Key ideas
Core concepts
- Having knowledge makes it difficult to imagine not having that knowledge, so expertise blinds teachers to the student perspective.
- Expert fluency makes complex processes seem effortless, which leads teachers to underestimate student difficulty. Compensating requires deliberate effort: systematically identify prerequisites and over-explain rather than assume understanding.
The curse of knowledge is a cognitive bias where having knowledge of a topic makes it difficult to understand the perspective of someone without that same knowledge (Hinds, 1999).
Expert teachers possess a blindspot: their fluency in mathematics makes retrieving and applying knowledge feel effortless, creating the illusion that the tasks they assign require minimal cognitive work (Hinds, 1999). When solving feels trivially simple, teachers struggle to imagine the vast network of chunked knowledge this “basic” problem actually demands (understanding equality symbols, algebraic notation, inverse operations, and maintaining balance) (Miller, 1956; Cowan, 2001).
This bias leads teachers to underestimate time needed for mastery, skip essential prerequisites, rush through explanations, and provide insufficient practice (Hinds, Bernstein, & Loewenstein, 1988). Beginners overestimate their competence due to ignorance; experts underestimate the competence required of beginners due to their automated knowledge (Kruger & Dunning, 1999), an inverse of the Dunning-Kruger Effect. Effective teaching counteracts the bias through deliberate compensation: identifying all prior knowledge requirements, making invisible thinking processes visible, and over-explaining rather than under-explaining (Rosenshine, 2012).
Connected to
Experts and Novices Think Differently | Prior Knowledge | Explicit Teaching | Fluency | Surface and Deep Structure | Dunning-Kruger Effect
How the bias shows up in teaching
Teacher expertise creates this bias through fluency in retrieving information from long-term memory effortlessly (Hinds, 1999; Ericsson & Kintsch, 1995). Teachers can immediately recognise the deep structure of a question without consciously realising how they do so (Chi, Feltovich, & Glaser, 1981). This automated expertise makes it difficult to remember what it was like not to understand the mathematics being taught, reducing empathy with students struggling to learn (Hinds, 1999).
The bias appears in several teaching behaviours. Teachers provide rapid explanations on the assumption that content is obvious, whilst students face multiple new concepts simultaneously (Hinds, 1999; Cowan, 2001). Teachers skip steps in worked examples believing “they should know this”, yet students lack the prerequisite knowledge being assumed (Chi, Feltovich, & Glaser, 1981). Complex examples that seem to show the principle clearly to expert teachers create cognitive overload from multiple variables for novice learners (Sweller, 1988). Teachers provide minimal practice after one or two examples, assuming students understand, whilst learners need extensive repetition to achieve automation (Ericsson, Krampe, & Tesch-Römer, 1993).
Compensation strategies
Teachers can counteract the curse of knowledge through systematic analysis of prerequisites before teaching new skills. This means identifying all prior knowledge required, listing every component skill and concept students need, and testing what students actually know rather than what curriculum documents suggest they should know (Rosenshine, 2012).
Instructional delivery needs adjusting to compensate for expert blindness. Explicit teaching with clear, step-by-step instruction makes the invisible thinking processes of experts visible to novices (Rosenshine, 2012). Teachers should over-explain rather than under-explain, providing more detail than feels necessary to their expert perspective, and show all working steps that automated expertise might otherwise skip (Hinds, 1999).
Practice requirements differ from expert intuitions. Students need practice until they achieve fluency, not just initial understanding (Ericsson & Kintsch, 1995). Teachers should use formative assessment to monitor actual comprehension rather than assumed understanding, and provide multiple examples since single examples are insufficient for novices to recognise patterns (Rosenshine, 2012).
Practical implications
Expert teachers consistently underestimate the time students need for mastery (Hinds, 1999). Problems that seem “simple” to experts often require vast networks of automated knowledge invisible to conscious awareness (Chi, Feltovich, & Glaser, 1981). When students show confusion, this may indicate the curse of knowledge affecting teaching rather than student deficiency (Hinds, 1999). Curriculum pacing frequently reflects expert assumptions about learning speed rather than the reality of novice learning trajectories (Rosenshine, 2012).
Examples reveal the hidden complexity of apparently simple tasks. Solving 4a = 12 seems trivial to mathematics teachers but requires chunked knowledge of equality symbols, algebraic notation, inverse operations, and maintaining balance (each element that once required conscious effort) (Cowan, 2001). Similarly, instructions to “just factor the quadratic” assume students have automated their multiplication tables, perfect squares, factor pairs, distributive property, and trinomial patterns (Miller, 1956). In literacy, asking students to “find the main idea” presumes vocabulary knowledge, relevant background knowledge, text structure awareness, and inferential reasoning skills that novice readers may lack (Chi, Feltovich, & Glaser, 1981).
Teaching applications
Practical guidance
Teachers should carefully delineate the prior knowledge required when teaching a new skill (Rosenshine, 2012). Instruction should be explicit, with teachers over-explaining rather than under-explaining (Hinds, 1999). Students need practice until they achieve fluency before moving to new content (Ericsson & Kintsch, 1995).
References
Chi, M. T. H., Feltovich, P. J., & Glaser, R. (1981). Categorization and representation of physics problems by experts and novices. Cognitive Science, 5(2), 121–152. https://doi.org/10.1207/s15516709cog0502_2
Cowan, N. (2001). The magical number 4 in short-term memory: A reconsideration of mental storage capacity. Behavioral and Brain Sciences, 24(1), 87–114. https://doi.org/10.1017/S0140525X01003922
Ericsson, K. A., & Kintsch, W. (1995). Long-term working memory. Psychological Review, 102(2), 211–245. https://doi.org/10.1037/0033-295X.102.2.211
Ericsson, K. A., Krampe, R. T., & Tesch-Römer, C. (1993). The role of deliberate practice in the acquisition of expert performance. Psychological Review, 100(3), 363–406. https://doi.org/10.1037/0033-295X.100.3.363
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), 205–221. https://doi.org/10.1037/1076-898X.5.2.205
Hinds, P. J., Bernstein, E., & Loewenstein, J. (1988). Unpacking the curse of knowledge. In Proceedings of the Academy of Management.
Kruger, J., & Dunning, D. (1999). Unskilled and unaware of it: How difficulties in recognizing one’s own incompetence lead to inflated self-assessments. Journal of Personality and Social Psychology, 77(6), 1121–1134. https://doi.org/10.1037/0022-3514.77.6.1121
Miller, G. A. (1956). The magical number seven, plus or minus two: Some limits on our capacity for processing information. Psychological Review, 63(2), 81–97. https://doi.org/10.1037/h0043158
Rosenshine, B. (2012). Principles of instruction: Research-based strategies that all teachers should know. American Educator, 36(1), 12–19.
Sweller, J. (1988). Cognitive load during problem solving: Effects on learning. Cognitive Science, 12(2), 257–285. https://doi.org/10.1207/s15516709cog1202_4