The mode of a class impacts how learners encounter and make sense of new knowledge and skills. Designing the instructive part of a lesson involves clear, structured explanations and learning materials that support students in the achievement of the learning outcomes.

Key design tips:

• Avoid long lectures in all modes; attention drops after as little as 20 minutes in person and 8-12 minutes online. After this, students need to engage with the content before learning more (See “Interaction” below).
• PowerPoint slides, notes, and other instructional materials can be uploaded for students in all modes. Where these are supplemental to their presentation (i.e., live or recorded presentation), it’s possible for them to be structured, scaffolded, or outlined only.
• When creating instructional materials, try to make them as accessible as possible so that all students can benefit from them. For support with this, see our page on Accessibility and UDL.

AI Considerations

• We acknowledge that faculty are increasingly turning to Generative AI to produce primary and supplementary instructional materials. We encourage you to model academic and professional integrity to students by ensuring all generated materials are vetted by you to ensure accuracy, relevance, alignment with learning outcomes, absence of bias, and acknowledgement or citation of source material where appropriate.

Interaction is where students actively engage with content, peers, and the professor to deepen learning. If students have no opportunity to actively engage with their learning, new information and skills will be quickly forgotten. Well-designed activities create opportunities for application, discussion, and practice so that students don’t experience new ideas passively.

Key design tips:

• Whereas digital presence (e.g., quick email responses, meaningful feedback, and use of announcements) is required with online delivery, academic research and student feedback show that it is helpful in all modes. 
• Group work can be a great experience, especially for in-class, formative interactions, but it can become challenging the more that grades are involved. We advise that group interactions be structured so that opportunities for students to sidestep learning through task avoidance or GenAI offloading are minimized, especially with online delivery.

AI Considerations

• Discussions and reflection activities can be more meaningful if they are not purely text-based or generic enough that GenAI can easily complete them. Look for opportunities for students to show the authenticity of their responses with supporting media (audio, image, video).
• Even with in-person classes (but especially when online), it’s best to design interactions around personal perspective, immediate application or decision-making, and connection to specific course material and experiences so that students don’t rely on Generative AI to produce simulated outcomes that aren’t actually learned.

Assessment provides evidence of whether students have achieved the intended learning outcomes. It involves designing tasks that meaningfully evaluate learning, emphasizing application, process, and demonstration of understanding.

Key design tips:

• All modes benefit from scaffolding, i.e., smaller, low-stakes assessments that build towards more comprehensive ones. This gives students a chance to receive feedback and have early success (or failure) so that they know how to meet expectations for high-stakes evaluations.
• Although assignments may be presented differently to students in person vs. online courses, all students benefit when assignment descriptions, feedback, and grades are posted to FanshaweOnline. This gives a consistent and easy-to-find place for students to locate these critical items.
• In all modes of delivery, it is important to design assessments so that the majority of grades a student receives are authentic to their demonstrated ability. Because it can be difficult to hedge against, for instance, all plagiarism or misuse of AI on all assignments, it’s important to have certainty around evaluation validity for key outcomes and significant grade items.

AI Considerations

• Traditional, take-home assessments have largely been made obsolete by GenAI tools. Unless written under supervision or intentionally and demonstrably integrated with expected AI use, essays, reports, drafts, discussions, reflections, etc., are no longer valid means of assessment.
• Some online evaluations are still able to confound AI offloading where the information required to succeed is not available to the AI’s large language model. For instance, applying knowledge given in an in-class setting, or described orally, or related to an immediate and/or local context or verifiable lived experience of the student.