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

Indiana education is shifting from pilots to policy as districts aim to capture AI's time‑saving benefits while protecting students: Indianapolis Public Schools recently approved a districtwide AI policy after a 20‑staff pilot where district‑approved tools helped teachers and a principal save time on administrative work and even transform a secondary master schedule; the district plans a wider Phase 2 rollout using Google Gemini (see the IPS policy summary at Chalkbeat).

State guidance for K–12 underscores AI literacy, FERPA/COPPA compliance, and human oversight as core safeguards - practical guardrails that let schools deploy tutors, lesson‑differentiation, and automated reporting without sacrificing privacy (see Indiana K–12 AI guidance).

For educators and administrators preparing to implement these changes, structured professional learning such as the AI Essentials for Work bootcamp supports prompt writing, tool evaluation, and workplace-ready AI skills to turn pilot gains into sustained practice.

Bootcamp	Length	Early‑bird Cost	Registration & Syllabus
AI Essentials for Work	15 Weeks	$3,582	AI Essentials for Work registration and syllabus - Nucamp

“Eventually AI is not going to be a choice. Right now, it's a choice.” - Ashley Cowger, district chief systems officer

Selection prioritized use cases that matched three practical priorities for Indiana districts: measurable teacher time‑savings in short pilots, clear FERPA‑aligned data practices, and feasible scaling without expensive replatforming; evidence came from state and district pilots - Indiana's 2023–24 AI‑Powered Platform Pilot Grant (a one‑year, $2 million federal relief–funded rollout that left 53% of participating teachers reporting positive experiences) and Indianapolis Public Schools' 20‑staff pilot that informed a districtwide AI policy (Phase 2 expands staff use of Google Gemini) - so what: districts can run a semester‑long pilot and expect concrete workload relief rather than abstract promise.

Methodology steps included vetting vendor documentation, requiring professional learning plans, prioritizing tools that integrate with cloud infrastructure, and defining KPIs (teacher workload, student equity, data minimization).

These criteria align with state rollout guidance and SchoolAI's recommended playbook for focused pilots and steering committees, and they shaped the Top‑10 prompts to be classroom‑ready, privacy‑conscious, and evaluable within one academic term.

Selection Criterion	Evidence / Source
Short pilot, measurable teacher impact	Education Commission of the States overview of AI pilot programs in K‑12 settings
Policy & professional learning required	Chalkbeat report on Indianapolis Public Schools AI policy and pilot
State playbook for scaling responsibly	SchoolAI guidance on how states are rolling out AI in public education

“We want to make sure that staff feel well equipped to determine what the boundaries are for use of AI in a classroom.” - Ashley Cowger, Chief Systems Officer, Indianapolis Public Schools

Indianapolis Public Schools can reclaim hours of staff time by using Google's Gemini tools to automate routine administrative work: Gemini in Classroom helps draft lesson plans, generate quizzes that export directly to Google Forms, and create standard‑aligned rubrics and student feedback at scale (Google Gemini in Classroom launch announcement).

Built‑in admin controls and enterprise‑grade protections let district IT enable or restrict access, audit usage with Vault, and keep Workspace data from being used to train external models - important for FERPA and district policy compliance (Google Gemini for Education admin, privacy, and FERPA protections).

Pairing Gemini with a short, targeted PD sequence and Chromebook rollouts can move IPS from pilot to operational gains quickly; practical first steps include automating newsletters, permission‑slip templates, meeting notes, and tagging assignments to CASE standards for faster reporting (Indianapolis Google Gemini pilot guide for schools).

“the rubric generation tool… turning the repetitive task of making a rubric into a quick and easy one, bringing your rubric right into Classroom in a matter of seconds.” - Chris Webb

Ivy Tech pairs AI-driven early‑alert predictive analytics that monitor early course performance and flag at‑risk students with human outreach and habit‑based supports, turning data into timely interventions that keep students enrolled across Indiana: the analytics surface who needs contact, campus leads reach out by text, email, and in person, and pilots tying those alerts to Ivy Achieves' 10 high‑impact habits produced clear downstream gains - students with six habits were 94% registered for spring and the pilot (1,840 students on 10 campuses) demonstrated how timely nudges convert flags into persistence (GoBeyond case study: Ivy Tech AI predictive analytics, Chalkbeat report: Ivy Achieves retention pilot).

