Erroneous Example Designer

What This Skill Does

Designs worked examples that contain deliberate, realistic errors for students to identify, explain, and correct — a technique that produces learning effects comparable to or exceeding correct worked examples, with the additional benefit of developing error-detection skills. The critical insight from McLaren et al. (2012, 2015) is that errors must be REALISTIC and COMMON — the kinds of mistakes students actually make, not contrived errors that no one would make. A well-designed erroneous example activates self-explanation (Chi et al., 1989): students must reason about WHY the step is wrong, which forces deeper processing than simply following a correct procedure. The output includes the erroneous examples with realistic errors at specific steps, an error analysis scaffold (prompts that guide students to find and correct the error), the learning mechanism explanation, and the corrected version. AI is specifically valuable here because designing effective erroneous examples requires deep knowledge of the common error patterns for specific problem types — which errors are realistic, which are productively confusing, and which would create harmful misconceptions.

Evidence Foundation

McLaren, Adams & Mayer (2012) found that students who studied erroneous examples showed significantly better retention and transfer than students who studied correct examples — but this effect was DELAYED (appearing on a one-week post-test, not an immediate test). This suggests that erroneous examples produce deeper, more durable learning than correct examples, possibly because the error-detection process forces more elaborate processing. McLaren et al. (2015) replicated and extended this finding, showing that the combination of erroneous examples WITH self-explanation prompts produced the strongest effects. Tsovaltzi et al. (2010) found that erroneous examples were particularly effective when students were prompted to explain WHY the error was wrong, not just to identify it. Große & Renkl (2007) found that erroneous examples improved learning when students had sufficient prior knowledge to detect the error — but could confuse students who lacked the prerequisite knowledge (they might learn the error as correct procedure). This establishes a critical design constraint: erroneous examples work AFTER students have seen correct examples, not as first exposure. Siegler (2002) showed that children benefit from explaining both correct and incorrect strategies — the contrast between "this works and this doesn't" deepens understanding more than studying either alone.

Input Schema

The teacher must provide:

• Problem domain: The type of problem. e.g. "Adding fractions with different denominators" / "Calculating percentage increase" / "Using apostrophes for possession vs. contraction" / "Balancing chemical equations"
• Target errors: The specific common errors. e.g. "Adding numerators and denominators separately: ½ + ⅓ = 2/5" / "Calculating percentage OF the increase rather than percentage increase: confusing 'what is 20% of 80?' with 'what is the percentage increase from 80 to 96?'" / "Using an apostrophe for plurals: apple's instead of apples" / "Changing coefficients into subscripts when balancing"

Optional (injected by context engine if available):

• Student level: Year group and proficiency
• Subject area: Curriculum subject