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REVIEW 4 major objections 5 minor 119 references

"How can we learn and use AI at the same time?": Participatory Design of GenAI with High School Students

T0 review · 4 major / 5 minor · reviewed 2026-08-06 · deepseek-v4-flash

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Pith's one-line read Seventeen high school students designed GenAI tools and policies, and their ideas form six concrete guidelines for educational technology builders.

desk verdict A candid, well-run PD study with six useful but not yet generalizable guidelines; send it to review, but ask for scope qualifiers. read the letter →

arxiv 2506.15525 v2 pith:MKNC3BBF submitted 2025-06-18 cs.HC

Isabella Pu , Prerna Ravi , Linh Dieu Dinh , Chelsea Joe , Caitlin Ogoe , Zixuan Li , Cynthia Breazeal , Anastasia K. Ostrowski This is my paper · ORCID
classification cs.HC
keywords GenerativeAIParticipatoryDesignHighSchoolStudentsinEducationQualitativeStudyEducationalTechnologyPolicyAcademicIntegrity
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

Seventeen high school students took part in a participatory design workshop where they named the problems generative AI creates in school and designed tools and policies to address them. The paper argues that their self-identified concerns—bias and misinformation, crime and plagiarism, over-reliance on AI, and false accusations of academic dishonesty—translate into six actionable guidelines for educational technology designers. Those guidelines cover consent-based data practices, transparent collaboration instead of surveillance, adaptive accessibility, teacher AI literacy built with students, balanced AI use that protects foundational skills, and third-party accountability. A sympathetic reader would care because students are the primary users of these tools yet are rarely included in their design or in school AI policy.

What carries the argument

The load-bearing mechanism is a seven-step participatory design workshop: an introduction and pre-survey, a GenAI primer, problem-space brainstorming with affinity diagramming (grouping sticky-note concerns into themes), guided GenAI tool design worksheets with role-played skits, a benefits-and-drawbacks discussion, school policy design, and a post-survey. The workshop carries the argument by generating student-created artifacts and transcripts that are then coded inductively, turning student voices into design guidelines rather than imposing researcher priorities.

What would settle it

Run the same workshop with a larger, demographically representative sample of public high school students, including students with no prior AI interest; if their problem spaces and tool and policy proposals diverge substantially from the six guidelines, the guidelines' generalizability claim is not supported.

Watch

Extended reading notes

Core claim

The authors claim that high school students, a group largely absent from prior generative-AI design research, can produce concrete and actionable design guidance when given a structured participatory space. In the workshop, student groups developed tools such as a browser extension that cross-checks AI answers against trusted sources, a personalized tutor that gives incremental hints rather than direct answers, and systems jointly regulated by libraries and government bodies, plus four school policies covering source legitimacy, teacher AI use, parental consent, and data deletion. From these artifacts and discussions the authors derive six guidelines for educational technology designers and argue that schools should formally involve students in AI policy development.

Load-bearing premise

The load-bearing premise is that the concerns and design preferences voiced by these 17 U.S. students—recruited through AI-interest mailing lists, mostly from private schools—represent high school students broadly, and that the workshop's GenAI primer and teacher-interview-derived scaffolding did not materially steer those concerns.

Editorial extensions

If this is right

  • GenAI tools built for high schools would include built-in source citations and user-controlled data deletion as standard features rather than optional extras.
  • Schools would rely less on AI detectors and more on transparent, collaborative tools that let students disclose their AI use, reducing false accusations of cheating.
  • EdTech developers would prioritize accessibility across low-end devices and limited internet connections over raw computational power.
  • Teacher AI literacy programs would pair formal training with student 'AI Ambassador' co-educators, sharing the burden of keeping up with the technology.
  • AI tutoring tools would give incremental hints and reflection prompts instead of direct answers, preserving foundational skills while building AI proficiency.

Reading between the lines

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

  • An implication the authors leave implicit is that the same preference for system-facing fixes, if stable across larger samples, would shift the academic-integrity debate away from policing students and toward changing how tools are built.
  • The six guidelines could be operationalized as a scored certification checklist; a natural next study would test whether independent raters, teachers, and students interpret each guideline consistently enough for the proposed 'Transparent AI Certification' to be enforceable.
  • Because the workshop's problem-space brainstorming was deliberately open-ended, re-running the workshop with the GenAI primer shortened or removed would isolate how much of the students' framing was shaped by the primer itself.
  • A testable extension is that students' demand for source citation can be turned into a product requirement: an AI tool that retrieves and displays verifiable sources alongside each claim, which could be compared with current AI-tool designs that do not provide citations.
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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

4 major / 5 minor

Summary. This paper reports a participatory design workshop with 17 U.S. high school students (ages 14–17), in which participants brainstormed concerns about GenAI in education, designed GenAI tools, and developed school policies. From the workshop data, the authors identify three themes (AI tool features, school/classroom use, regulations) and four student-designed policy proposals, and they derive six guidelines for educational technology designers. The paper argues that these guidelines are more actionable than existing UNESCO and U.S. Department of Education frameworks and that student voices should be included in school AI policy development. The study is positioned as addressing the underrepresentation of high school students in participatory design research on GenAI.

