Top 10 AI Prompts and Use Cases and in the Education Industry in Los Angeles

California's education systems are moving fast from experimentation to operational use of AI: a UCLA advisory team found 66% of undergraduates reported using generative AI during the academic year, while the Los Angeles Unified School District piloted the “Ed” AI assistant with 55,000 students to deliver personalized alerts on grades, attendance and supports - early signs that districts need governance, literacy, and coordinated leadership to avoid uneven outcomes; the Los Angeles County Office of Education summit likewise emphasized governance and leadership for safe integration.

These realities mean Los Angeles educators require practical, job‑ready training in prompt design, tool selection, and ethical deployment - not abstract theory - so that AI augments teaching without amplifying bias or workload.

Nucamp's 15‑week AI Essentials for Work trains staff and faculty on prompt writing and workplace AI skills that map directly to these local needs.

Program	Length	Cost (Early bird)	Courses	Registration
AI Essentials for Work	15 Weeks	$3,582	AI at Work: Foundations; Writing AI Prompts; Job Based Practical AI Skills	Register for Nucamp AI Essentials for Work (15-week)

“AI has a lot of potential to do good in education, but we have to be very intentional about its implementation.” - Amy Eguchi

The top 10 prompts and use cases were selected through a three‑part, California‑focused filter: (1) pedagogical impact - preference for prompts that support CETL's transparent, equity‑centered practices and assignment redesigns (e.g., TILT and rubrics) and that can be introduced in a single 90‑minute workshop or play group from the Cal State LA Teaching & Learning with AI certificate program (Cal State LA Teaching & Learning with AI certificate program); (2) integrity and feasibility - avoidance of over‑reliance on brittle detectors given Turnitin's noted false positives and CETL's guidance on detection limits, so prompts emphasize process evidence, revision history, and metacognitive artifacts from the CETL recommendations for teaching and learning with AI (CETL recommendations for teaching and learning with AI); and (3) local demand and scale - alignment with campus survey findings about student and faculty AI use to ensure prompts meet real needs identified in the Cal State LA artificial intelligence (AI) surveys (Cal State LA AI surveys on student and faculty AI use).

The result: practical, workshop‑friendly prompts that reduce policing, elevate human judgment, and map directly to institutional professional development pathways.

“Every one of our graduates will be entering a workforce that will increasingly rely on artificial intelligence.” - CSU Chancellor Mildred García

Design a course‑level AI policy with clear, actionable options - ban, conditional use with required attribution and process evidence, or encouraged use with mandatory fact‑checking and citation - so instructors can align course rules to Cal State LA's evolving academic honesty standards and reduce ad‑hoc policing.

Cal State LA's CETL guidance breaks this down into succinct syllabus language and classroom practices (examples for prohibition, transparency, and incorporation), recommends teaching students how to use AI tools, and warns that detection tools are unreliable and costly - Turnitin's AI detector was deactivated June 1, 2024 - so policies should instead require process artifacts (revision histories, live demonstrations, or short reflective memos) and link to campus academic honesty procedures for enforcement (Cal State LA CETL recommendations for teaching and learning with AI; Cal State LA Academic Honesty Policy Chapter V).

For faster adoption, offer three syllabus templates (ban/transparency/incorporation), invite students to co‑create classroom norms for buy‑in, and embed a one‑page assignment redesign checklist so faculty can swap a single line in Canvas and keep assessments focused on distinctly human skills.

Policy Type	Key Syllabus Elements
Ban	Explicit prohibition, examples, consequences, link to campus honesty policy
Conditional	Permitted uses, required citations, process evidence (revision history)
Incorporate	Uses encouraged, AI literacy checklist, mandatory fact‑check/reflection

“CSU faculty and staff aren't just adopting AI - they are reimagining what it means to teach, learn, and prepare students for an AI‑infused world.” - Nathan Evans

Redesign assignments with a Bowen & Watson–inspired rubric adapter that shifts grading from final products to process evidence - require staged deliverables (proposal → draft → final), submission of AI interaction logs when tools were used, and a short reflective justification (200–500 words) describing how the student evaluated and validated any AI output; this approach both preserves California learning outcomes and follows practical guidance for preserving integrity found in the Bowen & Watson process template and the University of Alberta assessment design recommendations (Bowen & Watson process template; U of Alberta assessment design).

