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

Columbus-area educators face a near-term imperative: statewide and local data show scale, equity gaps and industry pressure that make practical AI skills a strategic priority.

The Fordham Institute's Ohio K–12 metrics - Ohio Education by the Numbers (Ohio K–12 metrics - Ohio Education by the Numbers), while regional reporting highlights a talent pipeline - more than 310,000 K–12 students across 67 districts and roughly 22,000 annual college graduates - that employers and colleges expect to serve (Columbus Region education and talent data: Columbus Region education and talent data).

Industry–academic partnerships, such as Central State's Intel-backed semiconductor program that aims to graduate 100+ certificate/associate students in its early years, show demand for workforce-ready AI skills.

This guide focuses on prompt use cases and ready-to-run prompts that school leaders, T&L centers, and district PD teams can adopt now - paired with practical training like Nucamp's AI Essentials for Work syllabus (Nucamp AI Essentials for Work syllabus) to teach staff how to write effective prompts and apply AI safely in schools.

Program	Length	Early-bird Cost	Syllabus
AI Essentials for Work	15 Weeks	$3,582	AI Essentials for Work syllabus

Selection focused on three Ohio-specific priorities drawn from local reporting: affordability, workforce resilience, and assessment integrity. Prompts were chosen first for cost-effectiveness, informed by reporting on AWS investments in Ohio lowering infrastructure barriers for Columbus education startups.

Second, priority went to use cases that enable instructional staff to evolve rather than be displaced - echoing guidance about curriculum writers pivoting toward pedagogy and AI oversight in Columbus education.

Third, each prompt was vetted for alignment with classroom safeguards referenced in our guidance on generative AI limits for Columbus classrooms, so recommended prompts include explicit guardrails for assignments and assessments.

The result: a top-10 list that balances low-running costs, actionable teacher workflows, and assessment-safe defaults - so districts can adopt prompts that scale locally without sacrificing pedagogical control.

Ohio State instructors and students can start with tightly scoped, classroom-ready prompts that prioritize pedagogy and assessment integrity: instructors use prompts to convert existing assignments into AI-aware rubrics (explicit AI-use declarations, required source lists, and instructor checkpoints) while students work from prompts that ask for process summaries and verifiable citations rather than final-only outputs.

These choices reflect local realities - lower-cost, cloud-hosted workflows enabled by the AI Essentials for Work syllabus: practical AI skills for the workplace (AI Essentials for Work syllabus - practical AI skills for the workplace) - and protect instructional roles by emphasizing oversight and pedagogy over automation, a pivot highlighted for curriculum writers in Columbus (learn how to pivot curriculum toward pedagogy and AI oversight with AI Essentials for Work registration: AI Essentials for Work registration - gain practical AI skills for any workplace).

Pair every prompt with the classroom guardrails recommended in local guidance on generative AI limits - simple defaults (disclose AI use, require sources, log drafts) that let departments pilot prompt-driven activities without new infrastructure or compromised assessment integrity (see the AI Essentials for Work syllabus for practical classroom guardrails: AI Essentials for Work syllabus - classroom guardrails for generative AI).

This approach lets Ohio State test prompt-based learning at course scale while keeping faculty in control.

InnovateOhio's K–12 AI Toolkit converts best practices into ready-to-run curriculum mapping templates and district policy prompts that help Ohio schools align AI activities with accessibility, assessment integrity, and grant-readiness: mapping templates tie standards to project-based AI tasks and built-in assessment checkpoints; policy prompts provide disclosure language, citation and draft‑logging requirements, and default accommodations (captioning, sign-language interpretation, clear‑language summaries) modeled on ODDC accessibility guidance; and short PD modules prepare teachers to supervise student use rather than replace pedagogy.

Each template suite includes a funding-aligned project outline so districts can scope pilots to match ODDC NOFA ranges ($20,000–$90,000) and matching-fund rules, while the toolkit's policy language is consistent with statewide accessibility expectations found in Ohio's DD Council materials.

Use the toolkit alongside local guidance on generative-AI classroom limits and implementation checklists to deploy safe, equitable pilots that scale across districts without new cloud infrastructure investments.

