AI Framework - The College of Education

The University of Utah College of Education AI Framework guides College of Education faculty, staff, and students in the responsible use of AI. This framework outlines the College’s goals and expectations for AI in teaching, learning, research, and service.

Strategic Artificial Intelligence (AI) Framework v1.0

A Message from the Dean

Artificial intelligence is transforming education at an unprecedented pace. It’s creating extraordinary opportunities to reimagine how we teach, research, and serve our communities. As Utah’s flagship institution and a leading AAU research university, the University of Utah is advancing responsible innovation through the One-U Responsible AI (RAI) Initiative. The College of Education is proud to contribute to this university-wide vision with the launch of our own Framework, a bold roadmap that positions our College at the forefront of responsible AI leadership in education. Rather than simply responding to technological change, we are committed to shaping its future through scholarship, educator preparation, and meaningful partnerships that advance educational excellence and the public good.

Our vision is grounded in a simple but powerful principle: AI must enhance human potential, not replace it.

As we prepare the next generation of educators, researchers, counselors, K-12 leaders, higher education leaders, psychologists, and policymakers, we have both an opportunity and a responsibility to shape how artificial intelligence is used. We must ensure it is developed and applied ethically, transparently, and equitably, with unwavering attention to the learners, families, and communities we serve. Through this framework, the College of Education embraces AI as a catalyst for innovation while reaffirming that human judgment, compassion, and integrity remain at the heart of education.

This framework reflects the collective expertise and thoughtful leadership of our faculty, staff, and AI Task Force. Together, we are establishing the University of Utah College of Education as a national leader in AI-supported education. We are advancing research, improving teaching and learning, informing policy, and preparing leaders to shape the future of education. As AI continues to evolve, so too will our commitment to leading with purpose, responsibility, and innovation, in partnership with the University of Utah’s One-U RAI Initiative and our shared vision of creating lasting societal impact.

Frankie Santos Laanan
Dean

Developed by
The University of Utah College of Education AI Task Force – May 2026,
David Woo, Jakob McIntosh, Sydnee Dickson, Tuba Yilmaz,
Udita Gupta, Chenglu Li, and Sharlene Kiuhara,
with the assistance of ChatGPT5.5Edu

Executive Summary

The University of Utah College of Education Strategic Artificial Intelligence (AI) Framework establishes the College’s strategic approach to responsible AI use in education, research, teaching, service, and community engagement. The framework aligns with the College’s Education is Utah 2030 Strategic Plan, the University of Utah’s Strategy 2030 and Impact 2030 vision, and the One-U Responsible AI Initiative (One-U RAI).

This framework positions the College of Education as a national leader in responsible AI innovation by preparing future educators and community leaders to engage thoughtfully with emerging technologies. Through interdisciplinary collaboration, research, and public engagement, this framework promotes AI practices that strengthen teaching, learning, and community success, while creating meaningful and responsible societal impacts.

Leading AI with purpose, responsibility, and humanity at the center.

PART I: “Human-Centered AI”

At a time when artificial intelligence is rapidly reshaping education, the University of Utah College of Education is committed to a human-centered approach that strengthens learning, protects vulnerable communities, and advances public good.

Reflecting this core belief, this framework establishes a clear vision for how the College of Education will lead in the responsible, ethical, and transparent use of AI in education.

We will prepare future educators, researchers, and leaders with strong AI literacy.

We will advance research that explores the ethical and equitable use of AI.

We will partner with schools and communities to ensure that AI tools serve the public good, particularly for those historically underserved.

As AI continues to evolve, our responsibility is clear: we must guide its development and application with integrity, compassion, and an unwavering focus on the people we serve. The future of education will be shaped not only by what AI can do, but by the values we choose to lead with.

PART II: Foundational Principles for Responsible AI Use

The College of Education has adopted a set of foundational principles for responsible AI use that are consistent with the College’s mission and aligned with the One-U Responsible AI Initiative.

