Artificial Intelligence
Best Practices for Artificial Intelligence in Research
Artificial Intelligence (AI) can support a range of research activities, including data analysis, software development, information discovery, and content generation. Researchers should review institutionally supported AI tools before adopting external services, particularly when working with sensitive data.
Researchers should select AI tools and models that are appropriate for their research objectives, design prompts thoughtfully, and critically evaluate AI-generated outputs. Optimizing prompts and selecting models suited to the task can improve performance, reduce costs, and make more efficient use of tokens.
AI Tools and Platforms
Institutionally supported AI tools and platforms are available for a range of research activities, from experimentation and model development to day-to-day research workflows. The resources below can help researchers identify tools that meet their needs.
University of Toronto
A hub that provides access to secure AI tools, data, and expertise, including comparative information on available AI models and platforms.
University of Toronto Libraries
A catalogue of institutionally supported software that incorporates AI features into existing research, productivity, and collaboration tools.
Guidance and Considerations for AI
Expand for further guidance and resources.
Determine the sensitivity of research data before selecting AI tools, models, or platforms.
Information Security
University of Toronto
Groups the university’s data into four levels based on importance, sensitivity and potential for misuse. Researchers can use this standard to determine the sensitivity of their data and the safeguards required to protect it.
Information Security
University of Toronto
A tool that helps researchers determine the appropriate data classification level using research-specific examples.
Ensure AI practices comply with applicable ethics approvals, agreements, and confidentiality obligations.
Division of the Vice-President, Research & Innovation
University of Toronto
Guidance on when research requires Research Ethics Board (REB) approval and how to obtain ethics clearance for studies involving human participants.
Division of the Vice-President, Research & Innovation
University of Toronto
Information on research agreements and contracts that may be required when sharing data or collaborating with external partners.
Division of the Vice-President, Research & Innovation
University of Toronto
Guidance on the retention, protection, and secure destruction of confidential and personally identifiable information used in research.
Review guidance on the responsible use of AI in research, including privacy, information security, copyright, and citation considerations.
Information Security
University of Toronto
Guidance on the responsible use, development, and deployment of AI, including considerations related to privacy, information security, and misinformation risks.
School of Graduate Studies
University of Toronto
Up-to-date guidance and FAQ on considerations for the appropriate use of GenAI tools in graduate academic research.
Training and Resources on AI
Explore learning resources and recorded sessions related to artificial intelligence.
Workshops and Courses
- Hands-on Workshop: Applying GenAI Tools to Scholarly Writing (CRIS)
November 12, 2026
Workshop on which parts of scholarly writing can be delegated to AI without loss. - AI Literacy Workshop Series (University of Toronto Libraries)
A series of workshops to build understanding of GenAI tools and their use in academic research. - DSI-CARTE Machine Learning for Faculty (Data Sciences Institute)
An intensive training program introducing faculty, librarians, and research staff to machine learning and deep learning methods. - Deploying AI Microcredential (Data Sciences Institute)
A part-time course covering the development and deployment of deep learning and generative AI models.
Documentation and Guides
- GenAI Considerations for Academic Research (CRIS)
A guide to the opportunities, limitations, and responsible use of generative AI in academic research. - GenAI Learning Resources for Researchers (CRIS)
Curated learning resources, courses, webinars, and recordings that help researchers build AI literacy and skills. - Generative AI Tools and Copyright Considerations (UTL)
Guidance on copyright considerations when using generative AI tools in research. - Citing Artificial Intelligence Generative Tools (UTL)
Guidance on citing generative AI tools in scholarly and academic work.
Recorded Webinars
- AI in Qualitative Research: Mapping the Landscape (CRIS)
Explores practical, ethical, and methodological considerations for using generative AI in qualitative research. - AI-Assisted Qualitative Data Analysis (CRIS)
Examines the use of generative AI for qualitative coding, synthesis, and analytical workflows. - AI-Assisted Quantitative Data Analysis: Harnessing Tools Appropriately (CRIS)
Demonstrates practical applications of generative AI for quantitative analysis and research workflows.
Get Support for AI
- AI tools, training and advice: ai.kitchen@utoronto.ca
- Customized ML training for research groups, help with ML workflows, and guidance on GPU platforms: carte@utoronto.ca.