Instructors at the University of Toronto report that generative AI is reshaping how students approach coursework and how faculty structure learning environments. As AI tools become common for drafting, summarizing and studying, faculty are rethinking traditional assignments and creating clearer guidelines to ensure students continue developing core analytical and writing skills. Many instructors say the shift is comparable to earlier transitions in digital learning but moves faster and affects a wider range of academic practices.
Classroom observations indicate that students now blend AI assistance with conventional study methods, using tools to organize readings, generate outlines or simplify complex topics before engaging with course material. Faculty members note that while some students rely heavily on AI-generated text, many use it as a starting point before refining work independently. This variation has prompted departments to refine expectations and build pedagogical approaches that incorporate AI literacy alongside subject-specific learning outcomes.
Why Teaching Methods Are Changing
Faculty say traditional take-home essays and open-ended writing tasks are increasingly difficult to evaluate without accounting for AI assistance.
Departments are shifting toward in-person assessments, oral examinations, project-based demonstrations and multi-stage assignments that require visible student reasoning.
Instructors are updating syllabus language to define acceptable AI use, clarifying boundaries between legitimate learning support and academic integrity violations.
Many instructors note that AI tools can help students understand dense material more quickly, but they also caution that overreliance may weaken the development of critical thinking if students bypass foundational work. As a result, some courses now include checkpoints where students must show drafts, research notes or progress logs. These adjustments aim to ensure that AI supplements rather than replaces core academic skills.
Faculty Perspectives and Student Behavior
Professors across humanities, social sciences and professional programs report that AI effects differ across disciplines. Writing-intensive courses see more frequent use of generative tools for drafting and idea organization, while technical programs often use AI to review problem sets or interpret code.
Students say they turn to AI for efficiency, language support and alternative explanations of complex topics. Many describe the tools as part of their study workflow rather than as shortcuts.
Faculty development workshops at UofT now include sessions on creating AI-aware assignments, evaluating AI-supported work and helping students understand tool limitations.
Some instructors emphasize that transparency is becoming essential. Students are encouraged to disclose when and how AI contributed to their work, allowing faculty to assess whether the technology supported learning or replaced it. This approach also helps identify patterns that can inform future teaching design.
Institutional and Sector-Wide Trends
Universities across Canada are navigating similar challenges as AI becomes embedded in student routines. Institutions are updating academic-integrity frameworks, building guidelines for faculty and offering training to help instructors integrate AI in ways that strengthen learning outcomes.
AI literacy is emerging as a key competency, prompting programs to incorporate discussions about algorithmic reliability, bias and appropriate use of language models.
Some departments are piloting assignments that require students to critique AI-generated content, encouraging analytical engagement rather than passive use.
As generative AI tools continue to evolve, faculty at the University of Toronto expect assignment structures and evaluation methods to keep shifting. The university’s focus remains on balancing student access to powerful digital tools with the need to maintain academic rigor and support the development of independent reasoning and communication skills.
