Top 10 AI Prompts and Use Cases and in the Education Industry in Chesapeake

AI is rapidly reshaping K–12 classrooms in Virginia - boosting personalized learning, instant feedback, and administrative efficiency while raising urgent concerns about privacy, bias, cost, and academic misconduct; nationally, a University of Illinois review notes that 27% of students were regular generative-AI users in 2023 versus just 9% of instructors, a gap that Virginia districts must close with training and policy to avoid widening inequities (University of Illinois study on AI use in schools).

Education leaders also report widespread optimism but worry about cyber risks and uneven teacher readiness (NAESP report on AI opportunities and risks in schools); one practical step for Chesapeake staff is targeted upskilling - Nucamp's 15-week Nucamp AI Essentials for Work 15-week bootcamp offers hands-on prompt-writing and tool use to make AI an instructional aid rather than a liability.

So what: without fast, local teacher training and clear procurement policies, Virginia schools risk lost learning gains and greater inequity.

Selection prioritized practical, evidence-based PBIS tools, state cohort models, and AI-ready prompt sets that Virginia districts can adapt for Chesapeake: authoritative guidance and downloadable artifacts from the Center on PBIS publications informed the list of recommended assessments and templates (Center on PBIS publications: PBIS guidance and downloadable resources), state-cohort models like Minnesota's SW‑PBIS cohort - which supplies team training plus fidelity tools such as the Tiered Fidelity Inventory (TFI) and the School-Wide Information System (SWIS) - guided the emphasis on measurable implementation (Minnesota SW-PBIS cohort overview and fidelity tools), and a curated set of AI prompts and starter templates helped choose prompts that automate routine PBIS tasks without replacing human oversight (KOI Education: 35 AI prompts for PBIS implementation).

The methodology favored resources that provide training/coaching, fidelity metrics, parent‑facing artifacts, and simple pilot checklists so Chesapeake leaders can test tools, track fidelity, and protect student data before scaling.

Prompt 1 - School Behavior Summary Prompt: Ask the model to produce a concise, school‑level narrative that highlights short‑term trend changes, subgroup disparities, and clear next steps for PBIS teams - then append practical artifacts so leaders can act: include Nucamp's pilot program checklist for schools (Web Development Fundamentals pilot program checklist and syllabus) to test any AI intervention with minimal risk and spend and the AI safety and procurement checklist (AI Essentials for Work syllabus and safety guidance) to vet tools before deployment; also signal staffing impacts by referencing the guidance on at‑risk educators and reskilling (Job Hunting bootcamp syllabus and reskilling guidance).

So what: a standardized summary plus these two checklists turns raw incident data into a low‑risk pilot plan that protects student data, limits wasted spend, and surfaces specific staff reskilling needs for Chesapeake schools.

Prompt 2 - Parent-facing chart explainer: Behavior Chart Explanation Prompt: Provide a clear, one-page guide parents can read in 60 seconds that explains the chart's purpose (daily progress monitoring), the meaning of colors or codes, how teachers record time‑period checks, and the simplest actions parents can take at home (ask about one success and one goal).

Use a send‑home folder chart or behavior report template that includes space for teacher comments and a parent signature so communication is documented (Behavior report templates for parents - TeachersPayTeachers) and offer a free printable daily chart that stays in the folder for quick, visual updates (Free behavior chart templates for send-home folders - TeachersPayTeachers).

Prefer a simple colored system (e.g., percent thresholds and color codes) and send a weekly color‑coded average so families and PBIS teams spot trends early; practical how‑to and tracking tips are detailed in a behavior charts & data approach that minimizes teacher time while producing actionable weekly summaries (Behavior charts and data tracking guide - ED Queen).

So what: a consistent daily chart plus a weekly color average turns anecdote into measurable trends that drive PBIS decisions and parent partnerships.

Collecting data is my cardio.... is it yours?

Suggested neutral announcement for Chesapeake schools when a data concern arises: briefly name the dataset or tool involved, state what is known and unknown (for example, “a potential exposure in an ed‑tech integration under review”), and describe specific next steps the district is taking - initiate a vendor and security review, document actions per federal/state rules, and open a clear feedback channel for families and staff.

