Employee Experience
If you work in HR, talent development, or people management, you have almost certainly encountered learning styles. The idea that employees can be categorised as visual, auditory, or kinaesthetic learners has become one of the most widely adopted concepts in corporate training.
The appeal is obvious. It offers a simple, actionable framework. Identify how someone learns best, match your training to that preference, and watch results improve. It feels intuitive. It sounds scientific. And it’s everywhere.
There’s just one problem: the scientific evidence does not support it.
A 2020 systematic review published in Frontiers in Education found that 89.1% of educators across 18 countries believe matching instruction to learning styles improves outcomes. Yet decades of rigorous research have consistently failed to find evidence that this approach works.
This guide will give you what most articles on this topic do not. You will learn what learning styles are, why the theory became so popular, what the research actually shows, and most importantly, what evidence-based approaches genuinely improve workplace learning.
Whether you’re building a business case for L&D investment, designing training programmes, or simply trying to separate fact from fiction, this article will help you.
Before examining what the research says, it is worth understanding exactly what learning styles theory claims and why it has proven so enduring in workplace settings.
The most common framework used in corporate training is VAK, often expanded to VARK. This model was popularised by Neil Fleming in 1987 and suggests that individuals have a dominant sensory mode through which they learn most effectively. You can explore the original framework at the VARK website.
The appeal of learning styles is not difficult to understand. The framework offers several things that busy HR and L&D professionals value highly.
Simplicity. Three or four categories are easy to remember, easy to communicate, and easy to act upon. Compared to the complexity of cognitive science, this feels manageable.
Actionability. The model gives managers something concrete to do. Identify a style, adjust delivery, problem solved. This feels like progress.
Face validity. Most people can relate to having preferences. We might enjoy videos more than reading, or prefer discussion to solo study. This personal recognition makes the theory feel true.
Respect for individuality. Learning styles positions itself as honouring individual differences. This aligns with values that most HR professionals hold dear.
These factors explain why the concept has proven so persistent. But intuitive appeal and scientific validity are not the same thing.
The core claim of learning styles theory is straightforward: if you identify someone’s preferred learning style and match instruction to that style, they will learn more effectively. Researchers call this the ‘meshing hypothesis.’
This hypothesis has been tested extensively. The consistent finding is that it does not hold up.
In 2008, a team of leading cognitive psychologists published a comprehensive review of learning styles research in Psychological Science in the Public Interest. The paper, authored by Harold Pashler, Mark McDaniel, Doug Rohrer, and Robert Bjork, was commissioned by the Association for Psychological Science.
Their conclusion was unambiguous: “We conclude therefore, that at present, there is no adequate evidence base to justify incorporating learning-styles assessments into general educational practice.”
This was not a single study with a small sample. It was a systematic evaluation of the entire research literature, conducted by experts with no commercial stake in the outcome.
An earlier review by Coffield and colleagues (2004) examined 71 different learning styles models and found that only 3 came close to meeting minimal psychometric criteria for reliability and validity. The authors described some of the most popular instruments as having “serious weaknesses” and recommended their use “should be discontinued.”
Here is where the confusion often lies. Learning styles theory makes a very specific claim: that individuals have stable, consistent cognitive traits that determine how they process and retain information.
But what the questionnaires actually measure is something different: preference. How someone prefers or enjoys receiving information.
These are not the same thing.
Preferences are unstable. They change depending on the topic, the context, and even our mood. Research consistently shows that people are poor judges of their own learning processes. We often prefer methods that feel easier or more enjoyable, even when those methods produce weaker learning outcomes.
As Professor Paul Kirschner wrote in Computers & Education: “What people prefer is often not what is best for them.”
One finding from the research is particularly important for L&D professionals to understand.
When learners receive instruction that matches their stated preference, they often report higher satisfaction. They find the experience more enjoyable. They rate the training more positively.
But satisfaction is not the same as learning. When researchers measure objective outcomes, such as knowledge retention, skill transfer, or performance improvement, the advantage disappears. In some studies, learners actually performed better when taught through a modality different from their stated preference.
This distinction matters enormously for organisations that rely on post-training surveys to evaluate effectiveness. Happy learners are not necessarily competent learners.
The scientific community has been so consistent in its findings that learning styles has been formally classified as a ‘neuromyth’ by the OECD’s Brain and Learning project.
A 2012 study published in Frontiers in Psychology found that 93% of teachers in the UK and 96% in the Netherlands endorsed the learning styles myth. Despite overwhelming scientific consensus against it, belief remains remarkably persistent.
