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?
What do detectors actually get wrong?
40 minFalse positive rates, who they land on, and why they cannot support a discipline decision.
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
Activity. Compute what a positive detector result means at your school's assignment volume.
What counts as cheating in fourth grade versus eleventh?
45 minDevelopmentally appropriate expectations across grade bands rather than one district rule.
Objectives
- Set expectations appropriate to each grade band
- Distinguish developmental support from academic dishonesty
- Align expectations vertically across the district
Topics
Activity. Draft grade-band expectations for elementary, middle, and high school.
How do you assess process on a 150-student load?
50 minProcess-based assessment scaled to what a secondary teacher can actually sustain.
Objectives
- Design process assessment that fits a real teaching load
- Use in-class artifacts as primary evidence
- Estimate and control the grading cost
Topics
Activity. Redesign one assignment and compute its grading cost across 150 students.
How do you teach disclosure as a skill?
40 minCiting and disclosing AI use as an academic practice students learn rather than a violation they hide.
Objectives
- Teach AI disclosure as a citation practice
- Design assignments where disclosure is required
- Assess the quality of the disclosure itself
Topics
Activity. Design a disclosure convention for your grade band and test it on one assignment.
How do you rewrite the handbook clause?
45 minHandbook language that is specific, proportionate, and survives a parent challenge.
Objectives
- Write assignment-specific rather than blanket prohibitions
- Set proportionate consequences
- Align with existing district discipline policy
Topics
Activity. Rewrite your handbook clause and have a colleague challenge it as a parent would.
How do you talk to a parent about an accusation?
45 minThe conversation that goes worst when the evidence is a detector score.
Objectives
- Structure the conversation around evidence you can defend
- Handle the multilingual family case specifically
- Know when to withdraw an accusation
Topics
Activity. Role-play the conversation with a colleague playing a parent challenging the evidence.
How do you design assignments where AI is the subject?
40 minPermitted-use design that turns the tool into the object of study.
Objectives
- Design assignments requiring AI use and critique
- Require students to find and correct model errors
- Assess judgment rather than output
Topics
Activity. Build an assignment where the student must identify and correct the model's mistakes.
Building your grade-band package
50 minThe workshop module: policy plus two redesigned assessments for one grade band.
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
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?
The hands-on modules run against agents already deployable on the ibl.ai platform for k-12.
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
U.S. Department of Education
Federal guidance on AI in K-12 teaching and assessment.
- ISTE
International Society for Technology in Education
Student technology standards informing the grade-band expectations.
- Student Privacy Compass
Future of Privacy Forum
Privacy implications of submitting student work to third-party detection services.
- AI Index Report
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.