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

Boise educators need to act now because local institutions are already reshaping curriculum, policy, and student expectations: Boise State CI+D AI initiatives, and the university offers an accessible AI for All certificate to build campus-wide literacy; meanwhile local research surveying 700+ students found over 40% admitted using LLMs in ways explicitly banned by professors, a clear early-warning for integrity and equity challenges (Stone's student survey on generative AI in higher education).

Practical responses for Boise classrooms: teach prompt literacy, test assignments against LLMs, and upskill staff with applied training (for example, targeted programs like Nucamp's AI Essentials for Work registration) so educators can turn AI from a disruption into a measurable learning advantage.

Bootcamp	AI Essentials for Work
Length	15 Weeks
Courses	AI at Work: Foundations; Writing AI Prompts; Job-Based Practical AI Skills
Cost	$3,582 (early bird) · $3,942 (after)
Registration / Syllabus	Register for Nucamp AI Essentials for Work · AI Essentials for Work syllabus

“It seems that those using AI the most may not always discriminate between use cases but instead apply the tool broadly,” - Brian Stone.

Selection prioritized Boise-relevant impact, practicability, and alignment with statewide guidance: prompts and use cases chosen scored highest where they directly support classroom learning outcomes, respect Idaho's emerging legal and policy landscape, and plug into existing campus capacity for professional development.

Three concrete filters guided choices - classroom efficacy (does the prompt make a measurable learning task harder for an LLM than a student?), equity/access (does it improve supports flagged in Boise State and U of I workshops?), and governance/compliance (does it sit comfortably alongside Idaho's new AI restrictions and the State Board's fellowship work).

Local sourcing came first: institutional practice and policy reporting from the Idaho Capital Sun informed detection-and-policy variation across campuses, the State Board press release framed statewide fellowship priorities, and Idaho's statewide professional-development calendar showed scalable training (including one-semester fellowships and $2,000 awards) that made classroom adoption plausible.

Each prompt in the top 10 maps to at least one local example or training pathway so Boise faculty can pilot, measure, and iterate quickly.

Criterion	How applied	Local evidence
Classroom efficacy	Tested vs. LLMs for learning value	Boise State CI+D workshops and assignment testing
Equity & accessibility	Supports for varied learners	U of I / BSU accessibility recommendations and webinars
Governance & compliance	Aligns with state policy and detection practices	Idaho Capital Sun reporting on campus policies
Professional development	Can be taught in a PD session	State Board fellowships and statewide AI workshops

“The Generative AI in Higher Education fellowships are intended to help create better understanding and eventual Board policies to ensure the technology is used in productive and ethical ways on Idaho's college and university campuses.”

Automated formative feedback can make draft revision cycles manageable across Boise courses by producing consistent, rubric-aligned comments instructors quickly review and personalize; start by linking campus guidance from the AI in Education Taskforce and eCampus resources at Boise State with an instructional prompt for ChatGPT that asks for targeted, actionable feedback rather than a final grade.

Example prompt to adapt in class:

You are a formative-feedback coach - using this rubric, highlight one clear thesis issue, two places where evidence needs tightening, one paragraph to reorganize for clarity, note tone/voice problems, and give two specific revision steps the student can complete in one 30-minute session.

Pair this workflow with faculty PD or consultation through eCampus - connect with local practitioner expertise such as Amy Vecchione (eCampus Center) - so automated comments become teachable artifacts students use to practice revision rather than substitutes for instructor judgment.

Resource	Details
eCampus seminar dates	August 9, August 16, August 23 (asynchronous week-long sessions)
AI in Education Taskforce co-chairs	Ti Macklin; Leif Nelson; Daniel Sanford; Amy Vecchione; Sarah Wilson
Contact	ai-group@boisestate.edu

Personalized tutoring and adaptive practice can scale targeted, mastery‑based work across Boise classrooms by using Copilot/ChatGPT to generate scaffolded hints, short practice sequences, and quick formative checks that instructors review and localize; Boise State's Center for Teaching and Learning already offers consultations and AI resources (including Google Gemini access for faculty) to help shape those workflows Boise State CTL AI in Teaching and Learning resources for faculty, and evidence from open research shows AI‑generated hints - when screened by a teacher - can match human tutor effectiveness while cutting content production time dramatically (see the OATutor adaptive tutor work) OATutor adaptive tutoring research from UC Berkeley.