A 2019 investigation by Education Next found that 29 US states still include learning styles in their teacher certification preparation materials, perpetuating the myth through official channels.
The UK’s Chartered Institute of Personnel and Development (CIPD) has been explicit on this point. In their evidence-based L&D podcast, they stated plainly: “Learning styles per se has been debunked many a time and quite rightly so.”
Beyond the lack of effectiveness, continuing to apply learning styles theory carries real costs for organisations.
Creating multiple versions of training content to accommodate different supposed styles consumes significant time and budget. Administering style assessments, training managers to identify styles, and redesigning programmes around these categories all require investment.
With corporate training expenditure in the US alone reaching $98 billion annually, misdirected investment matters. Research suggests that companies lose an estimated $13.5 million per 1,000 employees annually due to ineffective training.
Resources spent on style-matching could be redirected toward approaches that actually improve outcomes.
Perhaps more damaging is the psychological effect of labelling employees as a particular type of learner.
When someone believes they are a “visual learner,” they may avoid training modalities that are actually necessary for certain subject matter.
For instance, leadership development requires verbal and interpersonal practice. Technical procedures often require hands-on engagement regardless of preference. Data analysis benefits from visual representation for everyone.
The most effective learners are those who adapt their strategy to the content, not those who insist on a single approach. Learning styles encourages the opposite mindset.
Here’s an important clarification. Many of the tactics associated with learning styles advice do improve training outcomes. Using visuals alongside narration helps. Breaking training into shorter sessions helps. Incorporating hands-on practice helps.
But these tactics work for everyone, not just those with a supposed preference for that modality. The reason they work has nothing to do with individual learning styles and everything to do with how human brains actually process and retain information.
Breaking up training into shorter sessions works because of cognitive load management, not kinaesthetic preference.
Using visuals alongside narration works because of dual coding theory, not visual preference.
Hands-on practice works because of active learning principles, not kinesthetic style.
Understanding the real mechanisms allows you to apply these principles systematically, rather than guessing which employees might benefit.
If learning styles do not explain individual differences in training outcomes, what does?
Research points to several factors that L&D professionals should attend to instead.
The 2023 Programme for the International Assessment of Adult Competencies (PIAAC), conducted by the US Department of Education, found that 28% of American adults scored at Level 1 or below in literacy proficiency. A total of 57% scored below the proficient level.
This has significant implications for workplace training, which overwhelmingly relies on text-heavy content.
Consider this: when an employee expresses a strong preference for visual or video-based learning over text, it may not indicate a cognitive “style” at all. It may reflect literacy challenges that make text-based learning genuinely more difficult for them.
Effective training design accommodates varying literacy levels for everyone, rather than creating separate tracks based on supposed learning types.
Modern employees are overwhelmed with information. The average worker contends with constant email, instant messaging, notifications, and meetings.
According to the Association for Talent Development (ATD), average training hours per employee have dropped from 35 hours in 2020 to just 13.7 hours in 2024.
In this environment, the format of training matters less than its respect for cognitive constraints.
Short, focused modules work better than marathon sessions not because some employees are kinesthetic learners, but because human attention and working memory have genuine limits that affect everyone.
Moving beyond learning styles does not mean abandoning the goal of effective, engaging training. It means grounding your approach in what cognitive science has validated.
Rather than trying to identify and match individual styles, Universal Design for Learning (UDL) takes a different approach: design training to accommodate variability from the start.
Developed by CAST and updated most recently in July 2024, UDL is built on three core principles. You can explore the full framework at the CAST UDL Guidelines website:
The crucial difference from learning styles: UDL provides options to all learners, rather than categorising individuals and prescribing a single approach for each.
Cognitive Load Theory, developed by educational psychologist John Sweller, explains why training design matters regardless of individual preferences. You can read an excellent summary in this guide from the NSW Department of Education.
The core insight is that working memory has severe capacity limits. We can only hold and process a small amount of new information at once.
When training exceeds this capacity, learners become overwhelmed, disengage, and fail to retain what they have been taught.
Effective training design manages three types of cognitive load:
Practical applications include:
Dual Coding Theory, developed by Allan Paivio, explains why multimedia training often outperforms single-modality instruction.
The theory proposes that the brain processes verbal information (words, whether written or spoken) and visual information (images, diagrams, spatial relationships) through separate channels. When both channels are engaged appropriately, memory formation is enhanced.
