Living guidelines on the responsible use of generative AI in research

You’re listening to the report “Living guidelines on the responsible use of generative AI in research”, by European Commission, Directorate-General for Research and Innovation. Published in May 2026.

Living guidelines on the responsible use of generative AI in research

ERA Forum Stakeholders’ document

Third version, May 2026

Living guidelines on the responsible use of generative AI in research

European Commission Directorate-General for Research and Innovation Directorate E-Prosperity Unit E4 - AI in Science & Critical Technologies

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Manuscript completed in May 2026 Third edition

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Living guidelines on the responsible use of generative AI in research

ERA Forum Stakeholders' document

Table of contents.

1. INTRODUCTION.

Artificial intelligence (AI) has become an integral part of our daily lives, transforming how we live and work. Over the past few years, there has been a rapid and disruptive acceleration of AI capabilities, driven by significant advances in widespread data availability, computing power and machine learning techniques. Remarkable strides were made in particular in the development of foundation models - AI models trained on extensive amounts of unlabelled data1. These advancements have given rise to what is known as ‘General purpose AI’, capable of performing a wide array of tasks. This includes “generative AI” which can generate various forms of new content (text, code, data, images, music, voice, videos, etc.), usually based on instructions (also known as prompts) provided by the user.

The quality of the output produced by these models is such that it may be difficult to distinguish it from human-generated content.

The widespread and rapid uptake of generative AI has drawn a lot of attention and led to wide policy and institutional responses. The EU is taking the global lead with the AI Act, and other international governance efforts are taking place. These include the Hiroshima process2 led by the G7, the Bletchley Declaration3 signed after the first AI Safety Summit, the Paris Statement signed after the 2025 AI Action Summit4 and the Council of Europe Framework Convention on AI5.

Generative AI and research.

Generative AI provides many opportunities for different sectors. However, it also harbours risks, such as the large-scale generation of misinformation, intellectual property and data protection issues and other unethical uses with significant societal and environmental consequences.

Research is one of the sectors that could be most significantly disrupted by generative AI. AI has great potential for accelerating scientific discovery, leading to new research breakthroughs and significant productivity gains, and improving the effectiveness and pace of research and verification processes. Positive examples of the use of these tools by researchers include supporting non-native speakers in producing texts in multiple languages, producing text summaries from different sources across extremely large corpuses quickly, automatically retrieving and contextualising a wide body of knowledge and helping to write code to analyse data6. However, the technology also entails the risk of abuse. Some risks are due to the tool’s technical limitations, and others have to do with the (intentional or unintentional) use of the tool in ways that erode sound research practices.

Other risks for research in Europe could stem from the proprietary nature of some of the tools (for example, lack of openness, fees to access the service, use of input data), the 1 the linked source 3 the linked source concentration of ownership or the undesirable transfer of critical technology and intellectual property7.

The impact of generative AI on research and various aspects of the scientific process calls for reflection, for example, when working with text (summarising papers, brainstorming or exploring ideas, drafting or translating). In many respects, these tools could harm research integrity and raise questions about the ability of current models to combat deceptive scientific practices and misinformation.

Why these guidelines.

Different institutions, including universities, research organisations, funding bodies and publishers have issued guidance on how to use these tools appropriately to ensure that benefits of those tools are fully utilised. The proliferation of guidelines8 and recommendations has created a complex landscape that makes it difficult to decide which guidelines should be followed in a particular context.

For this reason, the European Research Area Forum9 (composed of European countries and research and innovation stakeholders10), decided to develop guidelines on the use of generative AI in research for: funding bodies, research organisations and researchers, both in the public and private research ecosystems.

These guidelines focus on one particular type of AI used in the research process: generative AI. This is an important step to prevent misuse and ensure that generative AI plays a positive role as part of research practices. One of the goals of these guidelines is that the scientific community uses this technology in a responsible manner. Yet, the development of a robust framework for generative AI in scientific research cannot be the sole responsibility of policymakers (at European and national levels). Universities, research organisations, funding bodies, research libraries, learned societies, publishers, research managers, research support staff and researchers at all stages of their careers are essential in shaping the discussion on AI and how it can serve the public interest in research.