Pair the rubric with quick, LA‑ready anti‑cheating prompts for graders - score process documentation, critical evaluation of AI outputs, and connection to class discussion more heavily than surface grammar - and build an in‑class baseline (short supervised writes) so large deviations trigger a respectful clarification meeting as recommended by UMass CTL (UMass guidance on suspected GenAI misuse).

The memorable payoff: replacing one high‑stakes essay with three scaffolded checkpoints plus an AI‑use log reduced opportunities for undetected fabrication while giving instructors concrete, rubric‑aligned evidence they can review in a single 10‑minute pass per submission.

Rubric Dimension	What to Submit	Why it Reduces Misuse
Process Evidence	Proposal, annotated draft, final	Makes authorship visible and auditable
AI Evaluation	AI interaction logs + 200–500 word reflection	Requires critical vetting and citation of AI outputs
Authenticity	In‑class baseline sample or oral check	Provides a verifiable stylistic benchmark

“We often suspect AI usage but cannot know for certain.” - Lance Eaton

Equip Los Angeles classrooms with a prompt‑engineering lesson plan template that turns vague AI requests into standards‑aligned, classroom‑ready materials: start with a clear role, audience, and output format (e.g., “act as a middle‑school science teacher; produce a 45‑minute lesson with objectives, materials, and an assessment”) and then chain smaller prompts to build hook, guided practice, and assessment - an approach proven to generate comprehensive plans in minutes and slash planning time (Teaching Channel: 65 AI prompts for K‑12 lesson planning).

Emphasize iterative refinement and rubric alignment so learning objectives become specific and measurable; Khan Academy's work shows that breaking prompts into sections and validating outputs against teaching rubrics yields clearer, student‑facing objectives and more reliable lesson components (Khan Academy guide to prompt engineering for effective lesson planning).

Pair ready‑to‑use K–12 prompt templates from prompt libraries with in‑service practice sessions (model prompts, refine, compare outputs) so faculty across LA can produce differentiated lessons tied to California standards in a single planning block; teacher teams report the concrete payoff: usable lesson drafts ready for classroom trial after one 30–60 minute prompt session (Flint K‑12 prompt engineering guide for educators).

“Create a 1 hour lesson plan for introducing the water cycle suitable for 4th graders, including specific objectives and activities such as group work and guided practice.”

When a submission raises a suspected‑AI flag, follow a short, scripted workflow that centers evidence, equity, and clear communication: (1) review the work for telltale signs (invented citations, vague claims, missing textual evidence) and immediately pull revision history, timestamps, or AI interaction logs as primary artifacts - Cal State LA's CETL recommends privileging process evidence over detector scores (Cal State LA CETL recommendations on AI in teaching and learning); (2) treat automated detectors as advisory only - California reporting shows Turnitin and similar tools produce false positives and create privacy and cost concerns, so avoid unilateral sanctions based on a score alone (news report on AI detectors and Turnitin in California); (3) hold a neutral, focused meeting with the student: ask them to explain step‑by‑step how the piece was produced, show drafts/revision timestamps or a 10‑minute live rewrite, and request any research notes or source scans (time‑stamped docs and short live demonstrations routinely resolve ambiguity); (4) document the exchange, follow your syllabus AI policy and campus academic‑integrity process, and offer remediation or referral to supports rather than immediate punitive action; and (5) if the student contests findings, preserve records and advise them of appeal rights or legal support resources (guidance for accused students recommends gathering drafts, notes, and timelines) (student defense checklist for accused students and AI-related allegations).

The practical payoff: a brief request for Google Docs revision history plus a short, respectful interview often settles cases faster than running multiple detectors and protects vulnerable students from biased false positives.

Step	Action
Review	Check citations, hallucinations, and revision history
Converse	Neutral meeting; ask for process artifacts or a live 10‑minute rewrite
Decide	Follow syllabus AI policy, document outcome, offer supports/appeal info

Proceed with caution.

“Studyhall” tutor

connects a course knowledge base (syllabus PDFs, lecture notes, Canvas exports, library guides) to an LLM so students get grounded, timestamped answers instead of confident hallucinations: the retriever finds relevant chunks, the generator synthesizes a student-facing reply, and the response includes 1–3 hyperlinked source excerpts plus a confidence cue and metadata for instructor review.