A ready classroom prompt to adapt: "Act as a stepwise tutor for this problem: give three graduated hints (from minimal to explicit), one concise explanation, and a two‑question quick check that diagnoses the next misconception"; pair AI hints with faculty review and campus PD so students get personalized practice without losing teacher control - a practical short win given Boise State's active AI trainings and policy discussion reported locally local news coverage of BSU AI policies and trainings.

Resource	Why useful for Boise classrooms
Boise State CTL AI resources	Consultations, workshops, and Google Gemini access for faculty
OATutor research (Berkeley)	Shows AI‑generated hints can be effective when screened by instructors
KTVB reporting	Summarizes campus policies and faculty training needs in Boise

“Many are open minded and just want more training and guidance,” Schneider said.

Turn assignments into resilient learning tasks by running an “assignment design tester” prompt against ChatGPT: give the model the full assignment text, rubric, and any permissible student resources, then ask it to (1) produce a complete student submission with step‑by‑step reasoning, (2) list which rubric elements it satisfied and why, (3) flag every step that used general knowledge vs.

required local/course data, and (4) suggest three concrete redesigns to raise cognitive demand (e.g., require process logs, timestamped campus data, or instructor‑graded reflections).

Structure the prompt with clear instructions, an output template (JSON or bullet matrix), and a chain‑of‑thought request so results reveal shortcuts an LLM uses - this borrows test‑case ideas from PractiTest's ChatGPT testing prompts and Azure's prompt‑engineering best practices for explicit format and grounding.

Finally, include a short safety check to detect prompt‑injection or PII exposure before using AI output in grading; see Cobalt's guidance on prompt‑injection risks for mitigation tactics.

Running this tester before release gives one memorable payoff: if the simulated LLM earns a high “LLM‑pass” score, the instructor gains three actionable rewrites that typically turn an auto‑solvable question into a verifiable, student‑authored task.

Check	Prompt to Ask ChatGPT	Action If AI Passes
Completeness vs. Rubric	"Produce full solution and map to rubric."	Increase process evidence or require local artifact
Use of Local Data	"Mark steps that depend on course/local sources."	Add instructor‑provided dataset or timestamps
Security/PII	"Scan output for sensitive data or injection patterns."	Sanitize prompts; restrict uploads

"System Message Attacks are 'one of the most effective methods of ‘breaking' the model currently.'"

PractiTest blog on ChatGPT prompts for software testing Azure OpenAI documentation: prompt engineering techniques Cobalt blog: prompt injection attacks and mitigation

AI-assisted lesson planning helps Boise teachers turn standards, local materials, and differentiation into ready-to-teach sequences in minutes: start by grounding the request in Idaho Department of Education science educator resources (Idaho Department of Education science educator resources) and use an AI planner to output objectives, a 5E or activity-based flow, tiered supports, and quick formative checks - tools like the CK-12 AI Lesson Planner show how to generate standards-aligned plans and differentiation options fast (CK-12 AI Lesson Planner for Standards-Aligned Lesson Plans).

Example prompt to paste into ChatGPT/Copilot:

"Create a 45-minute 6th-grade 5E science lesson using Idaho DOE links I'll provide; include learning objectives, three formative exit tickets at beginner/grade/advanced levels, a materials list using low-cost classroom supplies, and two modification options for English learners and IEP students."

The concrete payoff: teachers spend less time on admin and more time coaching students through differentiated tasks, while plans stay explicitly tied to Idaho resources and local PD pathways.

Tool / Resource	How it helps Boise teachers
Idaho DOE educator resources	Local examples and materials to ground lessons
CK‑12 AI Lesson Planner	Creates standards‑aligned, customizable lesson plans in minutes
TeachBetter framework (guide)	Provides multiple lesson templates (5E, project‑based, flipped) for differentiation

Accessibility and alternative formats turn AI outputs from nice-to-have into classroom-ready supports for Boise students: build prompts that ask an LLM to produce a plain‑language summary, time‑coded captions for Panopto/YouTube, a clear transcript for text‑to‑speech, and a translated version targeted for multilingual learners, then include a final note that flags any segments requiring live interpretation or EAC coordination (the Educational Access Center requires interpreter requests via the EAC Access Portal at least 5 business days before an event).

Example in-class prompt to adapt:

Given this lecture transcript, produce (1) a 3‑bullet plain‑language summary, (2) an SRT file for captions, (3) a clean transcript formatted for TTS with speaker labels, (4) a translated version suitable for multilingual students, and (5) a one‑sentence accessibility action item that names whether EAC interpreter request is recommended.

