# Academic Integrity and Assessment in K-12

> K-12 · AI Course · K12-5
> Source: https://ibl.ai/solutions/k-12/course/academic-integrity-and-assessment-k12
> Last updated: 2026-08-25

**What to do when every student has a writing machine — grade-band policy, assessment redesign, and why detection tools create more problems than they solve.**

## The Short Answer

**AI detectors produce false positives that land hardest on multilingual students, so a district cannot base a discipline decision on one. ibl.ai supports the alternative — process-based assessment with AI running on district infrastructure where you own all the code and the data, so student writing is never sent to an outside detector or model.**

On ibl.ai you own all the code and the data, run it model-agnostic across any LLM, and pay with no per-seat pricing — so you can deploy anywhere, from your own cloud to a fully air-gapped network.

[Request Access](https://ibl.ai/contact) · [Explore K-12](https://ibl.ai/solutions/k-12)

## Course facts

- **Level:** Foundational
- **Duration:** 5 hours across 8 modules
- **Format:** District workshop with assessment redesign labs
- **Modules:** 8
- **Catalog code:** K12-5
- **Frameworks covered:** District academic integrity policy, State academic standards

## What is this course about?

Districts responded to student AI use with detection tools that produce false positives landing disproportionately on multilingual students. This course builds the alternative: developmentally appropriate expectations by grade band, process-based assessment a full teaching load can sustain, and an enforceable handbook clause — plus the conversation with a parent whose child has been accused.

## Who is this course for?

- Secondary teachers across content areas
- Building administrators and deans of students
- Curriculum and instruction leaders
- Academic integrity and discipline staff

### What do I need before starting?

- Bring one assignment and your current student handbook integrity language
- No technical background required

## What will I be able to do afterwards?

- State what the evidence supports about AI detection accuracy and bias
- Set developmentally appropriate expectations by grade band
- Redesign assessment for a full secondary teaching load
- Rewrite handbook language so it is enforceable and proportionate
- Conduct a parent conversation about a suspected AI-use incident

## What does each module cover?

### Module 1 — What do detectors actually get wrong?

False positive rates, who they land on, and why they cannot support a discipline decision. _(40 min)_

**Objectives**

- Summarize the evidence on detector accuracy and bias
- Work through base rates at a real school's scale
- Determine what role detector output may play, if any

**Topics:** Accuracy evidence · Multilingual false positives · Base rate arithmetic · Evidentiary sufficiency

**Activity:** Compute what a positive detector result means at your school's assignment volume.

### Module 2 — What counts as cheating in fourth grade versus eleventh?

Developmentally appropriate expectations across grade bands rather than one district rule. _(45 min)_

**Objectives**

- Set expectations appropriate to each grade band
- Distinguish developmental support from academic dishonesty
- Align expectations vertically across the district

**Topics:** Grade band expectations · Developmental support · Vertical alignment · Consistency across buildings

**Activity:** Draft grade-band expectations for elementary, middle, and high school.

### Module 3 — How do you assess process on a 150-student load?

Process-based assessment scaled to what a secondary teacher can actually sustain. _(50 min)_

**Objectives**

- Design process assessment that fits a real teaching load
- Use in-class artifacts as primary evidence
- Estimate and control the grading cost

**Topics:** Load-realistic design · In-class artifacts · Draft checkpoints · Grading cost control

**Activity:** Redesign one assignment and compute its grading cost across 150 students.

### Module 4 — How do you teach disclosure as a skill?

Citing and disclosing AI use as an academic practice students learn rather than a violation they hide. _(40 min)_

**Objectives**

- Teach AI disclosure as a citation practice
- Design assignments where disclosure is required
- Assess the quality of the disclosure itself

**Topics:** Disclosure as citation · Required-disclosure assignments · Disclosure quality · Norm building

**Activity:** Design a disclosure convention for your grade band and test it on one assignment.

### Module 5 — How do you rewrite the handbook clause?

Handbook language that is specific, proportionate, and survives a parent challenge. _(45 min)_

