SIDLIT 2026

SIGs are a networking opportunity to allow professionals to collaborate on relevant topics. These are held over the lunch hour. Hosts prepare questions and facilitate a discussion on the topic of their choice.

In this presentation I will share the results of a recently completed study of graduate students' lived experience of a pedagogy of care in an online asynchronous learning environment. The results are compared to the experiences of undergraduate students reported in the scholarly literature.  The study focused exclusively on graduate students and aims to current research on the application of Noddings’ (2013) model of care to online contexts. Using an interpretative phenomenological approach, we analyzed participant-generated documents within an online, asynchronous course. Findings revealed few differences between graduate and undergraduate experiences of care pedagogy. Results support two proposed extensions to Noddings’ framework: Robinson et al.’s (2020) division of modeling to two contexts, course design and teaching, and Byrd et al.’s (2025) concept of anticipation. Although limited in generalizability, this exploratory qualitative study contributes to understanding an understudied population and reinforces evidence that care-centered pedagogy can mitigate negative aspects of asynchronous learning, such as feelings of isolation and disempowerment (Burke & Lamar, 2021), which are associated with reduced learning success.

During our inaugural year in partnership with the Center on Rural Innovation’s Artificial Intelligence Consortium (CORI-AI Consortium), Emporia State University (ESU) engaged in a collaborative, community-centered initiative to advance AI literacy and institutional capacity within a rural context. As part of the “Higher Ed AI Consortiums in Rural America” project, supported by Microsoft and LinkedIn, ESU partnered with Emporia Main Street and the Emporia Public Library to design and implement a scalable and sustainable framework for AI education delivery. What transpired from this partnership was the development of a flexible “template app” that supports the delivery of repeatable workshops across diverse audiences. Grounded in a train-the-trainer framework and a growing community of practice, the model enables facilitators to maintain consistency while adapting content to local needs. The resulting model offers implications for broader adoption, contributing to workforce readiness and regional economic development in increasingly AI-integrated landscapes. This presentation shares both the process and the outcomes of this first year, highlighting lessons learned at the intersection of community engagement and the push for equitable AI understanding, usage, and exploration.