For Indianapolis institutions evaluating retention tech, the lesson is practical: pair a narrow, auditable early‑alert model with campus leads and measurable onboarding habits to turn alerts into higher registration and clearer pathways to degree completion.

Metric	Value
System‑wide students served	157,000 (19 campuses)
Pilot participants	1,840 students on 10 campuses
Fall‑to‑fall retention (system)	47%
Registration if 6 habits complete	94% registered for spring

“Early momentum metrics and holistic onboarding can help students feel supported and belong in college.”

Purdue University's stackable AI microcredentials give Indiana educators and district staff a practical, low‑lift route to certify classroom‑ready AI skills: each mini‑credential averages about 15 hours to complete, covers applied topics from prompt engineering and NLP to AI policy and data storytelling, and awards a certificate for every course finished - so teachers can upskill between grading cycles or a short summer PD window without long absences (Purdue AI microcredentials program for educators).

The catalog lists 13 discrete courses and a transparent per‑course cost ($500), letting districts assemble targeted professional learning paths that are auditable and stackable for career advancement (Purdue AI micro‑credentials course catalog).

For Indianapolis institutions planning FERPA‑aware pilots and teacher time‑savings, these faculty‑taught, short courses provide concrete building blocks - prompt practice, ethics, and governance - that map directly to the operational needs shown in recent local pilots (Purdue AI certifications and degree programs).

Program Detail	Value
Average time to complete	15 hours
Number of courses	13
Individual course cost	$500

Georgia Tech's Jill Watson - now rebuilt on ChatGPT - demonstrates a pragmatic model Indianapolis educators can study when weighing virtual teaching assistants: the agent grounds answers in verified courseware (syllabi, transcripts, slides), keeps conversational history in agent memory, and uses a question‑answering pipeline with moderation and coreference resolution so responses remain consistent and auditable; empirically this approach not only outperformed OpenAI's Assistant in pass rates but also correlated with modest grade gains (A's ~66% with Jill vs ~62% without) and fewer low grades, suggesting a concrete “so what”: offloading routine Q&A can measurably free instructor time while improving teaching and social presence in large online classes.

For technical and policy teams, the full methods, architecture, and experimental results are documented in the project report (Jill Watson project report at AI‑ALOE: Jill Watson project report (AI‑ALOE)) and discussed in practitioner Q&As on VTA design (Georgia Tech Q&A on virtual teaching assistants and student success: Georgia Tech Q&A on VTAs and student success), offering a replicable starting point for Indianapolis pilots that must balance pedagogy, FERPA‑aware data practices, and human oversight.

Metric	Value
Answer accuracy (pre‑deployment)	~75%–97%
A grades (with Jill vs without)	~66% vs ~62%
JW‑GPT pass rate vs OpenAI‑Assistant	78.7% vs 30.7%

“The Jill Watson upgrade is a leap forward. With persistent prompting I managed to coax it from explicit knowledge to tacit knowledge. That's a different league right there, moving beyond merely gossip (saying what it has been told) to giving a thought‑through answer after analysis. I didn't take it through a comprehensive battery of tests to probe the limits of its capability, but it's definitely promising. Kudos to the team.”

University of Toronto researchers tested an MI‑style chatbot that used large language models to deliver brief therapeutic conversations and produced small but measurable changes - 349 participants showed a 1.0–1.3 point rise on an 11‑point quit‑confidence scale one week after a session - and the team reported major quality gains using newer models (GPT‑4 reflections ~98% appropriate vs ~70% with GPT‑2); however, the studies also documented harmful edge cases and stressed the need for human oversight and clear escalation pathways (University of Toronto MI chatbot coverage, JMIR study on ChatGPT for clinical workflows).

For Indianapolis schools and campus counseling centers, the practical takeaway is that a scoped chatbot can provide 24/7 motivational triage and reduce demand on clinicians - if deployed with auditable escalation protocols, clinician handoffs, and FERPA‑aligned data handling as outlined in local pilot checklists (Indianapolis edtech pilot checklist for FERPA‑aligned chatbots).

Metric	Value
Participants	349
Short‑term confidence gain	+1.0 to +1.3 (11‑point scale)
Reflection appropriateness (GPT‑4 vs GPT‑2)	~98% vs ~70%

“If you could have a good conversation anytime you needed it to help mitigate feelings of anxiety and depression, then that would be a net benefit to humanity and society.”