They should all actively engage in discussions about the responsible and effective deployment of AI applications, promoting awareness and cultivating a responsible use of AI as part of a research culture based on shared values. Rules and recommendations must go hand in hand with a broad engagement of those involved in public and private research, both organisations and individuals, to develop a culture of using generative AI in research appropriately and effectively.

These guidelines intend to set out common directions on the responsible use of generative AI. While non-binding, they should be considered as a supporting tool for researchers, research organisations and research funding bodies, including the ones applying to the European Framework Programme for Research and Innovation. They take into account key principles on research integrity as well as already existing frameworks for the use of AI in general and in research specifically. Users of these guidelines are encouraged to adapt them to their specific contexts and situations, keeping proportionality in mind.

These guidelines complement and build on the EU AI policy framework, including the AI Act11. They complement other policy activities on the impact of AI in science. These include the opinion of the Scientific Advice Mechanism12 (SAM) on AI and a policy brief13 published by the European Commission’s Directorate-General for Research and Innovation, framing challenges and opportunities.

Key principles.

The set of principles framing these guidelines are based on pre-existing relevant frameworks:

• the European Code of Conduct for Research Integrity14;

• the work and guidelines on trustworthy AI, developed by the High-Level Expert Group on AI15;

Building on the commonalities of the currently emerging guidelines from various stakeholders, the key principles behind these guidelines for the responsible use of generative AI in research are:

• Reliability in ensuring the quality of research, reflected in the design, methodology, analysis and use of resources. This includes aspects related to verifying and reproducing the information produced by the AI for research. It also involves being aware of possible equality and non-discrimination issues in relation to bias and inaccuracies.

• Honesty in developing, carrying out, reviewing, reporting and communicating on research transparently, fairly, thoroughly and impartially. This principle includes disclosing that generative AI has been used.

• Respect for colleagues, research participants, research subjects, society, ecosystems, cultural heritage and the environment. Responsible use of generative AI should consider the limitations of the technology, its environmental impact16 and its societal effects (bias, diversity, non-discrimination, fairness and prevention of harm). This includes the proper management of information, respect for privacy, confidentiality and intellectual property rights, and proper citation.

• Accountability for the research from idea to publication, for its management and organisation, for training, supervision and mentoring, and for its wider societal impacts. This includes responsibility for all output that a researcher produces, underpinned by the notion of human agency and oversight.

Next steps.

Generative AI may create possibilities and risks that can be hardly anticipated and fully comprehended today. These guidelines are based on the current state of the technology and the surrounding policy landscape. Given the dynamic nature of both, these guidelines will have to adapt and evolve continuously. They will be updated on a regular basis to ensure that they remain a useful resource for researchers and organisations. These guidelines were developed and will continue to be developed collaboratively as part of the ERA Forum activities. Users of these guidelines may provide feedback and suggestions for adaptations in the feedback form17.

2. GUIDELINES ON THE RESPONSIBLE USE OF GENERATIVE AI

IN RESEARCH

Consider the use of generative AI by third parties that could be interacting with you/your organisation.

• Researchers and organisations remain mindful that third parties may use generative AI tools. For example, in the context of meetings, note-taking, summarising discussions or creating overviews of documents (that could be confidential). Therefore, they can face similar risks to the ones described in this guidance, namely on confidentiality, data protection or intellectual property rights related to protected or original information.

• Inform third parties about the of use of these kinds of tools and the potential risks, for example by sharing this guidance.

• In your contacts with third parties inform them if you are the one using those AI tools and how you are protecting collected information.

RECOMMENDATIONS FOR RESEARCHERS.

For generative AI to be used in a responsible manner, researchers should: 1. Remain ultimately responsible for scientific output.

• Researchers are accountable for the integrity of the content18 they produce generated by or with the support of AI tools.

• Researchers maintain a critical approach to using the output produced by generative AI and are aware of the tools’ limitations, such as bias19, hallucinations20 and inaccuracies.

• AI systems are neither authors nor co-authors. Authorship implies agency and responsibility, so it lies with human researchers.

Generative AI models can exhibit limitations that impact both their responses and usability. The most common types are:

• Training Data Bias: Biases in the data used to train generative AI models can lead to skewed responses, reflecting inaccuracies or systemic biases within the source material.

