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Erroneous Example Designer

moderate evidence · ⏱ 3 minutes · Ai Learning Science

Design deliberately flawed examples that develop error-detection skills and deepen understanding. Use when students make characteristic errors and need practice spotting mistakes.

What it 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.

The evidence behind it

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.

Sources

McLaren, Adams & Mayer (2012) — Delayed learning effects with erroneous examples
McLaren, Adams, Durkin, Goguadze, Mayer & Rittle-Johnson (2015) — To err is human, to explain and correct is divine
Tsovaltzi, Melis, McLaren, Meyer, Dietrich & Goguadze (2010) — Learning from erroneous examples
Große & Renkl (2007) — Finding and fixing errors in worked examples
Siegler (2002) — Microgenetic studies of self-explanation

How to use it in your lesson

For the best results with EvidenceLesson, give it:

problem_domain — The type of problem or procedure where students make characteristic errors
target_errors — The specific, common errors students make — realistic misconceptions or procedural mistakes
student_level (optional) — Age/year group and proficiency level
subject_area (optional) — The curriculum subject
correct_examples_available (optional) — Whether students have already seen correct worked examples for this problem type
number_of_examples (optional) — How many erroneous examples to design
delivery_context (optional) — Whether delivered digitally, on paper, or discussed in class

Known limitations

Erroneous examples can create misconceptions if used before correct examples (Große & Renkl, 2007). Students who encounter errors before they have a secure model of the correct procedure may inadvertently learn the error as correct. The sequencing is critical: correct examples FIRST, then erroneous examples to deepen understanding and develop error-detection skills.

The learning effect is often DELAYED (McLaren et al., 2012). Students who study erroneous examples may not outperform students who study correct examples on an immediate test — but they show superior retention and transfer on delayed tests (one week later). Teachers should be aware that the benefit may not be immediately visible, and should not conclude the approach has failed based on a same-day assessment.

The quality of the error-analysis scaffold determines the learning effect. Simply showing students an error and saying "find the mistake" produces much weaker effects than providing structured prompts that require explanation and correction (Tsovaltzi et al., 2010). The scaffold above is designed to force self-explanation — but if students skip the explanation step and just identify the error without reasoning about it, the learning benefit is significantly reduced.