California Education and Artificial Intelligence Institutional Review Guide

California Education AI Institutional Review Guide

A recommended framework for examining how selected California schools and universities use artificial intelligence and whether their educational practices support the development of a well-formed mind.

Recommended institutional review

Every school and university should conduct a documented review of artificial intelligence in education. The review should connect the institution’s educational purpose to its approved tools, actual teaching practice, assessment methods, student and family communication, privacy safeguards, educator preparation, and measured results. It should also determine whether AI strengthens learning and judgment or displaces the intellectual, moral, relational, and practical work education is expected to develop.

Why the review is needed

Public policies and program descriptions often show that an institution is considering AI, but they do not necessarily show how guidance operates in classrooms or whether outcomes are measured. A responsible review should distinguish four evidence levels.

Required scope

• Educational purpose and the meaning of a well-formed mind
• AI policy governance approved tools privacy and intellectual property
• Curriculum assignments assessment academic integrity and unaided mastery
• Educator preparation student and family voice access and accommodations
• Mental fitness relationships attention confidence dependency and wellness
• COVID-era disruption school closures online learning family burden and lessons retained
• Measured benefits harms incidents corrections and unresolved questions

Recommended review process

Standards for a credible review

• Policies should identify an owner effective date covered population and approval authority.
• Examples should represent more than one discipline grade or course when practices vary.
• Tool availability and event programming should not be treated as proof of educational benefit.
• Outcome claims should identify the measure baseline comparison period and limitations.
• Student parent and educator perspectives should supplement rather than replace documentary evidence.
• Missing public evidence should remain unresolved rather than being treated as favorable or unfavorable proof.
• The institution should state corrective actions and a date for the next review.

Completion standard

A review is complete when the institution can show a traceable connection from educational purpose to policy, approved tool, implemented practice, assessment, safeguards, feedback, and measured result. Where that chain cannot be shown, the report should identify the gap, responsible office, corrective action, and review date.

Institution 1 UC Berkeley

The Berkeley AI Hub is sponsored by the Provost’s Advisory Council on AI and serves as a central directory for campus AI tools, training, and guidance. Research Teaching and Learning publishes the instructional guidance.

Why this review should be conducted

The institutional review should examine how UC Berkeley’s AI guidance is translated into teaching practice and how the university determines whether that practice strengthens independent reasoning and learning.

Offices and participants

Berkeley AI Hub and Research Teaching and Learning.

Evidence that should be examined

Campuswide guidance approved-tool rules training records representative course materials student and faculty feedback and measured learning or integrity results.

Recommended review actions

• Appoint one accountable review lead and record the participating offices and stakeholder groups.
• Collect current institutional documents and representative examples of actual educational practice.
• Compare stated policy with implementation and identify material differences across units or courses.
• Evaluate effects on unaided mastery judgment integrity relationships access and well-being.
• Separate COVID emergency practices from later decisions made for ordinary educational use.
• Publish verified findings unresolved gaps corrective actions owners and the next review date.

Public reference points

https://ai.berkeley.edu/

https://rtl.berkeley.edu/about/contact

https://rtl.berkeley.edu/resources/ai-teaching-learning

Public reference points last checked September 12 2026. Current institutional documents should be confirmed when the review begins.

UC Berkeley review questions

The review should answer each question with an institutional claim, implemented practice, measured result, or unresolved gap. Each source should identify its owner, effective date, scope, and access status.

1 Which office owns campuswide AI guidance for teaching and learning, and which policies are mandatory rather than advisory?

2 Obtain and examine the current guidance, its effective date, revision history, covered populations, and approval authority.

3 Which generative AI tools are institutionally licensed or approved for students and instructors, and what data classifications apply to each?

4 Obtain and examine representative current syllabi or assignment policies from at least three disciplines and explain how they were selected.

5 How does Berkeley distinguish productive AI assistance from substitution for reading, writing, analysis, calculation, or original judgment?

6 What training is required or offered to instructors, teaching assistants, and students, and what participation records are maintained?

