Open Access Manifest
Innovation thrives in the light. We are deeply committed to the principles of open science, sharing our discoveries, architectures, and validated datasets with the global learning science community.
Commitment to Open Knowledge
The future of AI-driven education cannot be monopolized by proprietary silos. To ensure collective advancement and peer-reviewed accountability, LearnAdapt operates on an Open Access framework. We actively publish our research findings, architectural critiques, and methodological frameworks in open-access academic journals and repositories.
Anonymized Aggregate Datasets
To catalyze research in Educational Data Mining (EDM), we periodically release de-identified, aggregated datasets containing learning trajectories, cognitive traces, and model interaction logs. These datasets strictly adhere to privacy standards while providing high-fidelity raw material for external researchers.
Core Tracing Architectures
We open-source the implementations of our foundational predictive models, including advanced variants of Bayesian Knowledge Tracing (BKT) and prompt-routing heuristics. This allows institutions to independently audit our logic and adapt the engines for their own localized pedagogical contexts.
Audit and Iterate
We invite academic researchers, security specialists, and educators to critically examine our platform. By making our platform's cognitive architecture transparent, we embrace the "many eyes" theory: continuous public auditing leads to more robust, ethical, and effective educational technology.
If you are a researcher interested in collaborating with our dataset or testing our architectural assumptions, please visit our Research Hub for access protocols.