arxiv: 2604.10851 · v1 · submitted 2026-04-12 · 💻 cs.HC

Recognition: unknown

Participatory, not Punitive: Student-Driven AI Policy Recommendations in a Design Classroom

Kaoru Seki , Manisha Vijay , Yasmine Kotturi

Authors on Pith no claims yet

Pith reviewed 2026-05-10 14:57 UTC · model grok-4.3

classification 💻 cs.HC

keywords AI policyparticipatory designstudent governancegenerative AIdesign educationworkshop serieszine

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The pith

Student-driven workshops in a design classroom generate AI policies that expose double standards between students and faculty.

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

The paper examines a student-led process for developing university AI use guidelines instead of relying on top-down punitive rules written without input from those most affected. In a graduate design course, eight participants held discussions without faculty present, shared their actual AI practices, and produced ten policy recommendations that they turned into a zine distributed across campus. These recommendations identified issues such as the requirement that students disclose AI assistance while instructors operate under no parallel expectations. The authors present the workshops as a way to reduce student confusion and fear while building ownership over the rules that govern learning. They describe the methods used as adaptable to other settings where students could shape technology-related policies.

Core claim

Through a three-part workshop series in a graduate design course at a minority-serving university, where student leaders facilitated discussions without faculty present, eight participants shared candid accounts of their AI use, co-authored ten policy recommendations, and visualized them in a zine that circulated across campus. The resulting policies surfaced concerns absent from top-down governance, such as the double standard of requiring students to disclose or abstain from AI use while faculty face no such expectations.

What carries the argument

The student-facilitated workshop series without faculty present that enables candid accounts of AI use, co-authorship of ten policy recommendations, and creation of a zine for campus circulation.

If this is right

Policies created through student participation can include balanced accountability measures that apply equally to students and faculty.
The workshop format can lower student fear by allowing open discussion of actual AI practices rather than enforcing secrecy.
Circulating recommendations as a zine offers a concrete method for sharing student-generated policies with the wider campus community.
The approach provides a model for shifting from punitive enforcement to inclusive governance on technology use in education.

Where Pith is reading between the lines

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

Similar participatory workshops could be tested in undergraduate settings or STEM courses to check whether the same policy gaps appear.
Tracking whether participants continue to advocate for these recommendations after the course ends would test if the engagement benefit lasts beyond the workshops.
Administrators reviewing official AI policies could use the zine as direct evidence of student priorities when updating rules.

Load-bearing premise

That the concerns and strategies identified by eight participants in one graduate design course at a single minority-serving university are transferable and representative of student experiences across other disciplines and institutions.

What would settle it

If similar student-facilitated workshops at other universities or in non-design fields produce no comparable double-standard concerns and fail to generate engaged policy recommendations, the claim of transferable value would not hold.

Figures

Figures reproduced from arXiv: 2604.10851 by Kaoru Seki, Manisha Vijay, Yasmine Kotturi.

**Figure 1.** Figure 1: Ten graduate design students at a minority-serving university co-authored 10 policy recommendations through student [PITH_FULL_IMAGE:figures/full_fig_p001_1.png] view at source ↗

**Figure 2.** Figure 2: Ten student-drive AI policy recommendations derived from our three-part design workshop series [PITH_FULL_IMAGE:figures/full_fig_p003_2.png] view at source ↗

**Figure 3.** Figure 3: Overview of the three-part workshop series. [PITH_FULL_IMAGE:figures/full_fig_p006_3.png] view at source ↗

**Figure 4.** Figure 4: Workshop 1 focused on policy drafting through [PITH_FULL_IMAGE:figures/full_fig_p007_4.png] view at source ↗

**Figure 6.** Figure 6: The Student-Driven AI Policy Recommendation zine displayed outside of the research team’s lab (right), in the main [PITH_FULL_IMAGE:figures/full_fig_p009_6.png] view at source ↗

read the original abstract

Generative AI is reshaping education, yet most university AI policies are written without students and focus on penalizing misuse. This top-down approach sidelines those most affected from decisions that shape their everyday learning, resulting in confusion and fear about acceptable use. We examine how participatory, student-driven AI policy design can address this disconnect. We report on a three-part workshop series in a graduate design course at a minority-serving university in the U.S., where two student leaders facilitated discussions without faculty present. Eight participants shared candid accounts of their AI use, co-authored ten policy recommendations, and visualized them in a zine that circulated across campus. The resulting policies surfaced concerns absent from top-down governance, such as the double standard of requiring students to disclose or abstain from AI use while faculty face no such expectations. We argue that engaging students in AI governance carries value beyond the resulting policies, and offer transferable strategies for fostering participation across disciplines -- a model for calling students in rather than calling students

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.

Desk Editor's Note private letter to a colleague

This is a clear case report of one student-led workshop that produced ten AI policy recommendations and flagged a faculty-student disclosure double standard, but the transferability claims rest on an untested single-site sample.

read the letter

The paper walks through a three-part workshop series in a graduate design course at one minority-serving university. Two student leaders ran the sessions without faculty present, eight participants shared their AI experiences, and the group produced ten recommendations that they turned into a campus zine. The account is direct and includes specific outputs like the double standard on disclosure requirements, which top-down policies had overlooked. That part lands as a useful concrete example of what students notice when they get to set the terms. The process description also shows practical choices, such as keeping faculty out to encourage candor and using zine-making to circulate the results. Those details give readers something they can actually try in similar settings. The main limitation is scope. Eight self-selected participants from a single design course is a narrow slice, and the paper offers no comparative cases, replication steps, or analysis of how the recommendations were distilled from the discussions. The argument that this model supplies transferable strategies for other disciplines therefore stays at the level of suggestion rather than demonstrated result. Readers working on participatory design or university AI policy will find the case useful as an existence proof and a source of example recommendations. It is less likely to serve as a template without additional evidence. The work deserves peer review because the core description is grounded and the double-standard observation is worth recording, even if the broader claims need tighter bounds on what can be claimed from this one instance.

