Using Artificial Intelligence to Support Peer-to-Peer Discussions in Science Classrooms

In successful peer discussions students respond to each other and benefit from supports that focus discussion on one another’s ideas. We explore using artificial intelligence (AI) to form groups and guide peer discussion for grade 7 students. We use natural language processing (NLP) to identify student ideas in science explanations. The identified ideas, along with Knowledge Integration (KI) pedagogy, informed the design of a question bank to support students during the discussion. We compare groups formed by maximizing the variety of ideas among participants to randomly formed groups. We embedded the chat tool in an earth science unit and tested it in two classrooms at the same school. We report on the accuracy of the NLP idea detection, the impact of maximized versus random grouping, and the role of the question bank in focusing the discussion on student ideas. We found that the similarity of student ideas limited the value of maximizing idea variety and that the question bank facilitated students’ use of knowledge integration processes.

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Publication Details

Journal
Education Sciences
Published
2024-12-23
DOI
https://doi.org/10.3390/educsci14121411
Citations
6
Primary Topic
Intelligent Tutoring Systems and Adaptive Learning
Type
article
Field-Weighted Citation Impact
2.08

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article

Using Artificial Intelligence to Support Peer-to-Peer Discussions in Science Classrooms

Jonathan Lim-Breitbart, Hsin‐Yi Chang, Marcia C. Linn, Kelly Billings
6 citations
Education Sciences
Intelligent Tutoring Systems and Adaptive Learning
2.08
article

Using Artificial Intelligence to Support Peer-to-Peer Discussions in Science Classrooms

Jonathan Lim-Breitbart, Hsin‐Yi Chang, Marcia C. Linn, Kelly Billings
article en
6 citations

Abstract

In successful peer discussions students respond to each other and benefit from supports that focus discussion on one another’s ideas. We explore using artificial intelligence (AI) to form groups and guide peer discussion for grade 7 students. We use natural language processing (NLP) to identify student ideas in science explanations. The identified ideas, along with Knowledge Integration (KI) pedagogy, informed the design of a question bank to support students during the discussion. We compare groups formed by maximizing the variety of ideas among participants to randomly formed groups. We embedded the chat tool in an earth science unit and tested it in two classrooms at the same school. We report on the accuracy of the NLP idea detection, the impact of maximized versus random grouping, and the role of the question bank in focusing the discussion on student ideas. We found that the similarity of student ideas limited the value of maximizing idea variety and that the question bank facilitated students’ use of knowledge integration processes.

Education SciencesVol. 14(12)
National Taiwan Normal University (TW), University of California, Berkeley (US)
National Science Foundation
Quality Education
Openalex Percentile: Top 11%
Intelligent Tutoring Systems and Adaptive Learning
2.08
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