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VOOZH | about |
The data layer for smarter educational AI. Integrate trusted instructional content and research directly into your AI-powered tools — improving precision, relevance, and instructional alignment.
Evaluation for AI outputs against trusted educational rubrics. Measure and improve content quality with research-backed rubrics — ensuring rigor, reliability, and alignment to classroom needs.
Chat-first toolkit for teachers to plan complete courses and lessons with multi-agent workflows, template-based artifacts, and MARP slide generation.
Instructional design skills for Claude AI
AI-powered learning analytics platform with A2A multi-agent pipeline — xAPI behavioral tracking, at-risk detection, and automated PDF/DOCX reports for instructional designers.
AI-SETT: A diagnostic assessment framework for AI models. Not a benchmark -- a tool to identify what models need. 600 observable criteria across 13 categories. Grounded in instructional design (SETT, Cognitive Load Theory, ZPD).
Transform PowerPoint presentations into LLM-optimized markdown while preserving instructional design narrative. Built for technical trainers.
a way to capture scenario choices made with Rise 360
Preserves external versions of my pedagogical guide to teaching and learning with Discord servers.
An ecological mystery game designed for science education.
Instructional design tool optimized for self-hosted websites leveraging AI
A Claude skill embodying a 30-year veteran ID practitioner — 15 engagement modes, 12 reference files, full L&D spectrum
Instructional design ontology for classroom or online.
Slide deck for EME5601, Module 3: Performance Analysis (Fall 2024)
Slide deck for EME5601, Module 6: Design & Development (Fall 2024)
A temporary Instructional Design Portfolio. I'm putting up a temporary Jekyll site while I sort out the issues with my new domain www.pulamusic-portfolio.com
An edtech project exploring how prompt engineering can support language teachers in lesson planning, material creation, and CEFR-aligned adaptation.
The SSWG/MVM framework is a recursive workflow generator that unites symbolic structure with semantic evaluation. It synthesizes workflows, validates them against schemas, measures clarity and coherence, and iteratively refines results until improvement plateaus. Through memory, metrics, and modular orchestration, it is both software and mindset.
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