Top 10 AI Prompts and Use Cases and in the Education Industry in Des Moines

Des Moines Area Community College's new partnership with Intel - the first Intel AI for Workforce College tie-up in Iowa - is bringing a non-credit AI certificate to the region this fall and weaving AI competency into college programs, giving local districts a pipeline of trained staff and ready-made classroom content; Intel will provide more than 700 hours of AI courses, modules and teacher training, so schools can adopt ethically framed, workplace-focused units without building curriculum from scratch (DMACC Intel partnership announces AI workforce courses at DMACC).

For district leaders balancing budgets and outcomes, this creates a practical route to teacher upskilling and student workplace readiness - see a local roadmap in Nucamp's overview for Des Moines schools (Nucamp AI Essentials for Work overview for Des Moines schools).

“It fits in every area of curriculum… every subject matter, whether it's technical or arts and sciences, can benefit from the AI process,” Denson said.

Selection prioritized prompts and use cases that are locally practical, legally defensible, and training-ready: each candidate had to align with Iowa's growing AI infrastructure and workforce pipeline, demonstrate clear classroom return (for example, reusing DMACC/Intel's 700+ hours of curriculum to cut teacher prep), and respect emerging state guidance on data and human oversight; sources such as the Corridor Business profile of Iowa's AI growth informed the infrastructure and industry filter, while the Gazette story on the Iowa Legislature's Legible pilot guided governance and transparency criteria.

Prompts were also scored for ease of integration with district IT and for amplification of existing local expertise (Tippie and University of Iowa faculty), so the final Top 10 favors scalable classroom lesson templates, 1:1 tutoring flows, and assessment generators that districts can pilot within existing budgets and policy guardrails (Iowa AI ecosystem report from Corridor Business, Iowa Legislature AI pilot coverage from The Gazette).

“AI is a once-in-a-generation type of technology, providing a set of tools and assets that can pivot or really move you into this next phase of productivity,” says Allie Hopkins, area lead for Google's Iowa and Nebraska data centers.

ChatGPT lesson-plan templates give Des Moines teachers a practical, standards-aligned shortcut for building differentiated classroom sessions: start with a copyable template to generate objectives, step-by-step activities, materials and formative checks, then use a step-by-step workflow to refine and personalize for ELLs, IEP accommodations, or gifted extensions; classroom-ready prompt libraries also include ready-made examples (for instance, a 50‑minute lesson scaffold) so teams can quickly adapt local DMACC/Intel modules into differentiated lessons that teachers can iterate with follow-ups and rubrics to meet district standards and diverse learner needs.

“Create a lesson plan about [Topic] and incorporate [materials list, interactive activities, assessments, differentiation strategies, etc.]”

Use the Athena AI copyable ChatGPT lesson-plan template to generate initial lesson scaffolds (Athena AI copyable ChatGPT lesson-plan template), then apply the AIforWork ChatGPT lesson-plan workflow to refine and align lessons with a quality rubric (AIforWork ChatGPT lesson-plan workflow), and consult the LearnPrompt ChatGPT prompts for future teachers library for ready-made prompt examples and classroom-ready variations (LearnPrompt ChatGPT prompts for future teachers).

Khanmigo brings district-grade, on‑demand tutoring and teacher-assistant tools that Des Moines classrooms can use to scale 1:1 feedback without hiring more staff: integrated with Khan Academy content, Khanmigo prompts students with guiding questions instead of answers, offers teacher-facing features like a rubric generator that cut one ELA chair's rubric time from an hour to 15 minutes, and includes text‑scaffolding and exit‑ticket generators to support ELLs and students below grade level (Khan Academy prompt-engineering overview, Khanmigo ELA teacher tools overview).

For district leaders in Des Moines weighing rollout costs and impact, Khan Academy's district program reports over 400 partner districts, notes users are 10x more likely to reach recommended weekly usage, and links recommended use to roughly 20% higher-than-expected gains on MAP Growth assessments - so the practical result is more individualized practice time for students and measurable lift for district assessments (Khanmigo district program information and impact).

“By facilitating misconceptions where students are struggling with certain answers, Khanmigo will push and ask them guiding questions to get them to come to the conclusion on their own.”