Ground this workflow in campus supports - connect outputs to Boise State's English Language Support Programs for multilingual review and the university Accessibility resources for captioning and document checks - to make one concrete payoff: turning a single lecture into three accessible formats in under 30 minutes so instructors can meet legal accommodations and reach more learners the same day.

Read more: Boise State accessibility resources and tools, Boise State English Language Support Programs, and Educational Access Center interpreter requests.

Resource	Key detail
Educational Access Center (EAC)	Provides sign language and text interpreters; request via EAC Access Portal at least 5 business days prior
English Language Support Programs	Free weekly English tutoring, UDL strategies, and support for multilingual students and faculty
Accessibility Resources & Tools	Tools for captioning (Panopto, Zoom, YouTube), document accessibility (Adobe Acrobat Pro, Grackle Docs), and professional development

Automate rubric-based grading in Boise classrooms by chaining Turnitin's assessment reports with a lightweight ChatGPT prompt and Gradescope's dynamic rubrics: run submissions through Turnitin Feedback Studio originality and AI writing detection to get similarity, draft history, and rubric scores, export those markers into Gradescope for AI‑assisted answer grouping and per‑item analytics, then use a short ChatGPT prompt to turn rubric items into student-facing, revision‑focused feedback.

Example prompt to adapt: “Using this student's Turnitin rubric scores and the instructor rubric (paste results), produce: one clear strength, two specific weaknesses tied to rubric criteria, three concrete revision steps the student can complete in a 30‑minute session, and a 25‑word summary for parents.” The combined workflow preserves academic integrity (Turnitin's detection), speeds graders (Gradescope can cut grading time by up to 80%), and produces consistent, actionable comments faculty can personalize during local PD sessions - so instructors spend less time transcribing feedback and more time coaching learning.

Gradescope dynamic rubrics and AI-assisted grading features

Tool	Key features
Turnitin	Feedback Studio, Originality checks, AI writing detection, rubric & grading forms
Gradescope	Dynamic rubrics, AI‑assisted answer grouping, per‑item analytics (cut grading time up to 80%)

“The faculty have really taken Gradescope on board and my colleagues have said it is brilliant and is making our life much easier.”

Early-warning analytics can convert routine LMS, attendance, and grade logs into action: Ivy Tech's cloud work scaled to ingest roughly 12 million student‑interaction data points and - through its Project Early Success - saw a measurable 3.3% drop in D/F grades in the program's first term, showing predictive models can lead to short‑term gains when paired with targeted outreach (Ivy Tech GCP case study, Ivy Tech predictive analytics results).

A practical prompt for Boise analytics teams or dashboards: “Using recent LMS activity, attendance, submissions, and grade trends, produce a ranked list of at‑risk students with primary risk drivers, a confidence score, a recommended first‑step intervention (email, advisor call, tutoring referral), and a one‑sentence outreach template for each student.” Campus analytics can also be repurposed beyond retention - tracking faculty outcomes or financial‑aid anomalies are comparable use cases to consider as part of governance and data‑privacy planning (Ivy Tech data analytics approaches).

Metric	Value / Note
Data scale	~12 million student interaction points (Ivy Tech GCP work)
Measured impact	Project Early Success: 3.3% reduction in D/F grades in first term
Additional uses	Faculty outcomes tracking; financial‑aid fraud detection (explored by Ivy Tech)

AI-powered administrative automation can collapse routine teacher admin into a reliable, school‑wide workflow: use a School‑to‑Home Liaison GPT to draft weekly classroom updates, behavior notices, and multilingual parent messages at scale (School‑to‑Home Liaison for teacher‑parent communications), pair ready-made conference-reminder prompts with a form engine to handle signups and pre‑conference surveys (parent‑teacher conference prompt templates), and push finalized copy into an educator‑friendly form and scheduling system for tracking and translations (JotForm parent survey & scheduling tools).

The practical payoff for Boise classrooms is concrete: consistent, translatable messages that reduce no‑shows and reclaim admin time - anecdotal reports and educator testimony suggest teachers can win back roughly three to four hours per week when routine communications are automated.

Try this starter prompt: “You are an experienced educator communicator; draft a friendly conference reminder for [DATE], include exact scheduling instructions, a 25‑word parent summary, and a Spanish translation,” then paste outputs into a JotForm template and pilot by grade team while scrubbing any student PII per campus privacy guidance.

