AI use cases in education: 12 systems schools and edtechs build

The AI use cases schools, universities and edtech companies build first are course-grounded tutors, feedback drafting for instructors, accessibility remediation of course materials, advising assistants and transfer credit evaluation. The ones that survive review keep teachers and advisors in charge of grades and decisions and handle student records within FERPA, plus COPPA when children under 13 use the product.

We cover twelve systems here, across teaching and learning, student success and campus or district operations. For each: what it reads, what it plugs into, why it is tricky, and a sample team and timeline. Ryz serves enterprises across industries, including edtech companies and the institutions they serve. Our AI pod teams build in your cloud and repos, working on your team with your product, IT and academic technology staff.

Where the figures come from. Sample teams and timelines are illustrative planning assumptions, not quotes. All costs use Ryz's public rates and nothing else: mid-level engineers $7,000 to $10,000, senior engineers $10,000 to $15,000 and tech leads $15,000 and up per engineer per month, quoted per team, with the lead priced at the $15,000 floor.

Privacy, accessibility and risk rules

• FERPA. The FERPA regulations (34 CFR Part 99) govern education records at institutions that receive federal funds. Vendors usually receive records under the school official exception, which requires direct control by the institution and limits on reuse, so agreements typically bar model providers from training on student data.
• COPPA. The FTC's amended COPPA Rule (16 CFR Part 312), with a compliance date of April 22, 2026 for most provisions, covers online services directed to children under 13, including separate parental consent before disclosing children's data to third parties in many cases.
• Accessibility. Public schools, colleges and universities are covered by the ADA Title II web rule, which requires WCAG 2.1 Level AA for web content and apps, including course content. Current compliance dates are April 26, 2027 or April 26, 2028, depending on the size of the public entity.
• EU AI Act. Annex III lists AI used for admission, evaluating learning outcomes, assessing education level and proctoring as high-risk, with those obligations now applying from December 2027.
• State student privacy laws. Many states add limits on targeted advertising and the sale of student data. Check each state you sell into.

Teaching and learning

1. Course-grounded tutor

A tutor answers student questions using only the course's own readings, slides, problem sets and instructor notes, asks guiding questions instead of handing over answers, and tells the student when to go to office hours. Instructors set the rules for each assignment.

• What it reads: course materials, assignment instructions, instructor-written hint policies.
• Plugs into: the LMS (Canvas, D2L Brightspace, Blackboard, Moodle) through LTI 1.3, identity provider, analytics store.
• Why it is tricky: students ask for the answer in endless ways, and a tutor that gives it away on a graded problem loses faculty trust in a week. Evaluation sets need adversarial student prompts. See RAG development.
• Sample team and timeline: tech lead plus three senior engineers, four to six months: $45,000 to $60,000 a month, $180,000 to $360,000 in total.

2. Feedback drafting on written work

For essays, lab reports and short answers, the system drafts rubric-aligned comments for the instructor, pointing to specific sentences. The instructor edits, approves and assigns the grade; the model never posts a grade on its own.

• What it reads: submissions, rubrics, exemplar feedback from the instructor.
• Plugs into: LMS gradebook and SpeedGrader-style tools, plagiarism tools such as Turnitin.
• Why it is tricky: consistency across sections, and bias checks across student groups. In the EU, evaluating learning outcomes is a high-risk category.
• Sample team and timeline: tech lead plus two senior engineers, three to five months: $35,000 to $45,000 a month, $105,000 to $225,000 in total.

3. Standards alignment and content tagging

Publishers and edtech companies tag every item, lesson and video against state standards and skills, so teachers can find material and adaptive products know what a student is practicing. The model proposes tags; curriculum specialists approve.

• What it reads: content libraries, standards frameworks, previously tagged items.
• Plugs into: content management and authoring tools, the product's item library.
• Why it is tricky: fifty sets of state standards that change on their own schedules, and fine distinctions between neighboring skills.
• Sample team and timeline: tech lead plus one senior and one mid-level engineer, three to four months: $32,000 to $40,000 a month, $96,000 to $160,000 in total.

4. Accessibility remediation for course materials

With ADA Title II dates approaching, institutions have years of PDFs, slides and videos to fix. The system drafts alt text, generates and corrects captions, rebuilds PDF tag structure and flags math and charts that need specialist work, with accessibility staff reviewing.

