In this episode we explore what happens when AI writing feedback improves a paper without necessarily improving the writer. Using new research from the University of Pennsylvania on graduate students using an AI teaching assistant, we look at the difference between revision and real learning, why group feedback can mask individual understanding, and what teachers can do to make AI feedback more instructionally meaningful.

Topics covered:

• How AI feedback can lead to revision without deep learning
• What a University of Pennsylvania study reveals about student writing improvement
• Why group work can hide whether individual students actually learned
• The difference between fixing writing and understanding writing
• Why structured reflection should be part of any AI feedback workflow
• How teachers can help students process feedback more thoughtfully

• What district leaders should look for in AI writing-feedback tools
• The risk of overwhelming students with too much unprioritized feedback
• Why revision is evidence of compliance, not always evidence of understanding

Source:

https://learninganalytics.upenn.edu/ryanbaker/20_U%20Penn%20team%20-%20Do%20Students%20Learn%20from%20Writing%20Feedback%20from%20an%20AI%20Teaching%20Assistant.pdf