The A.C.C.E.S.S. Literacy Framework™ is a research-based instructional system I developed to help teachers make rigorous literacy instruction accessible for diverse learners—especially students receiving Special Education services and English Language Learners. The framework guides teachers through a clear instructional sequence that activates background knowledge, supports vocabulary development, breaks complex texts into manageable steps, and builds students’ ability to analyze and respond with evidence.
A co-teaching session at PUP Mulanay Quezon Campus—and what 56 pre-service teachers said when nobody was performing for a grade I want to tell you about a classroom in Mulanay, Quezon Province—about four hours south of Manila—where something happened this September that I’ve been thinking about every day since. I was a guest in Sir … Read more
A bilingual SPED teacher reflects on Project Zero’s Cultures of Thinking—and why, in the age of AI, thinking routines are the most urgent instructional investment you can make.
This AI tool lets us measure something more meaningful than whether a response is simply “right” or “wrong.” The tool examines the complexity of thinking and explanation separately from English proficiency, so a multilingual learner can demonstrate sophisticated thinking even when their English expression is still developing.
This tool addresses a major problem in AI-assisted assessment: an incorrect response does not necessarily mean a student lacks the knowledge. For our A.C.C.E.S.S. ecosystem, this tool explicitly separates content errors from language, access, task-design, and procedural issues.
The AI Misconception Mapper does not merely count wrong answers—it helps teachers see the different thinking patterns behind those answers, while clearly separating a true conceptual misconception from language, access, or task-related difficulty. A.C.C.E.S.S. AI Misconception Mapper | BilingualSPED.com A.C.C.E.S.S. AI Misconception Mapper “From wrong answers to patterns in student thinking.” Framework: A.C.C.E.S.S. Literacy Framework … Read more
This is much more powerful than a basic exit-ticket scorer. It identifies patterns across students, distinguish misconceptions from language/access barriers, and then turn those patterns into actionable instructional next steps—without treating an ELL/SPED student’s language production as evidence that they don’t understand the content. A.C.C.E.S.S. AI Exit Ticket Pattern Analyzer | BilingualSPED.com A.C.C.E.S.S. AI Exit … Read more
The AI Learner Agency Tracker should not simply track whether a student “used a strategy.” It tracks the progression from adult-provided support → student recognition → student choice → student request → student adaptation → student explanation → independent strategy ownership. That makes it a true Student Empowerment tool within our Human-Centered AI Teaching Continuum. … Read more
This is one of the tools I would prioritize highly in your ecosystem because it changes the starting point of the documentation. Instead of asking, “What is wrong?” it asks, “What is this learner already demonstrating, under what conditions, and how can we build from it?” I would also make the tool careful not to … Read more
Special education teachers spend a shocking chunk of their week on paperwork instead of teaching — national data puts IEP documentation alone at five-plus hours weekly, on top of everything else. AI won’t fix broken systems or unreasonable caseloads. But used well, it can hand back real hours for the part of the job that actually matters: your students.
This tool creates the missing bridge between our AI Learning Barrier Detector and our Student Learning Identity Card / Learning Passport. The key design principle that builds into the prompt is: the engine should not invent a new accommodation first. It should look at strategies the learner has already demonstrated success with and ask, “Could … Read more
