The rapid integration of Large Language Models (LLMs) into educational practice has created an urgent need for infrastructure that treats AI prompts not as disposable instructions but as reproducible scholarly artifacts. This paper presents PEDAL (Pedagogical Evaluation, Design, & Analysis Lab), an open-research platform implementing a three-tier Laboratory-to-Archive pipeline: (1) an Orchestration Layer with AI-assisted prompt generation and automated metadata extraction; (2) a Laboratory Layer supporting Git-style version control, LLM-as-a-Judge evaluation, and Mann-Whitney U statistical testing; and (3) a Public Archive Layer with per-version DOI minting via Zenodo, multi-format exports (JSON, CSV, LaTeX), and SEO-optimized discoverability. PEDAL's Scholarly Sync 2 (SS2) framework attaches a 24+ field metadata envelope encoding Bloom's Revised Taxonomy, Webb's Depth of Knowledge, SAMR levels, 5E phases, and NGSS alignment. A chemistry education exemplar demonstrates the full pipeline from Socratic inquiry scaffolding through statistical evaluation to DOI-minted archival. We further present NExAIE (Nexus AI & Education), applying PEDAL's infrastructure to AI-augmented peer review through a 42-prompt evaluation matrix spanning six quality dimensions, introducing Radical Transparency by publicly archiving all review rubrics with DOIs. Initial deployment data -- 4,684 views from 1,810 researchers within one month -- indicates strong demand for citable AI scaffolding in STEM education. Released under CC-BY-4.0 (DOI: 10.5281/zenodo.19474709).

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