Prompt Bias16: Models may align their answers with the perceived beliefs or • preferences of the user, a phenomenon known as sycophantic behaviour, potentially leading to misleadingly agreeable or biased outputs.

• Invented Citations and Incorrect Summaries: Generative AI models may generate plausible sounding but incorrect citations, which can mislead users who rely on those sources for research or verification. Even when the citations are correct (the paper exists, has the proper title...), the AI-generated summary of those papers may be incorrect. Therefore, researchers are responsible for checking all the references and content of the citations and the summaries.

• Interpretability: generative AI models operate as "black boxes," making it difficult to understand how specific responses are generated. This opacity underscores the importance of cross validation, especially in automated data analysis, where model responses can significantly impact conclusions.

2. Use generative AI transparently.

• Researchers, to be transparent, detail which generative AI tools have been used substantially21 in their research processes. When generative AI meaningfully shapes results, researchers transparently note its use according to the guidelines of their journal or standards in their discipline in the methods section (or equivalent) responsibly evaluating the extent of the contribution22. This transparency principle also applies to the use of AI to detect or screen bad practices (i.e. detect hallucinations or plagiarism) while keeping human supervision.

• Researchers take into account the stochastic (random) nature of generative AI tools, which is the tendency to produce different output from the same input. Researchers aim for reproducibility and robustness in their results and conclusions. They disclose or discuss the limitations of generative AI tools used, including possible biases in the generated content, as well as possible mitigation measures.

3. Pay particular attention to issues related to privacy, confidentiality and intellectual property rights when sharing sensitive or protected information with AI tools.

• Researchers remain mindful that generated or uploaded input (text, data, prompts, images, etc.) could be used for other purposes, such as the training of AI models. Therefore, they protect unpublished or sensitive work (such as their own or others’ unpublished work) by taking care not to upload it into an external AI system unless there are assurances that the data will not be re-used, e.g., to train future language models or to the untraceable and unverifiable reuse of data.

• Researchers take care not to provide third parties’ personal data to external generative AI systems unless the data subject (individual) has given them their consent and researchers have a clear goal for which the personal data are to be used so compliance with EU data protection rules23 is ensured24.

• Researchers understand the technical, ethical and security implications regarding privacy, confidentiality and intellectual property rights. They check, for example, their institutional guidelines, the privacy options of the tools, who is managing the tool (public or private institutions, companies, etc.), where the tool is running and implications for any information uploaded. This could range from closed environments, hosting on a third-party infrastructure with guaranteed privacy, to open internet-accessible platforms.

4. Respect applicable national, EU and international legislation

In particular, the output produced by generative AI can be especially sensitive in relation to the protection of intellectual property rights and personal data.

• Researchers pay attention to the potential for plagiarism (text, code, images, etc.) when using outputs from generative AI. Researchers respect others’ authorship and cite their work where appropriate. The output of a generative AI (such as a large language model) may be based on someone else’s results and require proper recognition and citation25.

• The output produced by generative AI can contain personal data. If this becomes apparent, researchers are responsible for handling any personal data output responsibly and appropriately, and EU data protection rules are to be followed.

5. Continuously learn how to use generative AI tools properly to maximise their benefits, including by undertaking training.

• Generative AI tools are evolving quickly, and new ways to use them are regularly discovered26. Researchers stay up to date on the best practices and share them with colleagues and other stakeholders.

• Researchers aim at minimising the environmental impact27 of generative AI, by evaluating whether and which AI tool is best suited for the intended task and by using the most effective prompting techniques.

6. Refrain from using generative AI tools substantially28 in sensitive activities that could impact other researchers or organisations (for example peer review29, evaluation of research proposals, etc).

• Avoiding the use of generative AI tools eliminates the potential risks of unfair treatment or assessment that may arise from these tools’ limitations (such as hallucinations and bias).

• Moreover, this will safeguard the original unpublished work of fellow researchers from potential exposure or inclusion in an AI model (under the conditions detailed above in the recommendation for researchers #3).

RECOMMENDATIONS FOR RESEARCH ORGANISATIONS.

For generative AI to be used in a responsible manner, research organisations should: 1. Promote, guide and support the responsible use of generative AI in research activities.