Principle	Description and Policy Implication
Act as responsible stewards.	All members of the College oversee the data of others, including students, research participants, staff, and community partners. We must never expose individuals or their data to AI tools that have not been approved under University of Utah enterprise agreements and policies. Faculty and staff must always confirm requests for data with the person who requested it.
Be transparent in how and why AI is used.	Faculty, staff, and students should default to citing the parts of their work where AI was used, including what tool(s) were used, how output was generated, and, when relevant, why. Records of substantive interactions should be retained to allow future review. In formal submissions such as course shells, assessment materials, and published research, substantive uses of generative AI in design or authorship should be disclosed through a brief annotation or author note. Instructors should establish expectations for how students will do the same in their courses.
Set a public example for accuracy and trustworthiness.	AI tools cannot hold authorship or intellectual responsibility. Only humans can claim authorship, co-PI, or creative ownership. All users are responsible for verifying AI outputs for accuracy and fairness. AI models can perpetuate or amplify systemic biases. Generative AI models are improving, but as primarily word prediction engines, their output is likely to include errors and biases. Whenever possible, College of Education researchers, instructors, and students should review outputs, working to evaluate and reduce any outputs containing errors or biases. It is important to minimize the perpetuation of false information.
Mitigate harm.	While AI is becoming more widely used in research and teaching, we do not endorse AI as an unaccountable agent. Because members of the College of Education work with youth, families, and other potentially vulnerable populations, we have a responsibility to identify the known and potential harms of AI use and to be public about how we will address them. As scholars and teachers, we engage with questions such as: What assumptions am I (or are we) making about AI as part of this work, and why am I making them? What known and potential harms does my (or our) AI use inflict on learners, families, and communities, and how might we mitigate those harms? Why was AI needed in this work, and could the work have been conducted without it? College of Education faculty, staff, and students are expected to interrogate these questions before, during, and after AI-assisted work, and to document their reasoning so that decisions remain open to scrutiny and revision.
Prepare our students for the future they will face tomorrow.	College of Education students will go on to work in many fields, including as teachers, leaders, and scholars. We must remain aware of advances that influence teaching and learning in myriad contexts and prepare our students to think critically about how those advances may shape their future work.

PART III: Core Strategic Pillars

Pillar A: Curriculum and Teaching Integration

Objective: Prepare graduates with deep AI literacy and pedagogical fluency, including the ability to use, evaluate, and critically interrogate AI tools.

Priorities:

AI literacy across programs. Integrate AI competencies into all undergraduate and graduate curricula, with attention to how students use tools, recognize bias, navigate privacy, and access tools equitably. Support students and adults with disabilities through differentiation and accessible technology.
Signature AI courses. Offer courses such as AI in Education Policy, Responsible AI Design for Equity, AI-enhanced Assessment Systems, and Leading AI in Education Initiatives.
Faculty development for AI fluency. Provide sustained training for faculty to integrate AI into pedagogy and research, in partnership with campus AI units.
AI teaching tools. Develop and deploy ethical AI assistants for personalized tutoring, feedback, and administrative support, with clear guidelines for the use of generative tools in assignments.

Next Steps:

Draft a College Syllabus Statement. Incorporate the One-U RAI Generative AI Acceptable Use Scale (Levels 0 through 4), recommending that faculty indicate the appropriate AI use level for each assignment.
Create a Shareable Canvas Assignment. This assignment will serve as a template that instructors can incorporate, to communicate AI expectations and to support student disclosures of AI use.
Map AI Fluency Expectations. Map AI fluency expectations vertically and horizontally across programs, so that students develop a defined baseline proficiency by graduation.
Pilot assignment-level disclosure forms. In these forms students will describe how they use AI, allowing the College to gather data on how disclosures shape student practice.
Initiate AI-Focused Professional Development. Use the existing College of Education AI Usage Survey as the baseline for faculty AI literacy and offer professional learning sessions and webinars built on the One-U RAI scale.

Pillar B: AI-Informed Research Innovation and Open Data Stewardship

Objective: Establish the College of Education as a premier research hub for AI in education, supporting College research initiatives and AI-ready data within traditional learning settings.

