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

2026-03-26

AI Summary

Amid the rapid proliferation of generative AI, adolescents often lack a deep understanding of its technical underpinnings and socioethical implications. This study addresses this gap by engaging 16 youth in a week-long participatory design workshop during which they constructed miniature generative language models to create recipes, scripts, and songs. Through this hands-on process, the research systematically investigates how participants developed conceptual and ethical understandings of AI/ML systems. The work identifies, for the first time, multidimensional elements of adolescents’ emerging comprehension during model construction and proposes a theoretical framework tailored to the cognitive development of AI/ML novices. Integrating micro-model building, participatory design, and thematic analysis, this study offers empirical grounding and a scalable pedagogical approach for AI literacy education.

Abstract

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.

Problem

• generative language models
• AI/ML literacies
• teen understanding
• socio-ethical understanding
• technical understanding

Innovation

• generative language models
• AI literacy
• constructionism
• socio-ethical understanding
• participatory design