provide definitive evidence of academic honesty policy violations. If instructors use GenAI detection tools in a course, they should disclose to students when and how the software will be used. Instructors should avoid using GenAI detection software as the sole basis for an academic honesty policy violation; instead, they should use it to converse with the student and conduct further investigation as needed.

Guidelines for students

These guidelines help students use GenAI effectively and responsibly in their studies.

Use GenAI to Support Learning

GenAI can be a valuable tool, but remember that university studies are about building students’ own skills and knowledge. Think of GenAI as a workout partner at the gym: It can help students track and plan, but if they let GenAI do the heavy lifting, they miss out on personal and academic growth. Students should use GenAI to enhance—not replace—their learning. They should aim to understand GenAI course policies, and how GenAI fits into the study process without allowing it to take over.

Critically Evaluate GenAI Content

GenAI can make mistakes or introduce biases. Students should always double-check GenAI-generated information for accuracy before using it in their work. For instance, if they ask GenAI to create a study guide, they should cross-reference its answers with their course materials or ask a teaching assistant or instructor to be sure it is accurate. Also, students should understand that GenAI relies on input quality—experimenting with prompts and learning practical ways to interact with GenAI will improve results over time.

Protect Privacy and Security

Many public GenAI platforms (like ChatGPT and Claude) collect data to improve their models. Non-public or sensitive University information should never be uploaded into public GenAI tools—whether free or paid—unless there is a university agreement with the vendor approved by one of the various AI governance groups. Accordingly, students should not share others’ personal information, course materials, or any proprietary content in these tools unless they have clear permission from the relevant parties, such as the instructor or content creator. Students should have the autonomy to decide whether to submit their own personal information or work into public GenAI tools.

Get Permission for Using GenAI in Coursework

If an instructor has not explicitly stated that GenAI is allowed for an assignment, assume it isn’t. GenAI use may vary by course, so it is up to students to understand and follow the specific policies for each one. When in doubt, ask the instructor for guidance. Unauthorized GenAI use may be considered academic misconduct, similar to plagiarism or other unauthorized assistance. In courses where GenAI is allowed, instructors may require transparency, such as students acknowledging their use or providing transcripts of their AI interactions.

Peer Educators Should Seek Instructor Guidance

If students are working as teaching assistants or in other peer educator roles, they should talk to the instructor about expectations for using GenAI. They should ask about GenAI’s role in their duties, such as grading, feedback, or instruction. Unless the instructor says otherwise, assume that using GenAI in these roles is not permitted.

Follow the University’s Academic Honesty Policy for the Applicable University of Rochester School

All GenAI use must align with the applicable academic integrity standards [9] [10] [11]. This means avoiding:

  • Plagiarism: Don’t present GenAI-generated work as your own without proper attribution.
  • Cheating: Don’t use GenAI to complete assignments unless you have explicit permission.
  • Data Falsification: Don’t use GenAI to generate or alter data in ways that misrepresent your findings.
  • Collusion: Don’t share GenAI-generated work with others unless the instructor allows it.

Recommendations for guideline implementation

This section provides recommendations to facilitate successful implementation of our guidelines across the university.

Centralize GenAI Tool Approval: Establish an agile university-wide system to evaluate and approve GenAI tools, ensuring they meet institutional standards for security, privacy, and educational value. A centralized approach streamlines procurement and reduces duplication of effort.

Invest in Resources and Infrastructure: Provide funding for institutional subscriptions to vetted GenAI tools and the infrastructure needed to support their use in teaching and learning. Equitable access to these resources ensures all students benefit. Additionally, solicit input from faculty and staff to identify resources that can help alleviate the workload of integrating and managing GenAI technologies in their courses.

Enhance GenAI Literacy: Implement GenAI literacy training programs for faculty, staff, and students, customized to address the needs of different disciplines and expertise levels. Programs should empower users to engage with GenAI tools responsibly and effectively, fostering critical evaluation and ethical use.

Streamline Access to GenAI Resources and Information: Create a centralized hub or repository for GenAI-related resources, including approved tools, training materials, best practices, and policy updates. This platform should be easily accessible to all faculty, staff, and students. Clear communication about available resources and how to access them ensures the university community can effectively utilize GenAI tools and guidelines.

Foster Innovation: Initiate targeted pilot programs to explore creative uses of GenAI in teaching, learning, and assessment. Involve diverse teams of faculty, staff, and students to encourage interdisciplinary collaboration and innovation. These pilot programs can test new ideas, address challenges, and inform best practices for broader adoption across the university.

Resources, references, and disclosure

Resources
References

[1] Source: National Artificial Intelligence Research Resource Task Force, 2023

[2] Examples currently include, but are not limited to, ChatGPT, Google Gemini, Microsoft CoPilot, ClaudeAI, Stable Diffusion, Midjourney, etc. This list is descriptive not prescriptive as it is a dynamically evolving market.

[3] https://www.qualitymatters.org/qa-resources/resource-center/articles-resources/CHLOE-9-report-2024

[4] AI Hacks for Educators 2024 (https://stars.library.ucf.edu/oer/9/)

[5] https://www.anthology.com/paper/ai-usage-in-higher-education-administration-where-do-we-need-it

[6] https://www.insidehighered.com/sites/default/files/2023-10/Benefits%2C%20Challenges%2C%20and%20Sample%20Use%20Cases%20of%20AI%20in%20Higher%20Education.pdf

[7] https://nvlpubs.nist.gov/nistpubs/ai/NIST.AI.600-1.pdf

[8] https://unesdoc.unesco.org/ark:/48223/pf0000386693

[9] https://www.rochester.edu/college/honesty/

[10] https://www.rochester.edu/graduate-education/academic-resources/regulations/

[11] https://www.esm.rochester.edu/registrar/files/2014/05/AcademicHonesty1.pdf

Disclosure

The outline and content of this document was conceived through multiple committee meetings over the course of several months. Subsequently, small writing groups created original drafts of individual sections of the guidelines, GenAI tools (ChatGPT, Claude) were used to edit drafts for some of the sections for clarity and cohesion. GenAI tools were also used to assist with combining the individual components into one complete draft document, checking for consistency of themes across the different sections. Finally, the committee reviewed and revised this draft document, resulting in the published guidelines dated December 2, 2024. Subsequently, the guidelines were further revised for clarity and completeness on March 7 and May 16, 2025.

Members of the education subcommittee

View all members on the education subcommittee of the University of Rochester AI Council.

Full membership list