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In our Personal Experience Insights series, members of the Everspring Learning Design department share first-hand accounts of creating online learning content and meaningful takeaways from their professional experiences.

Designing and delivering effective online courses requires careful consideration of numerous factors. As a result, it can be difficult to determine where to begin in the process, particularly for course developers and instructors who are new to online learning. This piece presents a curated list of resources aligned with 10 key considerations applicable across academic disciplines and degree programs.

Imagine planning a trip to a new city. A quick online search highlights the usual downtown tourist spots, but as you explore more, you uncover unique neighborhoods—a financial district bustling with experts, a hidden restaurant scene, and a college area alive with bookstores and cafés. Yet, none of these appeared in your initial search for “best places to visit.”

The rapid advancement of generative artificial intelligence (genAI) technologies has sent shockwaves through the education sector, sparking intense debates about academic integrity, assessment practices, and student learning (Roe et al., 2023; Rudolph et al., 2023; Susnjak & McIntosh, 2024; Swiecki et al., 2022; Yeo, 2023). Since the public release of ChatGPT in November 2022, educators have grappled with concerns about cheating and the potential erosion of traditional academic values (Gorichanaz, 2023; Sullivan et al., 2023). However, as our understanding of genAI capabilities evolves, so too must our approach to assessment and teaching (Lodge et al., 2023).

The rapid integration of AI into professional practice across disciplines makes AI literacy increasingly crucial, not just for technology-focused fields but for all areas of study. Even faculty who are skeptical of AI's value need to consider how it's transforming their disciplines. For example, scientific fields are seeing AI adoption in literature reviews, experimental design, and data analysis. In the humanities, AI tools are already being used for textual analysis, translation, and content creation. Creative disciplines must grapple with AI's impact on artistic production and copyright. Professional programs face increasing pressure from employers who expect graduates to understand AI applications in their field.