Significance. If the findings are transferable, the paper makes a useful contribution by foregrounding high school students' perspectives on GenAI, an underrepresented group in EdTech participatory design. The workshop design is well documented, the data analysis follows standard inductive coding with two independent coders and discrepancy resolution, and the paper transparently acknowledges its sample limitations in Section 6. The proposed guidelines are concrete and could provide a starting point for designers. However, the significance is tempered by the narrow, self-selected sample and by the fact that several guideline recommendations go beyond what the student data directly support.

major comments (4)
  1. [Abstract and §5.2] The abstract and Section 5.2 present the six guidelines as general designer-facing guidance for GenAI EdTech in high schools, without a scope delimiter such as 'for AI-interested U.S. students.' Yet Section 6 acknowledges that participants were recruited through AI/robotics mailing lists, 58.8% attended private schools, 16 of 17 had prior GenAI experience, and all were U.S.-based. This is load-bearing because the transferability of the guidelines to the broader high school population is not established. The claims should be tempered to reflect the sample's scope, or the paper should provide a clearly argued basis for why these specific concerns are likely to generalize.
  2. [§3.2 and §5.2.4] The workshop structure was shaped by themes from prior teacher interviews and included a GenAI presentation that covered example uses, benefits, risks, and strategies for exploring bias before the problem-space brainstorming. While facilitators avoided guiding discussion, this prior framing may have primed students to focus on particular problem spaces. More importantly, Guideline 4 recommends a peer-to-peer 'AI Ambassadors' model in which students help teachers learn AI, but the student data in Section 4.2 only show that students worried about teacher AI literacy and suggested that teachers take time-consuming classes; they did not propose peer teaching. This guideline appears to over-reach the data and needs either additional support from the transcripts or a clearly labeled inferential step.
  3. [§5.2.1] Guideline 1 recommends that GenAI tools include 'educational modules on data privacy' and 'regular prompts about data privacy.' In the findings, students emphasized consent, data deletion after graduation, and limiting data access (Section 4.3), but there is no quoted or reported student suggestion for educational modules or regular privacy prompts. If this recommendation is an author inference rather than a direct student value, it should be presented as such, with reasoning, rather than as something 'our findings revealed.' The same issue occurs in Guideline 3, where 'minimal internet requirement' and 'multilingual support' are recommended; Section 4.2 mentions cross-device accessibility but not offline functionality or multilingual support. These guidelines mix data-driven insights with designer judgment without separating the two.
  4. [§3.4 and §4.5] The paper describes an inductive coding process with two independent coders and discrepancy resolution, but it reports no inter-rater reliability metrics, such as Cohen's kappa or percent agreement. For a study whose central contribution is a set of themes and guidelines, the absence of any agreement measure makes it difficult to assess coding reliability. The survey results in Section 4.5 are also purely descriptive, with no statistical tests; this is acceptable for a small qualitative sample, but the paper should explicitly state that quantitative claims are limited to descriptive summaries and should avoid implying that pre-post changes are statistically significant.
minor comments (5)
  1. [§5.2.1] In Guideline 1, 'contexual knowledge' is a typo; it should read 'contextual knowledge.'
  2. [Table 1] In Table 1, the 'Previous AI Experience' row lists 'No Experience 1 5.9%' even though the text in Section 3.1 says '16 students had prior experience with GenAI,' so the table is internally consistent; however, the percentages in the table do not sum to 100% because respondents could select multiple options, which should be noted directly in the table caption.
  3. [§4.4.2 and §5.1.4] The sub-theme is inconsistently named 'The Roles of Teachers & Parents' in Table 2 but 'The Role of Teachers & Parents' in Section 4.4.2 and Section 5.1.4; please standardize the terminology.
  4. [§5.2.6] Guideline 6 introduces a 'Transparent AI Certification' program and an 'AI Accountability Dashboard' as concrete mechanisms, but the student data support a general desire for external oversight and distrust of centralized control. The leap from these preferences to a specific certification program should be more carefully flagged as a design proposal developed from, but not directly generated by, the student input.
  5. [§6] The limitations section is candid but could also mention that the workshop was a single-session event, so the durability of students' stated preferences and the stability of the derived guidelines over time are unknown.

