Georgia Tech's A4L Pipeline Replicates Educational Research Across 3 Domains

The paper, published on arXiv and presented at EDULEARN26, details the Architecture for AI-Augmented Learning (A4L) Data Analytics Pipeline—a highly configurable, modular system designed to collect, integrate, and analyze learner interaction data from educational AI assistants. The pipeline's design prioritizes flexibility, allowing it to handle heterogeneous datasets across different instructional contexts while maintaining a common set of statistical analysis methods. This enables researchers to systematically extend analytical capabilities developed for one domain to new domains, supporting personalized learning and bidirectional instructor-learner feedback loops.

The pipeline was evaluated through case studies involving three different educational AI assistants deployed in online learning environments at Georgia Tech. Results showed that despite differing data structures and instructional goals, the pipeline consistently replicated key analytical findings across domains. This replicability demonstrates that the A4L Analytics Pipeline can serve as reusable infrastructure for future educational AI systems, streamlining the process of deriving actionable insights. The work highlights how modular data architectures can accelerate research in AI-augmented education, potentially enabling more scalable and effective personalized learning experiences.