Rejecting both uncritical enthusiasm and technological panic, she sees AI not as a threat to academia, but as a mirror reflecting long-standing issues: gaps in methodological training, unresolved questions of authorship, and a culture that still treats scientific integrity as a bureaucratic protocol rather than a core value.

The arrival of AI tools in everyday research has prompted organizations such as the Brazilian National Council for Scientific & Technological Development (CNPq) to publish unprecedented regulatory guidelines—a sign that the technology is already part of academic practice and demands clear governance standards.

For Scussel, the greatest risk is not the technology itself, but researchers who delegate essentially intellectual tasks to machines.

In this interview with Science Arena, she explains how to select AI tools critically, why cross-validation is nonnegotiable, and what separates responsible AI use from careless application.

Science Arena — What are the biggest practical challenges researchers face when using AI without compromising the quality of their work?

Fernanda Scussel – AI has highlighted fundamental problems in academia that already existed but were neglected, such as questions of authorship, authenticity, and weaknesses in how research methodology is taught in Brazil.

The challenge is not only using the tool ethically, but also examining academic culture as a whole.

The focus needs to shift away from the technology itself and back to the research process. In other words, AI should be viewed as a tool for specific stages of research—not as a magical solution capable of writing dissertations or reading papers on its own. 

Many researchers feel overwhelmed by the sheer number of tools available. What criteria do you use to determine what is actually useful?

The main criterion is suitability for the task. Researchers should first define the problem they need to solve and only then choose the technology. Another important factor is testing: I recommend exploring no more than three or four tools and focusing on the one you feel most comfortable using.

Good results come from making the most of a specific tool, because consistent use enables machine learning and allows the model to better understand the user’s needs over time.

What validation strategies are paramount to ensure that a tool is trustworthy?

We need to understand that AI platforms are companies, and they contain what I call a “flattering element” designed to encourage engagement—they want to please you.

That means researchers must develop their own validation criteria. One crucial strategy is cross-checking, to verify whether what the AI claims actually matches what the author of a paper wrote.

Another critical error is delegating essential tasks, such as reading, to the machine. Use AI to accelerate the process—for example, to help navigate a complex text—but never relinquish intellectual responsibility for the content.

How do you stay up to date and distinguish genuine innovation from mere hype?

You need to be very careful with hype because it creates an environment of anxiety. If you are already using a tool that delivers reliable and satisfactory results, there is no need to migrate to the “tool of the week” simply because of external pressure.

Maintaining focus on a single tool also strengthens the machine-learning process I mentioned earlier and reduces the anxiety of constantly chasing the next technique.

As a professor, I test many different options, but for most researchers, the ideal approach is to tune out the noise and concentrate on what works for their own process.

What are the most common mistakes people make when using these tools?

The first is not knowing how to create an effective prompt. You need to know how to “direct” the AI—that is, provide context and establish limits.

The second is the lack of interaction: many people simply copy and paste the first response they receive, which results in generic writing and increases the risk of plagiarism.

Finally, there is a lack of technical understanding, such as ignoring the “context window,” which can lead to hallucinations when too much information is inserted at once.

How to structure an effective prompt for scientific research

What are the greatest opportunities AI offers today for democratizing Brazilian science?

AI has practically eliminated the English-language barrier, for example, and to me that has been a major breakthrough. Researchers not fluent in English can now understand complex texts and participate in classes on a more equal footing.

In your view, what are the next steps for this technology?

We now need to focus on training educators so they are better prepared to incorporate these tools into their own practices and teach students how to use them correctly.

We need to discuss all of this responsibly and without taboo. AI is here to stay, and therefore it deserves serious attention and a thoughtful approach.

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Published in the journal Clinical and Experimental Optometry, the text examines the advantages and risks associated with being an “early adopter” (a scientist who invests early in innovative methods) and the implications of this approach within academia.

According to Efron, entering a relatively unexplored field increases the likelihood of producing original results, publishing more quickly, and becoming a leading authority on a specific topic. In emerging areas, there is less direct competition and greater scope for pioneering discoveries.

“The greatest rewards for those who adopt new technologies early are reserved for those who invent them,” the author notes. “However, that opportunity rarely arises.”

By engaging with these technologies at an early stage, he not only contributed to their refinement but also published high-impact research while the field itself was still expanding.

New technologies, new questions

The QUT professor also points out that new technologies often open the door to new scientific questions. Emerging methods—such as, in the past, advanced data analysis tools—have made it possible to investigate previously inaccessible phenomena.

This creates an opportunity for young researchers to establish original lines of inquiry in fields still taking shape.

In practice, this means that early studies help define parameters and standards that guide subsequent research, reinforcing the strategic role of those who arrive first.