Priorities:

Interdisciplinary research clusters. Co-lead with One-U RAI clusters focused on AI in learning sciences, assessment systems, educational equity, and human-AI collaboration. Recruit faculty fellows in AI and education with joint appointments (e.g., computing and education).
Centers and labs. Pursue a Center for AI in Education and Learning Analytics that hosts seed grants, doctoral fellows, and postdocs focused on responsible AI research. Build partnerships with engineering, computing, policy, and business units.
Applied research portfolios. Advance projects in personalized adaptive learning, ethical AI assessment frameworks, AI-powered teacher support systems, and learning analytics governance, with explicit attention to harms mitigation.

Next Steps:

Establish the Baseline: Establish a baseline of AI-related activities currently underway in research, teaching, service, and community partnerships across the College.
Measure Success. Identify aligned metrics for measuring progress and quality, including peer-reviewed publications, practice-facing publications, presentations, and grants.
Automate Reporting. Pull data from existing faculty reporting systems (e.g., Elements submissions through the AD for Faculty), to relieve faculty of additional reporting responsibilities.

Pillar C: Responsible AI Governance and Policy

Objective: Align College of Education AI governance with the University of Utah’s Office of AI, One-U Responsible AI Initiative, and existing university policies so that AI use remains responsible, transparent, accountable, and human-centered.

Priorities:

University-Aligned Governance. Coordinate College AI guidance with the University of Utah Office of AI, which leads AI strategy, policy, and governance across the university, and with One-U RAI’s responsible AI framework, including its foundations of ethical technology development, interdisciplinary collaboration, knowledge and skill sharing, community engagement, and responsible AI policy and practice.
Policy and Standards Alignment. Apply existing university standards to AI use in teaching, research, service, administration, and community engagement, including the University AI Guidelines and Policies hub, Policy 4-001 on Institutional Data Management, Policy 4-004 on Information Security, Rule 4-004A on Acceptable Use, Rule 4-004C on Data Classification and Encryption, Policy 4-050 on University Software, and Policy 6-410 on academic conduct.
Responsible oversight and transparency. Support human oversight, compliance with policies and regulations, security and privacy, accessibility, and critical thinking in all College AI activities, consistent with the Office of AI guiding principles. Where AI is used in research, teaching, grading, communication, or administrative decision support, require clear human responsibility, appropriate disclosure, and review for accuracy, bias, privacy, and harms mitigation, consistent with university AI in Research Guidance.

Next Steps:

Create a College-level policy crosswalk that maps AI-related teaching, research, administrative, and partnership activities to existing university policies and guidance, including the AI Guidelines and Policies hub and the One-U RAI framework.
Use university guidance on AI in research, AI in teaching, responsible AI-assisted grading, and approved chatbot use when developing College syllabus language, research protocols, staff workflows, and student-facing expectations.
Review College AI governance practices annually against updated university guidance, recognizing that the university may revise or introduce additional AI policy guidance as technologies, legal requirements, and institutional practices evolve.

Pillar D: Community and Partnership Engagement

Objective: Build societal impact through partnerships, workforce development, and public education, with particular attention to underserved populations and families.

Priorities:

K-12 and District Innovation Hubs. Partner with local districts to pilot AI-powered instructional tools and to provide professional development for teachers. Pursue a statewide AI in Education Consortium drawing on school, university, and EdTech partners.
Industry and Policy Partnerships. Forge collaborations with EdTech firms, nonprofits, and policymakers to co-design practical solutions and to positively influence AI policy.
Public Scholarship and Outreach. Host an annual AI in Education Summit, publish public-facing research briefs and toolkits, and offer certifications and professional learning aimed at community engagement, including with underserved populations and families.

Next Steps:

Take Inventory. Inventory current partnership activities involving AI to identify entry points for pilots and collaborations.
Get Community Feedback. Identify community partner needs through the existing surveys and direct outreach, with attention to underserved populations.

Pillar E: Infrastructure, Data, and Computational Capacity

Objective: Build College of Education AI infrastructure and data practices that are consistent with the University of Utah’s approved tools, cyberinfrastructure, data governance, and One-U RAI commitments to responsible, accessible, and reliable AI.