Follow the Student Privacy Communications Toolkit for Schools & Districts to be clear, specific, and equitable by publishing a one‑page privacy FAQ that lists currently used ed‑tech vendors and a single privacy contact (with translated versions and low‑bandwidth access), explain FERPA‑related rights and any opt‑out options using plain language, and schedule a community meeting to listen and collect questions (Student Privacy Communications Toolkit for Schools & Districts).

Also reference FERPA and Washington state student privacy guidance when describing legal protections and reporting practices so stakeholders know how data is secured and shared (FERPA and Washington state student privacy guidance); for any AI tool under review, couple the announcement with a short pilot/safety checklist before resuming use (AI safety and procurement checklist - Nucamp AI Essentials for Work syllabus).

The so‑what: a single, public FAQ plus a named contact turns anxiety into actionable oversight and restores trust.

Prompt 4 - Stakeholder invitation: Draft a concise, action‑oriented invite that names the meeting purpose (co‑create an AI pilot and review privacy/procurement safeguards), lists pre‑reads, and gives simple attendance logistics: ask participants to read a short pilot program checklist and an AI safety & procurement checklist ahead of time (Chesapeake Public Schools school board access and contact information, Local pilot program checklist for low-risk classroom trials, AI safety and procurement checklist for vendor vetting), prepare one prioritized concern or outcome to share, and note that citizens may attend school board sessions and that printed materials will be available at the Clerk of the Board office; for copies or questions, provide the Clerk's contact so families without internet can get materials.

Useful links to include in the invitation: the Chesapeake Public Schools school board connection page for meeting access and contact details, a local pilot program checklist to structure low‑risk trials, and an AI safety/procurement checklist to vet any vendor before a classroom test - this keeps meetings practical, accessible, and firmly tied to district oversight.

Prompt 5 - SMART goals for PBIS/MTSS: Implementation Plan Prompt: Require teams to translate selected strategies into a written action plan with SMART goals, explicit steps, assigned resources, a named data lead, a timeline, and a communication plan - as recommended in the Missouri tiered PBIS team workbook - then append success criteria so teams can “determine results” and stop or scale work based on evidence (Missouri SW‑PBIS Tier 2 action-plan templates and timeline (PBIS workbook)); pair that plan with a low‑risk pilot program checklist to test interventions without large upfront spend (School pilot program checklist for low-risk interventions) and an AI safety and procurement checklist to vet any vendor or analytic tool before use (AI safety and procurement checklist for educational vendors and analytics).

So what: a short, evidence‑linked implementation plan that names who, when, and how produces measurable PBIS fidelity, protects student data during pilots, and prevents months of unfocused spending when Chesapeake districts scale supports.

Prompt 6 - Multi-modal content generation: Channel‑Optimized Summary Prompt: Instruct the model to output a small suite of ready‑to‑use artifacts tailored to each channel - a one‑page, plain‑language fact sheet built from the CDC's downloadable templates for print and web (CDC communication templates for youth public health materials), two short, branded social posts (sample copy + image suggestions) using the CEC social media toolkits and accessibility guidance so captions and alt text are included (Council for Exceptional Children social media communication templates and toolkits), and a classroom multimodal exemplar (podcast clip or infographic with a rubric) following Ohio State's multimodal activities approach to replace - not add - work for teachers (Ohio State University multimodal activities for engaged learning).

Include a one‑line teacher script for each artifact and a note to use CEC's Heebo/Tinos branding to keep messaging consistent; so what: this prompt produces immediate, accessible materials teachers and family liaisons can deploy the same day, bridging data to action without extra design time.

Prompt 7 - Staff workshop plan: Behavior Dashboard Workshop Prompt: Build a short, practical staff workshop that pairs hands‑on dashboard navigation with data‑hygiene stations and emergency‑communication checks so classroom teams can turn behavior signals into rapid, coordinated action; use the Chesapeake Public Schools safety guidance to structure an activity where staff verify ParentVUE entries (confirm a primary mobile number, set 3–5 emergency contacts, and practice who is listed as custodial) and map those records to dashboard alert fields (Chesapeake Public Schools emergency preparedness guidance), run a live scenario to validate that dashboard alerts align with the district goal to refresh notifications during a crisis (coverage of CPS large-scale reunification drill and technology goals), and finish with a low‑risk pilot checklist for testing any AI analytics or automated reports before wider use (pilot program checklist for schools on AI analytics).

