Evolution of A4L: A Data Architecture for AI-Augmented Learning

TL;DR

This paper presents the development and evolution of A4L, an open standards-based data architecture designed to support scalable, personalized AI-augmented online learning for adult learners, integrating data from various educational systems.

Contribution

The paper introduces A4L2.0, an improved architecture leveraging open standards for secure, interoperable data integration in AI-augmented education environments.

Findings

A4L1.0 supported analysis of meso- and micro-learning.

A4L2.0 enables secure, interoperable data integration.

The architecture includes modules for data ingestion, preprocessing, analytics, and visualization.

Abstract

As artificial intelligence (AI) becomes more deeply integrated into educational ecosystems, the demand for scalable solutions that enable personalized learning continues to grow. These architectures must support continuous data flows that power personalized learning and access to meaningful insights to advance learner success at scale. At the National AI Institute for Adult Learning and Online Education (AI-ALOE), we have developed an Architecture for AI-Augmented Learning (A4L) to support analysis and personalization of online education for adult learners. A4L1.0, an early implementation by Georgia Tech's Design Intelligence Laboratory, demonstrated how the architecture supports analysis of meso- and micro-learning by integrating data from Learning Management Systems (LMS) and AI tools. These pilot studies informed the design of A4L2.0. In this chapter, we describe A4L2.0 that leverages 1EdTech Consortium's open standards such as Edu-API, Caliper Analytics, and Learning Tools Interoperability (LTI) to enable secure, interoperable data integration across data systems like Student Information Systems (SIS), LMS, and AI tools. The A4L2.0 data pipeline includes modules for data ingestion, preprocessing, organization, analytics, and visualization.