AI Guidelines & Resources

 

Guidelines for College of Education ÀóÖ¦ÊÓƵ on the Use of Artificial Intelligence

Recommendations of the CED AI Advisory Committee, May 5, 2026

Preamble

The College of Education promotes equity and excellence in education. It prepares educators, counselors, and administrators to exercise independent judgment, think critically, and serve students and communities with integrity.  

Used thoughtfully and in ways consistent with our professional values, artificial intelligence tools can support research, strengthen practice, and expand what is possible in teaching and learning. These guidelines are intended to provide a framework that is grounded in equity, integrity, and the understanding that human judgment comes first. 

1. Scope and Governance

These guidelines establish baseline expectations for AI use across all College of Education coursework, clinical practice, and professional activities.

AI refers to technology that uses computational models and large datasets to generate media, analyze complex information, and recognize patterns. As of this writing, there are many models commonly used in education, like ChatGPT, Copilot, Claude, and Gemini (general purpose AI tools), Grammarly (writing feedback), Khanmigo (tutoring and lesson planning), Adobe Firefly (images), and Zoom AI Companion (meeting summaries).

Improper use of AI may constitute a violation of academic integrity subject to ÀóÖ¦ÊÓƵ’s Policy on Academic Integrity Regarding Cheating and Plagiarism, College of Education Graduate Programs Policy on Academic Integrity, and/or the CED Student Code of Professional Conduct. 

ÀóÖ¦ÊÓƵ may expect guidance from instructors on their policies regarding AI use through syllabus statements and other means. Because instructors may vary widely in how they allow or restrict the use of AI in their courses, it is the student’s responsibility to learn and follow the specific expectations outlined by the instructor for each course. AI is continuously evolving, and instructor expectations may also differ depending on the instructional circumstances.  ÀóÖ¦ÊÓƵ should ask their instructors for clarification regarding their AI policies. These course-specific expectations operate within the broader framework of College and University instructional practices and must be consistent with them.  

2. Transparency and Disclosure

Transparency is foundational to academic and professional integrity. As AI tools increasingly shape how people conduct research, write, and communicate, it is essential that all members of the College community are prepared to engage with them responsibly. Instructors may require disclosure and transparency if AI tools contribute to any assignment, project, or deliverable. Disclosure may include a brief statement describing how AI was used (e.g., brainstorming ideas, editing for clarity, generating visuals), along with the name of the tool. 

3. Critical Thinking and Academic Integrity

The College of Education prepares reflective practitioners, and that reflection must be authentically yours. If AI use is permitted by the instructor, it may support the thinking process through brainstorming, feedback, or idea exploration. It can be used to support critical thinking but may not substitute for it.  Submitting AI-generated analysis or judgment as one’s own violates the University and College academic integrity policies.

4.  Evidence-Based Verification

ÀóÖ¦ÊÓƵ are accountable for all work they submit, regardless of how AI tools contributed to it. What that accountability requires depends on the nature of the task. When AI generates factual or research-based content, students must verify it against credible sources, because AI tools can produce inaccurate, outdated, or fabricated information, including false citations. When AI is used to generate artifacts such as graphics, survey instruments, or instructional materials, students are responsible for evaluating their accuracy, appropriateness, and alignment with professional standards. When AI assists with structural or stylistic decisions, students must exercise their own professional judgment in accepting, modifying, or rejecting those suggestions. In all cases, students are responsible for the integrity of what they submit. 

5. Student and Community Privacy 

ÀóÖ¦ÊÓƵ must be mindful that personal information shared with AI tools may not be kept private. When using AI systems, students should consider relevant privacy policies. An advantage of using AI chatbots offered through ÀóÖ¦ÊÓƵ is that these systems are typically not permitted to use user-entered data to train their AI models. For the most up to date information about privacy policies of AI products used at ÀóÖ¦ÊÓƵ, see university sources. While students may elect to disclose their own information to AI systems, they must never input Personally Identifiable Information (PII) about others, including PK-12 students, families, school staff, or community members, without proper consent or permission from the owners of the data. For example, names, demographic data, behavioral records, assessment data, or case study details from field placements and clinical experiences should not be shared with AI tools. Doing so may violate FERPA, HIPAA, professional ethics codes, and the trust of the communities we serve.

