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Exploring the Impact of Integrating STEM-AI Curriculum and Human-AI Collaboration Using Generative AI Tools in Middle School Settings /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Exploring the Impact of Integrating STEM-AI Curriculum and Human-AI Collaboration Using Generative AI Tools in Middle School Settings // Keunjae Kim.
Author:	Kim, Keunjae,
Description:	1 electronic resource (165 pages)
Notes:	Source: Dissertations Abstracts International, Volume: 86-12, Section: B.
Contained By:	Dissertations Abstracts International86-12B.
Subject:	Educational technology. -
Online resource:	https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=32046434
ISBN:	9798286431731

Exploring the Impact of Integrating STEM-AI Curriculum and Human-AI Collaboration Using Generative AI Tools in Middle School Settings /
Kim, Keunjae,

Exploring the Impact of Integrating STEM-AI Curriculum and Human-AI Collaboration Using Generative AI Tools in Middle School Settings /Keunjae Kim. - 1 electronic resource (165 pages)

Source: Dissertations Abstracts International, Volume: 86-12, Section: B.

The emergence of generative AI has introduced new opportunities for human-AI collaboration in K-12 education. However, several critical gaps remain underexplored, including limited understanding of how students engage with generative AI as collaborative learning partners, a lack of age-appropriate and integrated STEM-AI curricula, and insufficient emphasis on developing foundational competencies such as students' attitudes toward AI, ethical awareness, and the ability to create effective prompts. This mixed-methods study examined how students interacted with generative AI tools, including ChatGPT through a co-designed, integrated STEM-AI curriculum aimed at fostering AI attitudes and ethics, prompt engineering skills, and core competencies such as critical thinking and evaluation, collaboration and communication, critical problem solving, ethical and societal awareness, and reflective and metacognitive skills. Grounded in a design-based research approach, the study was conducted in two iterative phases using a one-group pre-posttest design. Pre- and post-test survey data were analyzed using repeated measures MANOVA and multiple linear regression analyses. Student-generated prompts were examined through integrative content analysis. At the same time, interview transcripts and classroom video recordings were analyzed using qualitative content analysis to identify emergent themes and interaction patterns.The study found that middle school students engaged with generative AI in diverse and meaningful ways within an integrated STEM-AI curriculum. Qualitative analyses revealed that students used AI primarily to explore multiple perspectives (40%), brainstorm ideas (35%), and refine arguments (25%), treating AI as a cognitive and collaborative partner. Students' prompt quality was significantly improved across dimensions, including role, context, tasks, and ethical consideration, which changed from exploratory and playful to strategic and goal-oriented approaches with ethically grounded inquiries. Students demonstrated varied interaction patterns with AI, question-based inquiry (69%), solution generation for school problems (57%), information-seeking (53%), and analytical reasoning (35%), with video analysis showing iterative refinement, emotional engagement, and critical validation through cross-referencing. Quantitative analyses using repeated measures MANOVA revealed significant improvements across all domains: AI attitudes and ethics, prompt engineering skills, and core competencies (critical thinking, collaboration, ethical awareness, and metacognition), with no significant interaction effects between time and domain. This indicates that the integrated STEM-AI curriculum facilitated balanced and evenly distributed learning gains, rather than favoring specific domains. Multiple linear regression analyses showed that AI accountability and privacy in ethical awareness were the strongest predictors of students' post-intervention core competencies, highlighting the importance of ethical awareness as a key factor for developing human-AI collaboration skills. While prompt engineering skills showed significant growth, they did not predict core competencies. It suggests that technical proficiency alone may be insufficient for deeper conceptual or collaborative learning in the human-AI collaboration approach. Affective factors such as motivation and confidence improved significantly. However, they were not direct predictors of competency gains, possibly serving a mediating or enabling role in student engagement with AI-supported tasks. Together, these findings highlight how generative AI can be integrated as a cognitive scaffold and a relational and dialogic partner in learning when meaningfully embedded in STEM instruction. Students moved beyond passive tool use and engaged with AI creatively and responsively, co-constructing knowledge through iterative prompting and validation. While students demonstrated significant growth in prompting skills technically, their AI accountability and privacy in ethical awareness most strongly predicted core competencies development, underscoring the foundational role of ethical awareness in developing human-AI collaboration skills. Additionally, students use AI relatively less commonly for analytical reasoning, highlighting the need for intervention and scaffolding to foster analysis and evaluation of AI-generated information when collaborating with AI. Although not directly predictive of learning outcomes, affective dispositions in motivation and confidence appeared to support sustained engagement and ethical reflection. These insights underscore the importance of tailored curriculum designs that emphasize affective dispositions and ethical awareness in AI-integrated learning, scaffolded prompt engineering, and opportunities for students to interact with AI as both collaborators and critical thinking partners.