• What it reads: course files, lecture recordings, institutional style rules.
• Plugs into: LMS file stores, video platforms (Kaltura, Panopto), accessibility checkers.
• Why it is tricky: alt text needs context about why an image is in the lesson, and STEM notation is hard to make accessible automatically. See multimodal AI.
• Sample team and timeline: tech lead plus two senior engineers and one mid-level engineer, four to six months: $42,000 to $55,000 a month, $168,000 to $330,000 in total.

Student success

5. Early alert and retention signals

Models combine LMS activity, attendance, grades and registration holds to flag students who may be struggling, and route each flag to the right advisor or support office with the reasons. The alert prompts a conversation, never a sanction.

• What it reads: LMS engagement, grades, attendance, holds, past outcomes.
• Plugs into: SIS (Ellucian Banner, Workday Student, PeopleSoft Campus Solutions), advising platforms such as EAB Navigate, CRM.
• Why it is tricky: models can encode existing inequities, so error rates need testing by student group, and students should know how the data is used. See machine learning development.
• Sample team and timeline: tech lead plus two senior engineers, four to five months: $35,000 to $45,000 a month, $140,000 to $225,000 in total.

6. Advising and degree planning assistant

Students ask "if I switch to economics, what still counts and when could I graduate?" The assistant reads the degree audit and catalog, builds possible plans and books an advisor when the question needs judgment.

• What it reads: catalog and program requirements by catalog year, degree audit data, course schedules.
• Plugs into: degree audit tools (Ellucian Degree Works, uAchieve), SIS, advising calendars.
• Why it is tricky: catalog year rules and exceptions approved by a department that live in email, not in the audit.
• Sample team and timeline: tech lead plus three senior engineers, four to six months: $45,000 to $60,000 a month, $180,000 to $360,000 in total.

7. Transfer credit evaluation

Registrars evaluate transcripts and syllabi from many institutions. The system matches incoming courses to existing equivalencies, proposes new ones from syllabus content with the reasoning, and queues them for faculty or registrar approval.

• What it reads: transcripts, syllabi, equivalency tables, past decisions.
• Plugs into: SIS transfer articulation modules, document imaging, admissions CRM (Slate).
• Why it is tricky: transcript formats vary widely, and syllabi are often missing or outdated. Approved equivalencies become reusable rules.
• Sample team and timeline: tech lead plus two senior engineers, three to four months: $35,000 to $45,000 a month, $105,000 to $180,000 in total.

8. Admissions reading support

The system organizes each application file, extracts academic data from transcripts, summarizes activities and checks for missing materials, so readers spend their time on judgment. It does not score or rank applicants.

• What it reads: applications, transcripts, recommendation letters, reader guidelines.
• Plugs into: Slate or another admissions CRM, document imaging.
• Why it is tricky: admissions is high-stakes and legally sensitive. Keep the system to extraction and organization, test it for consistent accuracy across school types, and document what readers see.
• Sample team and timeline: tech lead plus two senior engineers, four to five months: $35,000 to $45,000 a month, $140,000 to $225,000 in total.

Campus and district operations

9. Enrollment and financial aid service agent

A chat, text and voice agent answers questions about deadlines, aid status, missing documents and registration holds around the clock, and reaches out when a student's file is incomplete. A Ryz AI pod built an AI voice platform that has placed 1M+ outbound calls.

• What it reads: knowledge base, student account and aid status, outreach rules.
• Plugs into: SIS, CRM, contact center and texting platforms.
• Why it is tricky: identity verification before discussing any record, and calling and texting consent rules. See AI chatbot development.
• Sample team and timeline: tech lead plus three senior engineers, four to six months: $45,000 to $60,000 a month, $180,000 to $360,000 in total.

10. Financial aid verification document processing

When a FAFSA record is selected for verification, students submit tax documents, household forms and identity documents. The system extracts and compares the values against the federal record, flags discrepancies and drafts the follow-up request for an aid officer.

• What it reads: verification documents, ISIR data, institutional policies.
• Plugs into: financial aid modules of the SIS, document imaging, student portal.
• Why it is tricky: phone photos of documents, and federal tax information handling rules. Aid officers make every award decision.
• Sample team and timeline: tech lead plus two senior engineers, three to five months: $35,000 to $45,000 a month, $105,000 to $225,000 in total.

