Learning with erroneous visualizations modulates retention depending on perceptual richness and test type.

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.