AI Use Policy
Artificial Intelligence Use Policy. A framework for research with integrity, transparency and equity. Version 1.0 · 2026. Anglo Latin Culture 369 Corp. · Florida, United States.
Editorial note. This document is intentionally different from the policies that exist in the international academic publishing landscape. It does not arise from institutional comfort but from a conviction: the world of research has to change, and someone always has to take the first steps. There are thousands of brilliant researchers across the Americas whose transformative ideas are lost for lack of resources, time and access to tools. This policy is, among other things, a response to that reality. PanAmerican DH&AI does not apologize for that step.
1. Foundation and editorial philosophy
Artificial intelligence is now a reality in the research process. This is not an emerging trend: it is an established fact that the international scientific community recognizes, uses and, for the most part, practices in silence. A recent study in the Proceedings of the National Academy of Sciences (He & Bu, 2026) found that about 70% of the 5,114 journals it examined have some kind of AI policy, while in a full-text sample of 75,172 articles published after 2023 only about 0.1% (76 articles) disclosed the use of AI. The gap between real practice and official declaration is, in that context, a problem of scientific integrity, not of technology.
This journal has chosen a different path. Instead of prohibiting what cannot be verified, we have decided to build a framework that makes the declared use of AI an act of methodological rigor. Instead of treating AI as suspect by nature, we treat it as what it is: a tool that, when used well, extends the capabilities of the human researcher without replacing their thinking, their judgment or their responsibility.
There is also a dimension of justice that this policy recognizes explicitly. Scientific research has historically been a privilege of those with time, resources and infrastructure. A researcher at a well-funded university in the Global North can spend months reviewing literature, coding qualitative data or processing large-scale surveys. An equally brilliant researcher in Latin America, with limited access to databases, several teaching loads and no support team, cannot always compete on the same terms. Artificial intelligence does not close that gap, but it narrows it significantly. Ignoring that potential for reasons of institutional tradition is not prudence: it is conservatism disguised as rigor.
“Artificial intelligence needs brains to deliver excellent results. The researcher is the one who formulates the question, designs the methodology, critically evaluates the results and interprets their meaning. AI executes with speed what the researcher commands with intelligence.” Founding principle, PanAmerican DH&AI, 2026
2. The five principles of this policy
Principle I. AI is a methodological tool. This journal treats the use of artificial intelligence tools with the same rigor and the same normality as any other research instrument: statistical software, computer-assisted content analysis, automatic transcription, reference managers. None of those tools is questioned for its mere existence, and neither will AI be. The question this journal asks is not “was AI used?” but “how was it used, by what criteria, and what was the researcher’s critical contribution to its results?”
Principle II. The researcher is the only intellectual author and the only person responsible. No artificial intelligence tool may be named as author, co-author or official collaborator of an article published in this journal. This position is not negotiable and matches the international consensus of COPE, ICMJE, APA and Nature Portfolio. Intellectual and ethical responsibility lies entirely with the human author or authors, including responsibility for any error, bias or inaccuracy in the work, whether or not it was generated or processed with AI assistance. An author who declares the use of AI does not transfer responsibility: they confirm it. Some dimensions of the research process belong exclusively to the human researcher and no protocol of this journal changes that: the formulation of the research question, the theoretical positioning, the interpretation of what the findings mean, the conclusions, and the ethical, political and social implications of the work.
Principle III. Declaring the use of AI is an act of rigor, not a confession. This journal deliberately reverses the dominant narrative. Declaring the use of AI tools in a research process is not an admission of weakness, a confession of a shortcut or a sign of lower intellectual quality. In the framework of this publication it is a demonstration of methodological transparency, scientific confidence and commitment to the reproducibility of the research process. Articles that describe precisely and in detail how AI was used will be recognized for that rigor during the editorial evaluation. The journal’s reviewers are selected, among other criteria, for their understanding of technology-assisted research.
Principle IV. Epistemic equity as an editorial value. Artificial intelligence has a redistributive potential that this journal explicitly recognizes. It allows a researcher with limited resources to review thousands of articles systematically, analyze large corpora of interviews, process data from several countries and communicate findings in high-quality academic English, without any of those capabilities meaning delegating scientific thought. This journal sees that potential as an academic good: not an unfair advantage but a partial correction of the structural inequalities that have limited, for decades, the participation of Latin American researchers in the highest-impact international scientific conversations.
