What happens when instructional design expertise and artificial intelligence work together?<\/p>\n
Recently, I had the opportunity to explore that question while developing a new Introduction to OSCQR self-paced course.<\/a> Rather than fearing AI as a replacement for instructional design expertise, I approached it as a collaborator, that like any other tool, supported parts of the design process while I remained responsible for critical decisions, quality review, and learner-centered thinking.<\/p>\n <\/p>\n The Introduction to OSCQR course<\/a> is a self-paced training focused on applying the OSCQR (Open SUNY Course Quality Review)<\/a> rubric and review process. Similar to our SUNY Online Teaching (SOT) OSCQR webinars<\/a>, the course is designed to help faculty, instructional designers, and peer reviewers use OSCQR and apply its standards towards course quality review and continuous improvement efforts.<\/p>\n We developed this course with several goals in mind:<\/p>\n Like many instructional design projects, this one came with a combination of known requirements and practical challenges. Our small SUNY Online Teaching team were the subject matter experts, and we had already defined the course objectives, audience needs, and overall course structure. My challenge was to work efficiently within a short timeframe, while maintaining quality to create a clear, engaging, and accessible training course.<\/p>\n To help meet those goals, I used AI in three areas:<\/p>\n While AI helped progress the course development more quickly, every interaction with it still required my human review, refinement, and decision-making.<\/p>\n For content editing, I used Microsoft Copilot to refine language, reduce repetition, and improve clarity. I also asked it to take a learner’s perspective, which helped me think through the course flow and identify opportunities for light reorganization.<\/p>\n Throughout the process, I relied on my own contextual knowledge and instructional designer experience. Understanding how OSCQR is used, who the audience would be, and what information mattered was essential. AI could generate useful suggestions with clear directions, but it still had misconceptions or misalignments that required my continual review and revision.<\/p>\n The self-paced course includes auto-graded knowledge-check quizzes, and to help with that I used Lumi Pro, a Brightspace AI tool. Lumi helped me generate quiz questions and align them with Bloom\u2019s taxonomy levels. Simply having it generate incorrect answer options saved a significant amount of time.<\/p>\n Still, as the instructional designer, I made the key assessment decisions. I determined what learners should be assessed on, which question formats best supported the objectives, and what language to include or avoid. Though AI helped me draft questions more quickly, I remained responsible for ensuring that every item was accurate, aligned, and meaningful.<\/p>\n Perhaps the most impactful way I used AI was for course page coding and visual design.<\/p>\n Based on our needs, I wanted to create custom HTML pages that were accessible, visually appealing, and portable outside of a single LMS environment. To do that, I used Google Gemini to draft and refine HTML layouts and visual elements. Over time, I compiled them into a library of reusable templates that I could draw from as I built the course. Once I added content and language, I used Copilot, and its enterprise license with additional data protection, to help review the code for accessibility and identify potential issues.<\/p>\n AI helped me create consistent page layouts, improve visual presentation, support accessibility efforts, and address transferability requirements for campus adoption. These actions were incredibly helpful, but they also required the most testing, refinement, troubleshooting, and prompt iteration to ensure everything functioned correctly and met desired expectations.<\/p>\n The biggest takeaway from this project is simple:<\/p>\n AI can help, but human understanding, direction, and review are essential.<\/p>\n<\/blockquote>\n I found AI was most useful when I gave it a clear purpose and a well-defined framework. It helped generate options, provide alternative perspectives, and accelerate portions of the development process. Yet I remained responsible for instructional decisions, contextual understanding, quality assurance, and final judgment.<\/p>\n That balance of human expertise and AI support ultimately helped bring this project to life as a practical learning resource that supports faculty, instructional designers, and peer reviewers in their own OSCQR work.<\/p>\n The Introduction to OSCQR self-paced course is being piloted in Fall 2026 and is publicly available, free, and self-paced. Participants can register through SUNY CPD: Introduction to OSCQR Self-Paced<\/a>, and campuses may also request a copy of the course file<\/a> for local adaptation and use.<\/p>\n If you are interested in learning more about OSCQR and course quality improvement or seeing a real-world example of AI-supported course design, check out the Introduction to OSCQR self-paced course and see how these ideas come together in practice.<\/p>\n Have you experimented with AI in your own course design work? If so, how have you balanced its capabilities with your own expertise and judgment?<\/strong><\/p>\n","protected":false},"excerpt":{"rendered":" What happens when instructional design expertise and artificial intelligence work together? Recently, I had the opportunity to explore that question while developing a new Introduction to OSCQR self-paced course. Rather than fearing AI as a replacement for instructional design expertise, I approached it as a collaborator, that like any other tool, supported parts of the […]<\/p>\n","protected":false},"author":8299,"featured_media":12091,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":"","_members_access_role":[],"_members_access_error":""},"categories":[602,319,179,1],"tags":[87],"class_list":["post-12083","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-blog","category-instructional-design","category-teaching-online","category-uncategorized","tag-instructional-design-2"],"_links":{"self":[{"href":"https:\/\/online.suny.edu\/onlineteaching\/wp-json\/wp\/v2\/posts\/12083","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/online.suny.edu\/onlineteaching\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/online.suny.edu\/onlineteaching\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/online.suny.edu\/onlineteaching\/wp-json\/wp\/v2\/users\/8299"}],"replies":[{"embeddable":true,"href":"https:\/\/online.suny.edu\/onlineteaching\/wp-json\/wp\/v2\/comments?post=12083"}],"version-history":[{"count":5,"href":"https:\/\/online.suny.edu\/onlineteaching\/wp-json\/wp\/v2\/posts\/12083\/revisions"}],"predecessor-version":[{"id":12089,"href":"https:\/\/online.suny.edu\/onlineteaching\/wp-json\/wp\/v2\/posts\/12083\/revisions\/12089"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/online.suny.edu\/onlineteaching\/wp-json\/wp\/v2\/media\/12091"}],"wp:attachment":[{"href":"https:\/\/online.suny.edu\/onlineteaching\/wp-json\/wp\/v2\/media?parent=12083"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/online.suny.edu\/onlineteaching\/wp-json\/wp\/v2\/categories?post=12083"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/online.suny.edu\/onlineteaching\/wp-json\/wp\/v2\/tags?post=12083"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}The Introduction to OSCQR Self-paced Course Project<\/h2>\n
\n
\n
Why Use AI?<\/h2>\n
\n
Content: Refining and Organizing Information<\/h2>\n
Assessment: Faster Question Creation<\/h2>\n
Coding and Accessibility: AI-Powered Development Support<\/h2>\n
The Most Important Lesson: AI Helps, but Humans Lead<\/h2>\n
\n
Check Out the Introduction to OSCQR Course<\/h2>\n