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In this paper, we describe a study that explored the use of AI as a method for collecting data on the instructional practices used in undergraduate introductory statistics courses. Six instructors were asked to use an AI app to track classroom discussions over a 12-week term. Comparison with classroom observation data confirmed that classes were lecture-centric with only nominal amounts of student interaction. The findings point to the use of AI as a viable method for learning about postsecondary instructional practices at scale.
Study at a glance
- Question: Can an AI app effectively track classroom discussions and teaching practices in college courses?
- Setting and data: Undergraduate introductory statistics courses with six instructors over a 12-week term.
- Method: Comparison of AI app tracking of classroom discussions with classroom observation data.
Why it matters
Questions remain about what conversational AI can and cannot tell us about peer interaction. There is a need to explore whether AI can serve as a method for tracking classroom discussions and collecting data on instructional practices.
Key findings
- In six undergraduate introductory statistics courses, AI tracking and classroom observations confirmed that teaching was lecture-centric.
- Students participated in only nominal amounts of classroom interaction.
- AI is a viable method for studying postsecondary instructional practices at scale.
Summary drafted with AI from the paper’s abstract and may contain errors. See the full paper for details.
What is the application?
Who is the user?
Who age?
Why use AI?
Study design