Principle V. Reflection on the use of AI is new knowledge. This journal is itself devoted to digital humanities and artificial intelligence. From that standpoint, methodological reflection on how AI was used in a research process is not a bureaucratic appendix: it is an original contribution to the field. Authors are invited, when pertinent, to include a reflection on what the use of AI allowed them to see that would otherwise have been invisible, on the biases it may have introduced, and on how it affected the speed, scope or depth of the analysis. This reflection is not mandatory, but it is welcome and valued editorially.
3. The three-level model
The use of artificial intelligence in research is not homogeneous. There is a substantial difference between using AI to improve a text’s grammar and using it to code two hundred in-depth interviews. This policy sets three levels of use, each with different declaration requirements. The central logic is simple: the more AI takes part in the intellectual process, the more precisely its use must be described.
Level 1. Assistive use. No declaration required. The use of AI tools to improve the formal quality of the text without altering its intellectual content. It includes grammar and spelling correction, improvement of style and clarity, adjustment of academic tone, automatic formatting of citations and references, and assisted translation of drafts. This level is functionally equivalent to using spell checkers, advanced word processors or writing assistants. It requires no declaration in the manuscript. Examples: Grammarly, DeepL, the word processor’s spell checker, ChatGPT to rewrite sentences.
Level 2. Methodological use. Declaration in the methods section. The use of AI as a documented methodological tool within the research process. The researcher defines the objectives, the analytical criteria and the theoretical frameworks; AI executes processing, classification or analysis tasks on the basis of those definitions. The researcher checks, validates and critically evaluates all results. The declaration must include the name of the tool, the version or model (when available), the specific task for which it was used and how the researcher validated the results. This information goes in the Methodology section of the manuscript, with the same level of detail used to describe any other analysis software. It includes, among others: assisted systematic literature review (Elicit, Consensus, ResearchRabbit), thematic analysis of interviews, inductive coding of qualitative data, discourse analysis, automatic transcription of interviews, extraction of categories, bibliometric analysis, assisted statistical modeling, data visualization and corpus analysis.
Level 3. Collaborative use. Declaration in a dedicated section. The cases in which artificial intelligence takes a substantial part in generating, synthesizing or structuring the intellectual content of the manuscript. The researcher keeps critical control, selection and final responsibility at all times, but the contribution of AI goes beyond executing discrete tasks. This level requires a separate section titled “Artificial Intelligence Use Statement”, placed before the references. It must describe: (a) the tools used, by name and version; (b) the specific tasks in which they took part; (c) the supervision, verification and critical editing carried out by the researcher; and (d) optionally, a reflection on how the use of AI affected the research process. It includes, among others: literature synthesis with generation of an argument, predictive modeling with assisted interpretation, generation of complex statistical analysis code, structuring of reports from extensive qualitative data.
4. Declaration protocol by type of research
4.1 Qualitative research: in-depth interviews. Analyzing in-depth interviews with the help of AI is one of the most powerful and best documented uses in the recent methodological literature, and this journal adopts the protocols developed in that literature as its frame of reference. Researchers who have used AI for thematic coding, discourse analysis or extraction of categories from interview transcripts must declare in the Methodology section: the AI tool used and its version; whether the transcripts were anonymized before being entered into the tool; the coding criteria or analytical frameworks the researcher defined beforehand; how the categories generated were validated (review by the lead researcher, triangulation, a second coder, etc.); and whether the process was iterative and how many rounds of review were carried out.
Non-negotiable requirement: anonymization of participants’ data. No interview transcript containing identifiable information about human participants may be entered into publicly accessible AI platforms without prior anonymization. This rule responds to fundamental ethical obligations of data protection and to the principle of confidentiality in research with human beings. The author is solely responsible for ensuring compliance.