This pattern - document chunking, embedding, hybrid search, and prompt assembly - is the industry standard for accuracy and evaluation described in Azure RAG solution design and evaluation guide, and can be deployed quickly using serverless RAG APIs that index PDFs, Google Drive, and web pages as shown in the ChatBees serverless RAG workflow for document indexing; for multi-source routing, reranking, and HTML citations that students can click back to the original page, see the NVIDIA RAG Q&A workflow and HTML citation architecture.

The practical payoff for Los Angeles classrooms: on-demand tutoring that lowers instructor triage time and gives students verifiable, citation-linked help they can use to iterate on drafts.

Administrative teams and faculty can cut routine work and speed decisions by wiring Gemini into Google Workspace: use the “Help me write” composer and side‑panel summaries in Gmail to draft replies, summarize long threads, and extract action items; pull specific Drive docs into responses or draft grant and program templates in Docs and NotebookLM so messages reflect up‑to‑date institutional files; and automate Meet notes and follow‑ups so committees spend meetings on policy, not minute‑taking.

For California schools this is operationally significant because Google offers enterprise controls and a Google AI Pro for Education add‑on that lets admins assign licenses, restrict features, and keep data private while enabling faculty productivity gains - case studies show teams cutting 30–35% of time spent drafting messages and reducing triage load substantially.

Start small: automate triage for vendor and student emails, create shared Docs with canned prompts for course or HR templates, then scale with admin provisioning and training to preserve privacy and governance (Gemini AI features in Gmail for education, Google Workspace with Gemini for Education add-on and admin controls).

“Gemini in Gmail really lets our staff save time drafting emails. It gives you the basics so then you make it your own. It gives us more time to essentially change lives.” - Jeff Johnson, Executive Director, Can Do Canines

Data‑driven early‑warning and personalization systems can help California schools spot students at risk and tailor supports, but deploying them safely requires translating public‑health and conflict‑EWS lessons into clear local practice: combine multisource signals (attendance, LMS activity, tutoring logs) with community‑centered reporting, rigorous verification, and a documented response pathway so alerts trigger human review and proportionate supports rather than automated sanctions - a principle echoed in technology‑fusion case studies and ethical guidance on do‑no‑harm and community empowerment (Phuong & Vinck, HHR Journal: technology conflict early‑warning systems and public health); use multi‑hazard, context‑aware design and investment in local capacity so systems serve resilience not surveillance (UNDRR handbook: multi‑hazard early warning systems and early action in fragile and conflict‑affected contexts).

The concrete takeaway for Los Angeles districts: require informed consent, data minimization, end‑to‑end encryption, clear ownership of alerts, and a one‑page response playbook so a flagged case becomes a targeted outreach task for a counselor within 48 hours rather than an unexplained algorithmic penalty.

Ethical Principle	Practical Challenge
Respect for persons	Informed consent, opt‑out, clear data ownership
Beneficence	Do no harm: accuracy, verification, secure handling
Justice	Prevent unequal access or biased signals
Respect for law & public interest	Legal due diligence, transparency, source protection

Multimodal assignments help Los Angeles educators push beyond text‑only tasks by asking students to combine written work with images, audio, or short videos that surface creative choices and process evidence - formats current generative models struggle to fake reliably.

Design a scaffolded prompt that pairs a conventional deliverable (essay, data analysis) with a brief student‑created artifact and a 150–300 word reflection explaining three compositional decisions and sources; use a rubric that emphasizes rhetorical choices, accessibility, and process so instructors grade thinking rather than surface polish.

Build on best practices for assigning multimodal work (student‑generated criteria, reflection components, and clear assessment goals) from Georgetown's multimodal guidance, require media‑anchored responses or instructor‑created bespoke videos to limit AI reuse as recommended by Harmonize, and scaffold with research on how preservice teachers compose multimodally with generative AI to anticipate ethical and pedagogical concerns.

Offer multiple submission formats (audio, captioned video, annotated images) to follow Universal Design for Learning and include explicit directions about permissible AI use per your course AI policy.

The practical payoff for LA classrooms: multimodal prompts deepen rhetorical skill while producing verifiable, instructor‑visible evidence that's harder to outsource to text‑only models (Georgetown University guide to assigning and assessing multimodal projects, Harmonize guide to combating ChatGPT with multimodal assignments, Vanderbilt LIVE study on examining multimodal composing processes with generative AI).