7 How are privacy, intellectual property, accessibility, bias, and unequal access addressed in instructional use?

8 What student or faculty feedback has changed campus guidance, course design, or approved tools?

9 What academic-integrity procedures apply when AI use is unclear, and what evidence is considered reliable?

10 What measures assess learning, critical thinking, metacognition, dependence, integrity, or well-being in AI-supported courses?

11 What current practices arose from Berkeley’s COVID-era remote-teaching experience, and which were later revised or discontinued?

12 Which offices and stakeholder groups should be responsible for completing the review, validating the findings, and implementing follow-up actions?

Review record

Institution 2 UCLA

UCLA’s responsible-use guidance directs instructors to the Teaching and Learning Center for instructional questions and identifies Digital and Technology Solutions for campus technology support.

Why this review should be conducted

The institutional review should examine how UCLA’s responsible-use principles and student AI-use findings have affected course design, assessment, and student formation.

Offices and participants

Teaching and Learning Center and Digital and Technology Solutions.

Evidence that should be examined

Responsible-use guidance student AI-use findings course materials assessment changes approved-tool safeguards and evidence that findings changed practice.

Recommended review actions

• Appoint one accountable review lead and record the participating offices and stakeholder groups.
• Collect current institutional documents and representative examples of actual educational practice.
• Compare stated policy with implementation and identify material differences across units or courses.
• Evaluate effects on unaided mastery judgment integrity relationships access and well-being.
• Separate COVID emergency practices from later decisions made for ordinary educational use.
• Publish verified findings unresolved gaps corrective actions owners and the next review date.

Public reference points

https://dts.ucla.edu/initiatives/ai/guiding-principles-responsible-use

https://teaching.ucla.edu/

Public reference points last checked September 12 2026. Current institutional documents should be confirmed when the review begins.

UCLA review questions

The review should answer each question with an institutional claim, implemented practice, measured result, or unresolved gap. Each source should identify its owner, effective date, scope, and access status.

1 Which office owns universitywide AI guidance for teaching and learning, and how is responsibility divided among the Teaching and Learning Center, Academic Senate, and Digital and Technology Solutions?

2 Obtain and examine current policies, guidance, approval records, effective dates, and the populations to which each document applies.

3 How did UCLA’s student AI-use research change institutional guidance, faculty support, assignments, or student communication?

4 Which AI tools are approved for instructional use, and what privacy, security, retention, intellectual-property, and accessibility controls apply?

5 Obtain and examine representative course policies and assignments showing prohibited, limited, and intentionally integrated AI use.

6 How do instructors verify a student’s independent reasoning, writing, and mastery when AI assistance is permitted?

7 What faculty and teaching-assistant development is available, and how does UCLA assess whether it changes practice?

8 How are student voice, unequal access, disability accommodations, and alternative assignments addressed?

9 What academic-integrity procedures apply to suspected AI misuse, and what limits has UCLA placed on AI-detection claims?

10 What measures assess learning quality, critical thinking, dependence, well-being, or integrity rather than tool adoption alone?

11 Which COVID-era online-learning practices remain in use, and what evidence supports retaining or changing them?

12 Which offices and stakeholder groups should be responsible for completing the review, validating the findings, and implementing follow-up actions?

Review record

Institution 3 Stanford University

The Teaching Commons publishes Stanford’s Artificial Intelligence Teaching Guide and coordinates AI Meets Education at Stanford resources and programs.

Why this review should be conducted

The institutional review should examine how Stanford’s AI literacy and AI Meets Education work moves from guidance and exemplars into implemented practice and measured learning.

Offices and participants

Teaching Commons with University IT privacy and academic-integrity participation as needed.

Evidence that should be examined

AI literacy materials AI Meets Education examples course policies assignments participation evidence student input and measures of critical thinking or metacognition.

Recommended review actions

• Appoint one accountable review lead and record the participating offices and stakeholder groups.
• Collect current institutional documents and representative examples of actual educational practice.
• Compare stated policy with implementation and identify material differences across units or courses.
• Evaluate effects on unaided mastery judgment integrity relationships access and well-being.
• Separate COVID emergency practices from later decisions made for ordinary educational use.
• Publish verified findings unresolved gaps corrective actions owners and the next review date.

