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REVIEW 3 major objections 5 minor 41 references

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

T0 review · 3 major / 5 minor · reviewed 2026-08-16 · deepseek-v4-flash

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Pith's one-line read A case study shows three teenagers successfully building a small generative language model, not just using one.

desk verdict A genuinely novel but carefully scoped case study of teens building a dataset and evaluating a small GPT; the 'building' claim overreaches, but the paper earns a serious read. read the letter →

arxiv 2504.14769 v1 pith:E4DD7J3N submitted 2025-04-20 cs.HC cs.CY

Luis Morales-Navarro , Daniel J. Noh , Yasmin B. Kafai This is my paper · ORCID
classification cs.HCcs.CY
keywords GPTlanguagemodelsyouthLLMscomputationalempowermentdatapracticesmachinelearningartificialintelligence
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

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

The reading

This paper reports a five-day workshop in which 35 ninth graders built very small generative language models, which the authors call 'babyGPTs', and then focuses on one three-person team that created a Marvel screenplay generator. The central claim is that the case demonstrates the feasibility of engaging youth as designers of GLMs, not merely as users: the three teenagers collected and curated screenplay scripts, tokenized them into an 80,050-token dataset, wrote prompt 'seeds', chose training durations, and then critiqued the model's outputs for missing structure and narrative coherence. Along the way they discussed whether it is acceptable to train on copyrighted screenplays and who deserves credit for generated text. The authors argue that this construction activity made the process of building a GLM transparent and allowed the youth to engage with data practices and ethical considerations concretely. If the feasibility claim holds, it suggests a path for AI literacy education that has young people building models rather than only interacting with them.

What carries the argument

The central object is the 'babyGPT' construction activity: a small generative language model that youth build by choosing a text domain, assembling a curated dataset of roughly 75,000 to 300,000 tokens, tokenizing it with a provided script, designing prompts as generation 'seeds', and submitting a training request specifying how long the model should train. The paper's analysis then codes video recordings and artifacts against an inventory of AI/ML data practices (cited as [38]) and a construction process framework (cited as [26]), which together supply the categories that turn the observed classroom activity into evidence that the youth engaged in data practices and ethical reasoning. The training itself is carried out by a researcher after the workshop using a lightweight GPT training framework (cited as [30]), so the machinery that carries the argument is the designed pipeline plus the analytic coding, not the model training itself.

What would settle it

A direct test would run the same workshop with the training step left to the youth themselves (for example, on unlocked computers with a simplified training interface) and observe whether they can complete it; a finding that they cannot, or a replication with a randomly selected group showing no spontaneous data-quality or ethical reasoning, would narrow or undercut the feasibility claim as stated.

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Extended reading notes

Core claim

On its own terms, the paper's discovery is that three 14- to 15-year-olds, given a scripted construction pipeline, can carry out the human parts of building a small generative language model. The team of Dillpickle, Optimus, and Cyclops selected Marvel screenplays, debated which films to include and exclude on quality grounds, ran a tokenization script, monitored the resulting token counts, requested a 5-minute and a 1-hour training run, and then evaluated the trained model's outputs, observing that the text lacked screenplay structure and a coherent narrative arc. When asked about using copyrighted scripts, they reasoned about copyright, attribution, and authorship, with one member imagining selling the model's script to Marvel and reflecting that the AI, not a human writer, produced it. The authors frame this as evidence that youth can engage in ML data practices—collecting data, controlling data quality, preparing data, and evaluating performance—and that constructing GLMs is a feasible route to AI/ML literacy.

Load-bearing premise

The load-bearing premise is that selecting, curating, and tokenizing a dataset, writing prompts, and evaluating outputs counts as 'building a GLM', even though the model training itself was performed by a researcher after the workshop rather than by the teenagers.

