This resource highlights key questions discussed during the February 3, 2025, GenAI Symposium, organized by theme. The answers provided reflect insights shared by presenters, along with resources available through UBC’s GenAI initiatives.
Please visit the Teaching and Learning Questions about AI webpage to explore frequently asked AI questions.
Pedagogical Approaches & Course Design
Note: The following suggestions represent a range of possibilities that should be adapted based on your specific discipline, course level, learning objectives, and student context. Not all approaches will be suitable for every teaching situation, and instructors should exercise their professional judgment in determining which strategies align with their pedagogical goals.
Questions about how to effectively integrate GenAI into course design and teaching approaches.
What are some examples of how we might adapt assessments of learning to account for student use of AI generated content?
- Process-oriented assessment: Focus on the overall process rather than just the final product. This includes scaffolding assignments with drafts, outlines, and revisions that demonstrate critical thinking and development of ideas.
- AI usage transparency: Have students submit AI usage statements with their assignments that include critical reflection on how AI was used and what they learned from the process.
- Diverse assessment methods: Incorporate more diverse types of assessments, including oral presentations, in-class activities, and collaborative projects that emphasize skills AI cannot easily replicate.
- Authentic assessments: Design assignments that connect to real-world problems requiring original analysis, personal experiences, or application of concepts in ways that are not easily addressed through generic AI responses.
Faculty members noted that these approaches not only address AI usage but often strengthen overall assessment practices.
What considerations should be made when integrating oral assessments?
- Inclusive assessment design: Recognize that students have diverse communication preferences and provide flexible assessment options where possible.
- Structured preparation: Provide clear guidelines and preparation time for oral assessments to reduce anxiety and ensure equitable opportunities for all students.
- Multiple modes: Consider using a combination of written and oral components rather than replacing one with the other entirely.
- Skills development: Use the shift to oral assessment as an opportunity to develop communication skills that will benefit students in their future careers.
- Recording options: When appropriate, allow students to record rather than present live, which can help reduce anxiety while still requiring original work.
How can AI be leveraged to enhance student learning?
Faculty shared various approaches:
- Prompt engineering skills: Teaching students how to effectively craft prompts helps them become better critical thinkers and more effective AI users.
- AI as a learning partner: Framing AI as a collaborative tool rather than a replacement for learning helps students see it as enhancing rather than shortcutting their education.
- Critical evaluation: Teaching students to evaluate AI outputs critically develops important information literacy skills that transfer to other contexts.
- Focus on higher-order tasks: With AI handling certain routine tasks, instructors can focus class time on deeper conceptual understanding, application, and analysis.
Resources for teaching effective AI use are available through the CTLT workshops.
How should AI hallucinations and misinformation be addressed in classrooms?
This was identified as a critical issue during the symposium and faculty noted that this presents an opportunity to emphasize the value of human expertise and judgment in an AI-rich environment.
- Information literacy focus: Instructors are encouraged to explicitly teach students how to verify information from any source, including AI.
- Deliberate demonstration: Some faculty have found success with classroom exercises that demonstrate AI hallucinations to help students understand the limitations.
- Framework for evaluation: Providing students with evaluation frameworks specific to AI-generated content helps them develop critical assessment skills.
- Domain expertise development: Emphasizing the development of core knowledge that enables students to recognize errors is becoming even more important.
Academic Integrity & Assessment
Questions about maintaining academic integrity while using GenAI and adapting assessment strategies.
How might instructors handle undisclosed AI use in writing assignments?
- Transparent AI policies: Establish clear guidelines on acceptable AI use.
- Assignment redesign: Incorporate in-class components or unique applications that AI cannot generate effectively.
- Pedagogical conversations: Engage students in discussions about their process rather than relying solely on punitive measures.