Public reference points

https://teachingcommons.stanford.edu/teaching-guides/artificial-intelligence-teaching-guide

https://teachingcommons.stanford.edu/about-stanford-teaching-commons

Public reference points last checked September 12 2026. Current institutional documents should be confirmed when the review begins.

Stanford University review questions

The review should answer each question with an institutional claim, implemented practice, measured result, or unresolved gap. Each source should identify its owner, effective date, scope, and access status.

1 Which Stanford office coordinates AI policy and guidance for teaching, and what authority does the Teaching Commons or AI Meets Education at Stanford hold?

2 Obtain and examine current policies, guidance, approval records, effective dates, and covered populations.

3 How many instructors or courses have used the Critical AI Literacy resource, workshop materials, or AI Meets Education examples?

4 Which examples are pilots, recommended practices, or adopted practices, and how does Stanford identify those distinctions publicly?

5 Obtain and examine representative assignments that use AI and comparable assignments that intentionally exclude it to protect specific learning objectives.

6 How does Stanford assess critical thinking, metacognition, original writing, disciplinary mastery, and the ability to work without AI?

7 How are student perspectives incorporated into course policies and university guidance?

8 Which enterprise tools are approved, and what privacy, retention, intellectual-property, bias, accessibility, and security controls apply?

9 What academic-integrity procedures apply, and how are students protected from unsupported AI-detection conclusions?

10 What evidence shows benefit, harm, dependence, displacement of effort, or changes in faculty-student interaction?

11 How did the COVID-era redesign of teaching support influence current AI and disruption-preparation practices?

12 Which offices and stakeholder groups should be responsible for completing the review, validating the findings, and implementing follow-up actions?

Review record

Institution 4 USC

USC’s Center for Excellence in Teaching provides AI teaching resources. USC’s enterprise guidance directs teaching-design questions to that Center and integrity questions to the Office of Academic Integrity.

Why this review should be conducted

The institutional review should examine the educational rationale, classroom implementation, safeguards, and measured effects of USC’s enterprise AI tools, including ChatGPT Edu.

Offices and participants

Center for Excellence in Teaching Office of Academic Integrity and Information Technology Services.

Evidence that should be examined

ChatGPT Edu rationale adoption data course materials training integrity procedures privacy controls accommodations and measured educational effects.

Recommended review actions

• Appoint one accountable review lead and record the participating offices and stakeholder groups.
• Collect current institutional documents and representative examples of actual educational practice.
• Compare stated policy with implementation and identify material differences across units or courses.
• Evaluate effects on unaided mastery judgment integrity relationships access and well-being.
• Separate COVID emergency practices from later decisions made for ordinary educational use.
• Publish verified findings unresolved gaps corrective actions owners and the next review date.

Public reference points

https://cet.usc.edu/contact-us/

https://academicintegrity.usc.edu/faculty-resources/

https://itservices.usc.edu/ai/chatgpt-edu-at-usc/

Public reference points last checked September 12 2026. Current institutional documents should be confirmed when the review begins.

USC review questions

The review should answer each question with an institutional claim, implemented practice, measured result, or unresolved gap. Each source should identify its owner, effective date, scope, and access status.

1 Which office owns the educational purpose and evaluation of ChatGPT Edu, separate from licensing, access, and technical operation?

2 Obtain and examine the current generative AI policy, teaching guidance, data rules, approval records, effective dates, and covered populations.

3 What adoption data are maintained by school, course, role, and instructional purpose, and what do those data not establish?

4 Obtain and examine representative syllabi and assignments showing prohibited, limited, disclosed, and intentionally integrated use.

5 How does USC protect independent reading, writing, analysis, creative work, and the ability to perform without AI assistance?

6 What training is offered or required for faculty, staff, students, and academic-integrity decision makers?

7 How are confidential data, chat retention, intellectual property, accessibility, bias, and accommodations governed?

8 How are students expected to disclose and cite AI use, preserve work history, and explain the contribution of the tool?

9 What academic-integrity case data exist concerning AI, and what evidentiary limits apply to detection tools or probabilistic indicators?

