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arxiv: 2603.25852 · v3 · submitted 2026-03-26 · 💻 cs.HC · cs.CY

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

Luis Morales-Navarro , Daniel J. Noh , Lucianne Servat , Carly Netting , Yasmin B. Kafai , Dana\'e Metaxa This is my paper

Pith reviewed 2026-05-14 23:54 UTC · model grok-4.3

classification 💻 cs.HC cs.CY
keywords teensgenerative language modelsAI literacyconstruction activitiessocio-ethical understandingstechnical understandingsparticipatory designworkshop
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The pith

Teens develop technical and socio-ethical understandings when constructing small generative language models.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

Sixteen teenagers took part in a week-long participatory design workshop where they built very small generative language models to produce recipes, screenplays, and songs. Thematic analysis of their activities revealed specific technical pieces of understanding, such as how models learn from data and generate outputs, alongside socio-ethical pieces including biases, fairness, and representation issues. A sympathetic reader would care because these construction activities may support the development of balanced AI literacies as generative technologies spread among young people. The work also supplies a theory-backed framing for investigating how novices come to understand AI and ML systems more broadly.

Core claim

The paper claims that teens exhibit identifiable technical understandings about model training, data, and generation processes and socio-ethical understandings about biases and societal implications while designing very small generative LMs, and that these pieces of understanding can be captured through thematic analysis of participatory construction activities.

What carries the argument

Pieces of understandings, discrete technical and socio-ethical insights identified via thematic analysis of teenagers' design and building activities with very small generative language models.

If this is right

  • Construction activities allow teens to develop concrete technical knowledge of how generative language models are trained and produce text.
  • Socio-ethical issues such as bias in data and fairness in outputs become visible to teens through the act of building the models.
  • Participatory workshops provide an effective route to AI literacies that combine technical function with ethical awareness.
  • A theory-backed framing can guide systematic examination of how novices acquire understandings of AI and ML systems.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • Workshops of this kind could be adapted for school settings to introduce AI concepts through direct building rather than lectures alone.
  • The same construction method might surface comparable understandings when applied to other generative AI tools beyond language models.
  • Tracking participants over months or years could test whether the understandings influence how teens later evaluate or use commercial AI systems.

Load-bearing premise

The week-long workshop tasks and thematic analysis surface representative technical and socio-ethical understandings without major distortion from the specific activities, small sample, or researcher interpretations.

What would settle it

A follow-up study with a larger or differently composed group of teens that finds no distinct technical or socio-ethical pieces of understanding emerge during comparable model-construction tasks.

Figures

Figures reproduced from arXiv: 2603.25852 by Carly Netting, Dana\'e Metaxa, Daniel J. Noh, Lucianne Servat, Luis Morales-Navarro, Yasmin B. Kafai.

Figure 1
Figure 1. Figure 1: Workshop activities by day. Manuscript submitted to ACM [PITH_FULL_IMAGE:figures/full_fig_p009_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: Screenshots of the TV Meals recipe generator designed by Dunkin Lover and Darwin (left) and the Transformer Script generator [PITH_FULL_IMAGE:figures/full_fig_p012_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: Connections between technical pieces (blue edges), connections between socio-ethical pieces (red edges), and connections [PITH_FULL_IMAGE:figures/full_fig_p019_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: Examples of connected technical pieces (left), connected socio-ethical pieces (center), and the connection between a technical [PITH_FULL_IMAGE:figures/full_fig_p019_4.png] view at source ↗
read the original 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.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 1 minor

Summary. The paper reports on a week-long participatory design workshop with sixteen teenagers who constructed very small generative language models for generating recipes, screenplays, and songs. Through thematic analysis of the workshop data, the authors identify technical and socio-ethical pieces of understandings exhibited by the teens during construction and contribute (a) empirical evidence of these understandings and (b) a theory-backed framing for studying novices' AI/ML understandings.

Significance. If the thematic analysis proves robust and the findings can be shown to generalize beyond the specific tasks and small sample, the work would offer valuable empirical grounding for constructionist approaches to AI literacy in teens, strengthening the evidence base in child-computer interaction and informing educational interventions that address both technical and socio-ethical dimensions of generative models.

major comments (2)
  1. [Methods] Thematic analysis description (abstract and methods): no details are supplied on the coding process, inter-rater reliability, data saturation, member checking, or how themes were validated against the raw data. This absence directly undermines assessment of the reliability of the identified technical and socio-ethical pieces of understandings.
  2. [Results] Central claim (abstract and results): the attribution of observed understandings to the act of constructing small LMs rather than to the fixed tasks (recipes, screenplays, songs), workshop scaffolding, or group dynamics lacks supporting controls or comparison conditions, leaving open the possibility that different prompts or a larger sample would yield substantially different themes.
minor comments (1)
  1. [Abstract] Abstract: the sample size (16) and workshop duration (one week) are mentioned but could be stated more prominently to allow readers to immediately gauge scale.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for their constructive feedback, which highlights important areas for strengthening the manuscript. We address each major comment below and indicate the revisions we plan to make.

read point-by-point responses

  1. Referee: [Methods] Thematic analysis description (abstract and methods): no details are supplied on the coding process, inter-rater reliability, data saturation, member checking, or how themes were validated against the raw data. This absence directly undermines assessment of the reliability of the identified technical and socio-ethical pieces of understandings.

    Authors: We agree that the methods section requires substantially more detail on the thematic analysis to allow readers to evaluate the reliability of the findings. In the revised manuscript, we will expand this section to describe the full coding process (including inductive code development from the data), inter-rater reliability procedures and scores, assessment of data saturation, member checking with participants where applicable, and the steps taken to validate emergent themes against the raw transcripts and artifacts. revision: yes

  2. Referee: [Results] Central claim (abstract and results): the attribution of observed understandings to the act of constructing small LMs rather than to the fixed tasks (recipes, screenplays, songs), workshop scaffolding, or group dynamics lacks supporting controls or comparison conditions, leaving open the possibility that different prompts or a larger sample would yield substantially different themes.

    Authors: We acknowledge that the study is exploratory and single-condition, with no control or comparison groups to isolate the effects of construction from the specific tasks, scaffolding, or group interactions. We will revise the abstract, results, and discussion to temper the attribution language, explicitly frame the findings as exploratory evidence within a constructionist lens, and add a dedicated limitations subsection discussing these factors and the need for future comparative studies. revision: partial

Circularity Check

0 steps flagged

No circularity detected in qualitative empirical study

full rationale

This paper reports results from a week-long participatory workshop with 16 teens using thematic analysis to surface technical and socio-ethical understandings during small LM construction. No equations, fitted parameters, derivations, or predictions exist that could reduce to inputs by construction. The contribution rests on direct empirical observation rather than any self-citation chain, uniqueness theorem, or ansatz smuggling. The study is self-contained against external benchmarks as a standard qualitative HCI report with no load-bearing circular steps.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

The central claim rests on the domain assumption that thematic analysis of construction activities can extract genuine pieces of understanding and that constructionist theory applies directly to small generative LMs. No free parameters or invented entities are introduced.

axioms (1)
  • domain assumption Thematic analysis of workshop observations reliably identifies technical and socio-ethical pieces of understandings in teens.
    The paper uses thematic analysis to derive its evidence without specifying validation steps in the abstract.

pith-pipeline@v0.9.0 · 5489 in / 1198 out tokens · 46279 ms · 2026-05-14T23:54:30.572121+00:00 · methodology

discussion (0)

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Reference graph

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