A4L: An Architecture for AI-Augmented Learning

TL;DR

This paper introduces A4L, an architecture designed to support AI-augmented online learning for adults by enabling data collection, analysis, and personalized feedback at scale.

Contribution

It proposes a novel data architecture, A4L, tailored for AI-supported adult online education, addressing personalization and scalability challenges.

Findings

Preliminary applications demonstrate A4L's potential for personalized learning.

A4L architecture supports scalable data collection and analysis.

Advances in AI-augmented learning for adult education.

Abstract

AI promises personalized learning and scalable education. As AI agents increasingly permeate education in support of teaching and learning, there is a critical and urgent need for data architectures for collecting and analyzing data on learning, and feeding the results back to teachers, learners, and the AI agents for personalization of learning at scale. At the National AI Institute for Adult Learning and Online Education, we are developing an Architecture for AI-Augmented Learning (A4L) for supporting adult learning through online education. We present the motivations, goals, requirements of the A4L architecture. We describe preliminary applications of A4L and discuss how it advances the goals of making learning more personalized and scalable.