10 What measures examine learning, judgment, dependence, student confidence, well-being, or faculty-student interaction?

11 How did COVID-era remote teaching affect current technology expectations, course design, and student-support practices?

12 Which offices and stakeholder groups should be responsible for completing the review, validating the findings, and implementing follow-up actions?

Review record

Institution 5 Caltech

Caltech’s online-teaching support page publishes CTLO’s main inbox. The Hixon Writing Center and Information Management Systems and Services publish related writing and tool guidance.

Why this review should be conducted

The institutional review should examine how Caltech governs generative AI while protecting original problem solving, scientific reasoning, writing, and disciplinary mastery.

Offices and participants

Center for Teaching Learning and Outreach Hixon Writing Center and Information Management Systems and Services.

Evidence that should be examined

Institute and divisional rules representative assignments approved-tool controls training integrity procedures and evidence of unaided mastery.

Recommended review actions

• Appoint one accountable review lead and record the participating offices and stakeholder groups.
• Collect current institutional documents and representative examples of actual educational practice.
• Compare stated policy with implementation and identify material differences across units or courses.
• Evaluate effects on unaided mastery judgment integrity relationships access and well-being.
• Separate COVID emergency practices from later decisions made for ordinary educational use.
• Publish verified findings unresolved gaps corrective actions owners and the next review date.

Public reference points

https://teach.caltech.edu/

https://ctlo.caltech.edu/

https://writing.caltech.edu/resources-for-instructors/generative-ai-and-college-writing

https://www.imss.caltech.edu/

Public reference points last checked September 12 2026. Current institutional documents should be confirmed when the review begins.

Caltech review questions

The review should answer each question with an institutional claim, implemented practice, measured result, or unresolved gap. Each source should identify its owner, effective date, scope, and access status.

1 Which office owns institute-level AI guidance for teaching, and which decisions remain with divisions, options, or individual instructors?

2 Obtain and examine current institute and divisional policies, their effective dates, scope, approval authority, and revision history.

3 Which AI tools are approved or prohibited, and what data classifications, retention rules, and security controls apply?

4 Obtain and examine representative course policies and assignments from science, engineering, humanities, and writing instruction.

5 How do instructors distinguish AI-supported exploration from substitution for derivation, calculation, coding, laboratory reasoning, reading, or writing?

6 How is a student’s unaided mastery verified when AI assistance is allowed during part of the learning process?

7 What training or consultation is available to faculty, teaching assistants, and students, and what evidence shows changes in practice?

8 What academic-integrity process applies to AI-related concerns, and what evidence is considered sufficient?

9 How are privacy, intellectual property, accessibility, bias, and unequal access addressed?

10 What measures examine reasoning quality, mastery, dependence, error detection, or student well-being?

11 Which COVID-era instructional changes remain, and what evidence supports their continued use?

12 Which offices and stakeholder groups should be responsible for completing the review, validating the findings, and implementing follow-up actions?

Review record

Institution 6 LMU

LMU’s Center for Faculty Development publishes AI-in-teaching resources. The university has also described a Provost-level generative AI task force and other AI work.

Why this review should be conducted

The institutional review should examine how LMU connects its AI work to teaching practice, human judgment, ethics, and its Jesuit and Catholic educational purpose.

Offices and participants

Center for Faculty Development Provost GenAI task force Information Technology Services and mission or ethics faculty.

Evidence that should be examined

Task-force recommendations implementation records course materials faculty development tool controls mission alignment and measured outcomes.

Recommended review actions

• Appoint one accountable review lead and record the participating offices and stakeholder groups.
• Collect current institutional documents and representative examples of actual educational practice.
• Compare stated policy with implementation and identify material differences across units or courses.
• Evaluate effects on unaided mastery judgment integrity relationships access and well-being.
• Separate COVID emergency practices from later decisions made for ordinary educational use.
• Publish verified findings unresolved gaps corrective actions owners and the next review date.

Public reference points

https://academics.lmu.edu/cfd/resources/teachinghub/aiinteachinglearning/

https://academics.lmu.edu/

https://its.lmu.edu/ai/aitoolsservices/

Public reference points last checked September 12 2026. Current institutional documents should be confirmed when the review begins.

