Human-Centered Design: The Disclosure of Generative Artificial Intelligence for Emerging Professionals

Sydney Lee

arxiv: 2606.24136 · v1 · pith:UQZGJFXPnew · submitted 2026-06-23 · 💻 cs.HC

Human-Centered Design: The Disclosure of Generative Artificial Intelligence for Emerging Professionals

Sydney Lee This is my paper

Pith reviewed 2026-06-25 23:18 UTC · model grok-4.3

classification 💻 cs.HC

keywords human-centered designgenerative AIAI disclosureemerging professionalsAI transparencyworkflow augmentationdeskilling

0 comments

The pith

Structured AI use with advocate roles and disclosures in a design course allows transparent augmentation of workflows while preserving student autonomy.

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

The paper reflects on a human-centered design course where students completed a semester project using generative AI under explicit rules. An AI advocate role on each team, combined with milestone checkpoints and a required high-level disclosure report, forced justification for every AI step and required human feedback loops. The author reports that this setup produced AI-augmented processes that increased efficiency and supported co-creation without replacing deep work. If the pattern holds, similar structures could let emerging professionals adopt AI productively while keeping core design skills intact. The approach is presented as a practical response to minimal industry regulation on AI use.

Core claim

In the ITIS8300 course, assigning a generative AI advocate, enforcing milestone-based projects, and requiring detailed disclosure reports enabled safe, justified, and transparent AI integration that advanced human-centered design through augmented workflows, supported co-creation, and raised productivity while maintaining autonomy and avoiding deskilling.

What carries the argument

The course structure that assigns an AI advocate role, imposes milestone checkpoints, and mandates high-level disclosure reports to enforce justification and human feedback.

If this is right

AI-augmented workflows can increase productivity when every use is justified against human-centered criteria.
Co-creation between humans and AI becomes feasible once disclosure and feedback requirements are built into the process.
Transparent reporting reduces the incentive to hide AI use and thereby limits ethical and bias risks.
Clear boundaries around AI preserve the need for iterative human feedback in design work.

Where Pith is reading between the lines

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

The same advocate-and-disclosure pattern might transfer to non-educational design teams if organizations adopt comparable internal checkpoints.
Without repeated testing across multiple cohorts or longer time spans, it remains unclear whether the observed productivity gains persist once students enter unregulated workplaces.
Extending the model to include quantitative measures of design quality before and after AI use would strengthen claims about maintained autonomy.

Load-bearing premise

Positive results seen in one semester of one course with its specific roles and reporting rules will generalize to other emerging professionals and produce the claimed benefits of maintained autonomy.

What would settle it

A comparison study tracking early-career autonomy and skill retention between graduates of courses that used the advocate-plus-disclosure structure versus those that did not.

read the original abstract

As the Human centered design continues to grow, generative AI has the potential to streamline the research process by iterating tasks within established workflows to increase efficiency. However, integrating AI raises concerns surrounding ethical bias, complexity, and the lack of prioritization of humanistic values. Emerging professionals represent a cohort with the opportunity to learn Human Centered Design principles, yet without this foundation AI becomes more of a crutch than a tool, leading to reduced experience with deep work, decreased autonomy, and deskilling of key foundations. Disclosures are a common method to self report AI usage, but they provide little clarification on appropriate implementation and may encourage omission to avoid consequences. This paper reflects on experiences in the Human Centered Design course ITIS8300, which emphasized optimizing user experience, enhancing innovation and collaboration, and improving efficiency through iterative user feedback. A semester long project, structured through milestones and team roles including a generative AI advocate, resulted in a high level disclosure report detailing design processes, methodology, findings, and rationale for AI usage. The course offered freedom in execution while setting clear boundaries for incorporating human feedback, reinforcing justification for HCI workflows and encouraging transparent AI use. This approach mirrors an industry with minimal regulation, demonstrating that when AI usage is safe, justified, and transparent, it can significantly advance the field through AI augmented workflows and support co creation an increase productivity.

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

A single-course reflection on AI disclosure and team roles in one HCD class, with no data to back the claims about advancing the field or reducing deskilling.

read the letter

This paper describes how the author ran a semester project in ITIS8300 with an AI advocate role, milestones, and a high-level disclosure report that includes rationale for AI use while keeping human feedback as the limit.

It does a reasonable job spelling out those concrete elements. The advocate role and the requirement to justify AI choices in the report give a clear structure that other instructors could copy if they want to handle generative AI in student work.

The main issue is that the bigger claims rest on nothing beyond the author's own narrative of that one class. The statement that safe, justified, transparent AI use can significantly advance the field through augmented workflows, co-creation, and higher productivity has no numbers, no student feedback scores, no time or quality measures, and no comparison to a class without these features. The evaluation is circular by design.