Priorities:

Approved Tools and University Infrastructure. Use university-approved AI tools and infrastructure for teaching, research, administrative, and partnership activities, following the Office of AI Tools and Infrastructure guidance. When a needed AI tool is not listed as approved, route the use case through the university AI Tool Request process for review of data handling, legal, compliance, technology, and security considerations.
AI-Ready Data Stewardship. Govern College data used with AI according to university data policy, including Rule 4-004C on Data Classification and Encryption and Policy 4-001 on Institutional Data Management. College AI projects should identify whether data is public, sensitive, or restricted; confirm appropriate data steward conditions of use; and avoid entering sensitive or restricted data into public or unapproved AI tools, consistent with university AI use guidelines.
Responsible Research and Learning Infrastructure. Align College infrastructure planning with One-U RAI’s investment in advanced cyberinfrastructure, the Center for High Performance Computing, the Scientific Computing and Imaging Institute, UCloud, and university-supported AI platforms. College projects should use University infrastructure to strengthen learning, research, and public good while maintaining privacy, security, accessibility, and human oversight.

Next Steps:

Direct faculty, staff, and students to the university’s current Tools and Infrastructure page before adopting AI tools for College work, and use the AI Tool Request Form when a tool is not already approved for the intended use.
Partner with University Information Technology (UIT), Center for High Performance Computing (CHPC), the Office of AI, One-U RAI, and the Martha Bradley Evans Center for Teaching Excellence to support AI-ready instructional and research infrastructure, including training, consultation, and responsible use guidance.

PART IV: Cross-Cutting Implementation Strategies

The College of Education will implement a set of cross-cutting strategies designed to support responsible AI integration across all areas of the framework. These strategies build upon existing faculty expertise, institutional partnerships, and operational infrastructure throughout the College.

Meet faculty where they are: Use baseline survey data to scaffold professional learning rather than assuming a uniform starting point.
Provide professional learning: Offer lunch-and-learns, webinars, and seminar series focused on AI curriculum and teaching integration, responsible use, and tool selection.
Upskill across roles: Build supports for faculty, staff, graduate students, and undergraduates, recognizing that each group has distinct needs and use cases.
Align policies and standard operating procedures: Review and update relevant policies, SOPs, and syllabus expectations to reflect AI in coursework, research, and service.
Adopt a shared rubric: Use the One-U RAI Generative AI Acceptable Use Scale across the College to ensure consistent language for students, faculty, and staff.
Build a crosswalk: Map the College of Education foundational principles to the One-U RAI principles and to the five pillars so alignment with the university-level initiative is visible.
Ensure ethical use: Operationalize the foundational principles, including the Mitigate Harm principle, in every pillar by requiring documentation of assumptions, harms analysis, and the case for AI use.

PART V: Measurement and Continuous Improvement

The College of Education will regularly evaluate the implementation and effectiveness of this framework to ensure that AI initiatives remain responsible, impactful, and aligned with institutional priorities. The following areas will help guide ongoing assessment, improvement, and future planning across research, teaching, policy, partnerships, and community impact.

Research: AI-focused publications, grants, presentations, and interdisciplinary projects.
Teaching and Learning: AI literacy competencies, syllabus adoption, and instructional integration.
Policy and Principles: Documentation of ethical AI practices and harms mitigation.
Partnerships: K-12 collaborations, professional learning offerings, and community engagement.
Impact: Educational, organizational, and societal outcomes associated with responsible AI innovation.

PART VI: Implementation Roadmap

Year 1 (AY 2026 - 2027)

Publish disclosure and integrity guidance for research and instructional use.
Launch the professional development series, including responsible use, prompting basics, bias and fairness, and fact-checking.
Pilot courses or modules integrating AI literacy and a syllabus statement built on the One-U RAI scale.
Select one to two district pilot sites and announce the Fall 2026 AI in Education Summit.
Complete an inventory of current AI-related activities to establish a baseline for measurement.
Establish or phase in the Center for AI in Education and Learning Analytics, including seed grants and fellows.
Scale curriculum integration and micro-credentials across programs and expand faculty development.
Run K-12 pilots, collect equity and impact evidence, and host the first AI in Education Summit.