Component	Purpose	Resource
Curriculum mapping templates	Standards → AI project + assessment checkpoints	Generative AI limits guide for Columbus K–12 education
District policy prompts	Disclosure, citation, accommodations defaults	ODDC accessibility and clear-language resources
Grant-ready project outlines	Budgeted pilots sized to funder ranges	ODDC NOFA: Pathways to Progress grant details ($20k–$90k)

Ohio's AI in Education Coalition released a statewide strategy in May 2024 that “identifies key themes illustrating how AI is transforming the educational landscape and workplace” and offers recommendations for K–12 schools, institutions of higher education, and the State - resources that districts can convert into practical rollout prompts and short PD scripts; view the full Ohio AI in Education Coalition strategy report (May 2024) and the Department's technology resources page describing the coalition's three workgroups and professional development series for statewide coordination and virtual meetups: Ohio Department of Education technology resources and coalition workgroups.

Note the guidance page was updated 6/17/2025, underscoring that districts can build on a recently refreshed set of state resources.

Useful rollout prompts grounded in the report include:

• Summarize the strategy's key themes and list actionable recommendations for schools, higher‑ed, and the State.
• Map recommended actions to district professional development modules and timeline.
• Identify industry partners and operational supports referenced by the Industry, Operations, and Instructional workgroups.

Workgroup	Focus
Industry	Align AI skills with workforce and employer needs
Operations	Address systems, infrastructure, and implementation
Instructional	Integrate AI into teaching, curriculum, and professional development

Marketing AI Institute chief growth officer Cathy McPhillips offers Columbus educators a practical outreach playbook rooted in the “A.I. Midwest” ethos: prioritize responsible, human-centered messaging, pilot problem-based use cases, and start with free tools before scaling - advice captured in her interview on the A.I. Midwest Prompt podcast (Cathy McPhillips interview on the A.I. Midwest Prompt podcast).

For regional outreach, convert that playbook into three ready prompts: (1) draft an event outreach script that emphasizes ethics and human connection to boost MAICON-style attendance, (2) create a pilot framework that targets one measurable problem (e.g., enrollment or churn) with KPIs and a 6–8 week test, and (3) generate a privacy-safe audience segmentation brief that strips identifiable data before modeling.

Districts can pair these with local training (see the AI Essentials for Work syllabus (Nucamp)) so marketing teams run small pilots that save staff time on repetitive outreach while preserving the personal touches that actually drive participation.

Prompt	Purpose	Tool / Tape
Event outreach script	Increase attendance with ethical messaging	Descript / Opus Clip
Problem-based pilot brief	Test one measurable issue (enrollment/churn)	Free LLMs → measure conversion
Privacy-safe segmentation	Audience modeling without PII	Data stripping + manual review

“The free [A.I.] tools are actually really good, start there. You don't need to jump in and buy the tool, or three of them.” - Cathy McPhillips

University libraries and law librarians - led locally by Rebecca Fordon and collaborators - are converting benchmarking research into practical prompt worksheets and rubrics Ohio campuses can reuse: the "Evaluating Generative AI for Legal Research" project outlines a task typology, draft questions, and scoring rubrics that let clinic instructors, reference librarians, and teaching‑and‑learning centers run consistent comparisons across tools and catch errors early (Evaluating Generative AI for Legal Research benchmarking project).

Those worksheets map vendor behaviors to three reusable task buckets (Find; Learn & Investigate; Create/Summarize), recommend simple audit‑trail prompts to expose retrieval sources, and embed hallucination checks - practical steps that matter in Ohio where many academic libraries have Lexis+ AI access but vendor coverage varies.

For teams building local pilots, the project and companion resources on LLM benchmark categories provide concrete templates to score accuracy, factuality, and reasoning so campus services can offer trustworthy, assessment‑safe AI help to students and faculty (AI Law Librarians legal research tag, LLM benchmark categories guide).

Task Type	Example Use / Purpose
Find	Quick statute or case lookup for reference answers
Learn & Investigate	Sift results, assess relevance, follow research threads
Create / Synthesize / Summarize	Draft memos, synthesize rules, produce classroom summaries

It is difficult to test Large-Language Models (LLMs) without back-end access to run evaluations.