4.2 Systematic literature review. Using AI tools to support literature review, including the initial screening of articles, the extraction of key data and the narrative synthesis, is a methodological use that this journal fully recognizes. The available tools (Elicit, Consensus, Semantic Scholar, ResearchRabbit, among others) allow an individual researcher to carry out reviews that would otherwise take whole teams months. The declaration must specify which tools were used, at what stage of the process (initial search, screening, extraction, synthesis) and how the researcher validated the results. Where possible, authors are encouraged to include the list of search terms given to the AI tool.
4.3 Quantitative analysis and data modeling. Using AI to generate statistical analysis code (Python, R), interpret model results or visualize data is a Level 2 methodological use. The researcher must declare which tool was used, for what task and how the validity of the code and of the results was checked. The researcher is responsible for the design of the model, the interpretation of the results and the conclusions drawn.
4.4 Academic writing in a second language. This journal has an explicit policy of linguistic equity. Using AI tools to improve the quality of the academic English of authors whose first language is Spanish, Portuguese or French is a Level 1 use and requires no declaration. The thinking, the argument and the research belong to the author. The tool of communication is an accessory, not a substitute for the intellect. This position rests on a principle of equity: no valuable research should be rejected or downgraded because its author has not mastered, since childhood, a language that is not their own.
5. Absolute limits
The following provisions are mandatory and admit no alternative interpretation:
| Provision | Reason |
|---|---|
| No AI tool may be named as author, co-author or official collaborator | Authorship carries legal and ethical responsibility that no AI system can assume. Universal consensus: COPE, APA, ICMJE, Nature |
| Personal or identifiable data of human participants may not be entered into AI tools without prior anonymization | Fundamental ethical obligation in research with human beings. Real risk of leakage on publicly accessible platforms |
| All factual information generated or retrieved with AI must be independently verified by the researcher | Language models can generate citations, data and references that do not exist. The researcher is ultimately responsible for accuracy |
| Undeclared Level 2 or Level 3 use is a violation of the editorial policy | Transparency is the foundation of scientific integrity. Undeclared use undermines the transparency this policy seeks to build |
| The research may not be presented as a method or finding of the AI | Conclusions, theoretical positioning and judgment about meaning are the exclusive responsibility of the human researcher |
6. Declaration of AI use at submission
Every manuscript submitted to PanAmerican DH&AI must include the AI use declaration as part of the Similarity and AI Form, a separate document attached at the time of submission. If no AI was used at any level, the author must say so explicitly in the corresponding field. In Part C of the form the author states the level of AI use (none; Level 1; Level 2; Level 3) and, for Levels 2 and 3, completes a table with the tool or model, version, stage of the process, specific task and validation method. In Part D the author confirms that no personal or identifiable data of participants was entered into AI tools without prior anonymization, that all factual information generated or retrieved with AI was independently verified, and that the author takes full responsibility for all the content of the manuscript, including any assisted by AI. The form can be downloaded from the journal’s Submissions page.
7. Context: why this framework matters for the Americas
Latin America faces a paradox: it has an extraordinary cultural, social and human wealth, with urgent problems that call for rigorous research and evidence-based solutions. At the same time, regional scientific output has historically been underrepresented in the highest-impact international publications. The reasons are many and well known: lack of funding, heavy teaching loads, limited access to international databases, language barriers, and the gap between the time rigorous research takes and the resources available to do it. A researcher who works under those limits is not less capable. They are more valuable, because their questions arise from contexts that the Global North does not know from the inside.
Artificial intelligence can act as a capability multiplier. It does not remove the need for critical thinking, theoretical knowledge, cultural sensitivity or ethical commitment. But it can compress weeks of routine work (systematic bibliographic review, interview transcription, initial coding of data) into hours. And that frees the researcher to do what no algorithm can: think in depth, interpret with context and propose solutions with wisdom.
The research published in this journal can become evidence for public policy in health, education, migration, the creative economy and technology governance. Those policies will change real lives. That they were produced with the help of artificial intelligence does not make them less rigorous. It makes them possible.
Reference. He, Y., & Bu, Y. (2026). Academic journals’ AI policies fail to curb the surge in AI-assisted academic writing. Proceedings of the National Academy of Sciences, 123(9), e2526734123. https://doi.org/10.1073/pnas.2526734123