Multimodal Task	Why it helps (teaching + integrity)
Video reflection + essay	Shows process and oral reasoning AI can't easily mimic; supports assessment of rhetorical choices
Response to bespoke instructor video	Uses instructor‑created media to limit web‑sourced model advantage and anchors local context
Group multimedia project	Encourages peer accountability, authentic collaboration, and multimodal composition skills

A compact CETL‑style, three‑session faculty workshop helps Los Angeles campuses turn AI curiosity into classroom change by following proven, scaffolded adult‑learning practice: session one introduces evidence‑based teaching principles and ethical guidelines for educational developers; session two centers hands‑on practice and peer observation so instructors pilot a syllabus clause, rubric tweak, or short prompt in a safe cohort; session three focuses on reflective revision, documentation for promotion dossiers, and next‑step scaling across departments.

This design mirrors Cal State LA's CETL approach - praised by WASC and convening cohorts of about 25 faculty each semester - while grounding the workshop in the “authentic professional practice” model that research links to improved student success (Cal State LA CETL faculty development approach, Research on scaffolded, authentic faculty development).

Add a brief session on confidentiality and developer ethics so participants can safely share drafts and revision histories in peer review (CETL ethical principles and POD guidelines).

The payoff: a small, time‑bounded cohort that practices, documents, and iterates concrete classroom changes rather than receiving one‑off tips.

Item	Detail
Model	Scaffolded, constructivist, peer‑based CETL programming
Typical cohort size	~25 faculty per semester
Recognition	Commended by WASC as a national model

Equip Los Angeles districts with an equity‑first, actionable AI policy brief that ties board governance to classroom practice: use the CSBA AI Taskforce playbooks and GAMUT sample policies to draft board‑approved language and scenario responses, follow Policy Analysis for California Education's urgent recommendation that districts adopt clear AI rules and educator training for the school year rather than rely on bans, and monitor state action captured in the NCSL 2025 AI legislation summary so local rules align with evolving procurement and ADS disclosure obligations; the concrete, memorable step: publish a one‑page district AI policy template that requires pre‑term educator training, defines permitted student uses, mandates process evidence (revision histories or AI interaction logs) instead of detector scores, and pairs with shared procurement to lower tool costs and centralize legal review - so policy protects students, preserves instructional integrity, and keeps districts out of costly compliance surprises.

Resource	What it Provides
CSBA AI Taskforce playbooks and GAMUT sample policies	Board resources, scenarios, sample policies (GAMUT)
Policy Analysis for CA Education	Urgent guidance to adopt district AI policies and teacher training
NCSL 2025 AI legislation summary of state actions affecting AI procurement and ADS disclosure	State legislative trends affecting procurement, ADS disclosure, and oversight

Therefore, all districts need to enter the 2023–24 academic year with a clear policy for use of AI and educator training to support the policy.

Next steps for Los Angeles educators and institutions are pragmatic: adopt an equity‑first, board‑approved AI policy that requires pre‑term educator training and clear classroom rules; prioritize process evidence (revision histories, AI interaction logs, staged deliverables) over brittle detector scores; fund small CETL‑style faculty cohorts to pilot syllabus clauses and multimodal assignments; centralize procurement and legal review to lower tool costs and ensure vendor contracts prohibit unauthorized student data use; and operationalize ethical early‑warning playbooks so a flagged case becomes a targeted counselor outreach within 48 hours rather than an unexplained algorithmic penalty.

State‑level guidance urges leaders to “keep calm” while building capacity - translate that into a one‑page district policy template, a 90‑minute in‑service for every instructor, and rolling audits of tool safety.

For policy framing see the Stanford PACE state policy brief on AI in education (Stanford PACE state policy brief: State Education Policy and the New Artificial Intelligence) and for classroom practice consult Cal State LA's CETL recommendations (Cal State LA CETL recommendations for teaching and learning with AI); teams seeking job‑ready prompt and tool training can consider a focused option like Nucamp's AI Essentials for Work (Nucamp AI Essentials for Work (15-week) registration).

Program	Length	Early bird cost	Registration
AI Essentials for Work	15 Weeks	$3,582	Register for Nucamp AI Essentials for Work (15-week)

“keep calm, engage stakeholders, and plan carefully.”