A4L: An Architecture for AI-Augmented Learning
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Abstract (arXiv)
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.
Key facts. Cited by 4 papers in the map (data through 14 Oct 2026). Builds on 3 of its 17 references that are in the map. First posted on arXiv 8 May 2025.
Builds on
3 of 17 references.
References · 3
Built on by
Citing papers per year: 2026: 4.
4 papers.
- 2026Evaluating Learner Representations for Differentiation Prior to Instructional Outcomescited by 3
- 2026Memory-Based vs. Context-Only Conditioning Produces Distinct Behavioral Patterns in Stateful Personalizationcited by 1
- 2026Surfacing Isolated Learners with Outcome-Independent Mediation of Feedback between Teachers and Students Using AIcited by 1
- 2026Detecting Knowledge Gaps from Conversational AI Interactions Using Curriculum Prerequisite Graphscited by 0
- 2026Detecting Knowledge Gaps from Conversational AI Interactions Using Curriculum Prerequisite Graphs9 Jun 2026
- 2026Surfacing Isolated Learners with Outcome-Independent Mediation of Feedback between Teachers and Students Using AI28 May 2026
- 2026Memory-Based vs. Context-Only Conditioning Produces Distinct Behavioral Patterns in Stateful Personalization14 Apr 2026
- 2026Evaluating Learner Representations for Differentiation Prior to Instructional Outcomes7 Apr 2026