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

Madison's schools and campuses are at a pivotal moment: statewide guidance and campus reports urge educators to move from forbidding generative AI toward transparent, policy-driven use that teaches students when and how to employ these tools responsibly - see UW L&S Instructional Design Collaborative AI classroom considerations (UW L&S Instructional Design Collaborative AI classroom considerations) and the Wisconsin Department of Public Instruction K–12 AI guidance for schools and libraries (Wisconsin DPI K–12 AI guidance for schools and libraries).

For Madison practitioners who need hands-on upskilling, Nucamp's AI Essentials for Work is a 15‑week program that teaches prompt design and workplace use-cases - one concrete route to building campus capacity so faculty and staff can design assignments that assess learning, not AI output (Nucamp AI Essentials for Work syllabus (15‑week)).

“We should stop and take a breath - the robots can't do that, not yet anyway - and realize artificial intelligence like ChatGPT could change education for the better.” - David Williamson Shaffer

Methodology prioritized prompts and use cases that are locally grounded, pedagogically defensible, and immediately adoptable: selection favored examples reported in campus coverage and research - see the On Wisconsin profile of Preston Schmitt on AI at UW–Madison (On Wisconsin profile: Preston Schmitt on AI at UW–Madison) - and cases that bridge disciplines, ethics, and classroom practice as illustrated by UW work in social genomics (Biosocial Lab article “The Truth in Our Genes” on social genomics: Biosocial Lab - The Truth in Our Genes).

Each candidate prompt was evaluated against three filters: clear classroom learning objective, alignment with assessment integrity, and feasibility for local rollout through upskilling pathways such as Nucamp's campus-focused resources (Nucamp AI Essentials for Work syllabus and campus resources: Nucamp AI Essentials for Work - Syllabus & Campus Resources); one practical tie-breaker was whether the use case could shorten time‑to‑market for new course materials, a real outcome for busy Madison instructors.

“As scientists learn how genes interact with social environments, their findings could transform health care and public policy.”

Classroom tutors for coding can use AI as a live pair‑programmer: teach students to craft debugging prompts that include language, framework, the exact error message, sample input/output and the intended behavior - an approach detailed in Addy Osmani's Prompt Engineering Playbook for Programmers by Addy Osmani - and combine that prompting discipline with browser-first or IDE tools so feedback is immediate.

Local instructors in Madison can pair in‑class exercises with free or student‑friendly assistants to turn

hours of head‑scratching

into guided fixes and short lessons (see the Top 10 Free AI Code Tutors for Students: Browser Tutors and Copilot Student Pack, which shows browser tutors and Copilot's student pack that generate runnable snippets and unit tests).

For practical campus rollout, link these exercises to upskilling pathways like Nucamp's AI Essentials for Work bootcamp - practical AI skills for the workplace so faculty can grade learning (designing tests and rubrics) rather than penalize tool use; the payoff is concrete: teach one reproducible‑prompt pattern and students produce targeted fixes, explanations, and unit tests instead of vague code dumps.

Design assessment rules that make honest work the path of least resistance: put clear, syllabus-level GenAI and academic-integrity expectations front and center (include UW–Madison policy text and an example honesty pledge as the first exam item), scaffold major writing assignments into milestones so instructors can see drafts in development, and prefer authentic or lower‑stakes assessments that require application and reflection rather than verbatim recall - practices UW–Madison guidance and the University of Oregon both recommend to reduce cheating and build skills (UW–Madison Assessment & Feedback - Academic Integrity; University of Oregon Designing Assessments for Academic Integrity).

Clarify the three W's - what tools are allowed, when they may be used, and why - so students know when disclosure or citation is required, following Faculty Focus's practical checklist for AI‑era syllabus language (Faculty Focus: Five Tips for Writing Academic Integrity Statements in the Age of AI); the payoff is immediate: one transparent policy and a handful of scaffolded checkpoints turn fuzzy enforcement into teachable moments that preserve grading for learning, not for detecting AI.

“Academic Integrity is critical to the mission of the University of Wisconsin-Madison, a research institution with high academic standards and rigor. All members of the University community play a role in fostering an environment in which student learning is achieved in a fair, just, and honest way.”

Designing AI-integrated courses for Madison classrooms means aligning modular materials to clear learning outcomes, explicit syllabus AI statements, and iterative prompt workflows so faculty grade understanding rather than AI text; practical steps include using prompt-design frameworks (RTRI/RTTG and CARE) to scaffold student interactions and asking generative tools to produce a 12‑module course outline with objectives, activities, and assessment checkpoints - an approach educators can trial with a single module before scaling campus‑wide.