6. Critical and Equity-Centered AI Practice

ÀóÖ¦ÊÓƵ must critically evaluate AI-generated content for bias. This includes assessing how outputs reflect or omit perspectives related to race, ethnicity, language, gender, sexual orientation, disability, and socioeconomic status. Such biases may appear not only in what AI produces but also in what it overlooks, whose knowledge it centers, and how it describes non-dominant communities. 

AI systems can offer a range of benefits in educational settings, including support for differentiated instruction, early intervention, or administrative efficiency. AI systems have also been used to surveil students, automate discipline, and make algorithmic recommendations about placement and academic trajectories, with disproportionate harm to students of color, students with disabilities, English language learners, and students from low-income communities. Responsible practice means bringing professional judgment to bear on both dimensions: making use of AI's benefits while recognizing and resisting its harmful applications.

7. Professional Judgment Over AI Dependency

AI tools should never replace the professional judgment students bring as current or future educators, counselors, or administrators. Decisions that affect student learning, wellbeing, placement, or discipline must be grounded in professional training, ethical obligations, and human relationships. While AI may inform or support professional decision-making, it cannot account for the full context, complexity, and care that ethical practice requires. Defaulting to AI outputs in place of your own reasoned judgment is inconsistent with the standards of care expected across education professions. 

8. A Final Word

These guidelines will evolve. ÀóÖ¦ÊÓƵ’ professions will continue to define what responsible AI use looks like. What the College asks of students throughout that evolution is that they bring integrity, critical judgment, and care for the communities they serve to every decision they make. Questions about uses of AI in a course should be directed to the instructor. 

Further resources on AI use at ÀóÖ¦ÊÓƵ and CED are available at: ÀóÖ¦ÊÓƵ AI Technology, the CED Teaching and Learning Portal, and linked below.

AI Resources & Professional Learning Opportunities 

Linked in the accordions below

Professional learning opportunities and resources and from the CSU Chancellor’s Office, ÀóÖ¦ÊÓƵ, and our district partners. 

CSU System-wide Resources 

    • The ÀóÖ¦ÊÓƵState University system’s central hub for AI resources, training opportunities, and microcredentials for students, faculty, staff, and alumni.
    • Training opportunities from the CSU and AI partners to use AI ethically, responsibly, and efficiently.
    • Free microcredential courses for students, faculty, staff, and alumni on using AI for teaching, learning, and working in the CSU.
    • Paid opportunities for CSU students partnering with members of the AI Workforce Acceleration Board. Micro-Internships are short-term, paid, professional assignments that are similar to those given to new hires or other internships.

Campus-wide Resources  

    • ÀóÖ¦ÊÓƵ’s enterprise AI assistant, available to students, faculty, and staff with institutional data protection. 

From the ÀóÖ¦ÊÓƵDepartment of Education (CDE) 

    • This guidance emphasizes keeping human connection, ethical decision-making, and adaptable system design at the center of AI’s role in TK–12 education. 

From our District Partners 

    • AI guidelines and community engagement materials from ABC Unified School District, a key clinical practice partner for CED students. 
    • AI guidance, professional development, and the LACOE Generative AI in TK-12 Education Guidelines from the Los Angeles County Office of Education. 

Resources on disclosure, citation, and the ethical boundaries of AI use in academic and professional work. 