The assumption that the approach prevents deskilling and preserves autonomy for emerging professionals is stated but not tested against any external benchmark or even within the course itself.

This is the kind of piece that might interest HCI or design educators who are writing their own AI policies for courses. It does not have the empirical grounding or analysis for a research journal.

I would not send it for peer review as a research paper. It reads like a teaching note that could go to an education-focused workshop or short article venue if they accept case descriptions.

Referee Report

3 major / 1 minor

Summary. The paper reflects on experiences implementing generative AI disclosure in the ITIS8300 Human-Centered Design course. It describes a semester project with milestones, an AI advocate team role, and high-level disclosure reports that detail AI usage rationale. The authors conclude that when AI usage is safe, justified, and transparent, it can significantly advance the field via augmented workflows, co-creation, and productivity gains while preserving autonomy and preventing deskilling.

Significance. If the described practices were shown to produce measurable benefits, the work could offer a pedagogical model for ethical AI integration in HCI education and practice. The current manuscript provides only a single reflective case, so its potential significance remains limited to illustrative rather than evidentiary value.

major comments (3)

[Abstract] Abstract: the claim that the approach 'can significantly advance the field through AI augmented workflows and support co creation an increase productivity' is unsupported by any quantitative data, task metrics, iteration counts, quality ratings, or controlled comparisons.
[Course project description] The reported benefits of the disclosure method and AI advocate role are drawn directly from the same ITIS8300 course narrative, with no independent external benchmark, separate validation cohort, or falsifiable outcome measures.
[Abstract] The generalization that positive outcomes from this specific project structure (milestones, team roles, human feedback boundaries) will extend to other emerging professionals and reduce deskilling is asserted without evidence of broader testing or applicability.

minor comments (1)

The manuscript would benefit from explicit separation between descriptive course elements and any prescriptive claims for the wider field.

Simulated Author's Rebuttal

3 responses · 0 unresolved

We thank the referee for the detailed feedback. The manuscript is a reflective case study of pedagogical practices in a single course rather than an empirical evaluation, and we will revise the abstract, add a limitations section, and adjust claims to ensure they remain within the scope of the available evidence.

read point-by-point responses

Referee: [Abstract] Abstract: the claim that the approach 'can significantly advance the field through AI augmented workflows and support co creation an increase productivity' is unsupported by any quantitative data, task metrics, iteration counts, quality ratings, or controlled comparisons.

Authors: We agree that the manuscript provides no quantitative data, metrics, or controlled comparisons to support claims of significant field advancement or productivity gains. The work is a reflective account of course experiences. In revision we will remove or substantially qualify these claims in the abstract, limiting the text to describing the observed practices and their rationale within the specific course context. revision: yes
Referee: [Course project description] The reported benefits of the disclosure method and AI advocate role are drawn directly from the same ITIS8300 course narrative, with no independent external benchmark, separate validation cohort, or falsifiable outcome measures.

Authors: This characterization is accurate. All reported observations originate from the single ITIS8300 implementation. We will add an explicit limitations section stating that the described benefits lack independent benchmarks, validation cohorts, or falsifiable quantitative measures, and that the account is illustrative rather than evidentiary. revision: yes
Referee: [Abstract] The generalization that positive outcomes from this specific project structure (milestones, team roles, human feedback boundaries) will extend to other emerging professionals and reduce deskilling is asserted without evidence of broader testing or applicability.

Authors: We accept that the manuscript asserts broader applicability and deskilling prevention without evidence from additional settings or testing. The revised version will remove these generalizations and present the project structure solely as one concrete example implemented in ITIS8300. revision: yes

Circularity Check

0 steps flagged

No significant circularity; reflective case study with interpretive generalization

full rationale

The paper is a qualitative reflection on a single semester course (ITIS8300) that describes its structure (milestones, AI advocate role, disclosure reports, human feedback boundaries) and draws conclusions about benefits for AI-augmented workflows. No equations, fitted parameters, predictions of related quantities, or self-citations appear in the provided text. The central claim is presented as an interpretive demonstration from the described experience rather than a derivation that reduces to its inputs by construction. This matches the expected non-finding for reflective, non-mathematical papers that do not invoke uniqueness theorems or smuggle ansatzes.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

The paper is an experiential reflection on educational practice with no formal parameters, derivations, or new entities; it rests on the domain assumption that human-centered values must be actively protected against AI efficiency pressures.