Practical guidelines for young researchers

Based on his experience, Efron lists guidelines for those who wish to follow this path:

• Seek collaboration with groups operating at the frontier of knowledge;
• Combine new approaches with solid theoretical foundations;
• Monitor trends and methodological innovations in your field;
• Be willing to learn tools that are not yet fully established;
• Assess the growth potential of emerging technologies or fields;
• Stay up to date through regular reading, networking, and conference participation.

Risks also exist

Despite the advantages, the author warns that being an “early adopter” involves risks. Not every promising technology becomes established, which can lead to significant investments of time and effort in approaches that fail to gain traction within the scientific community.

Efron also notes that working with methods still under development may require a longer adaptation period and involve technical uncertainties, potentially delaying results or hindering early publications.

Even so, he believes that, particularly early in one’s career, the potential benefits tend to outweigh the risks.

“Read extensively, attend conferences, and keep an eye out for new and emerging technologies,” Efron advises. “If you have an intuition that a particular application holds promise, give it a vote of confidence and strive to become a pioneer in its adoption.”

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The shift presents researchers with an important question beyond merely how to use AI: who defines the limits, objectives, and rules governing its use? This is a question that encompasses technology, ethics, institutional governance, and public policy, and that was discussed in a recent Science Arena report..

AI as a driver of productivity and methodological change

Generative AI and data analytics tools are already part of everyday scientific practice. They assist with tasks that might previously have required weeks of human work, such as structuring texts, organizing reference databases, or identifying patterns in large volumes of data, helping to speed up processes and expand exploratory capacities.

However, this technological integration goes beyond efficiency alone—it is changing the methods of knowledge production. 

In fields such as bioinformatics, genomics, and big data analysis, AI is almost indispensable, said veterinarian Rebeca Scalco, a PhD candidate in digital pathology and bioinformatics at the University of Bern, Switzerland, in an interview with Science Arena.

“The AI tool SciSpace, for example, helps explain complex articles, but it is still the researcher who interprets or critically analyzes them,” she says.

Governance of scientific AI: an institutional necessity

While many educational efforts and ethical recommendations focus on individual best practices, such as declaring or recording when and how AI is used, the broader discussion revolves around governance and institutional policy. 

In recent years, several universities and research centers worldwide, including Brazilian institutions such as the University of Campinas (UNICAMP) and the Federal University of Bahia (UFBA), have begun creating formal guidelines on the use of AI, with a focus on:

• Transparency and accountability, requiring researchers to document and justify the role of AI in their results.
• Data protection and confidentiality, preventing the exposure of sensitive information on platforms outside the institution’s oversight.
• Preservation of the researcher’s authorship and intellectual agency, ensuring that AI does not compromise the originality and integrity of scientific work.

These guidelines are not just recommendations. Many universities are discussing how to incorporate AI into their internal research regulations, with specialized committees and official documents to guide students, faculty, and research groups. 

AI, public policy, and global regulation

On an international level, the United Nations Educational, Scientific and Cultural Organization (UNESCO) has issued global recommendations encouraging the development of ethical frameworks for AI in science and society, emphasizing that the technology needs to be used in line with human rights, data protection, and shared ethical values. 

In some regions, such as the European Union, specific legislation is already being created to define risks, transparency requirements, and oversight mechanisms. 

In other countries, draft legislation addresses governance, civil liability, and ethical principles that can guide the use of AI in scientific research and technological innovation. 

Challenges and conflicts: autonomy, corporate power, and scientific sovereignty

The debate about AI governance is not neutral, Scalco warns. “Academia and civil society need to be involved in regulation, ensuring that the public interest remains central, especially in fields like health,” she says.

Most advanced AI tools and platforms are controlled by private corporations (the so-called big tech companies) with global economic interests, sparking concerns about who controls the technology that is now influencing the production of scientific knowledge. 

This conflict raises additional questions:

• Should science relinquish sensitive, long-term data to external platforms? 
• What criteria should academic institutions adopt to protect their researchers and research subjects? 
• How can the use of AI be balanced with preservation of the critical thinking, creativity, and methodological rigor that characterize science? 

The role of researchers in formulating governance

The ethical governance of AI in science is not solely the responsibility of committees or regulatory bodies. 

“AI tools should be treated like ‘good interns,’ eager to learn. But just as a doctoral student would never submit an intern’s unreviewed text for publication, nor should they submit work done with AI without proper verification,” says Scalco. In this sense, AI should be seen not merely as a tool, but as an agent of structural change, the ethical and political implications of which need to be understood, debated, and incorporated into institutional systems and scientific training.

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