So what: confirming contact data and running one scenario in the workshop gives immediate proof that dashboards feed the right people and measurably reduces the time to notify families during an incident.

“In the worst possible moment for a parent, we want to make sure that we have a system that is as smooth as possible, as seamless as possible, to reduce that amount of stress and anxiety for parents as much as we can.” - Penny Schultz; CPS aims to send notifications every 15 minutes in a crisis.

Prompt 8 - Reunification checklist & ID script: Reunification Procedures Prompt: Produce a single‑page reunification checklist and a two‑line ID‑verification script (what staff say, what parents present) modeled on Chesapeake's large‑scale drill at Deep Creek Middle and High Schools (Aug.

14, 2025), which tested a reunification center, an ID‑check system, and community alert technology; include clear role assignments (intake, ID verifier, runner, family liaison), a short list of acceptable IDs, instructions to log releases, and a parent-facing note about expected notification cadence so families know when updates arrive.

Anchor the checklist to district practice - verify ParentVUE contact info, use the tested ID workflow, and follow the district's notification timing - so districts can cut release time, lower parent anxiety, and replicate the drill's tech checks in real events (WAVY news coverage of the Chesapeake Public Schools reunification drill, Chesapeake Public Schools official reunification drill press release).

The so‑what: one script plus one checklist aligned to tested tech and a 15‑minute update cadence turns rehearsed roles into faster, less stressful reunifications for families.

“In the worst possible moment for a parent, we want to make sure that we have a system that is as smooth as possible, as seamless as possible, to reduce that amount of stress and anxiety for parents as much as we can.” - Penny Schultz, Assistant Director for Safety and Security, CPS

Prompt 9 - AI privacy & redaction script: Provide a compact, copy‑ready redaction script template that removes personal identifiers before any external model call and pair it with a low‑cost pilot program checklist so Virginia districts can test the script on limited data with minimal risk and spend (pilot program checklist for Virginia schools to test AI redaction); include the district‑facing AI safety and procurement checklist to ensure vendors meet basic privacy controls, logging, and retention expectations before wider deployment (district AI safety and procurement checklist for privacy controls).

Add a brief staff note linking to local reskilling guidance so IT and instructional teams know who to train if redaction changes workflows (reskilling guidance for Virginia educators on AI and redaction workflows).

So what: a tested redaction script bundled with two short checklists turns privacy theory into a repeatable, low‑cost safeguard that prevents accidental exposure while keeping procurement scrutiny up front.

Prompt 10 - Monitoring dashboard spec: Routine Monitoring & Alerts Prompt: Ask the model to output a compact, action‑ready dashboard spec for Chesapeake that names routine metrics (daily incident counts, subgroup breakdowns, staff reporting rates), clear alert rules (who is notified and by which channel), audit logging and redaction hooks, and an embedded pilot plan so new analytics are tested with minimal risk; attach Nucamp's AI Essentials for Work pilot program checklist for schools (AI Essentials for Work syllabus and pilot checklist) and the district AI safety and procurement checklist from Nucamp's AI Essentials for Work resources (AI safety and procurement checklist for schools) so every alert workflow is privacy‑checked before wider use; include a short staff training cue that links to local reskilling guidance for Virginia educators from Nucamp's Web Development Fundamentals bootcamp (Web Development Fundamentals syllabus and reskilling guidance for educators).

So what: a single, tested dashboard spec plus these two checklists turns reactive notification chaos into a repeatable, low‑cost monitoring routine that protects student data and limits wasted spend when Chesapeake scales automated alerts.