LMU review questions

The review should answer each question with an institutional claim, implemented practice, measured result, or unresolved gap. Each source should identify its owner, effective date, scope, and access status.

1 What recommendations did the Provost’s generative AI task force make, which were adopted, and who owns implementation?

2 Obtain and examine current policies and guidance, their effective dates, approval authority, covered populations, and revision history.

3 Which AI initiatives are universitywide practices, school or course practices, research projects, pilots, or public events?

4 Obtain and examine representative syllabi and assignments showing how AI is prohibited, limited, disclosed, or intentionally integrated.

5 How does LMU connect AI use to intellectual virtue, conscience, ethical judgment, human dignity, care for the whole person, and responsibility to others?

6 How are original reading, writing, reflection, dialogue, and the ability to perform without AI protected?

7 What faculty development is offered or required, and how does LMU evaluate whether it changes course practice?

8 Which tools are approved, and what privacy, retention, intellectual-property, bias, accessibility, and unequal-access controls apply?

9 How are student perspectives, academic-integrity concerns, accommodations, and alternative assignments handled?

10 What measures assess learning, judgment, dependence, relationships, well-being, or alignment with LMU’s educational mission?

11 How did COVID-era online teaching affect current technology practices, faculty preparation, and student support?

12 Which offices and stakeholder groups should be responsible for completing the review, validating the findings, and implementing follow-up actions?

Review record

Institution 7 Thomas Aquinas College

The College’s public AI and Human Flourishing symposium addresses AI through philosophical and theological questions. The official contact page uses a general inquiries form.

Why this review should be conducted

The institutional review should examine the relationship between Thomas Aquinas College’s public examination of AI and Human Flourishing and its curriculum, pedagogy, student guidance, and institutional practice.

Offices and participants

California dean tutors technology staff and the AI and Human Flourishing symposium organizer.

Evidence that should be examined

Any AI policy or guidance symposium follow-through course and honor materials tutor practice student experience and evidence concerning intellectual formation.

Recommended review actions

• Appoint one accountable review lead and record the participating offices and stakeholder groups.
• Collect current institutional documents and representative examples of actual educational practice.
• Compare stated policy with implementation and identify material differences across units or courses.
• Evaluate effects on unaided mastery judgment integrity relationships access and well-being.
• Separate COVID emergency practices from later decisions made for ordinary educational use.
• Publish verified findings unresolved gaps corrective actions owners and the next review date.

Public reference points

https://www.thomasaquinas.edu/contact

https://www.thomasaquinas.edu/ai

https://www.thomasaquinas.edu/

Public reference points last checked September 12 2026. Current institutional documents should be confirmed when the review begins.

Thomas Aquinas College review questions

The review should answer each question with an institutional claim, implemented practice, measured result, or unresolved gap. Each source should identify its owner, effective date, scope, and access status.

1 Did the AI and Human Flourishing symposium lead to a policy, faculty guidance, curriculum review, student discussion, or continuing institutional initiative?

2 Does the College have a formal policy concerning student or faculty use of generative AI, and who approved and owns it?

3 Are AI tools used in coursework, tutorials, writing, translation, research, administration, or student support? distinguish each use.

4 How does the College determine whether AI can or cannot serve the formation of intellect, will, judgment, wisdom, and moral responsibility?

5 How are direct engagement with original texts, classroom discussion, memory, writing, and unaided reasoning protected?

6 What guidance helps tutors distinguish legitimate assistance from substitution for a student’s intellectual work?

7 Obtain and examine representative course, assignment, honor-code, or student-handbook materials that address AI or related technology.

8 How are privacy, intellectual property, factual error, bias, spiritual concerns, and dependency addressed?

9 What student and tutor perspectives have been gathered, and have they changed institutional practice?

10 Does the College measure any effect of technology or AI on reasoning, participation, writing, integrity, attention, or well-being?

11 Did the COVID period change the College’s use of remote instruction or technology, and what lessons influence present practice?

