Evolution of A4L: A Data Architecture for AI-Augmented Learning
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| Format: | Preprint |
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2025
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| _version_ | 1866917081130205184 |
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| author | Thajchayapong, Ploy Carbonaro, Suzanne Couper, Tim Helmick, Blaine Rugaber, Spencer Goel, Ashok |
| author_facet | Thajchayapong, Ploy Carbonaro, Suzanne Couper, Tim Helmick, Blaine Rugaber, Spencer Goel, Ashok |
| contents | 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. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2511_11877 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Evolution of A4L: A Data Architecture for AI-Augmented Learning Thajchayapong, Ploy Carbonaro, Suzanne Couper, Tim Helmick, Blaine Rugaber, Spencer Goel, Ashok Emerging Technologies Computers and Society 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. |
| title | Evolution of A4L: A Data Architecture for AI-Augmented Learning |
| topic | Emerging Technologies Computers and Society |
| url | https://arxiv.org/abs/2511.11877 |