Use Case 1 - Behavior monitoring & PBIS insights for Chesapeake Public Schools: adopt a data‑first workflow so PBIS teams can move from anecdotes to targeted action - Center on PBIS guidance emphasizes continuous improvement driven by routine data review (Center on PBIS data-based decision making guidance), while PBIS Rewards shows how consistent analysis of positive and negative records reveals priorities and increases referral consistency (PBIS Rewards guide to PBIS data analysis).

Practical steps for Virginia districts include collecting the “Big 5” incident fields (what, where, when, who, how often) and running weekly dashboards to spot hotspots - Panorama's MTSS approach highlights using those patterns to design tiered interventions and monitor progress (Panorama Education behavior data for MTSS).

So what: a simple, weekly Big‑5 check can reveal a repeat peak (e.g., pre‑lunch hallway incidents) and justify reallocating supervision or a brief Tier‑2 check‑in within days, turning raw logs into measurable reductions in referrals and lost instructional time.

Use Case 2 - Personalized learning pathways for Deep Creek Middle School: combine classroom instruction with adaptive platforms to create tiered, data‑driven learning paths - use NWEA MAP Growth to place students into targeted ELA and math modules, then monitor real‑time progress so Tier 1, 2, and 3 supports adjust automatically (HMH Personalized Path MAP Growth integration and tiered supports).

For middle‑grade math, an evidence‑backed adaptive engine like DreamBox (highlighted among adaptive learning examples) has shown large gains when schools schedule consistent use - research and vendor guidance recommend roughly 60–90 minutes per student per week and report up to 2.5× growth versus traditional methods - so plan short, scheduled lab sessions or blended rotations rather than optional homework (Adaptive learning examples: DreamBox and Khan Academy research).

Practical next steps for Chesapeake: pilot one grade at Deep Creek, map MAP Growth placements to platform pathways, require weekly usage targets, and train counselors/teachers to interpret the platform's dashboards so interventions trigger within one reporting cycle - anchoring personalization to measurable minutes and MAP scores delivers faster skill gains and reduces time students spend off‑level instruction.

Learn local logistics for running a school pilot at Deep Creek Middle (Deep Creek Middle School contact and location).

Use Case 3 - Early risk/prediction analytics (Chesapeake Public Schools): deploy an Early Warning System (EWS) that stitches together the ABCs - attendance, behavior, and course grades - to assign each student a red/yellow/green risk status and trigger tiered MTSS responses; EAB's research explains how districts use predictive analytics and local historical data to set thresholds (for example, flagging a student after three discipline referrals) so interventionists can act before graduation timelines slip (EAB research on Early Warning Systems in K‑12 schools).

A well‑designed EWS is more than a model: Branching Minds emphasizes five operational elements - team roles, accurate indicators, shared reports, mapped interventions, and progress evaluation - so Chesapeake can turn noisy logs into coordinated supports (Branching Minds guide to Early Warning Systems and MTSS components).

High‑quality, consolidated data matters: Innovare shows that incomplete or fragmented records undermine timely flags, so plan data governance and periodic audits up front (Innovare: Why data quality matters in schools and its impact on interventions).

So what: a simple weekly ABC dashboard with agreed thresholds can move a student from “unnoticed” to supported within days, preserving instructional time and keeping on‑time graduation achievable.

“To put a system in place by which we can pull in the data, use the data to identify those students who have hit thresholds that would cause concern, that would tell us that they're at risk of falling behind.” - Emily‑Rose Barry, Branching Minds

Use Case 4 - Virtual tutoring & on‑demand help at Deep Creek High School: deploy a school‑scoped, curriculum‑aligned AI tutor to give students instant, scaffolded support outside class while preserving teacher oversight - mirror Penn Engineering's Jean by indexing local course materials so responses are specific to Deep Creek syllabi and route all AI answers into a TA/teacher review queue within 24 hours (Jean AI tutor (Penn Engineering) case study); pair that design with the kinds of two‑way nudges proven to move outcomes - Georgia State's “Pounce” chatbot increased the chance of earning a B or higher by 16% and produced an 11‑point boost for first‑generation students - so a vetted pilot at Deep Creek that requires weekly teacher validation, privacy redaction, and explicit academic‑integrity prompts can scale one‑on‑one tutoring without replacing human feedback (Georgia State Pounce chatbot study and results).

