The Mistake
Promote your top performer into a training role, and something strange tends to happen. Sessions drag on, questions pile up, and new hires still trip over tasks the expert would call basic. Deep skill, it turns out, can get in the way of teaching that same skill to someone else.
Effort and personality have little to do with it. Something else is going on: expertise reshapes how a person relates to a task. Once L&D teams understand that mechanism, building training around Subject Matter Experts (SMEs) looks completely different.
The mistake many organizations make is assuming that the best performer is automatically the best person to explain the work. Research suggests the opposite: expertise changes how people perceive tasks in ways that make teaching much harder than it looks.
Why Expertise Distorts The Teaching Lens
Back in 1999, psychologist Pamela Hinds ran a study that gets at this problem head-on. Published in the Journal of Experimental Psychology: Applied, her research asked experienced salespeople to guess how long a novice would take to learn a set of cellphone tasks. Experts lowballed the real time by roughly half. Novices and people with moderate experience came far closer to the mark.
That gap held up across several experiments, and it points to something counterintuitive: the more someone knows a task, the worse they get at judging what a beginner needs. Skill compresses memory of the learning curve until early struggle barely registers anymore.
Ask an SME to build training content, and that same bias travels straight into the material. A step feels obvious because years of repetition wore down the friction that once surrounded it. This same blind spot hits terminology too—a term feels self-explanatory because the expert forgot what it was like to hear it cold. For L&D teams, that’s the real cost: content pulled straight from an SME’s head ships with hidden assumptions nobody flagged, and those gaps surface only once learners hit them.
A Blind Spot With A Name
Teachers run into a version of this too. Education researchers Mitchell Nathan and Anthony Petrosino tracked it in a 2003 study for the American Educational Research Journal. Preservice teachers with stronger math backgrounds consistently misjudged which parts of algebra problems would trip students up.
They called it the „expert blind spot,“ and the term stuck for good reason. Subject experts tend to structure instruction around the logic of their discipline rather than the actual struggles learners face on the ground. Deep content knowledge naturally organizes itself around formal principles. A beginner still climbing the learning curve wants concrete, situational problems first, with abstraction layered in later, once the ground feels solid.
Workplace training runs into this constantly. A top rep can explain a pricing objection through margin-and-value logic in their sleep, but a trainee wants the actual words to say on the call first. For L&D teams, that gap explains a common failure mode: SME-built modules read like a highlight reel of conclusions instead of a path a beginner can follow. Reasoning that convinces an expert points a novice in a different direction entirely.
Automatic Skill, Invisible Steps
There’s a third layer to this, and it gets at why the blind spot forms in the first place. Educational psychologist David Feldon, writing in Educational Psychologist in 2007, dug into how automaticity shapes teaching performance in the classroom.
His review found that skilled practitioners often run complex tasks through automated mental routines, bundling multiple decisions into one fast, largely unconscious move. Automaticity itself frees up mental bandwidth and lets experts stay fluid under pressure, a genuine advantage on the job. It comes with a catch, though: those individual steps drop out of conscious awareness, which makes them tough to narrate when someone else needs a walkthrough.
Ask a veteran project manager how they triage a crisis, and the answer often boils down to a shrug and „I just know what matters first.“ Every micro-decision behind that instinct got folded into instinct itself, invisible even to the person running it. Getting to the actual sequence of judgment calls underneath that shrug usually takes several rounds of pointed follow-up questions, necessary to get insights from an SME build the training around.
How To Design Training Around Subject Matter Expertise
Knowing where expertise breaks down is only half the job. David Feldon, the researcher behind the automaticity findings above, tested a fix in a 2010 study: pull expert reasoning out through structured questioning, then rebuild it into direct instruction. Students taught that way turned in stronger lab reports and stuck with the course longer than peers who got the standard version.
That same mechanic shows up far from any lab too, in messier, real-world settings. One executive, asked to draft a negotiation module, handed over a list of principles that read like axioms—accurate, but disconnected from any starting point a beginner could use. A designer paired with him kept asking one question: „what do you notice first when you walk into the room?“ That single question flipped the module from abstract theory into a concrete sequence new managers could follow into their first negotiation. That is how to build training using SME insights.
A Four-Step Framework For Working With SMEs
- Design together, not sequentially
Bring Instructional Designers into the process before an outline exists. A learner’s-eye view catches assumptions SMEs no longer notice from inside their own expertise. - Capture expertise in action
Ask SMEs to think out loud while performing a task rather than explaining it afterward. Real-time narration surfaces decision points that memory often skips. - Validate with beginners
Pilot training with novice learners before scaling it. Even one session quickly reveals which „obvious“ steps still need explanation. - Unpack the reasoning
Don’t stop at the SME’s first answer. Follow-up „why“ questions uncover the tacit knowledge that experienced performers rarely articulate on their own.
Responsibility sits with L&D here, translating fluency an SME already skipped past, one deliberate step at a time. Four small process changes turn a blind spot into a training program a beginner can actually use.
Closing The Gap Between Knowing And Teaching
Great performance and great teaching draw on separate skillsets, and mixing them up costs organizations real training quality. An expert’s fluency comes from years spent compressing complexity into instinct. Good instruction means decompressing that same complexity back into steps a beginner can actually follow, one deliberate layer at a time.
Learning teams that build this decompression into their SME workflows, rather than assuming strong subject knowledge speaks for itself, end up with training that actually sticks. Given how much organizational knowledge lives inside a handful of top performers, getting that translation right carries stakes well beyond any single course.
References
- Hinds, P. J. 1999. „The curse of expertise: The effects of expertise and debiasing methods on prediction of novice performance.“ Journal of Experimental Psychology: Applied 5 (2): 205–21. https://doi.org/10.1037/1076-898X.5.2.205
- Nathan, M. J., and A. Petrosino. 2003. „Expert blind spot among preservice teachers.“ American Educational Research Journal 40 (4): 905–28. https://doi.org/10.3102/00028312040004905
- Feldon, D. F. 2007. „Cognitive load and classroom teaching: The double-edged sword of automaticity.“ Educational Psychologist 42 (3): 123–37. https://doi.org/10.1080/00461520701416173
- Feldon, D. F., B. C. Timmerman, K. A. Stowe, and R. Showman. 2010. „Translating expertise into effective instruction: The impacts of cognitive task analysis (CTA) on lab report quality and student retention in the biological sciences.“ Journal of Research in Science Teaching 47 (10): 1165–85. https://doi.org/10.1002/tea.20382