Teaching & Learning centers in Columbus can turn Ohio State's evidence‑based guidance into concrete PD and assessment prompts that faculty can use immediately: the Michael V. Drake Institute's “Artificial Intelligence and Instruction” guidance frames AI integration around backward design, transparency, active learning, and iterative assessment, and the Institute's hands‑on “Prompting for Teaching and Learning” workshop (90 minutes; CarmenZoom, 12:00–1:30 pm) trains instructors to write prompts that produce learning goals, AI‑aware rubrics, and scaffolded assignments while building in formative checkpoints and audit trails for academic integrity - so a single session can leave a faculty team with a completed rubric, two AI‑scaffolded assignment prompts, and a plan to collect mid‑course feedback.

Use the Drake Institute guidance to map prompts to assessment data (formative vs. summative) and the workshop to practice prompt refinement and critique of GenAI output before classroom rollout (Drake Institute: Artificial Intelligence & Instruction guidance, Prompting for Teaching and Learning workshop details and registration).

PD Prompt	Purpose	Quick Action
Convert an assignment into an AI‑aware rubric	Clarify expectations, require sources, set checkpoints	Use backward design and co‑develop the rubric with students
Create an AI‑scaffolded active‑learning task	Shift emphasis from product to process	Design formative checkpoints and revision milestones
Design assessment reflection scripts	Measure AI's effect on learning	Collect SGID or mid‑course feedback and analyze summative data

“Artificial intelligence is transforming the way we live, work, teach and learn. In the not-so-distant future, every job, in every industry, is going to be impacted in some way by AI. Ohio State has an opportunity and responsibility to prepare students to not just keep up, but lead in this workforce of the future.” - Walter “Ted” Carter Jr.

District leaders can lean on ready-made parent communications and classroom activity packs developed for Ohio - combining InnovateOhio's parent-facing templates and AI Snapshots with OESCA's district policy and implementation supplements - to launch transparent, assessment-safe pilots that parents can understand and support; InnovateOhio's Part 5 offers a sample parent letter, a Sample Student Agreement, and short classroom AI Activities and Challenges for immediate use (InnovateOhio Part 5: Parent Resources, Student Agreement, and Classroom AI Activities), while the Ohio ESC Association's AI Policy Toolkit supplement and aiEDU partnership package practical professional development and model policy language districts can adopt (OESCA Ohio AI Policy Toolkit: Model Policy Development Protocol and Guidance, OESCA / aiEDU AI Education Project: ESC Support and Professional Development).

So what: districts can stand up a parent-communications plus classroom-activities pilot in weeks, backed by ESC-trained AI champions (aiEDU aims to train 1–2 representatives per ESC) and ESSER-funded PD, ensuring clear disclosure, student agreements, and short formative checkpoints before any scale-up.

Resource	Purpose	Use
InnovateOhio - Part 5	Parent templates, classroom activities, FAQs	Send parent letter, deploy AI Snapshot warm-ups
OESCA - AI Policy Toolkit	Model policy & PD supplement	Adopt district disclosure & draft-logging policy
aiEDU / ESC network	PD series, ESC AI champions, summits	Train 1–2 champions per ESC to support pilots

“Safe and effective use of AI in schools requires a partnership between parents and teachers.”

Advising centers can turn chatbots into reliable, retention‑focused tools by combining operational prompts (appointment booking, enrollment checkpoints, deadline reminders) with clear escalation rules and transparent data sources: Ohio State's review highlights chatbots' 24/7 support and use as virtual TAs while warning that human oversight, audits, and source transparency are required to avoid misinformation (Ohio State report on chatbots in higher education).

Practical first steps taken locally show the path: Columbus State's new chat tool soft‑launched after testers created FAQs and excluded sensitive pages so the bot only consumes vetted PDFs and site content (Columbus State chat tool pilot and rollout details), and Ohio State's Drake Institute promotes the TRACI prompt framework (Task, Role, Audience, Create, Intent) to script prompts that produce consistent, auditable responses for advisors (Drake Institute guidance on the TRACI prompt framework).

So what: a staged rollout - train with curated PDFs, require an escalation script for complex cases, and log bot interactions - can free frontline staff from routine queries while preserving advisor judgment and student safety.

