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I moved from public school to a private school with no autism-specific training, and I found students on the spectrum struggling with one-size-fits-all lessons. AI helped close that gap for both me and my students in about five minutes per lesson. I take a standard lesson and rebuild it around the individual student. I upload only the blank assignment—never any student data—and prompt AI to adapt it to that learner. I include their favorite colors and niche interests, replace abstract examples with personalized ones, add sentence starters, and allow them to draw responses instead of writing dense paragraphs. Step-by-step: 1. I take an existing lesson and create a blank version of the assignment. 2. I upload only the blank assignment to my AI tool, without including any student data. 3. I provide the student’s interests, preferred colors, and reading level. 4. I ask the AI to rebuild the lesson for that specific student. 5. I use personalized examples, sentence starters, and drawing-based response options to make the lesson more accessible. The result is no shutdowns, no walls, and a genuinely engaged and grateful student. One untrained teacher can now individualize any lesson for a unique learner in minutes.

I built a reusable AI skill that turns any topic and source material into a self-paced course. It follows the same assembly line every time: modules with learning goals, real lesson text, working YouTube videos, quizzes with explanations for wrong answers, and a simple local web course packaged in a folder-based library. Each new course gets its own folder. It works across subjects, but I still review high-stakes content myself. I also treat video quality as more than whether a link works. Step-by-step: 1. I provide a topic and source material. 2. The skill splits the topic into modules and defines learning goals for each one. 3. It writes the lesson text. 4. It finds real YouTube videos and verifies that they work. 5. It builds quizzes that explain incorrect answers. 6. It packages everything as a simple local web course. 7. It adds the new course to the folder-based library in its own folder. 8. I review high-stakes content and assess video quality beyond whether the links work.

Teachers give assessments on devices provided to students, but students can use AI to cheat. I built TypeWriter to bring the level of security found in tests like the ACT and SAT to everyday assessments. Teachers can create assessments from existing materials, post them in a locked environment, and grade submissions and provide feedback on the same website. I built TypeWriter as a high school teacher, and it is free for teachers. Step-by-step: 1. Teachers create an assessment using their existing materials in TypeWriter. 2. They post the assessment in TypeWriter’s locked environment. 3. Students complete the assessment on the provided devices. 4. Teachers grade the assessments and provide feedback on the same website.

I teach AI in Business at Western Washington University, and I built a free, case-based AI textbook with Claude and GitHub Pages: https://prof-califf.github.io/ai-in-business/ The 11-chapter digital textbook includes seven real company cases—EveryCure, Netflix, Spotify, Uber, Waymo, Airbnb, and Epic—plus chapters on AI’s environmental cost, ethics, regulation, and the future of work. Seven chapters include hands-on Python labs, and Chapter 8 has an interactive calculator that models the energy and water footprint of a reader’s own AI usage. The textbook is free, has no publisher, and costs $0 to host. The problem was that AI textbooks are stale before they ship. An 18-month publishing cycle means students can pay $200 for a book that is already two model generations behind. Textbooks also tend to teach theory first and postpone business relevance until much later. My students do not need to derive backpropagation. They need to understand why Spotify built a recommender, how it works, what broke, and what it cost—then build one themselves. My stack is Claude, GitHub Pages, VS Code, Google Colab for the labs, and n8n for the Chapter 7 agentic lab. The project started when I was assembling a reading list and could not find anything usable—only outdated textbooks and paywalled cases. I already had years of lecture notes scattered across documents. Step-by-step: 1. I locked in a framework first. I use the AI Factory model—Data → Model → Prediction → Decision → Value → loop—and run every company through it. This is the step people skip: it builds transferable skills for students and gives Claude a stable structural contract across every chapter. 2. I set up the repository before writing. I created a new repo, added `index.html`, opened Settings → Pages, and confirmed that the site deployed. Five minutes up front was better than debugging after 40,000 words. 3. I built one chapter completely and used it as the template. Each chapter is a single, self-contained HTML file with no build step or dependencies. Then I prompted Claude: "Here's my finished Chapter 1 as the format reference. Here are my notes on Uber. Draft Chapter 4 in the same structure and voice." A finished exemplar worked better than an abstract description. 4. I started from my existing material instead of using a blank prompt. Claude structured and clarified my notes, but it did not decide what I think. I rewrote anything that did not sound like me. That distinction preserved the resource’s voice and avoided generated filler. 5. I made the labs builds rather than exercises. Students reconstruct each system in Google Colab using Claude as a coding partner, then publish their work to their own GitHub repositories. They finish with artifacts. 6. I shipped an incomplete version and update it like software. I published with fewer chapters, then edit and push updates when regulation changes so students automatically get the current version.

I’m building GradePath because schoolwork gets scattered quickly. A lecture, my notes, and the files for a class can all end up in different places. Then, when it’s time to study, I have to dig around to find what I need. The idea is simple: record a class, keep the recording and course materials together, and return to them later. If I ask Tutor about something, it should show me the relevant part of my notes, files, or class recording. If it can’t find an answer, it should say so. I also want students to be able to share useful study sheets with people who are actually in their class. I’m not trying to turn school into another content feed. I want to make it easier to keep up with a class and find the right material when I need it. Step-by-step: 1. I record a class. 2. I keep the recording, notes, and course materials together in GradePath. 3. I return to those materials when I need to study. 4. I ask Tutor about a topic and have it show the relevant part of my notes, files, or class recording. 5. If Tutor can’t find supporting material, it says so instead of presenting an unsupported answer. 6. I share useful study sheets with students who are in the same class.

When my sixth-grade daughter needs math help, I sometimes feel a bit lost myself. Some of the teaching methods have changed since I was a kid, and I don't always have time to dig through her textbooks. So I use ChatGPT to walk me through problems step by step — in French, since that's how she learns — so I can actually explain them to her in a way that makes sense. Step-by-step: 1. I brought ChatGPT the sixth-grade math problem and the teaching method being used in class. 2. I asked it to solve the problem step by step instead of jumping directly to the answer. 3. I requested the explanation in French, since that is the language my daughter uses at school. 4. I asked follow-up questions about any step I did not understand myself. 5. I used the final explanation to teach the method back in a way that matched her classroom.

Opposite of high school science class, I love that I never have to worry about being embarrassed to ask Claude when I don't understand something. The icing on the cake is to have it use the new chat visualizations feature. After watching (the wonderful) Hail Mary Project Movie with my kids, I needed some help trying to explain the science of Time Dilation. It helped me get a little closer. Step-by-step: 1. I told Claude the science concept I was struggling to explain: time dilation. 2. I asked it to walk through the idea in a patient, step-by-step way without making me feel rushed or embarrassed. 3. I turned on chat visualizations so the explanation included something concrete to look at. 4. I asked follow-up questions until I understood the relationship well enough to discuss it. 5. I adapted that explanation into language I could use with my children after the movie.