    • The American Psychological Association’s guidelines to properly acknowledge and explain any use of AI tools in academic writing.  
    • Practical APA guidance on how to cite AI-generated content in academic work. 
    • Examines how writing pedagogy must evolve in response to AI tools. 
    • Rethinking assessment for educators by offering practical guidance, examples, and resources for designing learning experiences that emphasize authentic, higher-order thinking from Teacher’s College, Columbia University.  
    • Practical strategies for educators in redesigning assessments to address the challenges of generative AI, while still ÀóÖ¦ÊÓƵmeaningful learning and protecting academic integrity from Johns Hopkins University.  

Resources for developing the capacity to evaluate AI tools, understand their limitations, and engage with them as critically informed professionals. 

    • Framework for identifying and organizing the skills and knowledge needed to help educators and students navigate generative AI from Stanford University. 
    • Overview of AI literacy competencies and why they matter in professional contexts. 
    • Framework for evaluating the credibility of information sources, such as AI-generated content. 
    • AI literacy curriculum from MIT, designed for educators and adaptable across grade levels and professional contexts. 
    • AI literacy resources for educators, families, and young people, with a focus on critical and ethical engagement. 
    • Pew Research Center (2025): Research on how young people engage with AI tools. 
    • Zhang et al. (2026): Research on enhancing university students’ AI and media literacy through multimodal digital composing. 
    • Strategies for building critical AI literacy across K–12 and higher education contexts from Harvard Graduate School of Education. 

Resources on bias in AI systems, deficit framing, cultural responsiveness, and the professional responsibility to recognize and challenge inequitable outputs. 

    • Explanation of how bias enters AI systems and manifests in outputs. 
    • Organization working to raise awareness of AI bias and its harms. 
    • Noble (2018): Scholarship on how search algorithms and AI systems reproduce and amplify racial and gender inequities. 
    • Verma (2019): Research that examines how algorithmic systems cause harm in high-stakes contexts including education, hiring, and criminal justice. 
    • Stewart & Rodgers (2025): A framework for scholars, instructors, and students to examine AI through a critical media studies lens, with attention to representation and equity. 

Resources on the documented harms of AI in educational and institutional settings, and on maintaining professional judgment in contexts where AI tools influence decisions about students. 

    • Research on the social and political factors that shape the use of AI in education, including how schools collect data and use monitoring technologies.  
    • UNESCO’s framework for teacher AI competency, including dimensions of professional judgment, ethics, and human-centered practice. 
    • UNESCO’s framework for student AI competency, which aims to help educators integrating artificial intelligence learning objectives into official school curricula to prepare students to be responsible and creative citizens in the era of AI. 
    • The National Association of Social Workers’ resources and guidance on AI use in social work and counseling practice, aligned with the NASW Code of Ethics. 
    • The American Counseling Association’s recommendations for using AI ethically in counseling – covering what clients need to know, how to protect their privacy, and the appropriate limits of AI in clinical work. 

Practical resources for integrating AI tools across professional roles, including instructional design, differentiated learning, clinical and administrative practice. 

Teaching and Instructional Design 

    • Bowen & Watson (2nd ed., 2025): A comprehensive practitioner guide to integrating AI thoughtfully into higher education teaching practice. 
    • Watson (2024): Practical guidance for K–12 educators on using AI tools to support student learning, companion volume to Teaching with AI. 
    • Research on AI-supported lesson design, with a focus on inquiry-based approaches. 
    • A curated collection of AI teaching resources, lesson plans, and professional learning materials. 

Differentiated Learning and Universal Design for Learning 

    • Strategies for using AI tools to support differentiated instruction across learning profiles. 
    • Approaches to AI-assisted differentiation that are sustainable for classroom teachers. 
    • Examples and frameworks for AI-supported differentiated practices. 
    • Accessible overview of AI applications for meeting diverse student learning needs. 
    • Research on AI tools that support inclusive and differentiated practices. 
    • Frameworks for integrating AI into UDL practice to expand access and representation. 
    • Guidance in aligning AI tool use with UDL principles. 

Self-Reflection and Professional Growth 

    • Research using AI as a tool for professional reflection within communities of practice.