Given the very rapid development of AI tools, these guidelines may evolve over time. It is vitally important that all students stay abreast of these developments.

As artificial intelligence (AI) is used more and more in into academic and scientific circles, particularly generative AI tools such as ChatGPT and Copilot, we need to think carefully about how it is used and make sure we comply with the highest standards of intellectual and research integrity.

INRS guidelines for the responsible use of AI in education

Best practices in the use of artificial intelligence

Issues and limitations of AI

INRS guidelines for the responsible use of AI in education

INRS’s Graduate Studies and Student Success Office has developed guidelines on artificial intelligence. They are set out in the document “Guidelines for the responsible use of artificial intelligence in graduate studies” and are summarized below.

Summary

  • Prioritize academic learning: Use AI as a support tool, not as a substitute for developing essential academic skills such as critical thinking, independent research, and writing.
  • Obtain prior authorization: Always check whether AI tools are permitted in a given context. Consult INRS’s policies and guidelines to avoid inappropriate use.
  • Take responsibility for the content: Take full responsibility for any content produced using AI. Systematically validate that all information is accurate, relevant, and original before incorporating it into your work.
  • Understand the associated risks:
    • Confidentiality: Sensitive or personal data may be compromised
    • Plagiarism: Generated content may include non-original elements without clear attribution.
    • Cybersecurity: Some AI tools may expose your information to cyberattacks.
    • Algorithmic bias: The results produced may reflect biases present in the training data.
    • Environmental issues: Remember that that these tools have a considerable environmental footprint

Maintain a critical mindset and be upfront about using AI tools.

CONSULT THE GUIDE (pdf)
French version (PDF)

Basic concepts

Artificial intelligence (AI)

“Artificial intelligence (AI) refers to the series of techniques which allow a machine to simulate human learning, namely to learn, predict, make decisions and perceive its surroundings. In the case of a computing system, artificial intelligence is applied to digital data.”

Source : Déclaration de Montréal, Glossaire, https://declarationmontreal-iaresponsable.com/glossaire

Generative AI (GAI)

“Generative AI refers to all artificial intelligence techniques used to produce content via algorithms and big data, usually in the form of text, sound, video, or image files. Generative artificial intelligence can lead to plagiarism or misinformation, particularly when data has been deepfaked. More broadly, chatbots  are types of generative artificial intelligence.”

Source : Office québécois de la langue française, Grand dictionnaire terminologique, https://vitrinelinguistique.oqlf.gouv.qc.ca/fiche-gdt/fiche/26561649/intelligence-artificielle-generative

“The term ‘generative’ refers to the ability of AI to automatically create information from large volumes of existing data on which it has been trained. AI does not simply copy and paste what it has analyzed—it imitates, improves, and creates an entirely new result based on a statistical recomposition of the patterns and structures it has identified during its learning process. These results, known as content, can be text, images, music, or computer code.”

Source : Université de Genève, Intelligence artificielle générative: Guide à l’intention de la communauté universitaire, Introduction à l’IA générative https://www.unige.ch/numerique/ia-generative-guide-unige/introduction-ia-generative

Differentiating between AI and GAI

Large language models (LLMs)

“An LLM is large because it has a huge number of parameters (in the order of several billion), all of which are pieces of information. It is a model because it is a neural network trained on a large amount of text to produce non-specific tasks. It is a language model because it reproduces the syntax and semantics of natural human language by predicting the sequence likely to follow a given input. This is also the source of the general “knowledge” it has acquired from its training texts.”

Content reproduced with the permission of the University of Geneva
Source : Intelligence artificielle générative. Guide pratique de l’UNIGE. Introduction à l’intelligence artificielle générative. Université de Genève.

Issues and limitations

When using AI tools, be aware of the associated challenges and limitations and consider whether they are being used appropriately and responsibly.

A hindrance to acquiring essential skills: AI should be a complementary tool and not a substitute for intellectual work. It should not compromise the acquisition of essential academic skills such as critical analysis or writing, research, or problem-solving skills.


Exposure of sensitive data: Personal, confidential, or unpublished data such as research results should never be shared with AI tools without explicit approval. These tools may store or analyze data in unpredictable ways, which can pose serious risks to confidentiality and cybersecurity. Remember—all uses involving sensitive data must comply with the INRS Privacy Policy.


Cybersecurity risk: Sharing data via online AI platforms may expose users to cyberattacks or data breaches.

Algorithmic bias and reliability of responses: AI tools do not guarantee that the responses they generate are accurate or objective. The results may also reflect biases in the training databases, which can hinder diversity and inclusiveness.


Intellectual property and plagiarism: Using AI-generated content may lead to potential breaches of intellectual property rights such as copyright. AI tools may rely on protected data or works without the permission to do so, exposing users to litigation. AI tools must be used in accordance with the rules governing citations, references, and attribution of sources.


Environmental and human issues: AI tools have direct and indirect impacts on the environment (e.g., raw materials to manufacture AI equipment, water to cool servers and energy to train AI tools, etc.) but also on society, particularly the labour market (i.e., automation of certain tasks, new skills for employees, etc.). etc.) but also on society, particularly the labor market (i.e., automation of certain tasks, new skills for employees, etc.).

Resources and references

Voir tout

Ressources

Sources consulted for the guidelines

Some AI glossaries (non-exhaustive list)

Additional resources and tools

Intellectual Integrity
Intellectual Integrity
Learn how to cite your sources
Learn how to cite your sources
Student Services and Resources (GARE)
Student Services and Resources (GARE)

Questions? Contact us at the following address

pedagogie.sesre@inrs.ca