12 Which offices and stakeholder groups should be responsible for completing the review, validating the findings, and implementing follow-up actions?

Review record

Institution 8 Mother of Divine Grace School

Mother of Divine Grace describes a Catholic classical distance-learning model in which parents, educational consultants, online teachers, and school resources share responsibility for instruction.

Why this review should be conducted

The institutional review should examine how Mother of Divine Grace addresses artificial intelligence within a parent-directed classical program delivered through both the home and online services.

Offices and participants

School leadership academic services educational consultants online teachers parents and technology or privacy staff.

Evidence that should be examined

Family and student guidance online-class rules parent and educator preparation privacy and consent controls integrity practices alternatives and evidence of family or learning effects.

Recommended review actions

• Appoint one accountable review lead and record the participating offices and stakeholder groups.
• Collect current institutional documents and representative examples of actual educational practice.
• Compare stated policy with implementation and identify material differences across units or courses.
• Evaluate effects on unaided mastery judgment integrity relationships access and well-being.
• Separate COVID emergency practices from later decisions made for ordinary educational use.
• Publish verified findings unresolved gaps corrective actions owners and the next review date.

Public reference points

https://modg.org/

Public reference points last checked September 12 2026. Current institutional documents should be confirmed when the review begins.

Mother of Divine Grace School review questions

The review should answer each question with an institutional claim, implemented practice, measured result, or unresolved gap. Each source should identify its owner, effective date, scope, and access status.

1 What current AI guidance applies to enrolled students, parents, consultants, teachers, tutors, grading, and submitted work?

2 Who approved the guidance, when did it take effect, which ages or courses does it cover, and how is it communicated to families?

3 Which AI tools or uses are approved, limited, or prohibited in lesson preparation, tutoring, research, writing, translation, grading, and feedback?

4 How does the school preserve the parent’s supervisory role when a student uses online classes, digital resources, or AI-assisted tools?

5 How are memorization, direct reading, narration, writing, discussion, and independent judgment protected within the classical sequence?

6 How do teachers and consultants verify that submitted work reflects the student’s own knowledge and ability?

7 What training or scripts help parents recognize useful assistance, dependency, factual error, inappropriate content, or academic dishonesty?

8 How are student data, recordings, chat histories, vendor access, age requirements, consent, and intellectual property governed?

9 What alternatives exist for families that decline an AI-enabled service or have limited connectivity or technical capacity?

10 How does the school assess effects on learning, family burden, parent-child relationships, attention, confidence, mental fitness, or spiritual formation?

11 What did the school learn from COVID-era disruption about family readiness, online instruction, social isolation, and unequal capacity to supervise learning?

12 Which offices and stakeholder groups should be responsible for completing the review, validating the findings, and implementing follow-up actions?

Review record

Institution 9 Harvard-Westlake

The official leadership page identifies the Head of School, Chief Information Officer, and division leaders. The contact page provides central office and department routes rather than relevant role inboxes.

Why this review should be conducted

The institutional review should examine how Harvard-Westlake addresses AI across its curriculum and how the school protects independent judgment, academic integrity, relationships, and student well-being.

Offices and participants

Head of School Chief Information Officer middle and upper school academic leaders counseling and library staff.

Evidence that should be examined

Middle and upper school rules representative assignments teacher guidance parent and student communication tool safeguards integrity procedures and wellness evidence.

Recommended review actions

• Appoint one accountable review lead and record the participating offices and stakeholder groups.
• Collect current institutional documents and representative examples of actual educational practice.
• Compare stated policy with implementation and identify material differences across units or courses.
• Evaluate effects on unaided mastery judgment integrity relationships access and well-being.
• Separate COVID emergency practices from later decisions made for ordinary educational use.
• Publish verified findings unresolved gaps corrective actions owners and the next review date.

Public reference points

https://www.hw.com/about/contact-us

https://www.hw.com/about/Leadership

https://www.hw.com/academics/About-the-Curriculum/Course-Descriptions

Public reference points last checked September 12 2026. Current institutional documents should be confirmed when the review begins.

