Adaptive Lesson Generator

For

Curriculum leads at charter school networks

Solves

Teachers spend evenings and weekends building lesson plans that rarely adapt to student pace.

Delivers

Daily adaptive lessons, interactive quizzes, voice discussion prompts, student mastery reports, intervention plans

Built in

about 2 weeks of creation time, MVP in 2 days

Investment

$5,500 for the MVP, $17,000 for the full product

Run it

Inside your business, or as part of your offer to clients

For curriculum leads at charter school networks, turn textbooks, standards, and notes into adaptive daily lessons with voice interaction.

1. Parse uploaded curriculum documents.
2. Generate lesson sequences with clear objectives.
3. Create video-like explanations.
4. Generate practice problems and quizzes.
5. Enable voice-based student interaction.
6. Provide real-time remediation and weekly summaries.

What goes in, what comes out

The workflow

1. In
   Start with

   Textbooks, state standards, teacher notes, pacing guides

2. 1

   Upload curriculum

3. 2

   Set pacing

4. 3

   Generate lessons

5. 4

   Run lessons

6. 5

   Monitor student progress

7. 6

   Receive weekly reports

8. Out
   Finish with

   Daily adaptive lessons, interactive quizzes, voice discussion prompts, student mastery reports, intervention plans

AI does the heavy lifting, people stay in charge

Use language models to interpret curriculum and generate explanations. Voice agents handle student interaction. Multimodal models grade math. The AI tracks mastery and prepares interventions. A human reviewer confirms pedagogical accuracy before delivery.

What your team sees

Key screens: Curriculum dashboard, lesson composer, student interaction hub, teacher analytics. Use a dashboard to view pacing and progress. The composer generates lesson content. The hub handles voice and video interaction. The analytics show gaps and strengths.

Accounts and administration

User roles, lesson versions, audit trail, permissions for curriculum access

Integrations and data access

Learning Management Systems, Student Information Systems, supplementary content libraries

We build with our own AI software development factory, so most implementations take days to a few weeks of creation time, not months. You see working software at every step, and exact timing depends on availability.

1. 1

   Scoping call

   Day 1

   Thirty minutes on your process, your data and how you want to run it: for your own team, or for your clients. You get a fixed scope and price for the MVP.

2. 2

   MVP

   2 days

   One buyer segment, one recurring use case. Manual review in the loop. Built by our AI software factory.

3. 3

   Paid pilot

   3 days

   Accounts, roles, review states, audit trail and the first integration, hardened for two to three paying pilot customers.

4. 4

   Full product

   6 days

   Self-serve onboarding, billing, monitoring and the wider integration set.

5. 5

   Run and improve

   Monthly

   We host, monitor and improve it for a fixed monthly fee, or hand it over to your team. How the retainer works.

Why we start with an MVP

An MVP, or minimum viable product, is the smallest version that your users can actually work with. It is not a cheap version of the full solution. It is a test, built to answer the questions that decide whether the rest is worth building.

1. Pick the riskiest assumption. Here: will curriculum leads at charter school networks use it to solve "teachers spend evenings and weekends building lesson plans that rarely adapt to student pace"?
2. Build only what tests it. One team, one use case, a few core modules. People do the rest by hand for now.
3. Run a paid pilot. Pilot proves value by comparing student progress in the AI-generated lessons versus traditional methods, establishing a baseline for engagement and mastery
4. Measure, then decide. Track student engagement rates and reduction in teacher planning time. Then expand, change course or stop, with evidence instead of opinions.

MVP scope for this solution. First cut: Charter school math lead, single textbook chapter, week of adaptive practice, manual review of generated content

After the MVP. Automated grading for all subjects, parent portals, integration with LMS, mobile apps

What the build depends on. Large language models, speech-to-text models, multimodal vision models

A planning range to start the conversation, not a quote. You pay per phase, so you can stop after the MVP.

Running costs per month

A rough indication of monthly hosting and AI model costs once it is live, not tested. Real costs depend on usage, file sizes and the models chosen.

Your brand, or this one

Run it under your own brand, or start from this concept style.

Selling it to your own clients: the go-to-market playbook

Pricing to test

Hypothesis: 5000 USD per school per year capped at 500 students

Message to test

Turn static textbooks into a living, voice-interactive daily lesson plan

Where to find buyers

EdTech conferences, charter school networks, direct sales to curriculum directors

Lead magnet

Free trial for one math unit

The first 30 days

1. Week 1: Deploy MVP for math curriculum.
2. Week 2: Gather feedback on voice interaction.
3. Week 3: Refine remediation logic.
4. Week 4: Prepare for pilot launch.

Paid pilot

Pilot proves value by comparing student progress in the AI-generated lessons versus traditional methods, establishing a baseline for engagement and mastery

Success metrics

Student engagement rates, reduction in teacher planning time

Retention and expansion

Annual subscriptions based on active student counts

Why clients would pick it

Rigorous testing of pedagogical sequences prevents hallucinations. Building a library of high-quality generated content over time

Alternatives and positioning

Traditional lesson planning software, static worksheets, generic AI chatbots

Main delivery costs

AI inference costs, development and maintenance

Strict guardrails on math answers, access controls for curriculum documents, audit logs for AI interactions