Structured PREreview of The Confluence of Code and Cognition: An Analysis of Generative AI’s Impact on ADHD Diagnosis Trends Among High School Students in Northern California, 2022-2025

Does the introduction explain the objective of the research presented in the preprint?

Yes

Yes, the introduction of the research preprint clearly explains the objective by framing the study around a "complex and urgent area of inquiry" at the intersection of transformative technology and adolescent neurodevelopment. The central objective is explicitly stated as the main research question: "How has the rapid and widespread adoption of generative AI tools from 2022 to 2025 influenced the trends, presentation, and diagnostic assessment of ADHD among high school students in Northern California?". The introduction also highlights the specific focus on Northern California due to its unique position as a global technology hub with high rates of AI adoption and a historically low state-level ADHD diagnosis rate, making it a critical case study. Furthermore, the abstract, which introduces the paper, summarizes the objective as providing a comprehensive analysis of the impact of generative AI on the diagnosis of Attention-Deficit/Hyperactivity Disorder (ADHD) among high school students in Northern California during that period.

Are the methods well-suited for this research?

Highly appropriate

The methods employed in the preprint are well-suited for the stated research objective because the approach directly addresses the complex, qualitative influence of generative AI on the "presentation" and "diagnostic assessment" of ADHD, rather than attempting a simple quantitative measure of prevalence. Recognizing the "most substantial limitation" is the absence of recent, geographically specific empirical data on ADHD prevalence for the 2023–2025 period, the authors appropriately utilized an inferential and theoretical methodology. This involved a comprehensive literature review synthesizing three distinct domains; generative AI adoption, the ADHD diagnosis landscape in California, and emerging neuropsychological effects on cognition, to build a foundational understanding. The core method then involved an in-depth analysis that systematically explores three primary pathways through which AI complicates diagnosis: as a catalyst for symptom exacerbation, as a masking agent that obscures deficits, and as a direct disruptor of assessment validity. This systematic qualitative analysis, focused on Northern California as a critical case study due to its unique technological adoption rates, is the necessary and effective method for constructing a logic-based argument about the likely impacts on the diagnostic process, fulfilling the paper’s goal to analyze the profound and nuanced impact on the nature of ADHD diagnosis itself.

Are the conclusions supported by the data?

Highly supported

The conclusions presented in the preprint are supported by the systematic synthesis of existing literature and the construction of a detailed, inferential analysis, which is the methodology chosen to address the research objective. The central conclusion, that the primary impact of generative AI is on the fundamental nature of ADHD presentation, assessment, and diagnosis, rather than raw prevalence, is evidenced through the systematic exploration of three primary pathways. Evidence supports the conclusion that AI acts as a “masking agent”, as studies show students with greater executive function deficits utilize AI tools to organize work and structure assignments, artificially resolving functional impairments that would otherwise prompt a diagnosis. Additionally the conclusion that AI acts as a catalyst for symptom exacerbation is supported by neuropsychological research indicating that the interactive nature of AI engages dopamine reward pathways on a variable-ratio schedule, which can intensify reward-seeking behavior and reinforce the attention fragmentation central to ADHD, potentially pushing subclinical issues past a clinical threshold. Lastly, the conclusion regarding the disruption of assessment validity is supported by findings that teacher ratings of academic functioning can be artificially inflated by AI-assisted work, and emerging research demonstrates that generative AI can coach students on feigning ADHD symptoms during clinical evaluations, challenging traditional diagnostic reliability.

Are the data presentations, including visualizations, well-suited to represent the data?

Highly appropriate and clear

The data presentations described in the preprint, which rely on systematic literature synthesis and structured inferential analysis rather than quantitative visualizations, are well-suited to represent the research because the study's methodology is necessarily inferential and theoretical, constructing a "logic-based argument" about the complex impact of generative AI on the diagnostic process. The authors acknowledge the most substantial limitation is the absence of authoritative, geographically specific empirical data on ADHD prevalence for the 2023–2025 period; thus, traditional quantitative visualizations (charts, graphs) of prevalence rates would not be appropriate for the core findings. Instead, the data is effectively presented through a comprehensive literature review synthesizing three distinct domains; AI adoption, the ADHD diagnosis landscape, and neuropsychological effects followed by an in-depth, structured analysis that systematically explores the three primary pathways (AI as a catalyst, masking agent, and assessment disruptor). This systematic textual presentation adequately supports the conclusion that the impact is nuanced and qualitative, affecting the fundamental nature of ADHD presentation and assessment, rather than a simple shift in raw prevalence numbers

How clearly do the authors discuss, explain, and interpret their findings and potential next steps for the research?

