Developing Machine Learning Agency Among Youth - Characterizing Youth Critical Use, Examination, and Production of Machine Learning Applications - PhD Dissertation

Aug 4, 2026

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Innovations in machine learning and artificial intelligence are reshaping society, with major implications for youth socializing, education, and future careers. This qualitative single-case study examines how middle school youths at an afterschool center, supported by adults and peers, used, examined, and produced AI applications for social good. Using thematic analysis and quantitative ethnography across multiple data sources, findings show that critical exploration improved youth ability to design interest-based applications and expand computational thinking as they progressed from using to producing AI tools. Adult guidance supported facilitation, co-investigation, and debugging, while peer testing enabled ethical troubleshooting. The study recommends authentic hands-on agency-building environments for youth.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages227

Reading Time9 h 32 min

AccessFree to download

Abstract

Research questions and study focus

Methods and data sources

Key findings

Roles of adults and peers

Limitations and recommendations

Clemson University TigerPrints | All Dissertations | Dissertations | | --- | --- | | 5-2024
Developing Machine Learning Agency Among Youth: Characterizing Youth Critical Use, Examination, and Production of Machine Learning Applications
Ibrahim Oluwajoba Adisa
Clemson University, [iadisa@g.clemson.edu](iadisa@g.clemson.edu)
Follow this and additional works at: [https://tigerprints.clemson.edu/all_dissertations](https://tigerprints.clemson.edu/all_dissertations)
Part of the Artificial Intelligence and Robotics Commons, and the Educational Technology Commons | | Recommended Citation Adisa, Ibrahim Oluwajoba, "Developing Machine Learning Agency Among Youth: Characterizing Youth Critical Use, Examination, and Production of Machine Learning Applications" (2024) . All Dissertations. 3581. [https://tigerprints.clemson.edu/all_dissertations/3581](https://tigerprints.clemson.edu/all_dissertations/3581) This Dissertation is brought to you for free and open access by the Dissertations at TigerPrints. It has been accepted for inclusion in All Dissertations by an authorized administrator of TigerPrints. For more information, please contact [kokeefe@clemson.edu](kokeefe@clemson.edu). DEVELOPING MACHINE LEARNING AGENCY AMONG YOUTH: CHARACTERIZING YOUTH CRITICAL USE, EXAMINATION, AND PRODUCTION OF MACHINE LEARNING APPLICATIONS | A Dissertation Presented to the Graduate School of Clemson University | | --- | | In Partial Fulfillment of the Requirements for the Degree of Doctor of Philosophy Learning Sciences | | By
Ibrahim Oluwajoba Adisa
May 2024 | Accepted by: Golnaz Arastoopour Irgens, Ph.D., Committee Chair Danielle Herro, Ph.D., Committee Member Edmond P. Bowers, Ph.D., Committee Member Nathan McNeese, Ph.D., Committee Member ABSTRACT Innovations in machine learning and artificial intelligence (AI) are reshaping society, with profound implications for youth socializing, education, and future careers. As these technologies become increasingly integral to youth lives, engaging youth in understanding, questioning, and shaping them is crucial. This study explores how middle school-aged youths at an afterschool center utilized, examined, and produced AI applications for social good with the support of adults and peers. Specifically, I answered the following research questions: (1) How do youth design and develop machine learning models that align with their interests and values? (2) How do youth employ computational thinking skills when using and developing machine learning models? (3) How do youth interactions with adults and peers facilitate their development of agency around machine learning? The study employs a qualitative single case study design. It uses thematic analysis and quantitative ethnography methods to analyze data from multiple sources, including field notes, interviews, focus groups, story completions, video recordings, and artifacts. Findings indicated that engaging youths in critical exploration of AI tools enhanced their ability to design interest-based AI applications that provide solutions for healthcare problems, security, and accessibility. Moreover, the computational thinking practices that youth engaged in increased as they progressed from using AI tools to producing AI tools, with youth engaging in more comprehensive computational thinking skills during the production and sharing of AI media. Additionally, adult guidance played multiple roles, acting as facilitators, coinvestigators, and guides as they supported youth in exploring, producing, and debugging AI applications that address social good. Finally, peer testing emerged as a significant avenue for adult-youth collaboration, fostering participants' ability to troubleshoot and consider the ethical dimensions of AI. Limitations in the study included the spontaneous nature of the afterschool center and minor technical issues that limited the number of AI tools youth could explore. The study recommends promoting you

FAQs

What does the study investigate about youth and machine learning?

The study investigates how middle school youths utilize, examine, and produce AI applications for social good, including how they develop agency around machine learning with adult and peer support.

What research questions guide the dissertation?

The dissertation asks how youth design and develop machine learning models aligned with their interests and values, how they apply computational thinking when using and developing models, and how adult-peer interactions facilitate youth agency around machine learning.

Which factors most influenced youth learning and agency in using AI?

Critical exploration of AI tools, progression from using to producing AI tools, and adult guidance acted as facilitators and guides. Peer testing also supported collaboration and troubleshooting while prompting ethical consideration.

Aug 4, 2026

Upload by Ezra