Tag: Adaptive Learning
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Correct Is Not the Same as Learned
AI tutors can help students reach the right answer faster, but correctness alone does not prove durable understanding. As AI becomes more embedded in education, we need to ask whether learners are truly building mastery, or simply performing well with assistance. The future of educational AI should not just optimise for immediate success, but for…
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LearnAdapt PedOS Plugins: From Prompt to Production in a Safe Educational AI Ecosystem
The LearnAdapt PedOS Plugin Lifecycle shows how educational AI plugins can move safely from prompt to production through artefact generation, human review, automated security checks, sandboxed previews, telemetry gating, and versioned publishing. It is a step towards a trusted Pedagogical Operating System where educators, researchers, and developers can co-create AI-powered learning tools without compromising safety,…
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The ES-LLMs Public Sandbox Is Now Live: From Untamed Black Box to Interpretable Pedagogical Orchestration
The ES-LLMs public sandbox is now live. This architecture moves beyond black-box LLM tutors by introducing a deterministic, multi-agent orchestration layer that enforces pedagogical constraints, improves interpretability, and enhances learning outcomes. Discover how structured AI systems can transform adaptive tutoring from opaque responses into auditable, trustworthy educational experiences.
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Formalizing Machine Pedagogical Intelligence: Announcing AIED 2026 Acceptance of our “ES-LLMs” Architecture
We are thrilled to announce the acceptance of our latest paper at AIED 2026. This research introduces the ES-LLMs architecture, providing the technical validation for “Machine Pedagogical Intelligence” by replacing monolithic “Black Box” models with a rule-governed, highly efficient team of specialized AI tutors.