Presentation of the cardinal points of artificial intelligence use: AIs cannot be credited as authors; the responsibility for an academic work rests entirely and exclusively with the author; authors must apply critical thinking in the use of AI, and the significative use of AI must be declared.
Consequences of these cardinal points applied to the use of AI in the production of evaluative academic work. Presentation of the Veritas framework: verification,evaluation, attribution, and editing as a procedure for transforming and filtering AI-generated content before applying it in academic work. Presentation of staggered critical interaction as the philosophy behind Veritas for the ethical use of AI, but also for mitigating or eliminating cognitive debt.
Keywords
Generative artificial intelligence; evaluative academic work; ethics; Veritas; staggered critical interaction
Introduction
As is well known, in just a few years, the use of generative artificial intelligence in academic work has become almost universal, or is on its way to becoming so. Before proceeding, for the purposes of what follows, I must clarify that by academic work I mean
academic work that has an evaluative character.
To make it clear, I should point out that among the most representative examples of
these productions are master’s theses and doctoral dissertations. We can also easily
add (quite the contrary) undergraduate theses, which often exhibit remarkable quality.
The important thing, especially in the case of doctoral theses, is that the highest quality
academic works make very relevant contributions with both academic and social
implications.
Furthermore, precisely because of the evaluative nature I mentioned earlier, successful
completion of these programs leads to qualifications that enjoy widespread social
recognition. Specifically, the final master’s thesis results in a Master’s degree, and doctoral
dissertations in a Doctorate, thus opening up new professional opportunities for
those who hold these qualifications.
An additional characteristic of this type of academic degree is that society expects
them to be awarded fairly, that is, that the respective courts only grant them to those
who have truly deserved them through their effort and not, for example, through the
use of artificial intelligence replacing the author’s effort.
What I will discuss here therefore refers to this type of academic production. However, I
believe that much of what will be presented can be extrapolated to other formats of academic communication, such as scientific articles, since, albeit through different means,
they also constitute evaluative work in some way.
(…)
The cardinal points of AI ethics applied to academia
Those responsible for university degrees, from undergraduate to doctoral programs,
are well aware that, to this day, there are numerous guides and guidelines on the use of
artificial intelligence in academia, not only in evaluative work, but also (and above all) in
research and scientific publication in general (see the References section).
And this is the point I wanted to make. Anyone who has examined some of these guidelines or regulations will realize that they are either abstract in nature or, when they go
into detail, they leave areas that require interpretation.
Fortunately, the above does not preclude the emergence of a clear core of consensus
from a careful reading of these documents. This emerging consensus is evident in at
least the four cardinal points that will be presented (remember that a cardinal point is
not necessarily a geographical location). Furthermore, these points generally overlap
with the ethical guidelines of scientific journals.
I list them below in a way that I hope the reader interested in these topics will easily
recognize:
- No authorship. AI cannot be credited as either author or co-author. This lack of
authorship discourages citing AI as an authoritative source. Instead, citing the original
source is encouraged. - Responsibility. The responsibility for the content of an academic work rests entirely
with the author. - Critical thinking. Authors must use AI by applying their critical thinking skills to
detect “hallucinations”, avoid biases, and prevent errors or inconsistencies. - Transparency. The significant use of AI in academic work must be declared in a
transparent and traceable manner .
