Designing for belonging: How to humanize politically sensitive content in asynchronous higher education
This presentation shares the design and development of a five-module asynchronous undergraduate course, Gender Issues in Education, created within an instructional design framework that prioritizes belonging and scaffolded intellectual risk-taking. The course addresses politically sensitive topics—including Title IX, anti-oppressive education, queer theory, and transformative pedagogy—while intentionally supporting non-traditional and working students through structured pacing, low-stakes writing, and community-centered interaction. Drawing on empathy mapping, learner analysis, and interviews with program graduates (Mortensen, 2020; Smith & Ragan, 2005), the design centers students whose educational trajectories are shaped by work, caregiving, and disrupted pathways.
Digital tools (Brightspace, Nearpod, Padlet, NotebookLM, collaborative artifacts) are integrated not for novelty, but for pedagogical purposes, recognizing that “technology is a tool, not a panacea” (UNESCO, 2024, p. vii) and that context-driven solutions do not need to be technologically maximal to be impactful. The course is structured around four commitments: moʻolelo as epistemological entry, writing as scaffolded resistance, belonging as instructional architecture, and AI as guided agency. Grounded in Hawaiian scholarship on relational and genealogical knowledge (Lipe, Oliveira, & Wright, 2015), students enter complex topics through identity, place, and lived experience. Writing unfolds as a protected process culminating in public argumentation, reflecting Kumashiro’s (2002) insistence that education must resist reproducing dominant frameworks of “learning well.” Belonging is structurally embedded across academic, dialogic, and place-based dimensions, informed by a Belonging-Centered Model for Distance Education (Ghersi, 2025) that is attentive to non-traditional learners’ realities. AI is integrated within instructor-guided spaces that preserve student agency and critical engagement. While EDUCAUSE (2025) reflects the sector-wide momentum and institutional framing of AI adoption in higher education, Hallström (2022) offers a needed counterpoint by cautioning against technological over-determinism and the erosion of human control in design. This course positions AI as a bounded research companion—not an author—so that technology supports learning without driving it.
This project models how asynchronous learning can be rigorous, relational, and equity-centered through intentional instructional architecture.
Vanessa Ghersi Cordano, LTEC Student, University of Hawaiʻi, US
Vanessa Ghersi is a Ph.D. candidate in Global and International Education at the University of Hawai‘i at Mānoa. She has taught and held academic leadership roles in higher education in Peru, and her background in the health sciences informs her interdisciplinary approach to education and social inclusion.
Her research examines distance education through organizational, cultural, and political lenses, with particular attention to how institutional structures shape access, belonging, and student success in higher education. Using qualitative and case study approaches, she focuses on fully online undergraduate programs and the experiences of non-traditional students in Hawai‘i and the Pacific.
In addition to her doctoral work, Vanessa serves as a Graduate Assistant at the University of Hawai‘i, where she supports digital learning initiatives, including faculty development, quality assurance, and online program evaluation. She is currently completing the Graduate Certificate in Online Learning and Teaching (COLT) through the Learning Design and Technology (LTEC) department to further engage with all dimensions of distance education and strengthen her practice as a planner, administrator, and instructional designer.
Her scholarly interests include distance education policy, organizational change, culturally responsive education, and the role of institutions in fostering equitable and inclusive learning environments.
