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

TL;DR

This study explores how teens develop technical and socio-ethical understanding of generative language models through participatory design activities, revealing their conceptual pieces and framing their learning process.

Contribution

It provides evidence of teens' understanding components and introduces a theory-backed framework for studying novices' AI/ML comprehension.

Findings

Teens exhibit specific technical understandings of language models.

Teens demonstrate socio-ethical considerations in model construction.

The study offers a new framework for analyzing novice AI/ML understanding.

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.