Year 2 (2027 - 2028)

Scale successful pilots, publish case studies and policy briefs, and deepen industry partnerships.
Launch postdoc and graduate fellowships and broaden national leadership and consortia engagement.
Release updated college AI guidelines reflecting lessons learned and evolving standards.

PART VII: AI in Research, Scholarship, and Service

Research Phase	Appropriate Use	Cautions/Ethical Considerations
Literature Review	Generate search terms, organize themes, summarize abstracts, identify possible gaps, and support citation workflows.	Verify every source manually. Do not rely on AI generated citations or summaries without checking the original source.
Data & Analysis	Support coding, debug scripts, summarize public or appropriately de-identified data, and assist with analysis planning.	Do not enter sensitive or restricted data into public or unapproved AI tools. Follow University of Utah data classification requirements for public, sensitive, and restricted data. AI may support analysis, but researchers must interpret findings.
Documentation & Reproducibility	Document workflows, methods, prompts, assumptions, analytic decisions, and AI-assisted processes.	Records should include the tool used, purpose, data classification, verification steps, harms considered, mitigation steps, and rationale for AI use.
Manuscript Drafting	Improve clarity, structure, grammar, formatting, abstracts, titles, and non-substantive editing.	AI must not replace human authorship, scholarly judgment, or interpretation of findings. AI may not be listed as an author. Human authors remain responsible for accuracy, originality, citations, and final language.
Dissemination	Prepare slide outlines, public summaries, captions, translations, visual concepts, and audience-specific materials.	Attribute substantive AI assistance when AI contributes to text, images, summaries, translation, analysis, or design. Verify all claims before dissemination.
Reviewing Manuscripts/Proposals	Organize reviewer notes, summarize nonconfidential feedback written by the reviewer, and edit feedback for tone or clarity.	Do not upload confidential, proprietary, identifiable, or unpublished materials into public or unapproved AI tools. Follow journal, sponsor, agency, committee, and institutional rules. Human reviewers remain fully responsible for the review.

Data Classification and Tool Use

College of Education AI-use must follow the University of Utah’s data classification model: public, sensitive, and restricted.

Public data may be used with AI when the use is appropriate, accurate, lawful, and consistent with university expectations.
Sensitive data may not be used with public or unapproved AI tools.
Restricted data may not be used with public or unapproved AI tools.
Sensitive or restricted data may be used with AI only when the tool, access, consent, IRB protocol, data steward conditions, contractual terms, and approved infrastructure permit.
De-identification must be meaningful and appropriate to the context. De-identification does not remove obligations related to consent, re-identification risk, community harm, contractual restrictions, or IRB review.

Transparency and Attribution

Substantive AI use in research, scholarship, service, and public materials should be disclosed when AI contributes to design, analysis, interpretation, writing, visuals, summaries, translation, or dissemination.

Disclosure should identify the tool used, purpose of use, type of assistance, substantive prompts or generation method when relevant, extent of human review, data classification involved, and any IRB, sponsor, publisher, consent, or data steward conditions.

AI tools cannot hold authorship, intellectual responsibility, or accountability. Human authors, reviewers, and presenters remain accountable for all final work.

Documentation Requirements

AI-assisted research, scholarship, and service must be documented in proportion to risk. Documentation should support review, reproducibility, and accountability.

At the minimum, faculty must document the task supported by AI, including the tool and model/version when available; date or timeframe; prompts or workflow summaries when relevant; data classification; approved tool status; IRB, sponsor, publisher, consent, or data steward requirements; assumptions; harms considered; mitigation steps; verification steps; and rationale for AI use.

PART VIII: Strategic Positioning and National Leadership

The University of Utah College of Education seeks to serve as a model for responsible AI adoption in education through research, policy engagement, and curricular innovation. The College will contribute to national conversations surrounding artificial intelligence in education by participating in professional consortia, advancing scholarship, and sharing expertise related to AI policy and practice.

Through this work, the College aims to support not only Utah’s educational ecosystem, but also broader national discussions and emerging standards surrounding responsible AI use. The College’s foundational principles, including its commitment to mitigate harm, reflect a visible and ongoing commitment to ethical, thoughtful, and human-centered innovation in education.

