发表机构
Nexus Research Group（Nexus研究组）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
PEDAL是一个开放平台，通过三层流水线将AI提示词转化为可引用学术制品，支持版本控制、统计评估和DOI归档，并已在STEM教育中验证需求。
AI 中文摘要
大型语言模型（LLMs）在教育实践中的快速整合，催生了对基础设施的迫切需求，该基础设施应将AI提示词视为可复现的学术制品，而非一次性指令。本文介绍了PEDAL（教学评估、设计与分析实验室），一个开放研究平台，实现了三层“实验室到档案”流水线：（1）编排层，提供AI辅助提示词生成和自动化元数据提取；（2）实验室层，支持类Git版本控制、LLM作为评审者的评估以及Mann-Whitney U统计检验；（3）公共档案层，通过Zenodo为每个版本铸造DOI，支持多格式导出（JSON、CSV、LaTeX）和SEO优化的可发现性。PEDAL的Scholarly Sync 2（SS2）框架附加了一个包含24+字段的元数据信封，编码了布鲁姆修订分类法、韦伯知识深度、SAMR层级、5E阶段和NGSS对齐。一个化学教育示例展示了从苏格拉底式探究脚手架、统计评估到DOI铸造归档的完整流水线。我们进一步介绍了NExAIE（Nexus AI与教育），将PEDAL的基础设施应用于AI增强的同行评审，通过一个涵盖六个质量维度的42提示词评估矩阵，引入了激进透明度，即公开归档所有带有DOI的评审量规。初步部署数据——一个月内来自1,810名研究人员的4,684次浏览——表明STEM教育中对可引用AI脚手架的需求强劲。该平台以CC-BY-4.0许可发布（DOI：https://doi.org/10.5281/zenodo.19474709）。
英文摘要
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).
Comments34 pages, 7 figures, 8 tables. Platform publicly accessible at https://kahveci.pw/pedal