• Research organisations provide and/or facilitate training for all career levels and disciplines, including for research managers and research support staff, on using generative AI, especially (but not exclusively) on verifying output, maintaining privacy, addressing biases and protecting intellectual property rights and sensitive knowledge.

• Research organisations promote an atmosphere of trust where researchers are encouraged to transparently disclose the use of generative AI without concerns for adverse effects.

• Research organisations provide support and guidelines to ensure compliance with ethical and legal requirements (EU data protection rules, protection of intellectual property rights, etc.).

2. Actively keep track of the evolution and use of generative AI systems within their organisations.

• Research organisations remain mindful of the research activities and processes for which they use generative AI to better support its future use26. This knowledge can: o be used to provide further guidance on using generative AI, help identify training needs and understand what kind of support could be most beneficial; o help anticipate and guard against possible misuse and abuse of AI tools; o be published and shared with the scientific community.

• Research organisations analyse the limitations of the technology and tools and provide feedback and recommendations to their researchers.

• Research organisations keep track of the environmental impact of generative AI within their organisations and promote awareness raising initiatives to help their staff pick the most sustainable option.

3. Reference or integrate these generative AI guidelines into their general research guidelines for good research practices and ethics.

• Using these guidelines as a basis for discussion, research organisations openly consult their research staff and stakeholders on the use of generative AI and related policies.

• Research organisations apply these guidelines whenever possible. If needed, they could be complemented with specific additional recommendations and/or exceptions that should be published for transparency.

4. Whenever possible and necessary, implement locally hosted or cloud-based generative AI tools that they govern themselves31. This enables their employees to feed their scientific data into a tool that ensures data protection and confidentiality.

• Organisations ensure the appropriate level of cybersecurity of these systems, especially those connected to the internet.

RECOMMENDATIONS FOR RESEARCH FUNDING ORGANISATIONS.

Research funding organisations operate in different contexts and follow different mandates and regulations, which may not align with a single set of guidelines. The recommendations below outline a set of measures and good practices for organisations to implement in ways that best fit their specific circumstances and objectives.

For generative AI to be used in a responsible manner, research funding organisations should: 1. Promote and support the responsible use of generative AI in research.

• Research funding organisations design funding instruments that are open, receptive and supportive of the responsible and ethical use of generative AI technologies in research activities.

• Research funding organizations require funded research and grantees to be in line with existing national, EU and international legislation (where applicable) and good practices for the use of generative AI.

• Research funding organisations encourage researchers, research managers, research support staff and research organisations to use generative AI ethically and responsibly, including respecting legal and research standards requirements.

2. Review the use of generative AI in their internal processes

They will lead the way by ensuring they use it transparently and responsibly.

• Research funding organisations remain fully responsible for the use of generative AI in their activities, in line with the accountability principle that emphasises responsibility and human oversight.

• Research funding organisations use generative AI transparently, in particular for activities related with the management of assessment and evaluation, and without compromising their fairness and the confidentiality of the content.. Generative AI could be used to improve internal processes for funders, but it cannot take a role in the assessment or evaluation of the scientific content of projects.

• When choosing and using generative AI tools, research funding organisations will carefully consider the tool’s adherence to standards and challenges in its use, when sharing sensitive or protected information with AI tools. Organisations take into account elements concerning quality, transparency, integrity, data protection, confidentiality and respect for intellectual property rights, as well as its environmental impact.

3. Request transparency from applicants on their use of generative AI facilitating ways to report it.

• Applicants declare if they substantially33 used generative AI tool(s) to prepare their application.

• Applicants provide information on the role of generative AI in the research activities proposed and carried out.

• Research funding organisations promote an atmosphere where researchers can disclose the use of generative AI transparently and without concerns of being disadvantaged24.

4. Monitor and get actively involved in the fast-evolving generative AI landscape.

• Research funding organisations promote and fund training and educational programmes for an ethical and responsible use of AI in scientific research.

5. When using generative AI as part of the internal processes, take into account the risks related to hidden prompts34 in content received by third parties, in particular in applications and proposals.

• Research funding organisations remain aware that content may contain hidden prompts or invisible instructions intended to influence generative AI tools.

• Where relevant, they set clear rules prohibiting such manipulation and introduce appropriate safeguards, in particular in the IT systems involved in the processing of that information.

BACKGROUND.

RESEARCH INTEGRITY.