11. Special education progress report drafting

For K-12 districts, the system gathers progress monitoring data, service logs and teacher notes for each IEP goal and drafts the progress report narrative for the case manager to edit, cutting hours of copying between systems.

• What it reads: IEP goals, progress monitoring data, service minutes, teacher notes.
• Plugs into: special education management systems, SIS (PowerSchool, Infinite Campus).
• Why it is tricky: the IEP is a legal document under IDEA. The draft only restates recorded data, and the team keeps every decision about services and placement.
• Sample team and timeline: tech lead plus one senior engineer, three to four months: $25,000 to $30,000 a month, $75,000 to $120,000 in total.

12. Research proposal and compliance pre-check

Before a grant proposal or human subjects protocol is submitted, the system checks it against the sponsor's instructions or IRB requirements: page limits, required sections, budget justification consistency and consent form elements, and lists what is missing.

• What it reads: funding announcements, proposal drafts, IRB templates and checklists.
• Plugs into: research administration systems (Kuali Research, Cayuse, Huron), document management.
• Why it is tricky: each sponsor has its own rules and they change by announcement. Research office staff sign off.
• Sample team and timeline: tech lead plus one senior and one mid-level engineer, two to three months: $32,000 to $40,000 a month, $64,000 to $120,000 in total.

How to pick your first use case

• Help staff before you grade students. Accessibility, transfer credit, aid verification and service agents save staff time without deciding anything about a student's grade or admission.
• Sort out the data agreement first. Decide which records the system may see, under which FERPA exception, and get no-training terms from every model provider.
• Find a faculty partner. Tutors and feedback tools succeed when one department shapes the rules and tells colleagues what it learned.
• Time it to the calendar. Aid, admissions and registration have peak months. Launch before the peak and measure it.
• Check readiness. The AI readiness scorecard covers data access and governance in about ten minutes.

What it costs

The math is engineers times months times monthly rate. At Ryz, mid-level engineers cost $7,000 to $10,000 a month, senior engineers $10,000 to $15,000 and tech leads $15,000 and up, quoted per team.

• Operations pilot (tech lead plus two senior engineers) for four months: $35,000 to $45,000 a month, $140,000 to $180,000 in total.
• Product pod for an edtech company (tech lead plus four senior engineers) for six months: $55,000 to $75,000 a month, $330,000 to $450,000.
• Full AI pod of about seven senior engineers including a tech lead and an ML engineer: $75,000 to $105,000 a month.

Cloud and model usage are billed to your accounts and are not included. See AI development cost for budgeting beyond the team. You meet the named engineers before the work begins, and a team can usually start in under two weeks once you say go. If you need an off-the-shelf tutoring product with no custom work, license one; Ryz pods build features in your own product or on your institution's systems.

Related

• Edtech engineering teams
• Hire edtech developers
• Generative AI development
• LLM evaluation
• AI use cases in the public sector
• AI use cases in media and entertainment

FAQ

What are the most useful AI use cases in education?

Course-grounded tutors, instructor feedback drafting, standards tagging, accessibility remediation, early alerts, advising assistants, transfer credit evaluation, enrollment and aid service agents, and document processing for aid verification are the systems that most often reach production.

Is it FERPA compliant to use a large language model with student records?

It can be, when the provider acts under the institution's direct control as a school official, uses records only for the stated purpose, does not train on them and meets the institution's security requirements. That needs a written agreement and a configuration that matches it.

Does COPPA apply to AI tutors?

If the service is directed to children under 13, or the operator knows it collects personal information from them, COPPA applies. Under FTC guidance, schools can generally authorize collection on parents' behalf only for educational use, and the 2025 amendments tightened rules on disclosure to third parties.

Can AI grade student work?

It can draft feedback and suggest scores against a rubric, but most institutions keep grading with the instructor. In the EU, systems that evaluate learning outcomes are high-risk under the AI Act.

Can a Ryz pod integrate with Canvas, Banner or PowerSchool?

Yes. Pods build LTI 1.3 tools, API integrations and data pipelines against LMS and SIS platforms, in your cloud and repos. You control access at all times and own all code and IP.

Questions we didn't answer? Email info@ryzlabs.com.