As the online learning landscape evolves, the need for personalization is increasingly evident. Although educational resources are burgeoning, educators face challenges selecting materials that both align with intended learning outcomes and address diverse learner needs. Large Language Models (LLMs) are attracting growing interest for their potential to create learning resources that better support personalization, but verifying coverage of intended outcomes still requires human alignment review, which is costly and limits scalability. We propose a framework that supports the cost-effective automation of evaluating alignment between educational resources and intended learning outcomes. Using human-generated materials, we benchmarked LLM-based text-embedding models and found that the most accurate model (Voyage) achieved 79% accuracy in detecting alignment. We then applied the optimal model to LLM-generated resources and, via expert evaluation, confirmed that it reliably assessed correspondence to intended outcomes (83% accuracy). Finally, in a three-group experiment with 360 learners, higher alignment scores were positively related to greater learning performance, χ2(2, N = 360) = 15.39, p <.001. These findings show that embedding-based alignment scores can facilitate scalable personalization by confirming alignment with learning outcomes, which allows teachers to focus on tailoring content to diverse learner needs.

Continuously, i.e. automatically and repeatedly checking at what geographical locations cloud service components are hosted aims at validating that the cloud service satisfies regulatory and other compliance requirements. Yet continuous validation is challenging since it requires location techniques to adapt to network changes over time. In this paper, we present adaptive location classification, an approach to continuously validate the location of cloud service components. Our approach combines supervised and unsupervised learning techniques and is capable of adapting to network changes over time. We demonstrate the feasibility of our approach by presenting experimental results where we continuously validate the locations of cloud service components hosted at 14 different locations of the AWS Global Infrastructure.