**Objectives**

- Write assignment-specific rather than blanket prohibitions
- Set proportionate consequences
- Align with existing district discipline policy

**Topics:** Clause specificity · Proportionate consequences · Policy alignment · Challenge resistance

**Activity:** Rewrite your handbook clause and have a colleague challenge it as a parent would.

### Module 6 — How do you talk to a parent about an accusation?

The conversation that goes worst when the evidence is a detector score. _(45 min)_

**Objectives**

- Structure the conversation around evidence you can defend
- Handle the multilingual family case specifically
- Know when to withdraw an accusation

**Topics:** Evidence-based conversation · Multilingual family context · De-escalation · Withdrawal decisions

**Activity:** Role-play the conversation with a colleague playing a parent challenging the evidence.

### Module 7 — How do you design assignments where AI is the subject?

Permitted-use design that turns the tool into the object of study. _(40 min)_

**Objectives**

- Design assignments requiring AI use and critique
- Require students to find and correct model errors
- Assess judgment rather than output

**Topics:** Required-use design · Error-finding tasks · Critique assessment · Judgment measurement

**Activity:** Build an assignment where the student must identify and correct the model's mistakes.

### Module 8 — Building your grade-band package

The workshop module: policy plus two redesigned assessments for one grade band. _(50 min)_

**Objectives**

- Produce grade-band policy and two redesigned assessments
- Estimate the load across a full teaching schedule
- Prepare the communication to students and families

**Topics:** Package assembly · Load estimation · Student communication · Family communication

**Activity:** Assemble the package and present it for peer critique.

## What is the capstone project?

**Grade-band integrity package with redesigned assessments.** Produce a complete grade-band package: developmentally appropriate expectations, two redesigned assessments with load estimates, rewritten handbook language, and the family communication plan.

_Deliverable:_ A package a building principal could adopt across a department.

## How are learners assessed?

- Handbook clause stress-tested by a colleague playing a challenging parent
- Load estimate checked against a real teaching schedule
- Redesigned assessments peer-critiqued for AI resistance and learning value

## What ships with the course?

- **Facilitator guide.** Session-by-session running order, discussion prompts, and the questions that reliably derail a room.
- **Learner workbook.** Exercises, checklists, and the templates each module's activity produces.
- **Hands-on lab environment.** A sandboxed ibl.ai deployment so exercises run against real agents, not screenshots.
- **Assessment bank.** Scenario questions and rubric criteria mapped to each stated learning outcome.
- **Source bibliography.** Every primary regulation and standard cited on this page, linked and dated.

## Which AI agents does this course use?

- [Assessment Agent](https://ibl.ai/solutions/k-12/agent/assessment-agent)
- [Writing Feedback Agent](https://ibl.ai/solutions/k-12/agent/writing-feedback-agent)
- [Curriculum Alignment Agent](https://ibl.ai/solutions/k-12/agent/curriculum-alignment-agent)
- [Administration Agent](https://ibl.ai/solutions/k-12/agent/administration-agent)

## Where does the course material come from?

Every module is grounded in primary sources — the regulation, standard, or research itself, not a summary of it. Each was resolved at authoring time.

- [Office of Educational Technology](https://tech.ed.gov/) — U.S. Department of Education. Federal guidance on AI in K-12 teaching and assessment.
- [ISTE](https://iste.org/) — International Society for Technology in Education. Student technology standards informing the grade-band expectations.
- [Student Privacy Compass](https://studentprivacycompass.org/) — Future of Privacy Forum. Privacy implications of submitting student work to third-party detection services.
- [AI Index Report](https://hai.stanford.edu/ai-index) — Stanford HAI. Model capability baseline for calibrating which assignments AI can complete.

