For over a century, educators have aspired to move away from the industrial model of schooling toward a student-centered approach—one that adapts to each learner’s interests, pace, and needs, as envisioned by thinkers like John Dewey. Despite periodic reform efforts, such models have rarely gained lasting traction in mainstream education. Barriers such as standardized testing regimes, rigid curriculum mandates, cultural expectations of uniform instruction, and the logistical burdens on teachers have consistently constrained personalization at scale.
Recent advances in artificial intelligence (AI), however, offer new momentum for this long-standing educational ideal. AI-powered learning systems can analyze student behavior in real time, personalize content, deliver immediate feedback, and adapt to individual learning paths with a level of precision and scalability previously unattainable. By automating routine tasks and supporting differentiated instruction, AI has the potential to free educators to act more as mentors and facilitators—key roles in student-centered learning environments.
This report investigates whether AI can overcome the systemic resistance that undermined earlier attempts at personalization. It reviews the capabilities of current AI tutoring platforms, writing assistants, and adaptive learning systems; evaluates risks related to data privacy, algorithmic bias, teacher readiness, and equitable access; and situates AI-based reform within the broader history of education innovation.
While challenges remain—particularly around trust, implementation, and maintaining human relationships in learning—AI distinguishes itself from past tools by combining technical power with a supportive policy and cultural moment. If implemented ethically and equitably, AI could finally bring student-centered education into the mainstream—not as a fringe innovation, but as a transformative shift in how we teach and learn.