So what: a tightly scoped, content‑connected tutor can deliver measurable grade lifts while freeing teachers for deeper instruction - if local content control, TA verification, and privacy checks are required before wider rollout.

“Eleven points is more than a full letter grade.” - Timothy M. Renick, on the impact of chatbot nudging at Georgia State

Administrative automation for Chesapeake Public Schools HR & enrollment pairs the district's existing electronic intake (the Office of Student Enrollment and Attendance already accepts special enrollments electronically) with configurable enrollment platforms to remove paperwork, validate required documents, and speed placements: platforms like EnrollWise enrollment platform automate matching and real‑time seat tracking, manage document workflows and role‑based staff access, and provide family‑facing dashboards and multilingual support that reduce back‑and‑forth calls; tying those workflows to Virginia's enrollment rules (birth certificate, immunizations, residency, foster/homeless exceptions) documented by the Virginia Department of Education enrollment requirements ensures compliance at intake rather than after placement.

So what: a tested automation stack that validates key documents up front and tracks seats by program turns manual triage into predictable workflows - freeing central office staff to resolve edge cases, improve accuracy for state reporting, and focus on family support instead of data entry (Chesapeake Student Enrollment & Attendance office).

“The EnrollWise team has transformed our application and placement system into a product that is user friendly, informed and efficient.”

Use Case 6 - Curriculum generation & lesson planning at Deep Creek Middle School: deploy generative AI as a standards‑unpacking and materials‑curation assistant so teachers spend planning time on pedagogy instead of formatting - start by feeding a Virginia Standard of Learning into an AI prompt to extract clear, measurable sub‑skills and then ask for three aligned assessment options (see the Edutopia supervisor's guide on AI‑assisted lesson planning for examples and guidance: Edutopia guide to AI‑Assisted Lesson Planning with classroom examples).

Combine those outputs with vetted middle‑school modules and ethics activities from AI4K12 (for example, AI plus Ethics for Middle School: AI4K12 curriculum materials for middle school AI and ethics) so lessons include both technical know‑how and responsible use.

Practical rollout tip: require teachers to edit and validate any AI draft and use AI only to produce hooks, objectives, differentiated tasks, and three assessment choices - researchers and vendors report that teachers find AI accelerates planning and improves alignment, with 55% of teachers saying AI has a positive classroom impact (see analysis of AI‑powered lesson planning benefits and use cases: Hurix blog on AI‑powered lesson planning benefits and use cases).

So what: a vetted AI prompt workflow turns an afternoon of solo planning into a repeatable, standards‑aligned pipeline that preserves teacher judgment while delivering ready‑to‑use, ethically framed lesson components.

Use Case 7 - Resource planning & operations for Chesapeake Facilities & Nutrition: combine the district's published meal‑service rules and emergency protocols with targeted pilot prompts to keep kitchens stocked and families fed during disruptions - Chesapeake's Chesapeake School Nutrition Services meal program already provides meals

without charge

on a first‑come, first‑served basis and can use short, testable AI prompts to generate prioritized substitute menus, supplier‑replacement checklists, and daily pick‑up routing options when items are scarce; national reporting shows supply‑chain shocks still force daily menu changes, so a low‑risk pilot that simulates substitutions and supplier lead‑time scenarios is practical (K‑12 Dive coverage of school foodservice supply‑chain issues).

Anchor pilots to emergency playbooks - verify ParentVUE primary mobile numbers and emergency contacts and keep the district's alert cadence (text shortcode 98900) aligned with meal‑site notifications per the district's emergency guidance (Chesapeake emergency preparedness guidance and alert procedures) - so the measurable payoff is clear: faster substitutions, fewer missed meals, and timely family alerts during a closure or shortage.

Use Case 8 - Reunification & emergency response support: codify a one‑page reunification checklist and two‑line ID‑verification script tied to the roles tested in Chesapeake's large‑scale Deep Creek drill (intake, ID verifier, runner, family liaison), require verification of ParentVUE contact data, log every release, and adopt the district's 15‑minute notification cadence so families know exactly when to expect updates; the Aug.

