Comprehensive Overview of the Concept and Applications of AI-based Adaptive Learning

Abstract

This paper provides a comprehensive understanding of the concept and the applications of AI-based adaptive learning, underlining its potential to revolutionize education in terms of personalization and optimization. Such systems offer personalized learning experiences by analysing vast pools of data so that the strengths and weaknesses of each student can be pinpointed. Personalization increases the learning outcomes and engagement of students by providing appropriate challenges and timely feedback. The SWOT analysis underpins a critical evaluation that AI-enhanced adaptive learning brings about: the strengths, weaknesses, opportunities, and threats in its adoption. Among the advantages are higher efficiencies, accessibility, data-driven decision making, and automated assessment. However, significant weaknesses have been well documented as a lack of personal contact, privacy issues, high costs, and possible technical problems. Furthermore, a Force Field Analysis is conducted, involving 112 test subjects, to investigate and analyse the driving and resisting forces of implementing AI adaptive learning. Driving forces include advancing technology, educational needs, social and economic pressures, facilitative government policies, and the ability to identify learning patterns, resisting forces emanating from technological failures, costs, privacy and security threats, lack of interpersonal interaction, and issues about ethics and sociology. The paper illustrates the dynamic interplay of these factors to provide strategies that would harness the supporting forces and mitigate the barriers in front. It concludes that AI-enabled adaptive learning faces several significant obstacles. At the same time, it is strongly potential for transformation into an inclusive and effective system.