Referee Report

2 major / 1 minor

Summary. The paper presents findings from a three-part workshop series held in a graduate design course at a minority-serving university in the U.S. Two student leaders facilitated discussions among eight participants without faculty involvement, allowing candid sharing of AI use experiences. Participants co-authored ten policy recommendations and visualized them in a campus-circulated zine. The work critiques top-down punitive AI policies for sidelining students and missing key issues like disclosure double standards. It posits that student-driven participatory design in AI governance provides benefits beyond the policies and offers strategies transferable to other disciplines as a model for inclusive engagement.

Significance. If validated, the participatory approach could shift university AI policy development towards more inclusive practices, reducing student confusion and fear by incorporating affected voices. The identification of overlooked concerns, such as unequal expectations for AI disclosure, underscores the limitations of faculty-only governance. The paper's strength lies in its direct documentation of student outputs and the positive framing of participation. However, the asserted transferability across disciplines remains untested, which if addressed could enhance its influence on HCI education and AI ethics discussions.

major comments (2)

[Abstract] Abstract: The assertion that the described workshop offers 'transferable strategies for fostering participation across disciplines' is central to the paper's contribution but is not substantiated beyond the single case study of eight participants in one design course. No mechanisms for transfer, comparative data from other contexts, or limitations discussion on generalizability are provided, making this claim load-bearing yet unsupported.
[Workshop Description and Findings] Workshop Description and Findings: The manuscript reports the co-authored policy recommendations and surfaced concerns (such as the faculty-student disclosure double standard) but provides no description of the qualitative analysis methods, coding procedures, or validation steps used to derive these from the discussions. This absence undermines evaluation of how reliably the outcomes reflect participant input and support the contrast with top-down policies.

minor comments (1)

[Abstract] The phrase 'calling students in rather than calling students out' is introduced in the abstract without definition or prior reference, which may reduce clarity for readers outside specific educational discourse communities.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive feedback on our manuscript. The comments highlight important areas for strengthening the presentation of our single-case study and improving methodological transparency. We address each point below and will incorporate revisions in the next version.

read point-by-point responses

Referee: [Abstract] Abstract: The assertion that the described workshop offers 'transferable strategies for fostering participation across disciplines' is central to the paper's contribution but is not substantiated beyond the single case study of eight participants in one design course. No mechanisms for transfer, comparative data from other contexts, or limitations discussion on generalizability are provided, making this claim load-bearing yet unsupported.

Authors: We agree that the claim of transferability is not empirically substantiated beyond this single exploratory case. In revision, we will qualify the language in the abstract and conclusion to describe the strategies as 'potentially transferable' based on detailed documentation of the process, rather than asserting broad applicability. We will also add a limitations subsection explicitly discussing the single-institution, design-course context and the absence of comparative data, while outlining how future studies could test adaptation in other disciplines. This preserves the core contribution of the participatory model without overclaiming. revision: partial
Referee: [Workshop Description and Findings] Workshop Description and Findings: The manuscript reports the co-authored policy recommendations and surfaced concerns (such as the faculty-student disclosure double standard) but provides no description of the qualitative analysis methods, coding procedures, or validation steps used to derive these from the discussions. This absence undermines evaluation of how reliably the outcomes reflect participant input and support the contrast with top-down policies.

Authors: We acknowledge this omission in the current draft. The outcomes were generated through iterative, participant-led thematic synthesis during the workshops, including real-time note capture, group affinity mapping of discussion points, and consensus validation of the final ten recommendations and zine content. In the revised manuscript, we will insert a dedicated 'Data Analysis' paragraph in the Workshop Description section that details these steps, including how the disclosure double standard and other themes were identified directly from participant statements and cross-checked for fidelity to the discussions. This will allow readers to assess the grounding of the findings. revision: yes

Circularity Check

0 steps flagged

No circularity: direct reporting of single-case workshop findings

full rationale

The paper presents a qualitative case study of a three-part workshop series involving eight participants in one graduate design course. Its central claims about the value of student-driven AI policy design and transferable participation strategies are derived directly from participant accounts, co-authored recommendations, and zine outputs without any mathematical derivations, fitted parameters, self-referential equations, or load-bearing self-citations that reduce the reported results to the inputs by construction. The analysis remains self-contained as empirical description of the observed activities and surfaced concerns.

Axiom & Free-Parameter Ledger

0 free parameters · 2 axioms · 0 invented entities

This is a qualitative case study relying on assumptions about the effectiveness of peer facilitation for candid disclosure and the broader applicability of findings from a limited sample, without external validation data or benchmarks.

axioms (2)

domain assumption Peer-led facilitation without faculty presence elicits more candid student accounts of AI use than faculty-led sessions.
Stated in the workshop design description as enabling open sharing.
ad hoc to paper Insights from this single design-class workshop generalize to produce transferable strategies for other disciplines.
Used to argue for value beyond the specific policies and across fields.

pith-pipeline@v0.9.0 · 5472 in / 1308 out tokens · 54763 ms · 2026-05-10T14:57:32.599035+00:00 · methodology

discussion (0)

Reference graph

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