Very clearly

The authors discuss, explain, and interpret their findings with clarity and depth by first establishing their central thesis that generative AI's primary impact is not on the raw prevalence of Attention-Deficit/Hyperactivity Disorder (ADHD) but on the fundamental nature of its presentation, assessment, and diagnosis, creating a profound paradox of symptom masking and exacerbation. This interpretation is systematically supported by explaining three pathways: AI as a catalyst(interpreting the interactive nature of AI as engaging dopamine reward pathways that can intensify impulsivity and attentional dysregulation), AI as a masking agent (explaining how AI provides an unprecedented scaffold that allows students to bypass core executive function deficits, thereby obscuring the functional impairment necessary for diagnosis), and AI as an assessment disruptor (interpreting research that shows AI can coach students on feigning symptoms and artificially inflate teacher ratings, compromising diagnostic reliability). The potential next steps for the research are clearly integrated into the discussion of implications, advocating for a necessary paradigm shift. For clinicians, the next step involves updating diagnostic protocols to include a "digital cognitive assessment" and a detailed history of generative AI use to accurately differentiate between masked, exacerbated, and induced symptoms. For educators, the authors recommend shifting pedagogy to "AI-resistant" assessments and expanding AI literacy curricula to include metacognitive training to prevent "cognitive over-reliance". Lastly, the authors clearly address limitations by calling for future longitudinal research to establish causality between AI use and ADHD symptoms and to acquire the currently absent, geographically specific empirical data on ADHD prevalence for the 2023-2025 period.

Is the preprint likely to advance academic knowledge?

Highly likely

The preprint is highly likely to advance academic knowledge because it addresses a complex and urgent area of inquiry: the intersection of transformative technology and adolescent neurodevelopment, by systematically analyzing generative AI’s influence on Attention-Deficit/Hyperactivity Disorder (ADHD) diagnosis. The research specifically focuses on Northern California as a "critical case study," leveraging its unique position as a global technology hub with high AI adoption rates and a historically low state-level ADHD diagnosis rate. Academic knowledge is advanced by shifting the focus from a simple quantitative measure of prevalence to a nuanced analysis of the "fundamental nature of its presentation, assessment, and diagnosis," thereby arguing that generative AI introduces a "profound confounding variable" into the diagnostic landscape. The paper achieves this by synthesizing findings from three distinct domains: generative AI adoption, the ADHD diagnosis landscape, and emerging neuropsychological effects to systematically explore how AI creates a complex duality, acting simultaneously as a masking agent that obscures deficits and a catalyst for symptom exacerbation

Would it benefit from language editing?

No

While the authors clearly discuss, explain, and interpret their complex findings with sufficient detail and structure, the systematic language and stylistic refinement provided by an editor are standard practices that enhance the final coherence and professional presentation of the work for publishers and a broader audience.

Would you recommend this preprint to others?

Yes, it’s of high quality

The preprint is highly recommended for researchers, clinicians, educators, and policymakers focused on the complex and urgent intersection of technology and adolescent neurodevelopment, as it successfully addresses the critical inquiry regarding generative AI’s influence on Attention-Deficit/Hyperactivity Disorder (ADHD) diagnosis trends

Is it ready for attention from an editor, publisher or broader audience?

Yes, after minor changes

The central argument and conclusions are robustly supported by the synthesis of neuropsychological and technological literature, making the content highly valuable; however, given that the document is explicitly labeled as an "Article Not peer-reviewed version" and an "early version of research outputs," it would likely benefit from the routine systematic language editing and refinement that precedes formal submission for publication.

Competing interests

The author declares that they have no competing interests.

Use of Artificial Intelligence (AI)

The author declares that they did not use generative AI to come up with new ideas for their review.