This is why training that combines relevant visuals with narration often produces better outcomes than either alone.
Critically, this benefit applies to everyone. It is not evidence for “visual learners” existing as a separate category. It is evidence that human cognition works in a particular way across individuals.
Practical applications include:
Perhaps the most underutilised evidence-based technique is retrieval practice, sometimes called the testing effect.
Research by Roediger and Karpicke, published in Perspectives on Psychological Science and later in Science, demonstrated that actively retrieving information from memory produces far stronger long-term retention than simply re-reading or re-watching material. Testing is a powerful learning strategy in its own right.
Spaced repetition amplifies this effect. Rather than covering material once and moving on, strategic revisiting at intervals dramatically improves retention.
A meta-analysis of 225 studies in STEM education, published in PNAS, found that students using active learning strategies showed 6% improvement in exam scores and were 1.5 times less likely to fail compared to traditional passive instruction.
Practical applications include:
Understanding the evidence is one thing. Applying it within an organisation that may have invested heavily in learning styles is another.
Here’s how to make the transition.
The first mindset shift is recognising that the nature of the content should determine how it is taught.
Communication skills training requires verbal practice and feedback. There is no visual alternative that produces the same outcome.
Technical procedures involving equipment require hands-on practice. Reading about it is insufficient regardless of preference.
Data analysis and financial modelling benefit from visual representation. Everyone needs to see the patterns, not just “visual learners.”
Help employees understand this.
The most effective learners are those who adapt their approach to what the content demands, rather than insisting on a single comfortable method.
This does not mean abandoning learner input entirely. Preference data can still be valuable when used appropriately.
Rather than using assessments to categorise employees into fixed types, use them to help employees think harder about their own learning processes.
Help employees understand that preferences exist but are not destiny. Encourage them to notice when they are avoiding a modality that might actually serve them well. Support self-advocacy: employees who understand their responses to different formats can better navigate the learning resources available to them.
The shift is from prescription (“you’re a visual learner, so you need visual content”) to awareness (“you tend to prefer visual content, but let’s ensure you’re also developing skills that require other approaches”).
If your organisation has embedded learning styles into its L&D practice, you may need to make the case for change. Here is an approach that tends to work:
Finally, shift your evaluation approach from satisfaction to outcomes.
Post-training surveys asking whether learners enjoyed the experience have their place, but they should not be the primary measure of success.
Research shows that only 1 in 10 organisations actively calculate the ROI of their learning programmes.
Better metrics include:
| Dimension | Learning Styles Approach | Evidence-Based Approach |
| Core assumption | Fixed individual types | Universal cognitive processes |
| Design goal | Match modality to style | Optimise for how brains work |
| Assessment focus | Categorise learners | Foster metacognition |
| Success metric | Learner satisfaction | Skill transfer and retention |
| Resource allocation | Multiple versions per style | Flexible, multi-modal design |
| Scientific support | Consistently debunked | Validated by cognitive science |
| Principle | What It Means | L&D Application |
| Universal Design for Learning | Design for variability from the start | Offer multiple ways to engage, represent, and express |
| Cognitive Load Theory | Working memory is limited | Use microlearning; reduce extraneous complexity |
| Dual Coding | Verbal + visual enhances memory | Combine narration with relevant diagrams |
| Retrieval Practice | Testing strengthens learning | Build in frequent low-stakes knowledge checks |
| Spaced Repetition | Distributed practice beats cramming | Revisit key concepts at intervals |
Learning styles will likely remain a popular concept for years to come. The intuitive appeal is strong, and habits are difficult to change.
But for HR and L&D professionals who want their programmes to deliver genuine results, the evidence is clear: the meshing hypothesis does not work. Categorising employees as visual, auditory, or kinesthetic learners and matching instruction accordingly does not improve learning outcomes.
What does work is designing training that respects how human cognition actually functions: managing cognitive load, engaging multiple processing channels through dual coding, building in retrieval practice, and providing flexible access through Universal Design for Learning.
The practical implications are significant. Rather than creating multiple versions of content for different supposed styles, invest in well-designed, multi-modal training that benefits everyone. Rather than administering style inventories, help employees develop self-awareness. Rather than measuring satisfaction, measure skill transfer and business outcomes.
According to LinkedIn Learning’s 2024 Workplace Learning Report, 94% of employees say they would stay longer at a company that invested in their learning and development.
Getting L&D right matters for retention, performance, and competitive advantage.
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