The European Code of Conduct for Research Integrity by All European Academies (ALLEA) lays down a set of principles to produce sound research, including ethical aspects. These principles include:

• reliability in ensuring the quality of research, reflected in the design, methodology, analysis and use of resources;

• honesty in developing, carrying out, reviewing, reporting and communicating on research transparently, fairly, thoroughly and impartially;

• respect for colleagues, research participants, research subjects, society, ecosystems, cultural heritage and the environment;

• accountability for the research from idea to publication, for its management and organisation, for training, supervision, and mentoring, and for its wider societal impacts.

TRUSTWORTHY AI.

The Ethics Guidelines for Trustworthy AI by the EU High-Level Expert Group on AI focuses on developing responsible AI systems (rather than using them). This work on trustworthy AI contains a set of ethical principles that were translated into operational requirements for developing AI systems. The requirements could be useful inspiration for drawing up guidelines on the use of generative AI.

The four ethical principles for AI systems are: 1. respect for human autonomy; 2. prevention of harm; 3. fairness; 4. explicability.

These ethical principles were used to develop these seven operational key requirements: 1. human agency and oversight35; 2. technical robustness and safety; 3. privacy and data governance; 4. transparency; 5. diversity, non-discrimination and fairness; 6. environmental and societal well-being; 7. accountability.

OTHER FRAMEWORKS OF PRINCIPLES.

KU Leuven principles were used in similar exercises and for the development of these guidelines:

• transparency on the use of generative AI, depending on the type of use;

• verification of the correctness of the generated output with attention to correctly attributing the source;

• respect for personal data and confidential information by not entering these on platforms that are not managed on proprietary servers;

• responsibility for the correct use of generative AI (primarily help and support) and the published output.

Specialists in AI, generative AI, computer science, psychology and sociology hosted by the Institute for Advanced Study at the University of Amsterdam identified the principles of accountability, transparency and independent oversight (in relation to developers of generative AI). Based on these principles, they proposed living guidelines for the responsible use of generative AI36.

These principles could be complemented with those from other sources. These include the principles put forward by the Association for Computer Machinery on generative AI Technologies that have a broad scope for those developing, deploying and using the technology. Other sources are the Values Framework for the Organisation of Research developed by Science Europe or the STM37 principles for ethical and trustworthy AI.

SYNTHESIS FOR THE DEVELOPMENT OF EU GUIDELINES.

The ALLEA principles in the European Code of Conduct for Research Integrity serve as guidance for ethical and responsible research. The same value system applies when using generative AI in research. The ALLEA principles can therefore be used as a basis for these guidelines and be enriched with parts from the other frameworks described in the previous sections.

ARTICLES AND PAPERS.

Birhane, A., Kasirzadeh, A., Leslie, D. & Wachter, S., ‘Science in the Age of Large Language Models’, Nature Reviews Physics, Vol. 5, 2023, pp. 277-280.

Conroy, G., ‘Scientific Sleuths Spot Dishonest ChatGPT Use in Papers’, Nature, 2023. the linked source

Ganjavi, C., Eppler, M., Pekcan, A., Biedermann, B., Abreu, A., Collins, G., Gill, I. Cacciamani, G., ‘Bibliometric Analysis of Publisher and Journal Instructions to Authors on Generative-AI in Academic and Scientific Publishing’, arXiv, 2023. the linked source

Li, Z., Liang, C., Peng, J., Yin, M., ‘How Does the Disclosure of AI Assistance Affect the Perceptions of Writing?’ Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing, pages 4849–4868 (2024). 10.18653/v1/2024.emnlp-main.279

Lorenz, P., Perset, K. & Berryhill, J., ‘Initial Policy Considerations for Generative Artificial Intelligence’, OECD Artificial Intelligence Papers, No. 1, OECD Publishing, Paris, 2023. the linked source

Lund, B.D., Wang, T., Mannuru, N.R., Nie, B., Shimray, S. & Wang, Z., ‘ChatGPT and a New Academic Reality: Artificial Intelligence-Written Research Papers and the Ethics of the Large Language Models in Scholarly Publishing, Journal of the Association for Information Science and Technology, Vol. 74, No. 5, 2023, pp. 570-581. the linked source

