arXiv subjects

Daniel J. Noh

Publications and source records attributed to Daniel J. Noh.

5 recordsLinked to original sources

arXiv2026

Understanding teens' self-beliefs when learning to construct and deconstruct AI/ML systems: Developing a survey instrument

Despite growing calls to foster AI literacy, there are few available survey instruments designed for children and youth that study computational empowerment alongside construction and deconstruction activities. In such activities, learners' beliefs about their abilities and attributes can impact their engagement. In this paper, we introduce and validate a survey instrument with constructs related to construction (creative expression and problem-solving self-beliefs) and deconstruction (auditing self-efficacy and fascination with auditing), along with more general self-beliefs related to design justice and the value of learning about AI/ML. We administered the instrument to 124 teenagers and assessed the six-factor structure of the instrument using confirmatory factor analysis. In addition to confirming the structure, we found that design justice beliefs strongly correlated with problem-solving, auditing self-efficacy, and creative expression.

cs.HC↗

arXiv2026

Building to Understand: Examining Teens' Technical and Socio-Ethical Pieces of Understandings in the Construction of Small Generative Language Models

The rising adoption of generative AI/ML technologies increases the need to support teens in developing AI/ML literacies. Child-computer interaction research argues that construction activities can support young people in understanding these systems and their implications. Recent exploratory studies demonstrate the feasibility of engaging teens in the construction of very small generative language models (LMs). However, it is unclear how constructing such models may foster the development of teens' understanding of these systems from technical and socio-ethical perspectives. We conducted a week-long participatory design workshop in which sixteen teenagers constructed very small LMs to generate recipes, screenplays, and songs. Using thematic analysis, we identified technical and socio-ethical pieces of understandings that teens exhibited while designing generative LMs. This paper contributes (a) evidence of the kinds of pieces of understandings that teens have when constructing LMs and (b) a theory-backed framing to study novices' understandings of AI/ML systems.

cs.HC↗

arXiv2026

"You Can Actually Do Something'': Shifts in High School Computer Science Teachers' Conceptions of AI/ML Systems and Algorithmic Justice

The recent proliferation of artificial intelligence and machine learning (AI/ML) systems highlights the need for all people to develop effective competencies to interact with and examine AI/ML systems. We study shifts in five experienced high school CS teachers' understanding of AI/ML systems after one year of participatory design, where they co-developed lessons on AI auditing, a systematic method to query AI/ML systems. Drawing on individual and group interviews, we found that teachers' perspectives became more situated, grounding their understanding in everyday contexts; more critical, reflecting growing awareness of harms; and more agentic, highlighting possibilities for action. Further, across all three perspectives, teachers consistently framed algorithmic justice through their role as educators, situating their concerns within their school communities. In the discussion, we consider the ways teachers' perspectives shifted, how AI auditing can shape these shifts, and the implications of these findings on AI literacy for both teachers and students.

cs.HC↗

arXiv2025

Building babyGPTs: Youth Engaging in Data Practices and Ethical Considerations through the Construction of Generative Language Models

As generative language models (GLMs) have gained popularity, youth are increasingly using them in their everyday lives. As such, most research has centered on supporting youth as users of GLM-powered systems. However, we know little of how to engage youth in the design of these models. Building on the rich legacy of child-computer interaction research that positions youth as designers of computing systems, we explore how to support young people in designing GLMs. Through a case study of three teenagers (ages 14-15) building a babyGPT screenplay generator, we illustrate how the team developed a model while engaging in artificial intelligence/machine learning-relevant data practices and addressing ethical issues. This paper contributes a case study that demonstrates the feasibility of engaging youth in building GLMs.

cs.HC↗

arXiv2025

Youth as Advisors in Participatory Design: Situating Teens' Expertise in Everyday Algorithm Auditing with Teachers and Researchers

Research on children and youth's participation in different roles in the design of technologies is one of the core contributions in child-computer interaction studies. Building on this work, we situate youth as advisors to a group of high school computer science teacher- and researcher-designers creating learning activities in the context of emerging technologies. Specifically, we explore algorithm auditing as a potential entry point for youth and adults to critically evaluate generative AI algorithmic systems, with the goal of designing classroom lessons. Through a two-hour session where three teenagers (16-18 years) served as advisors, we (1) examine the types of expertise the teens shared and (2) identify back stage design elements that fostered their agency and voice in this advisory role. Our discussion considers opportunities and challenges in situating youth as advisors, providing recommendations for actions that researchers, facilitators, and teachers can take to make this unusual arrangement feasible and productive.

cs.HC↗