Prompt / Script	Purpose	Operational Note
“List next available advising appointments and book one”	Reduce scheduling friction	Integrate calendar + human escalation
“Summarize required documents for financial aid and link PDFs”	Fast, accurate triage	Feed vetted FAQs/PDFs to the crawler
TRACI‑style intake: Task/Role/Audience/Format/Intent	Consistent, auditable responses	Store prompts & logs for regular audits

“AI is here to stay.”

Build a lightweight, repeatable LLM testing cadence that ties directly to classroom safeguards: run a fixed set of representative classroom prompts each month, score outputs against an AI‑aware rubric (disclosure, source citations, draft‑logging), and flag regressions for human review so faculty retain control before any wider rollout - this keeps assessment integrity front and center while instructional teams pivot to oversight roles outlined in local guidance on curriculum and pedagogy (AI Essentials for Work syllabus – AI pedagogy and oversight).

Host those monthly scripts on modest cloud instances made more affordable by recent infrastructure investments (Back End, SQL, and DevOps with Python syllabus – cloud deployment for education scripts), and align pass/fail criteria with the district's generative‑AI limits so every run verifies compliance with disclosure and citation requirements (AI Essentials for Work syllabus – generative AI classroom guidance).

So what: a simple monthly script turns vague worries about model drift into concrete, auditable checkpoints that let districts scale prompts confidently while preserving pedagogy.

Classroom-ready prompt engineering pedagogy turns the recipe from Prompt Engineering Ninja into hands‑on worksheets and short activities: give teachers a scaffolded prompt template that asks students to iterate from a simple request to a refined prompt (specify subject, level, question types, and explanations), pair it with a three‑level hint structure so homework help nudges toward understanding rather than answers, and use chain‑of‑thought or few‑shot examples to model reasoning steps; practical payoff: a single refined prompt can generate a full practice test (10 multiple-choice + 5 true/false + 5 fill‑in‑the‑blank items) with explanations and an answer key for immediate classroom use.

Use the exam‑prep recipe as a worksheet (prompt versions, QA checklist, learning‑outcome mapping) and the homework‑help guidance to design progressive hints and integrity guardrails that preserve learning while saving instructor time (exam preparation prompt templates, scaffolded homework help prompts).

Using the guidelines below, generate a detailed exam practice test for a high school mathematics course. The test must include three sections:

• Multiple-choice questions: Provide 10 questions, each with four answer options. Cover critical topics in algebra, geometry, and statistics.
• True/False questions: Include 5 questions to assess fundamental conceptual understanding.
• Fill-in-the-blanks questions: Generate 5 questions that require precise recall of essential formulas and definitions.

Next steps for Columbus educators and administrators: treat the state's guidance as a roadmap and move quickly from planning to small, auditable pilots - start by adopting an AI‑use policy that aligns with the state model (all K‑12 public districts must adopt policies by July 1, 2026) and use InnovateOhio's toolkit to translate principles into disclosure language, parent templates, and district PD prompts (InnovateOhio AI Strategy & Toolkit for K‑12).

Assemble a cross‑functional implementation team, tap Ohio's TechCred reimbursement for staff PD, and run 6–8 week classroom pilots that pair parent communications and draft‑logging with monthly LLM checks against an AI‑aware rubric so faculty retain assessment control.

For operational readiness, train frontline staff to write and audit prompts - practical training like Nucamp's AI Essentials for Work teaches prompt design, oversight workflows, and classroom guardrails that districts can deploy immediately (Nucamp AI Essentials for Work syllabus - AI skills for the workplace).

These steps convert policy into measurable practice and give districts a defensible, scalable path from pilot to systemwide rollout before the 2026 compliance deadline (EdWeek MarketBrief: Ohio requires AI policies for all K‑12 schools).

Action	Resource / Detail
Adopt AI use policy	State model; deadline July 1, 2026 (EdWeek MarketBrief on Ohio AI policy mandate)
Staff training	Nucamp AI Essentials for Work - 15 weeks; prompt writing & oversight (Nucamp AI Essentials for Work syllabus)
Pilot & assessment	6–8 week classroom pilots + monthly LLM rubric checks (InnovateOhio toolkit: InnovateOhio AI Strategy & Toolkit for K‑12)

“thou shalt not cheat, thou shalt not copy,” and if referencing AI‑produced content, “thou shall cite.”