(…)
Full PDF
Recommended citation
Codina, Lluís (2025). Ethical and efficient use of artificial intelligence in academic work: Veritas and staggered critical interaction. BiD, 55. https://doi.org/10.1344/BID2025.55.01
Useful links
References
Aguilera-Cora, E., Lopezosa, C., Fernández-Cavia, J., & Codina, L. (2024). Accelerating research processes with Scopus AI: A place branding case study . Pan-American Journal of Communication, 6 (1). https://doi.org/10.21555/rpc.v6i1.3088
Bhavsar , D., Duffy, L., Jo, H., Lokker , C., Haynes , R., Iorio , A., Marušić , A., & Ng, J. (2024). Policies on artificial intelligence chatbots among academic publishers : a cross-sectional audit . Research Integrity and Peer Review , 10 . https://doi.org/10.1101/2024.06.19.24309148
Crompton, H., & Burke , D. (2023). Artificial intelligence in higher education : The state of the field . International Journal of Educational Technology in Higher Education , 20 (1). https://doi.org/10.1186/s41239-023-00392-8
Font-Julián, C .I., Orduña-Malea, E., & Codina, L. (2024). ChatGPT Search as a tool for scholarly tasks : Evolution or return ? Infonomy , 2(5). https://doi.org/10.3145/infonomy.24.059
Gao, R., Yu , D., Gao, B., Hua, H., Hui, Z., Gao, J., & Yin, C. (2025). Legal regulation of AI- assisted academic writing : challenges , frameworks, and pathways . Frontiers in Artificial Intelligence , 8 . https://doi.org/10.3389/frai.2025.1546064
Hosseini, M., Resnik , D., & Holmes, K. (2023). The ethics of disclosing the use of artificial intelligence tools in writing scholarly manuscripts . Research ethics , 19, 449 – 465. https://doi.org/10.1177/17470161231180449 .
Hosseini, M., Rasmussen, L.M., & Resnik , D.B. (2024). Using AI to write scholarly publications . Accountability in Research , 31(7), 715–723. https://doi.org/10.1080/08989621.2023.2168535
Kasani , P.H., Cho, K.H., Jang , J.-W., & Yun, C.-H. (2024). Influence of artificial intelligence and chatbots on research integrity and publication ethics . Science Editing . https://doi.org/10.6087/kcse.323
Koplin , J. 2023. » Plagiarism , Academic Ethics , and the Utilization of Generative AI in Academics Writing .» International Journal of Applied Philosophy . https://doi.org/10.5840/ijap2023372202
Mondal , H., Mondal , S., & Behera , J. (2024). Artificial intelligence in academia Writing : Insights from journal publishers ‘ guidelines . Perspectives in Clinical Research , 16, 56 – 57. https://doi.org/10.4103/picr.picr_67_24.
Penabad -Camacho, L., Morera-Castro, M., & Penabad -Camacho, MA (2024). Guidelines for the use and reporting of Artificial Intelligence in scientific-academic journals . Educare Electronic Journal , 28(2), 1-18.
Resnik , D., & Hosseini, M. (2024). The ethics of using artificial intelligence in scientific research: new guidance needed for a new tool . AI and Ethics , 5 , 1499–1521. https://doi.org/10.1007/s43681-024-00493-8
Resnik, D.B., & Hosseini, M. (2025). Disclosing artificial intelligence use in scientific research and publication: When should disclosure be mandatory, optional, or unnecessary? Accountability in Research, 1–13. https://doi.org/10.1080/08989621.2025.2481949
Torres-Salinas, Daniel, Arroyo-Machado, Wenceslao (2025). Teaching and researching with artificial intelligence: a call for reflection. BiD , 54. https://doi.org/10.1344/BID2025.54.01.
UNESCO. (2023, September 8). Guidance for generative AI in education and research. https://www.unesco.org/en/articles/guidance-generative-ai-education-and-research
Veiga, A. (2025). Ethical guidelines for the use of generative artificial intelligence and artificial intelligence-assisted tools in scholarly publishing : a thematic analysis . Science Editing . https://doi.org/10.6087/kcse.352
Yousaf , M. (2025). Practical Ethical Considerations Implications of Using Artificial Intelligence in Writing Scientific Manuscripts . ACG Case Reports Journal , 12 . https://doi.org/10.14309/crj.0000000000001629
Additional reference materials
- Drive folder with guides and regulations on the use of AI in academic work
- GAIDeT (Generative AI Delegation Taxonomy): A taxonomy for humans to delegate tasks to generate artificial intelligence in scientific research and publishing
- Summary of the evidence generated with Consensus
https://consensus.app/search/evaluate-this-reasoning-if-there-is-a-consensus-on/50bZB47bQ5CMKcTXy4cc_Q/ - Summary of the evidence generated with Undermind
https://app.undermind.ai/report/6ba39cde4e09765bc0ca85a0531ebd8ec5db440d55acdca4718c4f91f5d34573