Automated task	Immediate benefit
Weekly classroom updates	Consistent, professional messages + translations
Conference reminders & signups	Fewer no‑shows; centralized scheduling and survey intake
Behavior notices & outreach	Faster, calibrated family communication with templates

“If technology allows a teacher to have three to four extra hours with their family every week, then that's something I think we have to capitalize on and hopefully do.” - Braxton Thornley

Advisors in Boise can build a career‑recommendation engine by combining student records (academics, interests, extracurriculars) with local labor‑market signals and employer partnerships so recommendations are both personalized and regionally actionable - this mirrors an AI counseling pilot that used real‑time labor data to improve student satisfaction, job fit, and graduate employment rates SMC AI-driven career counseling case study using real‑time labor data.

The practical path follows Strada's playbook: cultivate employer relationships, ingest labor market intelligence from public and private sources, and embed employer‑validated credentials into advising so colleges and employers share outcomes and incentives Strada report on employer–community college partnerships.

So what: a well‑configured engine turns opaque job‑market signals into specific, employer‑aligned next steps for students - making advising conversations measurable and more likely to lead to employment rather than generic career tips.

Advisor action	Why it matters
Ingest student academics + interests + extracurriculars	Enables personalized matches
Use local labor‑market data and employer input	Aligns recommendations to regional demand
Formalize employer partnerships and outcome tracking	Creates shared incentives and measurable employment gains

A mental‑health triage chatbot can be a practical first line for Boise campus counseling centers by offering consistent, 24/7 empathetic screening, brief motivational‑interviewing‑style support, and clear referral language that routes students to on‑campus crisis teams or community providers when risk is detected; an instructor prompt to start with: “You are a campus triage assistant - begin with brief validation, run a 5‑question risk check (incl.

current suicidal ideation), provide two short coping strategies, ask consent to log non‑identifying metadata, and if high risk, display the exact campus crisis script and require immediate warm‑handoff instructions to a counselor.” Ground deployments in three safeguards drawn from evidence: (1) human supervision and transparent labeling, (2) a forced escalation pathway for any affirmative self‑harm content, and (3) routine audit of outputs to catch generic or unsafe suggestions.

Research shows LLM responses can be judged as more compassionate than expert responders University of Toronto Scarborough study on AI compassion in crisis response, chatbots using motivational interviewing can move users toward change University of Toronto motivational interviewing chatbot for smoking cessation, and an RCT found a chatbot (Wysa) reduced depression and anxiety scores over four weeks JMIR randomized controlled trial of the Wysa chatbot reducing depression and anxiety, so Boise centers can pilot a triage assistant as a supplement - not a replacement - to stepped‑care pathways and on‑call clinicians.

Study	Key finding	Implication for Boise centers
U of T Scarborough compassion study	AI responses rated more compassionate than experts	Use AI for consistent empathetic engagement, with human oversight
U of T MI chatbot (smoking)	MI chatbot increased readiness/confidence to change	Include brief MI scripts to boost help‑seeking
JMIR RCT (Wysa)	Reduced PHQ‑9 and GAD‑7 scores over 4 weeks	Offer chatbot as short‑term supplement for mild–moderate symptoms

“AI doesn't get tired. It can offer consistent, high‑quality empathetic responses without the emotional strain that humans experience.” - Dariya Ovsyannikova

Boise educators can turn short-term disruption into durable gains by pairing local policy and training with practical guardrails: start by adapting Boise State's ready-to-use Boise State AI in Education faculty guidance so classroom rules are clear, require AI‑use disclosure on major assignments, and lean on campus microlearning and CTL consultations for faculty skill-building; a quick win with measurable impact is running the “assignment design tester” prompt on one high-stakes assessment to surface where an LLM can shortcut student work and then add process evidence or local data to protect authenticity.

For staff upskilling or district PD, consider a structured course like Nucamp AI Essentials for Work bootcamp registration to teach prompt literacy and workplace AI workflows, and follow the Boise action checklist to prioritize PD, policy, and pilot classrooms before scaling (Boise educators action checklist 2025).

The concrete payoff: clearer syllabi plus one tested redesign typically converts an LLM‑solvable task into verifiable student work, protecting integrity while unlocking time for targeted instruction.

Bootcamp	AI Essentials for Work - key facts
Length	15 Weeks
Courses included	AI at Work: Foundations; Writing AI Prompts; Job-Based Practical AI Skills
Cost	$3,582 (early bird) · $3,942 (after)
Registration / Syllabus	Register for Nucamp AI Essentials for Work · AI Essentials for Work syllabus