Editorial extensions

If this is right

  • If the feasibility claim holds, youth AI education has a working template: kids can meaningfully design small generative models through data curation, prompt design, and output evaluation even when the compute-heavy training step is handled by adults.
  • Construction activities of this kind can shift youth's stance toward generative AI from passive acceptance of outputs to critical examination—here, recognizing missing screenplay structure and narratively incoherent text.
  • Ethical questions of copyright and attribution arise naturally during dataset construction, which may make them more concrete and memorable than in abstract lessons.
  • The study motivates building easier-to-use tools for novices to train, validate, and fine-tune their own models, since the only barrier to hands-on training in this case was blocked terminal access at school.
  • It also indicates that youth's data practices are iterative and non-linear, which has design implications for how workshop time and scaffolding are structured.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • The paper's own evidence supports a narrower claim than the abstract's 'building GLMs': because the teenagers never ran the training, the demonstrated capability is building the data pipeline and evaluating outputs, not completing the full model-building loop. A stricter feasibility test would put the training step in the youth's hands.
  • The case was chosen for having the most complete data, so the demonstration likely represents a best case; whether typical groups engage this deeply is untested.
  • A testable extension would compare AI-literacy gains between youth who build babyGPTs and youth who only use commercial chatbots, measuring their later ability to explain how LLMs work or critique model outputs.
  • The youth's ethical reasoning, such as the scenario of selling a model-generated script to Marvel, suggests that building with copyrighted data can push adolescents toward nuanced thinking about derivative authorship; a future design could deliberately scaffold that into a fuller fair-use lesson.
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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

3 major / 5 minor

Summary. This paper reports a five-day workshop in which 35 ninth-grade students designed and built small generative language models ('babyGPTs') using the nanoGPT framework. The authors focus on a single team of three teenagers who created a Marvel-screenplay generator, curating and tokenizing a dataset of roughly 80,000 tokens, selecting prompts, submitting a training request, and evaluating the outputs of the resulting model. Using video recordings, artifacts, and saved files, the authors construct a descriptive case study and analyze it with Olari and Romeike's inventory of AI/ML data practices. They report that the youth engaged in data collection, data quality control, data preparation, and performance evaluation, and that they grappled with ethical issues around copyright, authorship, and attribution. The paper's central claim is that this case study demonstrates the feasibility of engaging youth in building GLMs, positioning them as designers rather than merely users of generative AI.

Significance. If the central claim is accepted, this paper makes a useful contribution to child-computer interaction and AI/ML education by extending constructionist approaches from classification tasks to generative language models. The strength of the paper lies in its rich qualitative data: the authors provide detailed vignettes of youth decision-making about dataset composition, their reasoning about copyright and attribution, and their critical evaluations of model outputs. The use of an external data-practices framework (Olari and Romeike) and the availability of artifacts (datasets, model files, outputs) lend transparency to the analysis. The paper also honestly reports the institutional constraint that prevented youth from performing training themselves (footnote 4). However, the framing of what constitutes 'building' a GLM is broader than what was actually demonstrated, and this overreach affects the paper's central feasibility claim. The result is a plausible and valuable exploratory case study whose scope needs to be stated more precisely.

major comments (3)
  1. [Abstract; Section 3.2, footnote 4; Section 5] The central claim that the case study 'demonstrates the feasibility of engaging youth in building GLMs' overreaches the observed activities. Although the youth curated and tokenized a dataset, chose prompts, and evaluated outputs, all model training was performed by a researcher after the workshop because school administrators blocked terminal access; the youth's only training-related action was selecting a training duration on a request form. The Discussion itself defers 'explore validation and training loss, adjust weights, or finetune pre-trained models' to future work, which concedes that these parts of the training loop were absent. The abstract should be reworded to claim feasibility of youth constructing datasets, specifying training requests, and evaluating outputs of a researcher-trained model, or the paper should explicitly define 'building' to include researcher-mediated training.
  2. [Section 3.3] The case was chosen because it had the most complete data (filled-in worksheets, saved datasets, attendance). The paper presents this as a feasibility demonstration without acknowledging that this is a best-case selection; readers cannot tell whether the observed engagement is typical or even achievable by other groups. The Discussion should explicitly state that the claim is limited to a best-case demonstration and that future work is needed to test generalizability.
  3. [Section 1 and Section 4] The introduction states that participants engaged in 'implementing a solution' as one of the data practices, but the Findings describe the youth running a provided tokenization script and filling out a training request form; they did not implement the model or training pipeline. This is internally inconsistent and should be corrected either by removing 'implementing a solution' from the list of observed practices or by explaining how tokenization and training requests constitute implementation in this context.
minor comments (5)
  1. [Section 3.2] The names 'Kaparthy' and 'Bathia' are misspelled; the references cite Andrej Karpathy and Aatish Bhatia.
  2. [Section 4, Figure 1] The text says 'see Figure 1' but the caption does not describe the output fragment; consider adding a caption that explains what is shown.
  3. [Section 3.1] The demographic description notes that all participants identified as White, but the paper does not discuss how this limits the generalizability of the findings; a sentence acknowledging this would be appropriate.
  4. [Title and Abstract] The title and abstract refer to 'babyGPTs' (plural), but the case study follows one team building one model; consider clarifying whether the workshop had multiple groups and that the case is a single exemplar.
  5. [Section 5] The sentence 'Our findings suggest that constructing GLMs should be an integral part of efforts to foster AI/ML literacies' is stronger than a single-case study supports; consider softening to 'may' or 'could'.

