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Area of Science:

  • Cognitive Psychology
  • Educational Technology
  • Human-Computer Interaction

Background:

  • Learners encounter AI-generated inaccurate visualizations.
  • Some learning strategies involve engaging with erroneous content.
  • Instructional visualizations vary in perceptual richness (realism).

Purpose of the Study:

  • To assess if testing with erroneous examples aids learning.
  • To determine if learning effectiveness depends on visualization realism.

Main Methods:

  • Two factors were manipulated: testing (error-spotting vs. no testing) and realism (schematic vs. realistic visualizations).
  • Two retention tests were used to assess learning outcomes.

Main Results:

  • Testing with erroneous examples was detrimental with schematic visualizations.
  • Testing with erroneous examples was beneficial with realistic visualizations in one test.
  • The effectiveness of error-spotting depends on the realism of the visualization.

Conclusions:

  • Learning from erroneous examples requires a sufficient level of realism.
  • Disclosure of inaccuracies in visualizations (e.g., AI-generated) is crucial.
  • Careful consideration of visualization realism is needed when using erroneous examples for learning.