Beyond Learning Styles: Rethinking Personalization in the Age of AI

I've noticed learning styles making a comeback in conversations about personalization and AI. It's understandable and an appealing idea: if we tailor instruction to how someone says they learn best, we should see better results.

The science, however, tells a different story.

The Persistent Appeal of Learning Styles

The concept of learning styles has remarkable staying power. Despite decades of research challenging their effectiveness, they continue to resurface in educational discussions, particularly now as we explore how AI might personalize learning experiences. The intuitive appeal is clear: we all have preferences for how we consume information, so surely matching instruction to those preferences should yield better results.

But preference and effectiveness are not the same thing.

What the Research Actually Shows

Decades of research have tested the VARK model, which labels learners as visual, auditory, reading/writing, or kinesthetic, and found little evidence that matching instruction to those preferences improves understanding, behavior change, or outcomes.

The most comprehensive reviews of learning styles research consistently reach the same conclusion: there is minimal to no evidence supporting the idea that tailoring instruction to a person's preferred learning style improves learning outcomes. What matters far more is matching the instructional approach to the content being taught, not to the learner's stated preference.

This doesn't mean that different people don't have different needs—they absolutely do. But those needs are far more nuanced than a simple four-category classification system can capture.

The Real Opportunity With AI

As interest in personalization grows, many people imagine AI allowing learning to feel more adaptive, human, and responsive. This is an exciting vision, and AI does offer unprecedented opportunities for personalization.

However, this opportunity with AI should not be about reviving old models or catering to declared preferences. Instead, it is about redefining personalization as something richer and more evidence-based.

What Effective Personalization Actually Looks Like

Effective personalization focuses on multiple dimensions that research has shown actually impact learning outcomes:

• Performance and mastery: Adapting based on what a learner has already mastered versus where they're struggling, providing appropriate scaffolding and challenge.
• Background knowledge: Connecting new concepts to what learners already know, using examples and contexts that resonate with their existing understanding.
• Lived context: Considering the real-world situations, roles, and challenges learners face, making content immediately relevant and applicable.
• Goals and motivations: Understanding what learners are trying to achieve and why, then tailoring pathways that align with those objectives.
• Cognitive load: Managing the complexity and pacing of information based on working memory constraints and prior knowledge.
• Metacognitive support: Helping learners develop awareness of their own thinking and learning processes, fostering self-regulation.

Rather than designing to a fixed label, effective personalization is dynamic, context-sensitive, and grounded in what we know about how people actually learn.

Moving Forward

The future of learning is about creating learning experiences that intelligently adapt to each learner's context, supporting deeper learning and long-term retention. AI gives us powerful tools to achieve this, but only if we apply them thoughtfully and ground them in sound learning science.

As instructional designers, we have an opportunity—and a responsibility—to shape how AI is used in education. We can push back against the temptation to automate outdated models and instead advocate for personalization that is truly evidence-based and learner-centered.

The question isn't whether to personalize learning with AI. It's how we personalize in ways that genuinely serve learners and honor what we know about how learning actually works.

Let's build the future of learning on solid ground—not on myths that sound appealing but fall short when put to the test.