Using AI to Foster Adaptive Learning Environments: Case Studies and Applications
Year: 2026
Type: conference-paper
Abstract: The advent of artificial intelligence to the field of education has triggered a change in the way an environment is conceived and built with the objective of allowing adaptive learning. A research focused on the power of AI on adaptive learning contexts are drawn in this study including empirical scenarios and case studies showing transformative concepts at the crossroads of technology and academia. Adaptive learning environments are dynamically personalised educational environments built upon the deployment of artificial intelligence by providing a personalised learning experience for learners. Consequently, these systems include the unique needs and preferences of each learner. Empowered by artificial intelligence, adaptive learning systems are making data-driven decisions based on student performance metrics and then adjust the instructional materials based on techniques such as machine learning, natural language processing, and data analytics. These systems function in real-time, dynamically adjusting not only the complexity of tasks, but also the modality of feedback, as well as additional resources depending on a learner’s progress in their learning and their learning style. This paradigm provides an easy, coherent and effective alternative to traditional one-size-fits-all educational models. Regulatory bodies have also mapped out the use of artificial intelligence in adaptive learning scenarios citing many case studies in this capacity. For example, Knewton and DreamBox offer AI-based personalised learning paths in learning areas such as reading and mathematics. Knewton use of analysis of student interaction to personalise classes and assessments which claim that this technological approach not only increases the engagement of students but also improves their academic performance.
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
SDG: Quality education
Open Access status: hybrid
OpenAlex ID: W7203689554