Circularity Check

0 steps flagged · score 0.0 of 10

No circularity: the six guidelines are a transparent empirical synthesis of student workshop data, with no fitted parameters, self-referential predictions, or load-bearing self-citations.

full rationale

The paper's derivation chain is an empirical one: a 17-student participatory design workshop produces transcripts, sticky notes, worksheets, and surveys; inductive qualitative analysis yields themes; the discussion synthesizes six designer-facing guidelines that are explicitly 'directly informed by student values' (Section 5). There is no fitted parameter, no equation, and no prediction that is then confirmed by the same data. The guidelines are a summary of the student-generated problem spaces and proposed solutions, which is the stated method of participatory design rather than a circular reduction. The self-citations that appear ([5, 44, 61] for AI-literacy teaching materials, [30] for a related RAISE policy report, [80] for a qualitative coding method) are background or methodological resources; the guidelines themselves are justified by workshop quotes and artifacts, not by these citations. The paper's acknowledged limitations (recruitment via AI/robotics mailing lists, 58.8% private school participants, 16 of 17 with prior GenAI experience, U.S.-only sample) concern the generalizability and transferability of the guidelines to broader high-school populations; that is a validity and scope concern, not circularity. No step in the paper's argument reduces to its own inputs by construction, and no load-bearing result is imported solely from the authors' prior work. Therefore the circularity score is 0.

Assumptions & free parameters 0 free parameters · 3 assumptions · 0 invented entities

No free parameters or invented entities are involved; the paper is qualitative. The main axiomatic burden is the transferability of insights from a small, self-selected student sample to high school contexts at large, plus the assumption that the workshop did not unduly shape student opinions.

assumptions (3)
  • domain assumption The workshop elicits authentic student perspectives rather than responses shaped by the workshop framing or facilitator influence.
    Section 3.2 states facilitators avoided guiding students, but the presentation and teacher-interview-informed structure may prime certain topics. This premise is needed for the guidelines to reflect students' genuine views.
  • domain assumption The 17 participants are treated as informative for high school students despite being self-selected and not demographically representative.
    Section 6 acknowledges the sample is not generalizable, yet the guidelines in Section 5.2 are proposed for high school EdTech broadly. This is a standard interpretive step in qualitative research but remains an assumption.
  • domain assumption Qualitative inductive coding with researcher triangulation yields reliable themes.
    Section 3.4 describes independent review and discrepancy resolution but does not report inter-rater reliability metrics, so the reliability of the thematic coding is assumed.

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Cite this review

Pith. "Pith review of "How can we learn and use AI at the same time?": Participatory Design of GenAI with High School Students." pith.science (2026). https://pith.science/paper/MKNC3BBF

@misc{pith2026250615525,
  author       = {Pith},
  title        = {Pith review of: "How can we learn and use AI at the same time?": Participatory Design of GenAI with High School Students},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/MKNC3BBF}},
  note         = {Machine review of arXiv:2506.15525}
}
read the original abstract

As generative AI (GenAI) emerges as a transformative force, clear understanding of high school students' perspectives is essential for GenAI's meaningful integration in high school environments. In this work, we draw insights from a participatory design workshop where we engaged 17 high school students -- a group rarely involved in prior research in this area -- through the design of novel GenAI tools and school policies addressing their key concerns. Students identified challenges and developed solutions outlining their ideal features in GenAI tools, appropriate school use, and regulations. These centered around the problem spaces of combating bias & misinformation, tackling crime & plagiarism, preventing over-reliance on AI, and handling false accusations of academic dishonesty. Building on our participants' underrepresented perspectives, we propose new guidelines targeted at educational technology designers for development of GenAI technologies in high schools. We also argue for further incorporation of student voices in development of AI policies in their schools.

Figures

Figures reproduced from arXiv: 2506.15525 by the authors.

Figure 1
Figure 1. A collection of brainstormed ideas from students in the participatory design workshop. Students were asked to [PITH_FULL_IMAGE:figures/full_fig_p001_1.png] view at source ↗
Figure 2
Figure 2. Diagram depicting the structure of our participatory design workshop with high school students. [PITH_FULL_IMAGE:figures/full_fig_p005_2.png] view at source ↗
Figure 3
Figure 3. Spotlight on two of the four worksheets (Left: Problem Space Identification; Right: Adding AI) that guide students [PITH_FULL_IMAGE:figures/full_fig_p006_3.png] view at source ↗
Figures from the paper (2 more)
Figure 4
Figure 4. Figure 4: (a) An example worksheet output filled out by student participants. (b) Students engaging with the workshop activities. [PITH_FULL_IMAGE:figures/full_fig_p006_4.png]
Figure 5
Figure 5. Figure 5: Results from the question “How did this workshop help you think differently about generative AI tools for education?” [PITH_FULL_IMAGE:figures/full_fig_p011_5.png]

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Pith tools

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