Differentiated Learning in the Age of AI: Personalised Education at Scale

One Size Doesn’t Fit All – And It Never Did

In today’s classrooms, diversity is the norm. Students come with different backgrounds, learning preferences, and paces. However, traditional educational models frequently employ a uniform approach. Enter Artificial Intelligence (AI), a powerful enabler that allows differentiated instruction to be delivered not just personally, but at scale.

Recent studies indicate that AI tools are not just futuristic ideas. They’re actively reshaping the way we approach personalised learning by drawing from data to understand each student’s strengths, weaknesses, and ideal learning paths (Zawacki-Richter et al., 2019; Holmes et al., 2022).

How AI Enables Differentiation

1. Adapting to Individual Learning Paces

AI-powered platforms use real-time analytics to track a student’s progress, enabling the system to automatically adjust content difficulty. For instance, intelligent tutoring systems like Squirrel AI and platforms like Carnegie Learning have demonstrated success in allowing learners to move at their own speed, revisiting material as needed or advancing when ready (Luckin et al., 2021).

This is especially powerful in mixed-ability classrooms where pacing is often the biggest challenge for teachers.

2. Personalized Learning Paths

AI can analyze students’ interaction data, like how long they spend on a problem or the types of errors they make, to create individual learning profiles. Tools like Knewton or Century Tech tailor educational content not just by difficulty but by preferred learning mode (visual, textual, interactive).

This aligns with research from Müller and Kyndt (2023), who show that adaptive learning systems enhance engagement and improve outcomes for neurodiverse learners and multilingual students.

3. Real-Time Feedback for Better Formative Assessment

One of the most underused yet powerful practices in education is formative assessment, frequent, low-stakes checks for understanding. AI automates this process. Platforms like ASSISTments and Gradescope provide immediate, actionable feedback that helps both teachers and students adjust learning strategies on the fly.

Real-time feedback has been shown to significantly improve retention and reduce dropout rates, particularly in large classrooms or low-resource environments (Holmes et al., 2022).

Beyond Technology: The Human Touch Still Matters

While AI is a remarkable tool for personalisation, it doesn’t replace the teacher. Instead, it amplifies the teacher’s ability to understand and meet individual student needs. Teachers can focus more on facilitation, mentoring, and social-emotional learning—areas where machines still fall short.

As differentiation scales through AI, we’re seeing a shift in the teacher's role: from content deliverer to learning architect.

Final Thoughts

Effective teaching has always relied on differentiated instruction, yet its consistent implementation has often been challenging, particularly in overcrowded or under-resourced classrooms. AI transforms the educational landscape. It offers a pathway for truly personalised education that adapts to the learner, not the other way around.

With the right balance of ethical design, professional development, and pedagogical insight, AI doesn’t just make differentiation easier. It makes it scalable.

References (2019–2024)

• Holmes, W., Bialik, M., & Fadel, C. (2022). Artificial Intelligence in Education: Promises and Implications for Teaching and Learning. Center for Curriculum Redesign.
• Luckin, R., Cukurova, M., & Holmes, W. (2021). AI and Education: Guidance for Policymakers. UNESCO.
• Müller, J., & Kyndt, E. (2023). Adaptive Learning Technologies and Their Role in Inclusive Education: A Systematic Review. Computers & Education, 195, 104699.
• Zawacki-Richter, O., Marín, V. I., Bond, M., & Gouverneur, F. (2019). Systematic Review of Research on Artificial Intelligence Applications in Higher Education – Where Are the Educators? International Journal of Educational Technology in Higher Education, 16(1), 39.