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<p>This study designed an AI curriculum that integrated constructionist learning principles with Social-Emotional Learning (SEL) to address the limited opportunities that rural schools and female learners often face, resulting in persistent disparities in access and engagement. The curriculum was implemented with 52 middle school students in a rural U.S. school (22 boys, 28 girls, and two non-binary students). A mixed-methods approach with one group pre-posttest design was employed. Pre- and post-tests of AI knowledge and attitudes, analyzed through a two-way repeated measures ANOVA. Interviews with students and the teacher were conducted and analyzed using a thematic analysis approach. Findings indicated significant improvements in both AI knowledge and attitudes across students, with gender influencing attitudes but not knowledge. Qualitative data further revealed that hands-on and collaborative activities, contextualized to engage emotions and address ethical considerations, fostered students’ confidence and engagement, particularly among girls. These results highlight the potential of Constructionism–SEL-based approaches to expand AI learning opportunities in rural contexts, reduce gender disparities, and promote more sustainable AI education.</p>

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Expanding Access To AI Education for Middle School Students and Bridging Gender Gaps with Constructionism-Social Emotional Learning Approach

  • 期刊论文

  • 发表日期：

    2025年10月30日

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  • Keunjae Kim,
  • Kyungbin Kwon

摘要

This study designed an AI curriculum that integrated constructionist learning principles with Social-Emotional Learning (SEL) to address the limited opportunities that rural schools and female learners often face, resulting in persistent disparities in access and engagement. The curriculum was implemented with 52 middle school students in a rural U.S. school (22 boys, 28 girls, and two non-binary students). A mixed-methods approach with one group pre-posttest design was employed. Pre- and post-tests of AI knowledge and attitudes, analyzed through a two-way repeated measures ANOVA. Interviews with students and the teacher were conducted and analyzed using a thematic analysis approach. Findings indicated significant improvements in both AI knowledge and attitudes across students, with gender influencing attitudes but not knowledge. Qualitative data further revealed that hands-on and collaborative activities, contextualized to engage emotions and address ethical considerations, fostered students’ confidence and engagement, particularly among girls. These results highlight the potential of Constructionism–SEL-based approaches to expand AI learning opportunities in rural contexts, reduce gender disparities, and promote more sustainable AI education.