Circularity Check

0 steps flagged · score 0.0 of 10

No circularity found: the case study's qualitative findings are coded with an external data-practices framework and do not reduce to the paper's own inputs.

full rationale

No load-bearing circular step is present. The paper's central claim—that a case study demonstrates the feasibility of youth engaging in GLM construction—is supported by observational data (video, artifacts, dataset and output files) and analyzed through external frameworks: Olari and Romeike's inventory of ML data practices and Iversen et al.'s construction process. There is no parameter fitting, no fitted quantity relabeled as a prediction, and no uniqueness theorem imported from the authors' prior work. The one self-citation (Tseng et al. 2024, Ref. [41], which includes one of the present authors) appears in a background list of research outlining data practices and is not load-bearing; the actual analysis relies on Olari and Romeike [38], an external source. The researcher-mediated training reported in footnote 4 is a transparently disclosed scope limitation: youth designed datasets, tokenized text, chose training durations, and evaluated outputs, while a researcher executed the training after the workshop. This narrows the strength of the feasibility claim but does not make the analysis circular, because the observed youth practices are not defined in terms of the conclusion, nor is any result derived by construction from its own inputs. The choice of the group with the most complete data is standard exploratory case-study sampling and is acknowledged in Section 3.3; it bears on generalizability, not circularity. Overall, the derivation chain is self-contained and the findings are externally anchored.

Assumptions & free parameters 0 free parameters · 4 assumptions · 0 invented entities

This is a qualitative case study; there are no fitted numeric parameters. The central claim rests on interpretive assumptions: that data curation counts as model building even when training is done by researchers, that observing one purposively selected team supports a feasibility claim, and that prompted discussions reflect youth reasoning.

assumptions (4)
  • ad hoc to paper Youth activity described as 'building a GLM' can include dataset creation and training requests even when model training is performed by researchers.
    Footnote 4 discloses that researchers trained the models; the central feasibility claim depends on this interpretation of 'building'.
  • domain assumption A descriptive case study of a single team selected for the most complete data is sufficient evidence of feasibility.
    Section 3.3 states the selection criterion; the feasibility claim is generalized from one team without comparison cases.
  • domain assumption Olari and Romeike's inventory of AI/ML data practices provides a valid and comprehensive coding framework.
    Section 3.3 says the analysis relied on this external framework; if the framework misses relevant practices, the findings are partial.
  • domain assumption Ethical reasoning elicited by researcher prompts reflects the youth's genuine considerations.
    The findings include a researcher asking the team about permission; the paper treats the responses as evidence of ethical engagement.

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Cite this review

Pith. "Pith review of Building babyGPTs: Youth Engaging in Data Practices and Ethical Considerations through the Construction of Generative Language Models." pith.science (2026). https://pith.science/paper/E4DD7J3N

@misc{pith2026250414769,
  author       = {Pith},
  title        = {Pith review of: Building babyGPTs: Youth Engaging in Data Practices and Ethical Considerations through the Construction of Generative Language Models},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/E4DD7J3N}},
  note         = {Machine review of arXiv:2504.14769}
}
read the original abstract

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.

Figures

Figures reproduced from arXiv: 2504.14769 by the authors.

Figure 1
Figure 1. Fragment from output of the Marvel babyGPT model. [PITH_FULL_IMAGE:figures/full_fig_p005_1.png] view at source ↗

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