Harvard-Westlake review questions

The review should answer each question with an institutional claim, implemented practice, measured result, or unresolved gap. Each source should identify its owner, effective date, scope, and access status.

1 What schoolwide AI policies or guidance apply to students, faculty, staff, admissions, counseling, libraries, and technology services?

2 Who approved each document, when did it take effect, and how do expectations differ between the middle and upper schools?

3 Obtain and examine representative policies and assignments from writing, humanities, science, mathematics, computer science, arts, and interdisciplinary study.

4 How does the school distinguish useful AI assistance from substitution for reading, writing, research, creativity, or original judgment?

5 How are teachers expected to verify unaided knowledge and maintain consistent standards across departments and courses?

6 Which AI-enabled tools are approved, and what privacy, retention, age, consent, intellectual-property, accessibility, and bias controls apply?

7 What faculty training is required or offered, and how does the school determine whether it changes teaching or assessment?

8 How are students and parents informed, consulted, and able to question or seek alternatives to an AI-enabled practice?

9 What academic-integrity process applies to suspected AI misuse, and what protections address unreliable detection methods?

10 What evidence examines effects on learning, attention, anxiety, confidence, friendship, faculty-student relationships, or mental fitness?

11 How did COVID-era remote instruction affect current device use, online learning, family expectations, and wellness supports?

12 Which offices and stakeholder groups should be responsible for completing the review, validating the findings, and implementing follow-up actions?

Review record

Institution 10 High Tech High

High Tech High’s student handbook references Information Technology and AI-use policies. Its Board operates as a public agency, and HTH Compliance publishes a required route for Public Records Act requests.

Why this review should be conducted

The institutional review should examine how High Tech High’s AI-use policy operates within project-based learning and how the network evaluates effects on student agency, judgment, integrity, and well-being.

Offices and participants

System academic leadership school leaders teachers students technology staff compliance staff and the Board when governance evidence is reviewed.

Evidence that should be examined

The complete AI-use policy approval history public deliberation project materials training vendor controls integrity records student agency evidence and relevant public records.

Recommended review actions

• Appoint one accountable review lead and record the participating offices and stakeholder groups.
• Collect current institutional documents and representative examples of actual educational practice.
• Compare stated policy with implementation and identify material differences across units or courses.
• Evaluate effects on unaided mastery judgment integrity relationships access and well-being.
• Separate COVID emergency practices from later decisions made for ordinary educational use.
• Publish verified findings unresolved gaps corrective actions owners and the next review date.

Public reference points

https://www.hightechhigh.org/about/contact/

https://www.hightechhigh.org/board-and-governance/

https://compliance.hightechhigh.org/records-requests

https://www.hightechhigh.org/hth/

Public reference points last checked September 12 2026. Current institutional documents should be confirmed when the review begins.

High Tech High review questions

The review should answer each question with an institutional claim, implemented practice, measured result, or unresolved gap. Each source should identify its owner, effective date, scope, and access status.

1 Obtain and examine the complete current Information Technology and AI-use policies referenced in the student and parent handbook.

2 Who approved the policies, when did they take effect, what public deliberation occurred, and how were student and staff views incorporated?

3 Do the policies apply uniformly across the network, or do schools, grades, projects, and teachers set additional rules?

4 Obtain and examine representative project instructions and assessments showing prohibited, limited, disclosed, and intentionally integrated AI use.

5 How does AI use fit High Tech High’s design principles and project-based approach without replacing inquiry, making, revision, collaboration, or public presentation?

6 How do teachers verify individual knowledge and contribution within collaborative or AI-assisted projects?

7 What educator training is required or offered, and what implementation records are maintained?

8 Which tools are approved, and what vendor, privacy, retention, age, consent, accessibility, intellectual-property, and bias controls apply?

9 What academic-integrity or disciplinary records concerning AI are maintained, and how are students protected from unsupported detection claims?

10 What evidence examines learning, student agency, dependence, relationships, attention, confidence, or mental fitness?

11 How did COVID-era closures and online education affect current technology practice, project design, family communication, and student wellness?

12 Which offices and stakeholder groups should be responsible for completing the review, validating the findings, and implementing follow-up actions?

Review record