The MegaSoTL Research and OER Development Process
The MegaSoTL Research and OER Development Process
Our Open Educational Resource (OER) was developed as part of a Virginia cross-institutional Scholarship of Teaching and Learning (SoTL) project on AI in teaching and learning.
Needs Assessment Results
To make our design process transparent and emphasize the collaborative nature of this project, we embedded a needs assessment poster we presented at the Teaching and Learning with AI 2024 conference, which summarized the needs assessment results and our research, design, and development process.
We began with a statewide needs assessment survey: What experience did higher education instructors have with AI, and what did they hope to learn from an OER on teaching and learning with AI in higher education? This needs assessment shaped every aspect of our resource, from its structure and content to delivery format.
Below, we will summarize how faculty feedback influenced our key decisions and led to meaningful improvement. You can learn more about this needs assessment from a poster we presented at the Teaching and Learning with AI 2024 conference. To interact with this graphic, enter your name/alias and then proceed to click on the interactive elements noted by an “i” encircled in yellow.
Design Decisions
Enhanced Interactivity and Structure of Content
One of the main takeaways from the needs assessment was the demand for engaging, structured, and interactive learning experiences. In response, we designed easy-to-follow chapter structures and developed H5P interactive modules with multimedia learning resources.
Focus on AI Literacy, Pedagogy and Ethics
The needs assessment revealed that AI literacy was the most critical area for educators, followed closely by pedagogy and ethics. Consequently, we adapted a robust AI literacy framework as the foundation of our OER. The framework guides the progression from basic to advanced AI knowledge and skills while embedding ethical considerations at every level. This ensures that educators not only understand AI tools, but also have the skills to teach students how to use them responsibly in diverse classroom environments.
In addition, we included an “Educator’s Kit: Apply and Reflect” section in every chapter featuring practical teaching strategies and reflection questions. We also included chapters on classroom application, which incorporate practical, specific examples—another key need identified—to help bridge the gap between theory and classroom implementation. These examples include real-world case studies and best practices. The “Course Adaptation in Action” section provides a step-by-step guide for integrating AI into lesson plans and fostering AI literacy among students.
Building a Collaborative Community
Given the strong interest in community features, we added spaces for collaboration and interaction. These include discussion forums, shared resource libraries, and opportunities for readers to submit their own classroom examples. These features are integral to fostering a network of educators committed to learning, sharing, and innovating together.
Adaptability as a Core Principle
Recognizing the importance of adaptability, the OER was designed to allow customization at multiple levels. Educators can reuse and embed the H5P interactive modules into their existing courses and adapt resources to suit various teaching environments. This flexibility ensures that the resource remains relevant and effective across a wide range of educational contexts.
By combining these elements—practical content, a focus on AI literacy, pedagogy and ethics, interactive modules, and community engagement—we created an OER that is not only grounded in educators’ current needs but also serves as a dynamic tool for advancing teaching with AI in higher education.
