Artificial Intelligence (AI) refers to computer systems that can perform tasks that typically require human intelligence, such as visual perception, speech recognition, decision-making and language translation. Examples of AI tools include Siri, Google Alexa, DeepL, ChatGPT and DALL-E.

AI is used in academia for various purposes such as research, teaching and improving learning outcomes. It can help researchers analyze large amounts of data, make predictions and discovering new patterns. It’s also used to enhance the learning experience through personalized feedback, recommendation systems and gamification.

AI needs to be used in a way that maintains academic integrity. For example, using AI-generated text as part of your academic work without permission or a citation is a violation of academic integrity.

It depends! Check your course syllabus and each assignment’s instructions for the academic integrity provisions. The University’s Regulation on Academic Integrity and Misconduct addresses some aspects of artificial intelligence, such as citing sources. However, there can be variations in AI use permitted between courses, and even between assignments in the same course (e.g., exams). If in doubt, always ask your professor!

If you’re not sure, consult your professor for guidance. The professor can provide you with information on using AI with academic integrity in your course.

Yes. You can:

• Clearly understand the uOttawa rules and guidelines.
• Properly cite and give credit to all sources, including AI-generated content if its use is allowed. Your professor may also ask you to explain how AI has been used in your work.
• Avoid using AI to generate answers or complete assignments that should be original and completed on your own.
• Ask for clarification and guidance from your professor if you’re not sure about expectations concerning AI.
• In research, be mindful about how you share and use data.

There are several limitations of AI in academic work, including:

• Bias and discrimination: AI systems can be biased and discriminate against certain groups if the data used to train them is biased or skewed.
• Lack of contextual understanding: AI systems may not fully understand the context in which a task is being performed, leading to inaccurate or misinterpreted results.
• Lack of creativity: AI systems can’t generate truly original ideas or creative thought. They can only make predictions based on patterns in data.
• Limited knowledge of the field: AI systems can only perform well within the specific fields they’re trained in, and may not have the broader data set necessary for certain tasks.
• Ethical and moral concerns: AI systems raise important ethical and moral concerns, particularly in regard to privacy, data security and the impact they may have on employment and other aspects of society. Be mindful of the data that AI systems collect; we strongly advise against sharing personal or private information
• Lack of interpretability: Many AI systems, particularly deep learning algorithms, can be difficult to interpret and understand, making it challenging to assess the accuracy of their predictions and decisions.
• Computational complexity: AI systems can be computationally intensive and require significant resources to develop and deploy, which can be a barrier for some institutions and researchers.
• Nonsensical information: At times, AI systems generate answers that can sound reasonable in parts, but overall don’t make sense
• False citations: AI can generate realistic-looking citations that actually don’t exist

Despite these limitations, AI has the potential to greatly affect and improve many areas of academia. That said, it’s important to be mindful of these limitations and address them responsibly and ethically.

• Responsibility: Ensure that the AI system is designed and used in a manner that’s safe, transparent and accountable.
• Fairness: Avoid creating or perpetuating biases and discrimination in AI systems and ensure equal treatment for all individuals.
• Privacy: Protect the privacy of individuals and ensure that personal data is collected, stored and used securely and responsibly, according to uOttawa and Tri-Council policies on research with human subjects.
• Transparency: Provide clear explanations of how AI systems work and make their decision-making processes transparent and understandable.
• Attribution: Cite the use of AI and the manner that it has been used in the research.
• Human control: Ensure that AI systems are designed to support, rather than replace, human decision-making and remain under human control.
• Non-maleficence: Avoid causing harm through the use of AI and ensure that AI systems aren’t used to cause harm or engage in unethical behaviour.
• Beneficence: Use AI to bring about positive outcomes and to benefit society as a whole.

Depending on the discipline and intended learning outcomes, professors could use AI in several ways, including:

• Teaching you to use AI so that you can become a more effective learner and have greater career readiness and success: AI is integrated into some courses as part of the learning activities, including learning how to use AI ethically and critically reflect on its strengths and limitations.
• Personalizing learning: AI can be used to provide personalized feedback and recommendations to you, based on your performance and learning preferences.
• Grading and assessment: AI can be used to grade assignments and exams, provide feedback and identify areas where you may need additional support.
• Creating content: AI can be used to generate study materials, such as summaries, flashcards and interactive quizzes, to enhance the learning experience.
• Analyzing data: AI can be used to analyze large amounts of data and make predictions, providing a more comprehensive understanding of a topic.
• Gamifying learning: AI can be used to create interactive, engaging educational games that motivate you to learn and retain information.
• Classroom assistance: AI can be used to assist professors in managing and organizing their courses, including scheduling and administrative tasks.
• Research assistance: AI can be used by professors to assist with their research projects by analyzing data, making predictions and discovering new patterns.
• Not at all: Some courses will use approaches that are independent of AI, for various reasons (e.g., because of the course’s focus, to emphasize learning core principles).

The use of AI in education is still in its early stages, and its impact will continue to evolve as technology advances. It’s important for professors and students to understand the limitations of AI and use it in responsible and ethical ways, while also considering its potential benefits for enhancing the learning experience.

All new undergraduate students must complete the training on academic integrity in their first term at uOttawa.

Undergraduate students in the Faculty of Medicine (other than those in the Translational and Molecular Medicine program) are exempt since they take different, though equivalent, training.

If you started undergraduate studies at the University before September 2021, you don’t have to complete the training.

Although only newly admitted undergraduate students take the module, key information is available to all on the academic integrity Web pages.

The training will help you develop essential academic integrity skills, help ensure you understand academic integrity and show you resources to succeed right from the start of your studies.

During your studies, including while you’re working on the module, many uOttawa services are available to help you with academic integrity and understanding uOttawa policies and regulations.

You must complete the training by the date shown in the table below. The module takes approximately two hours to complete.

You’ll automatically be enrolled through Virtual Campus (Brightspace). Complete the training well before the deadline so you can meet uOttawa academic integrity requirements right from the start. This will help you immerse yourself in the culture of academic integrity.

You have an unlimited number of attempts to pass the final evaluation. If you don’t pass the training by the deadline, your student account will be flagged until the module is successfully completed. If it is still not completed by the time enrolment open for the following year, your course enrolment will be blocked until the module is successfully completed or otherwise decided by your faculty.

Integrity is a key value in all university activities. The Regulation on Academic Integrity and Academic Misconduct lists the actions that can compromise academic integrity, describes the consequences of these actions and sets out procedures to deal with allegations and determine penalties. The mandatory training for undergraduate students is based on this regulation.

For questions related to accessing or operating Brightspace, including questions about your password, please consult Information Technology.

If you do not see the Academic Integrity module in your Virtual Campus (Brightspace), please contact Intégrité[email protected].