Adaptive Learning is a data-driven approach to differentiated instruction that uses technology to tailor educational experiences to the individual learner’s pace, level, and learning style. Unlike traditional teaching methods that deliver uniform instruction to all students, adaptive learning personalizes the content and sequence of learning activities based on a student’s ongoing performance and interaction with the system. This concept aligns with the broader goal of differentiated learning — recognizing that learners differ in readiness, interests, and learning profiles (Tomlinson, 2017).

At its core, adaptive learning employs algorithms and artificial intelligence to analyze how students respond to tasks and assessments, then adjusts the subsequent content accordingly. For example, if a student demonstrates mastery in a topic, the system may offer more challenging materials; if they struggle, it provides additional practice or remedial lessons. This real-time feedback loop helps ensure that each learner receives the right level of challenge and support.

A practical example of adaptive learning in action is the platform DreamBox Learning, which provides mathematics instruction that dynamically adjusts lessons to fit each student’s needs. Similarly, Duolingo, a language-learning app, adapts its lessons based on users’ responses and performance, ensuring that learning remains engaging and appropriately challenging. These tools embody the principles of differentiated learning through technology by making instruction more flexible and learner-centered.

Adaptive learning is particularly effective in e-learning environments, where educators can leverage analytics dashboards to track progress and identify learning gaps. By integrating adaptive systems, teachers can design inclusive and equitable classrooms that respect learner diversity and maximize student engagement.

References:
Tomlinson, C. A. (2017). How to Differentiate Instruction in Academically Diverse Classrooms. ASCD.
Walkington, C. (2013). Using adaptive learning technologies to personalize instruction to student interests. Journal of Educational Psychology, 105(4), 932–945.