Smart Sparrow's adaptive platform gives Indiana instructors granular control to build active, branched lessons and simulations that meet local course standards while scaling costly hands‑on experiences - useful for Indianapolis colleges that face lab space and equipment limits.

Educators author “If THIS, then THAT” adaptivity (just‑in‑time hints, differentiated pathways, and real‑time feedback), deploy through an LMS, and see live analytics to pinpoint misconceptions and intervene before failure; deployments show real gains in engagement and outcomes and practical savings such as replacing slow manual grading workflows with time‑limited, automatically scored adaptive exams.

For example, Smart Sparrow supports virtual labs, simulations, and adaptive tutorials that replicate expensive experiments at lower cost and enable instructors to reclaim instructor hours for high‑impact teaching - so what: a single adaptive exam workflow has been credited with saving roughly 30 hours of instructor grading per exam while surfacing data to guide interventions.

Learn more in Smart Sparrow's adaptive learning overview, explore institution demos, or review adaptive‑tutorial exam use cases.

Metric / Feature	Value / Example
Instructor control & adaptivity	Designed adaptivity (authoring “If THIS, then THAT” rules)
Reported impact	Engagement +57%, outcomes +28% (Doherty et al., 2018)
Time savings example	~30 hours saved per exam (UNSW adaptive tutorials deployment)

“The adaptive lessons developed using Smart Sparrow improved the pre-class learning experience of my students – both, cognitively and affectively.” - Dr. Autar Kaw

University of Alicante's accessibility suite - led by the AI‑powered “Help Me See” mobile app - demonstrates practical assistive AI Indianapolis campuses can model: Help Me See uses computer vision and machine‑learning to assist blind and low‑vision students with navigation and obstacle alerts, building on research that used a phone's 3D camera to warn users of obstacles roughly two meters away (NBC News report on the Help Me See obstacle-detection app); the UA platform also runs Navilens for indoor wayfinding and recently added an AI subtitling and transcription tool that generates time‑synced captions with processing kept on university servers to strengthen multimedia privacy and control (University of Alicante accessible apps overview, UACloud announcement on subtitling and transcription tool).

So what: Indianapolis institutions can replicate this stack to cut accommodation wait times, increase campus independence for students with visual or hearing impairments, and maintain local data control as part of FERPA‑aware deployments.

Tool	Capability	Privacy/Note
Help Me See	Computer‑vision navigation & obstacle alerts	Mobile on‑device sensing (3D camera research)
Navilens	Long‑range, high‑density indoor wayfinding markers	Pilot deployed on UA campus maps
Subtitling & Transcription	AI‑generated, time‑synced captions for videos/audio	Processing on UA servers for privacy

Juilliard's conservatory structure - centered on intensive applied training (students receive 15 one‑hour studio lessons per semester) and a Pre‑College practice‑buddy program where prep percussionists run half‑hour weekly Zoom sessions with elementary students - offers a concrete model Indianapolis music programs can mirror when introducing an AI “Music Mentor” that provides instant, practice‑level feedback between human touchpoints; so what: pairing quick, automated feedback on rhythm, dynamics, and posture with Juilliard‑style frequent coaching can accelerate deliberate practice cycles and free instructors to focus on ensemble work and higher‑order musicianship (see Juilliard's Bachelor of Music program - Juilliard 15 one‑hour lessons per semester and the Prep Division mentorship write‑up - Juilliard prep percussionists tutoring elementary students).

Local pilots should follow Juilliard's rules for unedited student recordings, teacher‑assigned repertoire, and clear audition/assessment timelines so automated feedback remains auditable, FERPA‑aware, and integrated with human oversight rather than replacing it.

Feature	Juilliard Example
Studio lessons per semester	Juilliard Bachelor of Music program - 15 one‑hour lessons per semester
Prep Division mentoring	Juilliard Prep Division mentoring - half‑hour weekly Zoom practice‑buddy sessions

“The main learning tool of El Sistema is the music ensemble, so having these kids learn to work together is perfect in terms of our theme of connections.”

