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Webinar Resources: AI-Enhanced Instructional Design: Scaling Quality, Accessibility and Innovation
Webinar Description
In the past, the development of meaningful, accessible and effective learning experiences has always been a complex and time-intensive process. Generative AI has demonstrated tremendous capability in the generation of course content, starting with learning objective and facilitating the generation of actual learning content. With this as a foundation, how can we then engage higher-level design principles like spacing, scaffolding, cognitive load management, and others. Importantly, how can we accomplish all this while ensuring accessibility for learners of varying abilities? This session will explore the ways in which designers can leverage generative AI to achieve these goals, and will provide practical prompting models for accomplishing these goals.
Resource Sharing
Name of resource
Description
Click the link and scroll down to the “Read Me” section to understand more about the uses for the files at the top of this Github space. The YouTube presentation above will walk through how these are used.
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Shared files
File
File use
This is an example of a document that is created after using the process from one of the Github resources to create a course blueprint. You can look at this output to see what works well and what is lacking (where you would be the human in the loop to make corrections).
This is an example of a document that is created after using the process from one of the Github resources to create a course syllabus. You can look at this output to see what works well and what is lacking (where you would be the human in the loop to make corrections).
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Questions from the audience
Question
Response
AI in Course Design, Instruction & Content Creation: How can educators effectively use AI to design courses, generate instructional materials, and automate content creation while maintaining alignment with curriculum standards, pedagogical quality, academic integrity policies, and human oversight?
Open
AI Integration, Systems & Interoperability: How can institutions build interoperable AI frameworks that integrate effectively with existing systems (such as LMS platforms) while maintaining instructional quality standards?
Open
AI Tools, Access & Platform Choices: Which AI tools or platforms (e.g., ChatGPT, Codex, or others) are most appropriate for course design and educational use, and what are the practical considerations when choosing between them?
Open
AI Reliability, Hallucination & Quality Assurance: Given issues such as hallucinations and inaccuracies, how can educators ensure the reliability and quality of AI-generated instructional content?
Open
Academic Integrity & AI-Generated Content Policies: How should institutions address academic integrity and quality concerns related to AI-generated content, including detection thresholds and acceptance policies?
Open
Standardization, Prompts & Workflow Design: Is it possible to standardize AI use in instructional design through generic prompts or structured workflows, and what are the limitations of doing so?
Open
Strategic Transformation & Workforce Impact: What is the long-term value and return on investment of shifting instructional designers from content creators to AI-augmented architects of learning ecosystems?
Open
Human Creativity, Agency & AI Impact: Does AI risk reducing human creativity and agency, and how can educators ensure that AI enhances rather than replaces creative thinking and problem-solving?
Open
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