Yallen Bai et al. (2026) — EDULEARN26.
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.
Key Contributions
- Reusable analytics infrastructure: Bai et al. present the A4L Data Analytics Pipeline as modular, domain-agnostic infrastructure for analyzing learner interaction data from educational AI assistants. The pipeline is designed to ingest heterogeneous datasets across different courses and tutoring contexts without rebuilding analytics from scratch.
- Cross-domain validation: The pipeline was evaluated through three case studies at Georgia Tech, each involving a different educational AI assistant deployed in real online learning environments. Results showed that a common set of statistical methods could be consistently applied across datasets with varying structures and instructional contexts.
- Extensibility demonstrated: Analytical capabilities initially developed for one domain were successfully extended to support richer analyses in another domain, proving the pipeline's extensibility. This positions the A4L pipeline as reusable infrastructure for future learning-analytics systems.
- Bidirectional feedback loop: The architecture supports a feedback loop between instructors and learners, enabling personalized-learning insights derived from AI-augmented learning data. This connects to broader conversations in edtech-platform design about how analytics infrastructure should scale across domains.
- EDULEARN26 publication suggests growing academic interest in systematizing analytics for educational AI, complementing work on ai-assisted-writing-research-teams and llm-sentiment-analysis-education-research which explore different facets of AI-augmented education research.
Related Pages
- learning-analytics — Educational data infrastructure and analytics pipelines
- intelligent-tutoring — AI tutoring systems and student modeling
- academic-integrity — AI's impact on academic honesty and policy
- over-reliance — Risks of student dependence on AI assistance
- ai-literacy — Building student and educator competency with AI tools
- student-experience — How students interact with and perceive AI in education
- edtech-platform — Platform and infrastructure design for educational technology
- llm-educational-question-cognitive-depth -- LLM-generated educational questions show varying cognitive depth; models excel a...
Citation
APA: Yallen Bai, Ploy Thajchayapong, & Ashok Goel (2026). Generalizing a Highly Configurable Analytics Pipeline to Replicate and Support Educational Research Across Multiple Domains. arXiv:2605.30303. EDULEARN26.