For concrete resources on prompt design and course planning, see UT Austin's Generative AI prompt-design workshop guidance (UT Austin Generative AI Prompt Design Workshop Guidance) and curricular examples for generating syllabi, learning modules, and AI-aware assignments from curriculum teams (UNR Integrating AI into Curriculum and Assignments - Syllabi & Module Examples); the payoff for Madison instructors is immediate: a reproducible module template that speeds new course rollouts while preserving assessment integrity.

Policy‑scenario prompting can turn national frameworks into a practical, campus‑ready whitepaper that UW–Madison faculty can use at department meetings and faculty senate: prompt a model to synthesize Chan's university AI education framework with WCET's three‑part policy taxonomy (Governance, Operations, Pedagogy) and to produce clear, role‑mapped recommendations for UW actors (Chancellor/President, Chief Academic Officer, CIO, Deans) along with sample syllabus language and measurable KPIs such as educational outcomes, operational efficiency, and ethical compliance - see the WCET institutional AI policy and practice framework for higher education (WCET institutional AI policy and practice framework (Dec 2023)) and the foundational research in Chan's comprehensive AI policy education framework (Chan 2023: Comprehensive AI policy education framework).

Use Faculty Focus's stepwise guidance to structure governance, pilots, and stakeholder engagement so the whitepaper is not just diagnostic but operational and adoptable by Madison faculty and administrators (Faculty Focus guide: Crafting thoughtful AI policy in higher education); the payoff is a single, reproducible document that aligns academic integrity language with campus roles and gives deans concrete levers to run low‑risk pilots and measure impact.

Madison educators and campus creators should treat the Zarya of the Dawn decisions and the U.S. Copyright Office's evolving guidance as an operational constraint on using generative imagery in curricula and course materials: the Office has repeatedly found MidJourney‑generated panels lacked the “human authorship” required for copyright, and its 2023–2025 guidance makes clear that repeated prompt revisions alone often won't meet that threshold, so instructors who assemble AI images into lecture packs or publish course‑adjacent works must document substantive human edits, disclose non‑de minimis AI content, and correctly identify human authors when filing registrations or public deposits - failure to do so can lead to cancelled registrations or worse if applications misstate authorship.

For practical campus policy, translate the guidance into simple requirements (disclose tool use on course pages, keep editable source files showing human decisions, and avoid claiming exclusive ownership of raw AI images) and coordinate with UW–Madison counsel before monetizing or widely distributing AI‑assisted materials; see the Creative Commons briefing on the USCO limits (Creative Commons briefing on Zarya of the Dawn and US Copyright Office limits) and a practitioner summary of the Copyright Office's two‑part guidance and registration rules (Whiteford Law client alert: Can works made with generative AI be copyrighted?) for examples Madison departments can adopt into syllabus and institutional‑repository checklists.

"absent significant and direct human creative input, generative AI outputs should not qualify for copyright protection."

Madison classrooms can reduce risky AI “facts” by building a short RAG‑aware workflow that stores context, question, and the model's answer, runs lightweight filters (token log‑prob or token/novelty checks) to drop obviously improbable sequences, then applies a sentence‑level LLM prompt detector that returns a 0–1 hallucination score and flags sentences for citation or human review - an approach and prompt template described for RAG systems by the AWS tutorial on detecting hallucinations in RAG systems (AWS tutorial: Detect hallucinations for RAG-based systems).

Complement this with semantic‑similarity or BERT‑stochastic checks for high‑recall review in graded or clinical assignments and use Deepchecks' catalog of detection techniques (seq‑logprob, similarity, novelty, SelfCheckGPT, RAG) to pick the right mix for cost and precision (Deepchecks guide to LLM hallucination detection and mitigation techniques).

The payoff for UW–Madison courses: a reproducible pipeline that catches fabricated citations and out‑of‑context claims before grading, preserving assessment integrity while keeping turnaround time reasonable.

To keep Madison classrooms equitable, pair a lightweight bias audit with a stakeholder simulation: use advanced, challenge‑style prompts to surface assumptions and missing perspectives (see prompts for mitigating bias and inaccuracies in AI responses by Geoff Cain Prompts for Mitigating Bias and Inaccuracies in AI Responses - Geoff Cain), convene cross‑role workshops that mirror the three stakeholder groups IRRODL identifies (students, teachers, institutions) to capture lived impacts and policy tradeoffs (IRRODL stakeholder perspectives on online learning research IRRODL Stakeholder Perspectives - Open Praxis), and follow a practical school‑leader audit playbook - map AI touchpoints, demand vendor transparency, pilot with diverse students, monitor disaggregated outcomes, and verify teacher override controls (SchoolAI school leader audit guide for edtech algorithmic bias SchoolAI: School Leaders' Audit of EdTech Algorithmic Bias).