English

ISBN: 9798286431731Subjects--Topical Terms:

517670
Educational technology.
Subjects--Index Terms:

Prompt engineering

Exploring the Impact of Integrating STEM-AI Curriculum and Human-AI Collaboration Using Generative AI Tools in Middle School Settings /
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520 $a The emergence of generative AI has introduced new opportunities for human-AI collaboration in K-12 education. However, several critical gaps remain underexplored, including limited understanding of how students engage with generative AI as collaborative learning partners, a lack of age-appropriate and integrated STEM-AI curricula, and insufficient emphasis on developing foundational competencies such as students' attitudes toward AI, ethical awareness, and the ability to create effective prompts. This mixed-methods study examined how students interacted with generative AI tools, including ChatGPT through a co-designed, integrated STEM-AI curriculum aimed at fostering AI attitudes and ethics, prompt engineering skills, and core competencies such as critical thinking and evaluation, collaboration and communication, critical problem solving, ethical and societal awareness, and reflective and metacognitive skills. Grounded in a design-based research approach, the study was conducted in two iterative phases using a one-group pre-posttest design. Pre- and post-test survey data were analyzed using repeated measures MANOVA and multiple linear regression analyses. Student-generated prompts were examined through integrative content analysis. At the same time, interview transcripts and classroom video recordings were analyzed using qualitative content analysis to identify emergent themes and interaction patterns.The study found that middle school students engaged with generative AI in diverse and meaningful ways within an integrated STEM-AI curriculum. Qualitative analyses revealed that students used AI primarily to explore multiple perspectives (40%), brainstorm ideas (35%), and refine arguments (25%), treating AI as a cognitive and collaborative partner. Students' prompt quality was significantly improved across dimensions, including role, context, tasks, and ethical consideration, which changed from exploratory and playful to strategic and goal-oriented approaches with ethically grounded inquiries. Students demonstrated varied interaction patterns with AI, question-based inquiry (69%), solution generation for school problems (57%), information-seeking (53%), and analytical reasoning (35%), with video analysis showing iterative refinement, emotional engagement, and critical validation through cross-referencing. Quantitative analyses using repeated measures MANOVA revealed significant improvements across all domains: AI attitudes and ethics, prompt engineering skills, and core competencies (critical thinking, collaboration, ethical awareness, and metacognition), with no significant interaction effects between time and domain. This indicates that the integrated STEM-AI curriculum facilitated balanced and evenly distributed learning gains, rather than favoring specific domains. Multiple linear regression analyses showed that AI accountability and privacy in ethical awareness were the strongest predictors of students' post-intervention core competencies, highlighting the importance of ethical awareness as a key factor for developing human-AI collaboration skills. While prompt engineering skills showed significant growth, they did not predict core competencies. It suggests that technical proficiency alone may be insufficient for deeper conceptual or collaborative learning in the human-AI collaboration approach. Affective factors such as motivation and confidence improved significantly. However, they were not direct predictors of competency gains, possibly serving a mediating or enabling role in student engagement with AI-supported tasks. Together, these findings highlight how generative AI can be integrated as a cognitive scaffold and a relational and dialogic partner in learning when meaningfully embedded in STEM instruction. Students moved beyond passive tool use and engaged with AI creatively and responsively, co-constructing knowledge through iterative prompting and validation. While students demonstrated significant growth in prompting skills technically, their AI accountability and privacy in ethical awareness most strongly predicted core competencies development, underscoring the foundational role of ethical awareness in developing human-AI collaboration skills. Additionally, students use AI relatively less commonly for analytical reasoning, highlighting the need for intervention and scaffolding to foster analysis and evaluation of AI-generated information when collaborating with AI. Although not directly predictive of learning outcomes, affective dispositions in motivation and confidence appeared to support sustained engagement and ethical reflection. These insights underscore the importance of tailored curriculum designs that emphasize affective dispositions and ethical awareness in AI-integrated learning, scaffolded prompt engineering, and opportunities for students to interact with AI as both collaborators and critical thinking partners.
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