Santa Monica College models a pragmatic pathway Indianapolis institutions can mirror for AI‑driven career and guidance systems: SMC's online EDUC 50 “AI for Educators” course - built from Lynn Dickinson's How to Use ChatGPT and which filled its initial 45‑student section within hours and expanded to two sections - prepares faculty to teach AI literacy and integrate ethical prompt practices into career advising, while SMC's hands‑on offerings (like the Python for Machine Learning & Data Science course at Santa Monica College) and its interaction‑design pipeline create stackable, job‑ready skills; pairing that supply of trained instructors and technical coursework with a campus degree‑planning platform such as Stellic degree-planning tools (used by large universities including Indiana University) lets advisors automate audits, show clear pathways to AI roles, and surface timely alerts so more students can convert interest into employment - so what: the combined system turns one‑off AI PD into scalable career routing that shortens time‑to‑placement while keeping human advisors in the loop.

Offering	Key fact
EDUC 50 - AI for Educators	Initial section capacity 45; doubled to two sections
Python for ML & Data Science	Comprehensive certificate (120 hrs); price listed $2,095
Stellic degree planning	Planner, audits, alerts; trusted by institutions including Indiana University

“It's about understanding AI's impact on teaching and learning, and learning how to use it ethically and effectively.”

Indianapolis Public Schools' new districtwide AI policy foregrounds ethics and governance by limiting use to district‑approved tools, requiring staff to sign responsible‑use agreements, and explicitly tying any deployment to federal privacy rules such as FERPA - practical safeguards include prohibitions on uploading a student's full IEP into generative models and a rule that classroom guidance for students is not yet adopted, keeping initial rollout focused on adults and operational gains; the policy also builds human oversight into every stage with an AI advisory board, monthly professional development for pilot participants, and an always‑available online PD repository so educators learn limits as well as capabilities (see the IPS policy summary on IPS AI policy summary on Chalkbeat).

For districts mapping next steps, Indiana AI Planning Guide for Schools offers a step‑by‑step framework to align pilots with instructional priorities, data minimization, and auditability - so what: these controls let Indianapolis convert quick admin time‑savings into sustained, FERPA‑compliant practices without exposing sensitive student records.

Policy Element	Detail
Acceptable uses	Teacher‑supervised quiz/material generation, lesson support, communications
Tool restrictions	Only district‑approved AI products; staff must follow responsible‑use agreements
Privacy & law	Must adhere to FERPA and data‑protection principles; restrict sensitive uploads
Training & PD	Monthly pilot PD + online repository; roadmap for broader staff learning
Governance	Creation of an AI advisory board to monitor trends and update practices

“We do not at any point encourage someone going in blindly to using AI. It can be a slippery slope, which is why we have put a lot of effort into developing the professional learning roadmap for AI for the pilot users for next school year.” - Ashley Cowger

Getting started in Indianapolis means pairing small, auditable pilots with clear privacy controls and focused professional learning: follow IPS' playbook of a 20‑staff pilot and a Phase‑2 rollout using Google Gemini (budgeted at about $122 per person) while enforcing district‑approved tools and data minimization in contracts (see the IPS AI policy on Chalkbeat IPS AI policy); require vendor vetting and a FERPA/COPPA checklist before any classroom data is shared (use SchoolAI's compliance primer at SchoolAI FERPA/COPPA compliance primer), and invest in short, practical upskilling - Nucamp's AI Essentials for Work is a 15‑week, hands‑on path to prompt writing and tool evaluation (early‑bird $3,582; registration and syllabus at Nucamp AI Essentials for Work bootcamp - 15‑Week Prompt Writing & AI for Work) - so what: a low‑cost, 20‑person pilot plus vendor controls and targeted PD can convert the district's early time‑savings into repeatable, FERPA‑compliant practice without large systemwide disruption.

Starter Step	Practical Detail	Source
Run a small pilot	~20 staff; Phase‑2 Gemini pilot (~$122/user)	Chalkbeat IPS AI policy
Lock vendor terms	Require no student‑data training, retention limits, and audit logs	SchoolAI FERPA/COPPA compliance primer
Train staff	Targeted PD: 15‑week bootcamp for prompts & tool evaluation (early‑bird $3,582)	Nucamp AI Essentials for Work bootcamp - registration & syllabus

“We do not at any point encourage someone going in blindly to using AI. It can be a slippery slope, which is why we have put a lot of effort into developing the professional learning roadmap for AI for the pilot users for next school year.” - Ashley Cowger