PART IX: Tools for Faculty

AI tools may support teaching, research, planning, and administrative work. AI does not replace faculty judgment, student learning, human authorship, or professional accountability.

Faculty must use AI in accordance with the University of Utah’s Office of AI Guidelines and Policies, and the College of Education’s principles of responsible stewardship, transparency, accuracy, human accountability, and harm mitigation. One-U RAI frames responsible AI use as work that advances societal good while protecting privacy, civil rights, and civil liberties - promoting fairness, accountability, and transparency.

Course Planning and Instructional Design

Faculty should begin with learning outcomes, not tools. AI may be used to draft lessons and assignments, but all outputs must be reviewed for accuracy, bias, accessibility, and alignment with course goals.

Instructors must clearly define acceptable and prohibited uses of AI in course syllabi and assignment instructions for students. Instructors may refer to the following Acceptable Use Scale for students:

Level of Use	Description	Example	Disclosure
0: No AI Use	Assignment is completed entirely on your own, without any AI assistance at any point.	In-class blue-book essay exam; Closed-book calculus exam, proctored with no devices.	No AI disclosure required.
1: AI-Assisted Idea Generation & Structuring	AI is a thought partner for brainstorming, outlining, and generating ideas. The content you submit is your own.	Discussion board post using AI to brainstorm angles; Presentation outline using AI for structure; Data science library search	Include an AI disclosure statement describing how AI was used.
2: AI-Assisted Editing	No new content created by AI. AI assists with clarity, quality, or debugging of student-created work.	Senior thesis chapter run through AI for revision; Programming assignment using AI to debug code.	Include an AI disclosure statement. Links to AI chats may be required per course.
3: AI for Specified Task Completion	AI generates content that may be used verbatim or lightly edited. You think critically and take responsibility for reviewing all AI output.	Course assignment generating ad copy drafts with AI; CS project using AI coding assistant for starter code.	Cite AI using APA style (in-text + reference). Chat links may be required per instructor.
4: Full AI Use with Human Oversight	AI is used freely as a co-pilot. You review everything, ensure accuracy, and the final product reflects your judgment.	AI-enhanced advising chatbot project; Design studio with AI throughout process; Engineering codex project.	Cite all AI use in APA style. Chat links may be required per instructor.

Instructors may tailor ‘acceptable AI use’ to their coursework, provided that changes adhere to University and College guidelines. All changes should be communicated in course syllabi and assignment instructions.

Responsible Use of AI in Grading

AI may support assessment design and feedback, but grading remains a human instructional responsibility. Consistent with the University’s Guidelines for Responsible Use of AI in Grading, faculty retain full responsibility for grades. Human oversight is a requirement, student privacy is paramount, and AI may not serve as the sole or final authority in determining grades.

AI must not assign final grades without human review, serve as the primary or exclusive grading mechanism, or make academic misconduct determinations without independent human evaluation.

If AI assists with grading or feedback, faculty must use institutionally approved tools, avoid uploading identifiable student information, and provide human oversight and review. Faculty should conduct pilot testing, compare AI-assisted suggestions with human grading, monitor for inconsistency or bias, and maintain brief documentation of the tool used, the nature of AI assistance, the validation approach, and known limitations. The University also provides an AI-Assisted Grading Syllabus Statement that faculty may adapt.

The University’s Center for Teaching Excellence further states that AI-detection tools are unreliable, are not on the list of UIT-approved AI tools, and have the potential to expose FERPA-secured data. Faculty should instead use process-based assessment, drafts, reflections, checkpoints, and careful review of student work over time.

AI Tools for Faculty (Teaching, Grading, and Research)

Faculty should use the University’s current Tools and Infrastructure list before adopting AI tools for work.

*The University notes that approved enterprise tools generally cover public data only and that only Gemini is currently approved for sensitive and restricted data, subject to tool-specific requirements.

