Before considering implications for AI, clarify the learning environment you are trying to cultivate. 

• What are the relevant disciplinary methodologies and epistemologies that students should understand and utilize in this course or program?
• What do I most want students to know, understand, and be able to do by the end of this course or program?
• What specifically do students need to practice in order to develop mastery in these areas of learning?
• Where do students most often struggle or make mistakes in these areas of learning that are foundational for students’ development?
• What do students need to demonstrate so that I can assess how they are learning, not simply what they are producing?
• What capacities or competencies do students need to transfer to other courses,  to professional contexts, or their lives beyond my course or even beyond SCU?

Resources and Support:

The Course Design page introduces the principles of learning goals and backwards design that will help to develop AI responsive course materials and learning environments. Departmental and disciplinary discussions will also be particularly helpful in thinking through these questions.

Clarify what you need to understand about AI's capabilities and limitations in order to make informed choices about your teaching.

• What is generative AI and how does it work?
• What are the capabilities and limitations of generative AI, especially in relation to tasks that my students will encounter?
• What are the implications for generative AI use in my course(s) or program? (e.g. accuracy, bias, privacy, attribution, access)
• How is AI changing knowledge production and the practices of my discipline(s)?
• Where are the gaps I need to address in my current understanding or experience with generative AI to make informed choices about my teaching?

Resources and Support: 

Making decisions for your courses and setting expectations for and with your students requires knowledge and understanding of these fast-evolving tools. SCU has extensive resources and opportunities for professional development across campus and we encourage you to explore more broadly available resources and trainings. At any step of the way, Faculty Development and Academic Technology are available for individual consultation to support your learning and development.

Integrate your understanding of AI with your priorities for student learning to inform course and curricular design.

• What questions about AI (e.g. ethical, institutional value-based, disciplinary) should students be engaging to be successful in my course?
• What assumptions am I making about my students’ performance, positionality, and practices?
• Where might AI support, extend, or create new possibilities for the learning I want students to develop?
• Where might generative AI diminish or fundamentally change learning that students need to practice themselves?
• What kinds of thinkers, practitioners, and people are we preparing students to become and how should that shape the role we give AI in their learning?
• What do I need to change, preserve, or more intentionally design in my learning experiences, assignments, and assessments so students can develop and demonstrate their learning?
• What do I need to observe, assess, or otherwise learn from students to determine whether these choices are supporting the learning environment?
• How will I engage students in ongoing conversations about AI, learning, and the choices we make together about its role in our learning environment?

Resources and Support:

The Teaching and Learning in the Context of AI page elaborates on key ideas about learning design in the context of AI. Select technology workshops are available from Instructional Technology to support learning design, and you can also join an AI Community of Practice to engage with key pedagogical questions and learning alongside other faculty.