# From Tools to Teacher-Built Teammates: No-Code Pedagogical Plugin Authoring with LearnAdapt Agentic Studio and PedOS 1.1 Lumina

arXiv: https://arxiv.org/abs/2607.09674 · HTML: https://mlsanity.com/paper/2607.09674/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Placed in Educational agents and math problem solving, one of 16 directions in LLM agents and tool use.
- Builds on 0 of its 4 references that are in the map.
- First posted on arXiv 5 Jun 2026.

## Placed in

- LLM agents and tool use / [Educational agents and math problem solving](https://mlsanity.com/agents/educational-agents-and-math-problem-solving/)

## Built on by (most cited first, 2 of 2)

- [EduPluginBench: Executable Assurance for AI-Generated Educational Plugins](https://mlsanity.com/paper/2608.00739/) (2026)
- [Beyond Prompt-to-App: Accountable Translation in Teacher-Facing Agentic Authoring](https://mlsanity.com/paper/2609.04679/) (2026)

## Abstract (arXiv)

Teachers and researchers need to adapt educational AI to local goals, but most systems remain difficult to customize or study without coding expertise. We present LearnAdapt Agentic Studio on PedOS 1.1 Lumina, a no-code authoring and governed runtime environment for educational AI plugins. A non-coder describes a desired learning interaction in plain English; the system prepares a previewable plugin artifact, runs safety checks, and supports submission for review. PedOS then deploys approved plugins into a directory for installation. Crucially, telemetry is strictly gated to authenticated users running approved plugins. The demo shows the complete lifecycle from prompt to governed evidence capture, shifting from fixed tools to teacher-built teammates.