axioms (1)

domain assumption Human-centered design requires prioritizing humanistic values and iterative human feedback over unchecked AI-driven efficiency.
Stated as the core concern motivating the course structure and disclosure requirements throughout the abstract.

pith-pipeline@v0.9.1-grok · 5763 in / 1279 out tokens · 36186 ms · 2026-06-25T23:18:15.503492+00:00 · methodology

discussion (0)

Reference graph

Works this paper leans on

5 extracted references · 4 canonical work pages

[1]

Chilana, Amy J

Parmit K. Chilana, Amy J. Ko, and Jacob Wobbrock. 2015. From User-Centered to Adoption-Centered Design: A Case Study of an HCI Research Innovation Becoming a Product. InProceedings of the 33rd Annual ACM Conference on Human Factors in Computing Systems(Seoul, Republic of Korea)(CHI ’15). Association for Computing Machinery, New York, NY, USA, 1749–1758. d...

work page doi:10.1145/2702123.2702412 2015
[2]

Interaction

Kasper Hornbæk, Aske Mottelson, Jarrod Knibbe, and Daniel Vogel. 2019. What Do We Mean by “Interaction”? An Analysis of 35 Years of CHI.ACM Trans. Comput.-Hum. Interact.26, 4, Article 27 (July 2019), 30 pages. doi:10.1145/3325285

work page doi:10.1145/3325285 2019
[3]

Jakob Nielsen and Rolf Molich. 1990. Heuristic evaluation of user interfaces. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (Seattle, Washington, USA)(CHI ’90). Association for Computing Machinery, New York, NY, USA, 249–256. doi:10.1145/97243.97281

work page doi:10.1145/97243.97281 1990
[4]

Hauke Sandhaus, Kashif Imteyaz, Mohammed Almutairi, Pooja Prajod, Divya Ramesh, Saiph Savage, Qian Yang, and Michael Muller. 2026. Interrogating GenAI Augmentation for CHIworkers: Strategies for Professional Autonomy and Ac- countability. InCHIWork ’26: Workshop Proceedings(Linz, Austria). Workshop at CHIWork 2026, June 22, 2026, Linz, Austria. https://ch...

2026
[5]

Dennis Wixon, Karen Holtzblatt, and Stephen Knox. 1990. Contextual design: an emergent view of system design. InProceedings of the SIGCHI Conference on Human Factors in Computing Systems(Seattle, Washington, USA)(CHI ’90). Association for Computing Machinery, New York, NY, USA, 329–336. doi:10.1145/97243.97304

work page doi:10.1145/97243.97304 1990

[1] [1]

Chilana, Amy J

Parmit K. Chilana, Amy J. Ko, and Jacob Wobbrock. 2015. From User-Centered to Adoption-Centered Design: A Case Study of an HCI Research Innovation Becoming a Product. InProceedings of the 33rd Annual ACM Conference on Human Factors in Computing Systems(Seoul, Republic of Korea)(CHI ’15). Association for Computing Machinery, New York, NY, USA, 1749–1758. d...

work page doi:10.1145/2702123.2702412 2015

[2] [2]

Interaction

Kasper Hornbæk, Aske Mottelson, Jarrod Knibbe, and Daniel Vogel. 2019. What Do We Mean by “Interaction”? An Analysis of 35 Years of CHI.ACM Trans. Comput.-Hum. Interact.26, 4, Article 27 (July 2019), 30 pages. doi:10.1145/3325285

work page doi:10.1145/3325285 2019

[3] [3]

Jakob Nielsen and Rolf Molich. 1990. Heuristic evaluation of user interfaces. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (Seattle, Washington, USA)(CHI ’90). Association for Computing Machinery, New York, NY, USA, 249–256. doi:10.1145/97243.97281

work page doi:10.1145/97243.97281 1990

[4] [4]

Hauke Sandhaus, Kashif Imteyaz, Mohammed Almutairi, Pooja Prajod, Divya Ramesh, Saiph Savage, Qian Yang, and Michael Muller. 2026. Interrogating GenAI Augmentation for CHIworkers: Strategies for Professional Autonomy and Ac- countability. InCHIWork ’26: Workshop Proceedings(Linz, Austria). Workshop at CHIWork 2026, June 22, 2026, Linz, Austria. https://ch...

2026

[5] [5]

Dennis Wixon, Karen Holtzblatt, and Stephen Knox. 1990. Contextual design: an emergent view of system design. InProceedings of the SIGCHI Conference on Human Factors in Computing Systems(Seattle, Washington, USA)(CHI ’90). Association for Computing Machinery, New York, NY, USA, 329–336. doi:10.1145/97243.97304

work page doi:10.1145/97243.97304 1990