## Delivery notes

Binding guidance for anyone preparing and delivering this course:

- Module 1's base-rate exercise must use the participants' own numbers. A cited false-positive rate does not change behavior; computing it for your own school does.
- Module 6 needs a multilingual educator in the room. The false-positive burden falls on their students and the conversation design should come from people who have had it.
- Do not recommend any detection product. If a district already owns one, teach them the limits of what its output can support rather than how to use it better.
- Secondary teachers will reject anything that ignores load. Every redesign in Module 3 must carry an honest grading-cost figure computed across a full schedule.
- Include elementary examples throughout. Most AI integrity material is secondary-only and elementary teachers correctly conclude it was not written for them.

## Why run AI training on a platform you own?

- **You own the course, not a licence to it.** Course content, learner data, and the platform run inside your perimeter — you own all the code and the data.
- **Model-agnostic delivery.** Run the course's AI components on any LLM — Claude, GPT, Llama, Gemini, Command — and switch anytime.
- **No per-seat training licences.** Usage-based or self-hosted, so cost tracks actual use rather than headcount.
- **Deploy anywhere.** Cloud, private VPC, on-premise, or fully air-gapped — including for cohorts that cannot use public AI tools.

## Frequently asked questions

### What does the Academic Integrity and Assessment in K-12 course cover?

Districts responded to student AI use with detection tools that produce false positives landing disproportionately on multilingual students. This course builds the alternative: developmentally appropriate expectations by grade band, process-based assessment a full teaching load can sustain, and an enforceable handbook clause — plus the conversation with a parent whose child has been accused. It runs 5 hours across 8 modules across 8 modules, at foundational level, and closes with a capstone: Grade-band integrity package with redesigned assessments.

### Who should take Academic Integrity and Assessment in K-12?

It is written for Secondary teachers across content areas, Building administrators and deans of students, Curriculum and instruction leaders, Academic integrity and discipline staff. Prerequisites: Bring one assignment and your current student handbook integrity language; No technical background required.

### Can we run this course on our own infrastructure?

Yes. ibl.ai is model-agnostic and deploy-anywhere — cloud, private VPC, on-premise, or fully air-gapped — and you own all the code and the data. Cohort data, submissions, and any material learners upload stay inside your perimeter, which matters for k-12 teams that cannot send work to a public AI tool.

### How do we get access to Academic Integrity and Assessment in K-12?

Request access and we will set it up for your cohort — hosted by ibl.ai, or running against your own deployment. Tell us the group size and timing you need, and whether it should run inside your own perimeter.

### How much does AI training for k-12 cost on ibl.ai?

There is no per-seat pricing — you pay for usage or self-host and pay only for the infrastructure, so a 5,000-person rollout does not cost 5,000 licences. 1.6M+ users across 400+ organizations run the platform this way, including NVIDIA, MIT, and Syracuse University.

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- [Safe AI Tutoring for Minors: Guardrails and Escalation](https://ibl.ai/solutions/k-12/course/safe-ai-tutoring-for-minors): Building a tutoring agent for children — content moderation, self-harm escalation, grooming-pattern detection, and the mandatory-reporter workflow behind it.
- [AI Lesson Planning Aligned to State Standards](https://ibl.ai/solutions/k-12/course/ai-lesson-planning-standards-aligned): Generate standards-aligned lessons that survive a curriculum audit — grounded on your state's standards, your adopted materials, and your scope and sequence.
- [AI in the IEP Process: Drafting, Compliance, and the Human Signature](https://ibl.ai/solutions/k-12/course/ai-in-the-iep-process): Cut IEP paperwork without ceding a legally binding decision — drafting present levels, goal writing, and the IDEA requirements no agent can satisfy for you.
- [Teaching AI Literacy: A K-12 Scope and Sequence](https://ibl.ai/solutions/k-12/course/k12-ai-literacy-scope-and-sequence): A vertically-aligned AI literacy progression from elementary through high school — what to teach at each band, and the activities that make it concrete.
- [District AI Procurement: Evaluating Vendors and Contracts](https://ibl.ai/solutions/k-12/course/district-ai-procurement): A procurement process a school board will approve and a privacy officer will sign — evaluation rubric, contract clauses, and the questions vendors dodge.