Work

Using Biterm Topic Modeling to Explore Gender Differences in Secondary Students’ Wonderings about AI Concepts

Year: 2024

Type: conference-paper

Abstract: The study reveals gender-based differences in how secondary students express curiosity in an AI curriculum.The study used biterm topic modeling to explore questions posed by male and female learners (n=239) in Grades 8-12.The findings revealed that keywords related to model accuracy and prediction such as Words and Weights were prevalent in both groups.Key variations were observed in technical terms, such as Machines.The discussions highlight the need for socially relevant AI curriculums.

Source: Proceedings.

DOI: 10.22318/icls2024.839258

Authors: Doreen Mushi, Jie Chao, Shiyan Jiang, Rebecca Ellis, Carolyn P. Rosé, Kenia Wiedemann

Institutions: North Carolina State University, Concord Consortium, Carnegie Mellon University

Language: English

Topic: Gender and Technology in Education

Subfield: Gender Studies

Field: Social Sciences

Domain: Social Sciences

SDGs: Quality education, Gender equality

Open Access status: bronze

Funder: National Science Foundation