Faculty Tasks	Responsible AI-Use	Required Guardrails
Teaching and course design	Draft learning activities, discussion questions, examples, study guides, and case prompts.	Manually verify accuracy, bias, accessibility, and alignment with learning outcomes.
Assessment design	Draft rubrics and criteria, create feedback templates, diversify question banks, and review prompts for bias.	Do not allow AI to determine instructional standards without human review.
Grading and feedback	Generate preliminary rubric suggestions, draft feedback language, or identify common patterns in student work.	Follow the University’s Guidelines for Responsible Use of AI in Grading: use approved tools, de-identify student work, disclose AI use, and provide human review.
Research and scholarship	Brainstorm, organize literature searches, summarize public sources, debug code, and polish prose.	Follow University AI research guidance, IRB requirements, data steward conditions, sponsor rules, and publisher policies.
Administrative workload	Draft agendas, summaries, templates, communications, and project plans.	Do not enter sensitive or restricted data into public or unapproved tools. Stick to approved university systems.

Faculty may request review of tools not on the approved list through the University’s AI Tool Request Form. Unapproved tools should not be used for university business when data privacy, compliance, intellectual property, or student records are involved.

Attribution and Citation

For assignment disclosures, when AI use is permitted or required by the instructor, students should briefly identify how AI contributed to their process using the following template:

DISCLOSURE TEMPLATE:

This assignment used generative AI (tool name, model/version, date) to support [brainstorming/outlining/draft revision/study questions/etc.]. All outputs were reviewed, verified, and revised by the author.

DISCLOSURE EXAMPLE:

This assignment used generative AI, ChatGPT Edu (GPT-5.5, July 4, 2026), to support brainstorming, outline organization, and revision for clarity. All outputs were reviewed, verified, and revised by the author.

When AI creates or substantially contributes to content included in a submitted assignment, that AI-created content should be cited in APA style with an in-text citation and reference entry. The University’s baseline format is the following:

CITATION TEMPLATE:

Author of AI tool. (Year). Name of AI tool (Version) [Type of AI model]. URL.

CITATION EXAMPLE:

Anthropic. (2026). Claude (Claude 4.1 Opus) [Large language model]. https://claude.ai/

IN-TEXT CITATION EXAMPLE:

(Anthropic, 2026)

For faculty research and scholarly work, follow the Office of AI’s Citing AI Use in Research and Scholarly Work.

AI tools should not be listed as authors because authorship requires accountability; substantive AI use in text, methods, analysis, or presentation materials should be documented according to publisher, funder, agency, or professional society requirements.

Plagiarism

The University’s CTE syllabus guidance states that academic honesty includes refraining from cheating, plagiarizing, misrepresenting one’s work, or inappropriately collaborating, to include the use of Gen-AI tools “without citation, documentation, or authorization.” Students who fail to comply may be subject to sanctions under Policy 6-410: Student Academic Performance, Academic Conduct, and Professional and Ethical Conduct.

Faculty should address AI-related concerns through clear expectations and evidence-based review. CTE cautions that AI-detection tools are unreliable, are not on the list of UIT-approved AI tools, and may expose FERPA-secured data. Instructors should instead use process-based assessments, drafts, reflections, checkpoints, student conferences, and individualized review.

Accessibility Considerations

AI-supported course materials must meet University accessibility expectations. CTE’s Accessibility Essentials states that digital educational content at the University must meet WCAG 2.1 AA standards, including alternative text for images, captions or transcripts for video, high contrast, descriptive links, and accessible course files.

Faculty should not require AI tools that create cost barriers, inaccessibility, privacy risks, or conflicts with accommodations. When AI is used to draft alt text, captions, translations, summaries, or accessible formats, faculty remain responsible for checking accuracy, clarity, and compatibility with assistive technologies.

Faculty Checklist

Before using AI for tasks or coursework, faculty should confirm:

Their tool is listed on the University’s approved tools page.
No sensitive or restricted data is being entered into public or unapproved AI tools.
Students have been given clear instruction on Acceptable and Unacceptable AI-Use.
Students know when AI must be disclosed or cited.
AI-assisted grading, if used, follows the University’s grading guidance.
Course materials meet accessibility standards.
AI outputs are checked for accuracy, bias, and fairness.
Human judgment remains visible, accountable, and final.

