The Expert Who Forgot What It Is Being A „Beginner“
Expertise does something invisible to the people who develop it. Over years of deliberate practice, complex multi-step processes become automatic. The mental effort required to perform them drops dramatically. What once demanded conscious attention now flows without friction. This is exactly what makes someone an expert. And it is exactly what makes many experts unreliable instructional communicators. Researchers Nathan and Petrosino described this phenomenon as the „expert blind spot“: as expertise becomes automatic, experts stop noticing the intermediate steps beginners still need. What feels obvious to the SME often remains invisible to the learner.
In workplace learning, the consequences appear everywhere. Onboarding modules skip foundational concepts because the SME assumes they are self-evident. Training decks become overloaded with terminology before learners understand the basics. LMS courses introduce multiple concepts in a single lesson without enough time for consolidation. Learners may complete the training, pass the assessment, and still struggle to perform the task in real work situations.
SMEs routinely build 90-slide decks because everything feels important from inside their expert perspective. Learners, meanwhile, are still trying to understand the first few concepts before the next 20 appear.
The training feels comprehensive to the person who designed it. To the learner, it often feels confusing, rushed, and difficult to apply.
Why The Expert Blind Spot Is So Difficult To Detect
One reason the expert blind spot is so difficult to correct is that experts often believe their explanations are already clear. Fisher and Keil found that experts consistently overestimate how well beginners understand their explanations. From the SME’s perspective, everything important has been covered. From the learner’s perspective, critical context is often missing. This has direct implications for training design. SME reviews alone are rarely sufficient to evaluate clarity because the same expertise that enables accurate content can make gaps invisible.
That is why pilot testing with novice learners is essential. If learners consistently struggle with a concept, skip a step, or interpret instructions incorrectly, the issue is usually not motivation—it is often a signal that the training reflects expert assumptions rather than learner reality. Learner feedback becomes particularly valuable because it reveals breakdowns the SME may not be able to see. What feels obvious to an expert is often exactly what needs additional explanation, examples, or practice opportunities.
How The Expert Blind Spot Creates Pacing Problems
The expert blind spot affects more than explanation quality. It also shapes how SMEs estimate learner readiness and training pace. Psychologist Pamela Hinds ran a study in which experts, intermediate users, and novices each estimated how long it would take a beginner to complete a complex task. Experts dramatically underestimated the time required. Intermediate users were the most accurate. The pattern held across domains.
The interpretation for L&D practice is direct: when SMEs design training at what feels like a reasonable pace to them, they are designing it at a pace calibrated to their own expert-level automatic thinking—not to a learner encountering the material for the first time. The result is training that moves too quickly through foundational material, stacks concepts before earlier ones have settled, and compresses too many ideas into a single lesson. LMS courses that look comprehensive on paper often become cognitively exhausting for learners encountering the material for the first time.
Learners nod along during the session, then immediately message coworkers afterward asking how the process actually works in practice. Hinds found that experts became more accurate only when they deliberately tried to reconstruct what learning the task originally felt like. The act of mentally reinhabiting an earlier cognitive state reduced the gap between expert estimates and actual novice performance. It is a transferable technique—and one worth building into any SME collaboration process.
Why „More Detail“ Often Reduces Learning
One of the most common ways the expert blind spot appears in training design is through content overload. Cognitive load theory, developed by John Sweller and refined across decades of educational research, demonstrates that working memory has strict capacity limits. When learners encounter more simultaneous elements than their working memory can handle, comprehension degrades—regardless of how well-organized or accurate the content is. The problem is architectural, not motivational.
More information does not automatically improve learning. This is why compliance programs sometimes achieve high completion rates while producing little behavioral change. Employees finish the course, pass the assessment, and still struggle to apply the required procedures in real situations.
One of the most robust findings in this research area is the worked example effect. For learners at an early stage, studying a fully worked example produces significantly better retention and transfer than attempting to solve an equivalent problem independently. Worked examples reduce cognitive strain, allowing learners to focus on understanding before independent problem-solving.
SMEs, almost universally, do the opposite. The result is familiar to most Instructional Designers: learners pass the knowledge check immediately after training, yet cannot perform the task independently a week later. They present the full conceptual framework first, follow it with extensive contextual detail, and then—if time permits—walk through an application. This sequencing makes intuitive sense from inside the expert’s cognitive state. From inside a novice’s working memory, it is a recipe for overload.
What Good SME Collaboration Actually Requires
Understanding this dynamic changes what effective collaboration between L&D professionals and SMEs looks like in practice. The SME’s role remains indispensable—domain knowledge is genuinely irreplaceable. The issue has never been whether expertise belongs in training design, but how it should be translated into practice. The Instructional Designer’s role is to serve as a translation layer: someone who understands what the learner’s cognitive state actually looks like and can restructure expert knowledge accordingly. In practical terms, that means building four disciplines into every SME collaboration:
- Start with the learner state, not the content inventory. Before an SME presents everything they know about a topic, the design conversation should begin with a clear-eyed assessment of what the target learner actually knows right now, and what specific capability they need to demonstrate afterward. This reframes the design task from „how do I convey this knowledge“ to „what pathway takes a person from here to there.“
- Build external comprehension testing into the design stage. Because expert self-assessment of clarity is unreliable, prototype materials should be tested with actual novice-level reviewers before finalization. The test should measure demonstrated understanding, not self-reported confidence—learners consistently overestimate their own comprehension after listening to explanations, a well-documented finding that compounds the original problem.
- Sequence for progressive disclosure, not completeness. Effective SME-driven training sequences foundational concepts first, confirms understanding before adding complexity, and uses worked examples to bridge the gap between concept and application. Each step should stand on the one before it, rather than assuming the learner can hold the whole structure in mind while new material arrives.
- Keep SME expertise and instructional judgment as separate functions. Subject matter experts bring knowledge. Instructional Designers bring a model of how human cognition acquires that knowledge. The most effective training design processes keep these roles distinct—and let each do what it does well.
Workplace learning is full of well-intentioned training that produces completion without application, onboarding programs that leave new hires uncertain of the basics, and assessments that certify knowledge without demonstrating performance. That observation carries no criticism of the experts involved. The expert blind spot is a natural, well-documented consequence of developing real mastery. The solution is structural, not personal.
As workplace learning becomes increasingly compressed, asynchronous, and AI-assisted, this problem may become harder to notice—not easier. Training systems can look polished, scalable, and information-rich while still quietly failing at the thing learners actually need: understanding how to operate inside real work.
References:
- Nathan, M. J., and A. Petrosino. 2003. „Expert blind spot among preservice teachers.“ American Educational Research Journal 40 (4): 905–98. https://journals.sagepub.com/doi/10.3102/00028312040004905
- Fisher, M., and F. C. Keil. 2016. „The curse of expertise: When more knowledge leads to miscalibrated explanatory insight.“ Cognitive Science 40 (5): 1251–69. https://cogdevlab.yale.edu/sites/default/files/files/Fisher2015.pdf
- Hinds, P. J. 1999. „The curse of expertise: The effects of expertise and debiasing methods on predictions of novice performance.“ Journal of Experimental Psychology: Applied 5 (2): 205–21. https://psycnet.apa.org/doi/10.1037/1076-898X.5.2.205
- Sweller, J. 2023. „The development of cognitive load theory: Replication crises and incorporation of other theories can lead to theory expansion.“ Educational Psychology Review 35: 95. https://link.springer.com/article/10.1007/s10648-023-09817-2