The production pilot of Learning 3.0—an adaptive learning architecture where faculty define course intent and multi-agent swarms generate personalized content.
A two-phase model: instructors define intent, agents generate and personalize, faculty approve before deployment
Faculty author Natural Language Specification files that describe learning outcomes, concept dependencies, and content structure. PathShaper's NLSpec viewer lets instructors see and edit their course blueprint.
Coordinated agent swarms generate personalized content across the curriculum. The generation dashboard tracks 39 content items: chapters, overviews, quizzes, studio guides, labs, rubrics, and discussions.
TextbookGenerator, AdaptiveEngine, and DigitalTwin agents work in coordination with xAPI learning analytics and persistent memory
Generated content goes through a faculty approval workflow before reaching students. The generation dashboard shows status across all content types with approve/reject/regenerate controls.
In the student phase, agents adapt content and pathways based on individual progress, prior knowledge, and learning pace. Students see personalized pathways through the concept graph.
Built on modern web technologies with a focus on visualization and adaptive learning
Enterprise database course at Gies College of Business
8-week curriculum spanning foundations, SQL, data modeling, Python integration, ETL pipelines, NoSQL, cloud databases, and data governance. 169 core concepts + 17 studio concepts.
39 content items tracked in the generation dashboard across 7 content types. Faculty can review, approve, or request regeneration for each item before student deployment.
3 milestone projects with increasing AI Assessment Integration Scale (AIAS) levels, designed to gradually build student competency from guided to independent work.