Abstract: Research into cloud-based AI-driven personalized learning offers insights into the defining concepts, properties, enabling technologies, methodologies, and challenges that characterize this rapidly-developing area. AI-driven personalized learning adapts to individual learners, fostering growth and engagement through analysis of extensive personal and behavioral data. Cloud computing affords the necessary scalable infrastructure for such platforms while meeting the requirements for data storage and processing in educational AI. Unifying System Theory suggests a natural evolution through levels of system integration, combining AI-driven learning in the cloud with crowd-powered content in a modularized, interoperable structure.
Adaptive education provides the theoretical foundations for personalized learning; however, student modeling, recommendation, and natural language processing remain prevalent areas for investigation. Quality and evaluation are equally important, encompassing experimental design and validation in AI-supported cloud ecosystems, along with considerations of bias, fairness, and transparency. Security, privacy, and compliance aspects of personalized learning in the cloud, including identity and data protection, risk management, auditing, and adherence to privacy frameworks such as FERPA, also warrant rigorous scrutiny.
Keywords : AI, Personalized Learning, Adaptive Learning, Cloud Computing, Technology Enhanced Learning, Educational Data Mining, Learning Analytics, Recommender Systems, Learning-as-a-Service, Learning Management System, Natural Language Processing, Evidence-based Education, Online Learning, Educational Technology, Cloud Computing, Educational Technology Evaluation, Process Mining, Artificial Intelligence in Education.

Downloads: | DOI: 10.17148/IJIREEICE.2023.111216