What is it?
As the academic landscape evolves, generative AI (GenAI) – a transformative technology capable of producing sophisticated content including text, code, images, and audio through user interaction – is fundamentally reshaping teaching and learning environments. These advancements present the academic community with both unprecedented opportunities and important pedagogical considerations. In response to AI platforms like ChatGPT demonstrating increasing proficiency in traditional academic tasks, instructors are invited to thoughtfully adapt their instructional approaches while maintaining the integrity of our educational mission.
Across disciplines, faculty members are embracing this transition through strategic curricular innovation, developing frameworks that either establish appropriate boundaries or meaningfully integrate AI tools. This adaptation extends to the creation of enriched educational resources, from dynamic case studies to comprehensive assessments and course materials, enhancing their capacity to deliver engaging, effective instruction.
As a faculty member at UBC, you’ll find that integrating generative AI in academic environments presents both transformative opportunities and significant ethical considerations that require thoughtful navigation.
The transformative potential of AI in higher education
Artificial Intelligence has the potential to transform teaching and learning in higher education through multiple applications. This technology offers opportunities to enhance educational experiences by supporting both faculty and students across various dimensions of academic work.
Key Applications of AI in Higher Education
- Rapid content creation: AI can generate instructional materials, case studies, and assessments tailored to evolving student needs and disciplines.
- Personalized learning: AI can adapt to individual learning profiles, offering scalable tutoring, targeted feedback, and adaptive support.
- AI as a learning partner: AI can support brainstorming, drafting, programming, and revision throughout the assignment process.
Considerations
Academic integrity and learning
Maintain academic integrity when integrating GenAI tools in teaching and learning by balancing their potential benefits with the risks of academic misconduct. How can we ensure that students use these tools ethically and transparently? Clearly communicating expectations, providing guidance on proper acknowledgment, and addressing ethical considerations such as copyright, accessibility, and bias can help foster responsible and intentional use. Read more about AI and Academic Integrity at the UBC Academic Integrity Hub.
Privacy and data protection
You’ll need to prioritize student privacy by using only supported AI tools at UBC that have completed a Privacy Impact Assessment (PIA). These institutionally vetted systems ensure compliance with provincial legislation and university data governance requirements, protecting sensitive information and preventing unauthorized data processing or storage. You can determine which AI systems are approved for use in the classroom through successful PIA on the CTLT AI website.
Equity
Additionally, consider the diverse needs of your students when implementing AI tools, paying particular attention to barriers to learning and inequities that might arise when AI is part of the assignment and assessment process. How can we ensure learning equity when some learners have access to paid AI platforms that tend to produce more accurate outputs? Additionally, some students will have more skills in prompting AI to produce better results. Providing students with scaffolding and helping them develop better skills using these tools can improve student equity.
Accuracy and bias
All outputs of GenAI for teaching and learning purposes should undergo human review before sharing. Users should think critically about outputs from GenAI, including their potential for producing false or misleading information, especially if sources of information in the outputs cannot be identified and/or verified.
Where to start
- Develop an understanding of AI and its capabilities and limitations in your discipline. Developing an understanding of AI and its capabilities and limitations can help you make informed decisions about its role in your discipline. Whether you choose to adapt assignments to mitigate AI use or explore ways to incorporate it effectively, the CTLT offers workshops and resources to support you in navigating these possibilities.
- Become familiar with UBC’s guidelines for the responsible use of Generative AI in the classroom, including key considerations related to privacy, intellectual property, and equity. As discussed, these issues are significant when using AI and understanding UBC’s approach to addressing them is essential.
- Redesign assessments to thoughtfully integrate or mitigate AI. AI is reshaping student evaluation. Identify when to limit AI use for essential skill development and when to leverage it as a learning partner to enhance disciplinary skills and approaches.
- Develop a plan for communicating AI use to students, including when it is appropriate or inappropriate to use and how it will be integrated into your course. Clear guidelines help students understand expectations, ensure appropriate use, and uphold academic integrity.
- Experiment with Generative AI in your discipline, by using it for lesson planning, content creation, assessment design, or student engagement. Exploring these applications helps you assess its benefits, challenges, and ethical implications while aligning with UBC’s guidelines.
Go further
To keep exploring and experimenting with AI in your teaching, there are plenty of UBC resources and professional development opportunities available to support you. Here are a few to get you started:
The Teaching and Learning with GenAI website provides resources to support teaching, learning, and assessment with AI.
The CTLT offers a variety of online and in-person AI workshops and studios to support faculty integrating AI in their teaching.
The UBC guidelines for responsible Generative AI emphasizes its transformative potential in education while addressing data privacy, algorithmic bias, and the balance between AI and human interaction.
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