One concrete detail: a 30‑minute kickoff plus a short, mixed‑cohort pilot that breaks out results by learner group frequently reveals whether an adaptive system quietly steers multilingual or low‑income students toward easier material - turning fairness principles into specific syllabus language, procurement questions, and vendor evidence faculty can require immediately.

“The bias that exists 'out there' is the same bias that exists in ourselves.”

Human‑centered educational assistants for Madison classrooms must combine clear transparency, task‑focused interfaces, and stateful interactions so students and faculty treat AI as a partner - not a black box: design requirements include persistent session history and “My Sessions” UX so instructors can replay a student's prompts and AI responses for grading and feedback, larger full‑page canvases for higher‑stakes tasks (grading, course design), and built‑in promptbooks and templates that teach reproducible prompt patterns rather than leaving users to guess - see UX Studio's guide to AI in education and UX for AI copilot design best practices on statefulness and promptbooks (UX Studio guide to AI in education; UX for AI copilot design best practices).

Operationally, require plugin or fine‑tuning hooks so assistants can reference campus data safely, and an onboarding flow that teaches AI literacy, citation prompts, and override controls - this mix preserves assessment integrity while making assistants practically useful for instructors pressed for time.

“SaaS is not entertainment but serious work. And copilots are now a serious business indeed.”

Administrative copilots can collapse the busiest points in Wisconsin enrollment cycles into fast, auditable workflows: tools like FlowForma student enrollment automation demo let offices design, build, and deploy student‑enrollment and onboarding flows in minutes - automating validation, notifications, document checks, and confirmations so departments avoid misplaced paperwork and long approval queues (FlowForma student onboarding automation demo).

Complementing this, Microsoft 365 Copilot education scenarios and Copilot Agents can summarize advisor notes, auto‑schedule appointments, and generate targeted outreach segments or multi‑step journeys for at‑risk prospects, turning CRM data into timely advisor briefs and follow‑up tasks (Anthology Reach: Microsoft Copilot for admissions and advising).

“We [Microsoft] are the copilot company […] We believe in a future where there will be a copilot for everyone and everything you do.”

Design campus-aware illustration prompts that anchor AI outputs in UW–Madison's real places and policies: point prompts to the Campus Art Exchange inventory to borrow locally circulating pieces at “no or little cost,” ask for styles matching the Media Solutions “Illustration” categories (graphic stylized, medical, scientific) to ensure pedagogical clarity, and require compliance checks against the university's Campus Art Management Policy so site proposals account for stewardship and siting constraints; one concrete workflow is to prompt an illustrator‑AI with a target building, desired media (mural, printed lecture slide, or four‑inch community canvas), and a short provenance note that documents human edits - this mirrors how students' four‑inch mini‑canvases were combined into a larger UW mural and makes rights and maintenance decisions visible to planners.

Use these local anchors so generated art fits campus scale, aligns with conservation rules, and can be vetted quickly by facilities and curators before installation (Campus Art Exchange UW–Madison collection, UW–Madison Media Solutions Illustration styles, UW–Madison Campus Art Management Policy UW-6044).

"A picture is worth a thousand words..." - Unknown

Madison's next steps are practical and immediate: adopt UW–Madison's vetted enterprise AI tools for course and career work to protect student and institutional data, pair a short 30‑minute kickoff with a mixed‑cohort pilot to reveal equity or accuracy issues, and embed clear syllabus language plus scaffolded checkpoints so instructors grade learning processes not AI outputs; detailed guidance and campus tools are available at UW–Madison's Generative AI Services hub (UW–Madison Generative AI Services - enterprise AI tools and guidance) and the Center for Teaching and Learning and Mentoring teaching resources (Generative AI in Teaching - CTLM resources and templates).

For skill-building that supports local rollout, consider cohort upskilling - Nucamp's AI Essentials for Work is a 15‑week, practical pathway that teaches prompt design, workplace use cases, and assessment-aware prompting so faculty can run pilots and flip from policing to coaching (Nucamp AI Essentials for Work - 15-week practical AI for the workplace).

A focused pilot, transparent policies, and one reproducible instructor workflow (prompt logging + scaffolded drafts) will quickly surface risks and preserve academic integrity while giving students real, career‑ready AI skills.

“Academic Integrity is critical to the mission of the University of Wisconsin-Madison, a research institution with high academic standards and rigor. All members of the University community play a role in fostering an environment in which student learning is achieved in a fair, just, and honest way.”