Oak National Academy's Aila experiments show how an AI-powered quiz builder can turn minutes of teacher time into vetted formative assessments by combining retrieval‑augmented generation (RAG) from a curriculum corpus, prompt engineering for high‑quality distractors, and a human‑in‑the‑loop workflow that keeps teachers editing every question; Oak's pilot produced thousands of quizzes and lesson plans while offering localization options (e.g., lowering reading age or changing context) so U.S. districts can replicate the pattern with local standards and content (Oak AI Experiments – Aila: AI-powered quiz builder and experiments, Oak blog on AI tools for teachers: practical classroom applications).

Crucially, Oak's auto‑evaluation work shows measurable quality gains - mean squared error versus teacher judgment fell from 3.83 to 2.95 and Quadratic Weighted Kappa rose from 0.17 to 0.32 - meaning generated multiple‑choice items and distractors more closely match expert review, so Des Moines districts can aim to free teacher hours for instruction while retaining human oversight and safety checks (Oak auto‑evaluation findings: improving AI-generated resource quality).

“Oak's AI lesson planner offers a really helpful starting point if stuck or low on inspiration and I was particularly impressed at how good it is at highlighting misconceptions for a topic.” - Alex Hawkes, English secondary teacher

Automated essay scoring (AES) tools can give Des Moines teachers a reliable first pass on student writing - generating rubric-aligned scores, highlighting patterns (grammar clusters, argument structure gaps), and producing draft feedback that teachers review and personalize - so turnaround for revisions shortens and conference time shifts from grading to targeted coaching; districts should pilot AES on low‑stakes drafts with a clear human‑in‑the‑loop workflow, local rubric validation, and strong data governance to prevent bias and protect student records, using statewide training pathways to upskill instructional staff.

For practical guidance on how AES systems work and why multi‑stakeholder oversight matters, consult the COL Automated Essay Scoring (AES) systems repository (COL Automated Essay Scoring (AES) systems repository), review analyses of local implementations (How AI Is Helping Education Companies in Des Moines), and consult Nucamp's AI Essentials for Work syllabus for training pathways and guidance on deploying AI solutions in schools (Nucamp AI Essentials for Work syllabus).

To guide successful development and implementation, we need to understand how AIEd works and ensure multi- stakeholder dialogue throughout its lifecycle (Hu et ...

Ivy Tech's NewT early‑warning system shows how Des Moines districts can use daily predictive analytics to find students who need help before grades collapse: the model scanned course interactions and engagement signals to flag roughly 16,000 at‑risk students in a two‑week pilot and supported about 3,000 to move to passing grades, with roughly 80% predictive accuracy inside the first fortnight - a concrete “so what” for school leaders balancing limited interventions and fast action.

The system combines grades, attendance, and online activity into daily predictions so outreach teams can triage cases, offer targeted supports, and track impact at scale; responsible rollout requires human‑in‑the‑loop triage, rubricled interventions, and strong data governance.

Districts in Iowa can adapt this pattern - pairing local data pipelines with vendor/cloud tools and district training - by studying the Ivy Tech case in detail (Ivy Tech predictive analytics case study - student success), the NewT/Google Cloud implementation notes (NewT predictive AI implementation on Google Cloud), and local planning resources for Des Moines districts (predictive student analytics planning resources for Des Moines districts).

“We had the largest percentage drop in bad grades that the college had recorded in fifty years.”

For Des Moines district offices, pairing a Workiva-style reporting platform with LLM-driven AI and the category of AI productivity tools described by DigitalOcean can turn time‑consuming communications into automated workflows: LLMs draft board reports and executive summaries from uploaded spreadsheets, Otter-style transcription tools capture meeting notes for instant minutes, Zapier-like automations push finalized narratives into SIS or finance systems, and Grammarly-class tooling polishes family letters and compliance text - minimizing manual overhead so business offices can redeploy staff time to grant writing and community outreach rather than formatting and copy‑editing (DigitalOcean guide to AI productivity tools, Nucamp AI Essentials for Work bootcamp - practical AI skills for the workplace).

The practical payoff: predictable, template-driven communications that maintain human oversight while freeing administrators to focus on strategic district priorities.

University of Alicante's “Help Me See” - an AI app that uses computer vision and machine learning to help visually impaired students navigate campus and access materials - offers a practical model for Des Moines districts seeking to boost classroom accessibility without reinventing assistive tech: schools can pilot campus‑navigation demos and build prompt libraries that generate accurate alt text, plain‑language image descriptions and audio summaries for LMS content so visually impaired learners gain independence while teachers regain prep time; crucially, any rollout must embed FERPA‑style data safeguards and human oversight from day one to avoid bias and privacy gaps (University of Alicante Help Me See AI assistive navigation case study, TUUTOR AI guidance on inclusive education and privacy for AI).