Concluding Statement

The future of education will be shaped by those who use emerging technologies with wisdom, care, and a deep commitment to human learning. Through this framework, the University of Utah College of Education affirms that AI should serve to strengthen teaching, expand research possibilities, support ethical innovation, and serve all communities with transparency and accountability. Our goal is not simply to adopt new tools, but to prepare educators, researchers, leaders, and partners to guide AI’s use in ways that advance accessibility and the public good. As AI continues to evolve, this framework will remain a living document, shaped by faculty expertise, student needs, community partnerships, and the College’s commitment to responsible, safe, human-centered innovation.

Acknowledgements

Additional feedback provided by:
Frankie Santos Laanan, Dean
David Stroupe, Associate Dean of Research
Mitchell Keahey, Sr. Supervisor of Marketing & Communications

This framework was authored collaboratively by members of the University of Utah’s College of Education AI Task Force, with editorial assistance from OpenAI’s GPT-5.5 model (ChatGPT, May 2026). The AI was used to integrate, format, and refine text from human-authored drafts. All intellectual content, ethical positions, and final decisions were determined, reviewed, and approved by human faculty and staff.

Version 1.0, August 2026

Resources

For the latest version of this framework and any future updates, visit:
https://education.utah.edu/coe-resources/ai-framework.php

AI & Teaching Guidance

https://cte.utah.edu/instructor-education/ai-for-teaching.php

CTE’s main faculty-facing resource for using AI in teaching, including syllabus expectations, assignment design, student use, grading cautions, and AI-detection concerns.

Mandatory Institutional Policies for Syllabi

https://cte.utah.edu/instructor-education/syllabus/institutional-policies.php

CTE’s required syllabus policy language, including academic misconduct expectations relevant to unauthorized or undocumented AI use.

AI Guidelines and Policies

https://ai.utah.edu/guidelines/

The University of Utah’s central hub for AI-related guidance, policies, responsible use expectations, and links to university-approved AI resources.

One-U Responsible AI Initiative

https://rai.utah.edu/

The university-wide responsible AI initiative that frames AI use around ethical technology development, interdisciplinary collaboration, public good, accountability, and transparency.

AI in Research Guidance

https://ai.utah.edu/_resources/documents/uofu-ai-research-guidance.pdf

University guidance for responsible AI use in research, including documentation, privacy, data classification, human oversight, and research integrity.

Policy 4-001: University Institutional Data Management Policy

https://regulations.utah.edu/it/4-001.php

University policy governing institutional data stewardship and responsible data management.

CTE Accessibility Essentials

https://cte.utah.edu/instructor-education/accessibility-essentials/index.php

University guidance for making digital course materials accessible, including WCAG 2.1 AA expectations, alt text, captions, color contrast, accessible documents, and Canvas course materials.

Policy 4-004: University Information Security Policy

https://regulations.utah.edu/it/4-004.php

University policy establishing expectations for protecting university information systems and data.

Education is Utah 2030 Strategic Plan

https://education.utah.edu/_resources/documents/strategic-plan-v5.pdf

The College of Education’s strategic plan, which grounds the AI Framework in the College’s mission, vision, values, priorities, and 2024-2030 goals.

Policy 6-410: Student Academic Performance, Academic Conduct, and Professional and Ethical Conduct

https://regulations.utah.edu/academics/6-410.php

University policy governing academic misconduct, professional conduct, and student academic responsibilities.

Gen-AI Acceptable Use Guidance

https://ai.utah.edu/ai-student-guide/acceptable-use.php

University guidance for student AI use, including acceptable-use expectations, disclosure practices, and assignment-level clarity.

Rule 4-004C: Data Classification and Encryption

https://regulations.utah.edu/it/rules/Rule4-004C.php

University rule defining public, sensitive, and restricted data classifications and related encryption requirements.

Institutional Review Board

https://irb.utah.edu/

University resource for research involving human participants, relevant when AI-supported research involves student, participant, school, family, or community data.

Tools and Infrastructure

https://ai.utah.edu/tools/

Office of AI list of approved, under-review, and not-approved AI tools and infrastructure resources for university use.