Artificial intelligence assistants deployed in online learning environments create new opportunities to collect large volumes of learner interaction data and generate insights to improve student outcomes. Architecture for AI-Augmented Learning (A4L) is a modular data architecture that enables the collection, integration, and analysis of learner interaction data from educational AI systems, supporting the generation of instructional insights that facilitate personalized learning and reinforce the bidirectional feedback loop between instructors and learners. This study examines the modular design of the A4L Data Analytics Pipeline, an extensible data infrastructure that enables the ingestion, processing, and analysis of heterogeneous datasets generated by educational AI assistants. We describe the design principles and development process used to extend the pipeline's analytical capabilities while preserving flexibility across domains. We evaluate the pipeline through case studies spanning three research domains corresponding to three educational AI assistants deployed in online learning environments at Georgia Tech. Results show that a common set of statistical analysis methods can be consistently applied across datasets with differing structures and instructional contexts, enabling the pipeline to reproduce key analytical findings across domains. We demonstrate how analytical capabilities initially developed for one domain can be extended to support richer analyses in another, illustrating the pipeline's extensibility. These findings suggest that the A4L Analytics Pipeline can serve as reusable infrastructure for analyzing data generated by future educational AI assistants. By enabling analytics that can be systematically extended to new domains, the pipeline provides a foundation for deriving insights that inform the design and evaluation of educational AI systems.

翻译：部署于在线学习环境的人工智能助手为收集大量学习者交互数据并生成改善学生成绩的洞察创造了新机遇。人工智能增强学习架构（A4L）是一种模块化数据架构，能够从教育AI系统中收集、整合并分析学习者交互数据，支持生成教学洞察以促进个性化学习，并强化师生间的双向反馈循环。本研究聚焦A4L数据分析流水线的模块化设计——这一可扩展的数据基础设施能够接收、处理并分析教育AI助手生成的异构数据集。我们阐述了扩展流水线分析能力时遵循的设计原则与开发流程，同时确保其跨领域灵活性。通过涵盖佐治亚理工学院在线学习环境中部署的三个教育AI助手对应的三个研究领域案例研究，我们对流水线进行了评估。结果表明，一套通用的统计分析方法可一致应用于结构各异、教学语境不同的数据集，使流水线能够跨领域复现关键分析结论。我们展示了最初为某一领域开发的分析能力如何扩展至另一领域以支持更丰富的分析，验证了流水线的可扩展性。这些发现表明，A4L分析流水线可作为可复用基础设施，用于分析未来教育AI助手生成的数据。通过支持可系统化扩展至新领域的分析功能，该流水线为衍生指导教育AI系统设计与评估的洞察奠定了基础。