The bottom line: a tested computer‑vision assistive pattern plus district‑curated alt‑text prompts equals faster, more equitable access to lessons for Iowa students with visual impairments.

Maths Pathway–style adaptive units can be built quickly with classroom-tested AI prompts that turn a single standard into a scaffolded learning sequence: use student‑interest word‑problem templates and multi‑level practice sets to generate basic/intermediate/advanced problems with step‑by‑step solutions, add diagnostic multiple‑choice items and targeted distractors to triage misconceptions, then atomize lessons into “I Do / We Do / You Do” sequences for mastery-based progression - all provably repeatable with the prompt patterns in Project Pals' AI math prompt templates for multi-level practice (Project Pals AI math prompt templates for multi-level practice).

Prompt structure matters: craft context, constraints, output format and examples, iterate with the model, and export polished worksheets via LaTeX/Overleaf when print or LMS-ready PDFs are needed (AI prompt structure and LaTeX workflow for teachers: AI prompt structure & LaTeX workflow for creating maths resources).

For Des Moines classrooms, the practical payoff is concrete: a teacher can produce a three‑tiered unit (problems, worked solutions, quick diagnostics) in minutes - freeing hours each week for small‑group interventions - using the 50+ differentiation and assessment prompt stems in EdTech Specialist's collection (50 AI prompts for math differentiation and assessment).

Use AI as a rapid brainstorming partner to turn local issues into rich, cross-curricular projects: feed a chatbot a brief (standards, grade, local context) and get back a scaffolded plan that ties science experiments, ELA research, and math data analysis into one authentic unit - Discovery Education shows how STEM projects built around local problems (for example, investigating an invasive species) can require research, writing, and quantitative testing while leading to community-facing products like presentations or prototypes (Discovery Education guide to cross-curricular STEM projects).

Classroom-ready AI activities from Edutopia - debates, story-collaborator exercises, and AI “study buddies” - give concrete templates for student roles, assessment checkpoints, and reflective tasks that deepen creativity and transfer (Edutopia AI classroom activities that spark student creativity).

Combine those activity templates with prompt libraries (see Panorama's 30+ K–12 prompts) to generate differentiated entry points for diverse learners, and guard every rollout with Panorama's privacy-first prompts and district FERPA/COPPA practices so enrichment scales without exposing student data (Panorama Education 30+ AI prompts for K–12 with privacy guidance).

Des Moines districts can use AI triage scripts - fed by local predictive student analytics - to surface early signs of distress and route cases for human escalation rather than replacing school counselors; practical rollouts pair the district's predictive models (see Nucamp AI Essentials for Work syllabus - predictive student analytics overview: AI Essentials for Work syllabus - predictive student analytics overview) with trained instructional aides who follow scripted, privacy‑aware triage flows informed by social‑emotional best practices (see Nucamp Job Hunt Bootcamp syllabus - instructional aide upskilling paths: Job Hunt Bootcamp syllabus - instructional aide upskilling paths); local workforce partnerships and accredited programs - like Nucamp scholarships and partnerships for workforce training (Nucamp scholarships and partnerships for workforce training) - supply trained staff to run human‑in‑the‑loop escalation, so the “so what” is clear: districts scale timely, humane responses while preserving counselor capacity for the highest‑risk students.

Start small, protect data, and build clear governance before scaling AI in Des Moines schools: pilot one low‑risk use case (for example, lesson‑plan templates or alt‑text generation) with de‑identified content while procurement and vendor agreements are reviewed; remember Des Moines Public Schools explicitly forbids using public‑domain AI tools with student data and already requires strong controls such as MFA for Office 365 and long staff passwords - concrete controls any district can adopt now (Des Moines Public Schools IT administrative regulations).

Pair pilots with privacy and FERPA-aligned policies from the Iowa Department of Education so families' rights and data‑redaction needs are met (Iowa Department of Education data access and privacy guidance).

Close the loop by investing in staff competency - use a structured training pathway like Nucamp's AI Essentials for Work to teach prompt design, vendor risk checks, and human‑in‑the‑loop reviews - so the “so what” is clear: safer pilots that free teacher time without trading away student privacy (Nucamp AI Essentials for Work bootcamp syllabus).

“The emotional and human impact is the hardest part; culture matters.”