14, 2025 exercise validated an ID‑check system and community alert tech with Chesapeake Police, the Sheriff's Office, CPS staff, and city departments and surfaced practical fixes that should be baked into every school script and checklist (see WAVY coverage of the drill and the district press materials).

So what: one short script plus a single‑page checklist aligned to tested technology and a predictable 15‑minute update rhythm turns rehearsed roles into faster, less stressful reunifications for families and makes tabletop-to-live transitions repeatable across Virginia schools.

“In the worst possible moment for a parent, we want to make sure that we have a system that is as smooth as possible, as seamless as possible, to reduce that amount of stress and anxiety for parents as much as we can.” - Penny Schultz, Assistant Director for Safety and Security, CPS

Use Case 9 - Mental health support & clinician assistance for Chesapeake school counselors: leverage TEAMMAIT‑style AI teammates - an NSF‑funded $2 million research project that supports clinicians by acting as a collaborative aide - to triage routine check‑ins, surface medication side‑effect reports, and draft evidence‑aligned referral summaries so counselors spend less time on paperwork and more on direct care (Complete AI Training overview of TEAMMAIT and AI mental-health research).

Begin with a low‑risk school pilot using a simple checklist to validate redaction, consent, and escalation rules before any external model calls (school pilot program checklist for AI implementation), and require the district AI safety & procurement checklist to ensure FERPA‑aligned logging and vendor controls (district AI safety and procurement checklist for FERPA compliance).

So what: a tested, privacy‑checked AI teammate can convert routine messages into documented, prioritized referrals - helping Chesapeake counselors identify higher‑risk students faster while keeping sensitive data under district control.

Use Case 10 - Prompt engineering & AI training for staff (CPS Training): build a pragmatic, Virginia‑focused staff pathway that pairs foundational AI literacy with hands‑on prompt labs so teachers and instructional coaches can safely use generative tools in planning and interventions; anchor training to district practice by linking to Chesapeake instructional resources and technology expectations (Chesapeake instructional resources and technology), ground session design in recent research that highlights prompt engineering and AI literacy as classroom priorities (Walter 2024 study on AI literacy and prompt engineering), and use Nucamp's AI Essentials materials as a pilot checklist and syllabus for rollout (Nucamp AI Essentials for Work syllabus and pilot checklist).

Train staff on a simple three‑part prompt template (context + explicit task + redaction hook) and require one redaction line before any external model call; this concrete habit converts creative prompts into auditable artifacts that streamline privacy review and let coaches evaluate classroom pilots quickly.

So what: a short, repeatable prompt protocol plus localized PD lets Chesapeake teachers adopt AI as a tool for planning and intervention while keeping Virginia FERPA risks under active district control.

“The role of the teacher is to create the conditions for invention rather than provide ready-made knowledge.” - Seymour Papert

Next steps for Chesapeake schools: codify the Deep Creek lessons into three practical actions - first, make ParentVUE verification and a one‑page reunification checklist standard at every school and run at least one full drill per year to validate the ID‑check and community alert workflow tested on Aug.

14, 2025 (see WAVY Deep Creek reunification drill report on Yahoo: WAVY Deep Creek reunification drill report (Yahoo)); second, pilot a bounded AI training track for coaches and data leads using the Nucamp AI Essentials materials and the district's pilot checklist so teachers learn prompt‑engineering, redaction, and procurement habits before any scale‑up (Nucamp AI Essentials for Work syllabus and pilot checklist); third, publish a plain‑language privacy FAQ and a named district contact, then run a stakeholder co‑creation meeting to surface concerns and translate them into measurable SMART goals.

Anchor these actions to the district's emergency guidance and ParentVUE procedures so drills, alerts (15‑minute cadence), and AI pilots share the same data hygiene and notification rules (Chesapeake emergency preparedness guidance).

“In the worst possible moment for a parent, we want to make sure that we have a system that is as smooth as possible, as seamless as possible, to reduce that amount of stress and anxiety for parents as much as we can.” - Penny Schultz, Assistant Director for Safety and Security, CPS