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Office of the Provost

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 both the significant potential these technologies hold for enhancing teaching, learning, and student services and the associated challenges. The University also recognizes the importance of preparing students to use AI effectively, safely, and ethically in their everyday lives, for lifelong learning, and in their future jobs, given the increasingly ubiquitous use of GenAI in our society. 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 course content, delivering lectures, facilitating course design, providing feedback on student performance, developing adaptive learning pathways, conducting data analysis, and enhancing accessibility across diverse learning needs. Potential uses for students include using GenAI as a personal tutor, summarizing information, generating ideas, drafting documents, and self-testing knowledge [3] [4] [5] [6]. However, using GenAI in teaching and learning introduces important challenges that must be carefully navigated with human oversight, course policies, and institutional guidelines. 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 challenges, and marginalizing diverse voices [7] [8].

Our guidelines are rooted in our core Meliora values of equity, leadership, integrity, openness, respect, and accountability. We emphasize academic freedom and autonomy, responsibility, honesty, privacy, data ownership, transparency, and equitable access and promote a human-centered approach to GenAI implementation. We recognize that different disciplines and educational contexts may require varied approaches. We advocate for university investment in necessary resources to balance innovation with responsible implementation and for an agile approach to evaluate new tools and promote 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

Higher education institutions and their community members have always responded to new technologies and their changing place in our society. As such, the following guidelines, designed for faculty, staff, peer educators, and students are intended to be adapted, refigured, and refined in relation to the broader educational landscape. Our framework follows a core set of principles that will allow individual stakeholders to respond to emerging trends, challenges, and other developments.

  • Student Learning: The primary purpose of GenAI in education is to support student success in achieving program and course learning outcomes and holistic student development. Faculty, staff, peer educators, and students should prioritize learning and engagement over-reliance on GenAI to complete tasks.
  • Academic Freedom: Instructors, individually and collectively, can determine whether, when, and how GenAI is integrated into teaching activities for which they are responsible. This principle recognizes the varied approaches needed across disciplines and educational contexts.
  • Accountability: Faculty, staff, peer educators, and students are responsible for the accuracy of all work created or assisted by GenAI. While GenAI can support teaching and learning, human oversight is essential in tasks such as grading, feedback, and decision-making. Users should actively question and rigorously evaluate all results produced by generative AI tools 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.
  • Bias: Faculty staff, peer educators, and students should recognize that all GenAI tools are trained on large, unmoderated datasets. This can result in biased, incomplete, or incorrect outputs, or in the failure to consider some views. Users should remain vigilant and avoid incorporating biases into their work.
  • Privacy and Security: Faculty, staff, peer educators, and students should adhere to established university principles of respect for intellectual property and avoid uploading confidential and/or proprietary information, including moderate or high-risk data from the University of Rochester, to GenAI platforms. They should also avoid sharing personal or sensitive data, including student information, with open or public GenAI tools and services.
  • Transparency: Faculty, staff, peer educators, and students should disclose when their work was 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: The university should ensure equitable access to GenAI tools and resources, recognizing the importance of inclusivity and the potential barriers some students may face.

Instructor guidelines

(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

The educational purpose of using GenAI is to facilitate student success in meeting program and course learning outcomes and holistic student development. Instructors should identify and communicate well-defined course learning outcomes to provide rationales for how and when students must, may, or cannot use GenAI in a particular course. GenAI-created errors could harm students by impacting their learning or performance on course assessments. Instructors should avoid student use of GenAI in assignments where it may undermine learning outcomes or give dishonest impressions of students’ abilities. Likewise, instructors should evaluate their own use of GenAI in their teaching practice through the lens of student learning outcomes.

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 are accountable and responsible for all materials created. They should thoroughly review all materials for accuracy and bias and make any necessary revisions before use with students and course staff. They should accurately document and disclose their GenAI use to students to model professionalism, transparency, and academic honesty within the classroom.

Develop Clear Course GenAI Policies

Instructors should create and communicate student GenAI course policies. The GenAI course policy should state when students must, may, or cannot use GenAI throughout the course as a whole and, if applicable, any deviations from the overall GenAI course policy for specific course activities. The course policy should be communicated to students in writing and, for courses with a synchronous element, in class. When students are permitted to use GenAI, instructors should communicate in their course and assignment policy how students should document their use, verify the GenAI output, and attribute their GenAI use.

Uphold Academic Honesty

Instructors should review their University of Rochester school’s academic honesty policy to ensure that their course GenAI policy and approach to policy violations are consistent with school policy. They are encouraged to engage their students in discussions of broader academic honesty issues, specifically identifying those concerning GenAI.

Ensure Equitable Access

If a course requires students to use GenAI, instructors should provide 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 consider whether there is a feasible alternative for the student that would meet the same learning outcomes.

Evaluate Accuracy of GenAI Output

When instructors provide a GenAI learning tool or interface specifically developed for their course (e.g., a custom tutor chatbot), they must ensure the tool provides 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. Students will not be held responsible for errors generated by the tool, provided they use it as directed by the instructor. Instructors should select or develop tools that preserve usage data. They retain responsibility for monitoring the tool’s outputs and correcting errors 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; to safeguard student privacy and intellectual property, instructors should exercise caution when inputting student data, assignments, or work into GenAI tools and should consider obtaining explicit written consent from each student before doing so. Further, instructors should ensure compliance with relevant data protection regulations and ethical guidelines.

Maintain Human Oversight in Grading and Feedback

Instructors and course staff should maintain human oversight of grading and feedback for student work. Grading and feedback are core teaching responsibilities that require a human in the loop. 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 course staff such as peer educators, lab managers, etc., the instructor should create and communicate course GenAI work policies to govern GenAI use by all course staff. If course staff is permitted to or required to use GenAI in their work, the instructor should provide access to the GenAI tool and training on GenAI, including GenAI strengths and limitations and potential intersection with state and federal laws and university policies. Course staff should adhere to the course staff work policy set by the instructor or inquire with the instructor before using GenAI for student assessment and/or feedback if the instructor has not yet provided a policy.

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.

Student guidelines

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 AI 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 growth. Students should use GenAI to enhance—not replace—their learning. They should aim to understand how GenAI fits into their 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 GenAI platforms (like ChatGPT and Claude) collect data to improve their models. Students should avoid sharing their own or 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.

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. Unauthorized GenAI use may be considered academic misconduct, similar to plagiarism or other unauthorized assistance. Even in courses where GenAI is allowed, instructors may require transparency, such as students acknowledging their use or providing transcripts of their AI interactions. When in doubt, ask the instructor for guidance.

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: Avoid using 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] 2024 CHLOE 9 Report

[4] AI Hacks for Educators 2024

[5] AI Usage in Higher Education Administration: Where Do We Need It?

[6] Benefits, Challenges, and Sample Use Cases of Artificial Intelligence in Higher Education

[7] Artificial Intelligence Risk Management Framework: Generative Artificial Intelligence Profile

[8] UNESCO Guidance for Generative AI in Education and Research

[9] University of Rochester Academic Honesty

[10] University of Rochester Graduate Student Regulations and Policies

[11] Eastman School of Music Academic Honesty

Disclosure

GenAI tools (ChatGPT, Claude) were used to create and edit this document, including revising original content for clarity and cohesion.

Members of the education subcommittee

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

Full membership list