JELAI: Integrating AI and Learning Analytics in Jupyter Notebooks

TL;DR

JELAI is a modular open-source platform that integrates fine-grained learning analytics with AI tutoring within Jupyter Notebooks, enabling real-time, context-aware educational support and research.

Contribution

It introduces a flexible, containerized architecture for embedding LA and LLM-based tutoring directly into Jupyter, facilitating authentic learning analytics and AI research.

Findings

Demonstrated system feasibility through performance benchmarks.

Showcased multi-modal data logging and analysis capabilities.

Enabled A/B testing of AI configurations within Jupyter.

Abstract

Generative AI offers potential for educational support, but often lacks pedagogical grounding and awareness of the student's learning context. Furthermore, researching student interactions with these tools within authentic learning environments remains challenging. To address this, we present JELAI, an open-source platform architecture designed to integrate fine-grained Learning Analytics (LA) with Large Language Model (LLM)-based tutoring directly within a Jupyter Notebook environment. JELAI employs a modular, containerized design featuring JupyterLab extensions for telemetry and chat, alongside a central middleware handling LA processing and context-aware LLM prompt enrichment. This architecture enables the capture of integrated code interaction and chat data, facilitating real-time, context-sensitive AI scaffolding and research into student behaviour. We describe the system's design, implementation, and demonstrate its feasibility through system performance benchmarks and two proof-of-concept use cases illustrating its capabilities for logging multi-modal data, analysing help-seeking patterns, and supporting A/B testing of AI configurations. JELAI's primary contribution is its technical framework, providing a flexible tool for researchers and educators to develop, deploy, and study LA-informed AI tutoring within the widely used Jupyter ecosystem.