Nogueira, L. A., Moltubakk, S. T., Fagervik, A., & Langfeldt, I. B. ‘Cutting through the noise: Assessing tools that employ artificial intelligence.’, IFLA Journal, 0, 2025 the linked source

Parrilla, J.M., ‘ChatGPT Use Shows that the Grant-Application System is Broken’, Nature, 2023. the linked source

International Association of Scientific, Technical and Medical Publishers (STM), ‘AI Ethics in Scholarly Communication: STM Best Practice Principles for Ethical, Trustworthy and Human-centric AI’, 2021. the linked source

Sharma, M., Tong, M., Korbak, T., Duvenaud, D., Askell, A., Bowman, S. R.... & Perez, E. (2023). ‘Towards understanding sycophancy in language models.’ arXiv the linked source

Van Dis EAM, Bollen J, Zuidema W, van Rooij R, Bockting CL., ‘ChatGPT: Five Priorities for Research’, Nature, Vol. 614, pp. 224-226 (2023) the linked source

United Nations Environment Programme, ‘Artificial Intelligence (AI) end-to-end: The Environmental Impact of the Full AI Lifecycle Needs to be Comprehensively Assessed - Issue Note’ (2024) the linked source

Zou, J., ‘ChatGPT is transforming peer review — how can we use it responsibly?’, Nature Vol. 635, p.10 (2024) the linked source

GUIDELINES ANALYSED.

Aalto University: the linked source

All European Academies (ALLEA): the linked source (DOI: 10.26356/ECOC)

Arizona State University: the linked source

Association for Computing Machinery (ACM): the linked source

Australian Government Tertiary Education Quality and Standards Agency (TEQSA): the linked source

Bockting CL, van Dis EAM, van Rooij R, Zuidema W, Bollen J. Living guidelines for generative AI - why scientists must oversee its use. Nature. 2023 Oct;622(7984):693-696.

Committee on Publication Ethics (COPE): the linked source

Deakin University: the linked source

Deutsche Forschungsgemeinschaft (DFG): the linked source apiere/2023/230921statementexecutivecommitteekiai.pdf

Elsevier: the linked source

Harvard University: the linked source

International Association of Scientific, Technical and Medical Publishers (STM): Global Principles for AI

International Association of Scientific, Technical and Medical Publishers (STM):

Ethical and practical guidelines for the use of generative AI in the publishing process International Society for Computational Biology (ISCB): the linked source

KU Leuven: the linked source Maastricht University: the linked source Science Europe: the linked source United Nations Educational, Scientific and Cultural Organization (UNESCO): the linked source University of Ljubljana: the linked source

University of Toronto: the linked source

GETTING IN TOUCH WITH THE EU.

In person.

All over the European Union there are hundreds of Europe Direct centres. You can find the address of the centre nearest you online (european-union.europa.eu/contact-eu/meet-usen).

On the phone or in writing.

Europe Direct is a service that answers your questions about the European Union. You can contact this service:

– by freephone: 00 800 6 7 8 9 10 11 (certain operators may charge for these calls),

– at the following standard number: +32 22999696,

– via the following form: european-union.europa.eu/contact-eu/write-usen.

FINDING INFORMATION ABOUT THE EU.

Online.

Information about the European Union in all the official languages of the EU is available on the Europa website (european-union.europa.eu).

EU Publications.

You can view or order EU publications at op.europa.eu/en/publications.

Multiple copies of free publications can be obtained by contacting Europe Direct or your local documentation centre (european-union.europa.eu/contact-eu/meet-usen).

EU law and related documents.

For access to legal information from the EU, including all EU law since 1951 in all the official language versions, go to EUR-Lex (eur-lex.europa.eu).

EU open data.

The portal data.europa.eu provides access to open datasets from the EU institutions, bodies and agencies. These can be downloaded and reused for free, for both commercial and non-commercial purposes. The portal also provides access to a wealth of datasets from European countries.

These guidelines are the result of a joint effort between the European Commission and the countries and stakeholders represented in the ERA Forum. They provide simple and actionable recommendations to key actors in the research community on the use of generative AI. These recommendations will bring clarity and reassurance to those using generative AI in their research activities. These are ‘living’ guidelines, they will be updated regularly to keep up with the very fast technological development in this area.

Research and Innovation policy.