TRACE Tuesday

Abstract: At Marshall, we often talk about belonging as a part of shared identity. From “WE ARE…MARSHALL” to becoming a part of “The Herd,” the idea that people can find connection and purpose here is central to who we are as an institution. But in the classroom, belonging doesn’t just happen automatically. It’s shaped, often in subtle ways, by how we design learning experiences and how we engage with students in the moments that matter.

This plenary explores the relationship between challenge, support, and student experience. When expectations feel overwhelming, students may disengage or question whether they belong. When expectations are too low, students may feel unchallenged and disconnected from the work. Meaningful learning tends to occur in the space between these extremes, where students are pushed to grow, but supported enough to succeed. This balance also plays a critical role in shaping students’ sense of belonging, confidence, and identity.

Drawing on examples from applied, field-based teaching as well as research on student learning, this session examines how instructional design, interaction, and expectations send signals about who belongs and who can succeed. Participants will be invited to reflect on how their own teaching, research mentoring, and community engagement practices create conditions that either support or undermine student connection and participation. The session concludes with a set of practical considerations for fostering environments where students are not only learning content, but also developing the capacity and confidence needed to apply it beyond the classroom.

Abstract: Artificial intelligence (AI) is swiftly changing the way people engage with information, especially in regard to mental and physical health. This gain in access to information is best characterized by Norman and Skinner (2006) where they note “electronic health resources are helpful only when people know how to use them.” These authors created the eHEALS, a health literacy scale designed to assess consumer’s perceptions of their skills in finding and evaluating electronic health information and applying it. However, the emergence of large language models (LLMS) has introduced complexities that go beyond traditional internet use. These models not only provide health information; they aggregate, synthesize, generate recommendations, and often function as informal health, fitness, and mental health coaches. This presentation will discuss recent research comparing human participants with pseudo-participants generated by several of the leading LLMs and comparing them on measures related to mental health literacy, electronic health literacy, and perceptions of credibility between popular public figures (Black, Fugett, & Williams, 2026). Specific attention will be given to the discrepancy in variation displayed by LLMs and how this does not mimic real-world data. Further, this presentation will address the ways in which individuals utilize LLMs as “coaches” to assist with fitness and nutrition goals (Williams, 2026). Lastly, future directions related to human and AI relationships will be discussed as it relates to mental and socio-emotional health, as well as, ethical collaboration between users and AI systems.

Abstract: Artificial intelligence is rapidly changing education, prompting faculty to reconsider how students engage with learning, demonstrate understanding, and develop the knowledge and skills our disciplines value most. Whether we are enthusiastic about AI, skeptical of its role, or simply unsure where to begin, one reality remains: our students are already encountering and using these tools.

This interactive session invites faculty to explore what meaningful teaching and learning look like in an AI-shaped world. Rather than focusing solely on academic integrity or learning how to use a particular AI tool, we will begin with a more fundamental question: What does it mean to learn well in the age of AI? Through discussion, examples, and shared reflection, participants will consider how AI is influencing student engagement, assessment, and the cognitive work we expect learners to perform.

Together, we will also consider practical considerations for course design, including opportunities to leverage AI to support learning while remaining mindful of privacy, transparency, bias, and related ethical concerns. Participants will also explore strategies for designing assignments and assessments that emphasize critical thinking, reflection, and authentic demonstrations of learning.

As a community of educators, we will collaboratively develop a set of guiding principles for teaching in the age of AI, drawing on the diverse perspectives and experiences represented across disciplines. The goal is not to arrive at a single right approach, but to begin building a shared foundation that supports thoughtful, responsible, and student-centered teaching as AI continues to reshape higher education.

Abstract: Named Oxford University Press’s word of the year in 2024, the slang term “brain rot” describes the “deterioration of a person’s mental state” after too much time spent consuming unedifying online content, as well as a popular category of digital media designed to be materially and intellectually worthless. While brainrotted content may seem diametrically opposed to the kind of material taught in university classrooms, many professors are familiar with the accusation that their lessons are pointless, a waste of time– or even that a college degree has lost its value. Rather than mounting a defense against that accusation, this talk approaches it with curiosity, asking why students might find educational content “worthless” and what makes worthless content so appealing in the right aesthetic packaging. Building on research from digital media studies, as well as scholarship on slow pedagogy, this talk will outline what educators can learn from brainrotted browsing as a practice of psychological decompression. It will offer practical strategies for decompression-oriented pedagogy that resists optimization and productivity culture in favor of deep learning.

Abstract: As AI becomes increasingly embedded in teaching and learning, community‑based learning (CBL) educators face pressing questions about how to use these tools without weakening the relational and human foundations essential to meaningful engagement. This session introduces a human‑in‑the‑loop approach that positions AI as a tool for listening to communities rather than speaking for them. Participants will learn strategies for designing prompts that foreground community strengths, elevate local expertise, and avoid deficit‑based narratives. Through examples of AI‑assisted interview analysis, partnership communication, and workflow planning, the session demonstrates how AI can support reflection, empathy, and deeper understanding while maintaining ethical storytelling practices. The workshop also addresses faculty sustainability, helping participants distinguish which CBL tasks AI can streamline—and which must remain rooted in human connection, co‑creation, and relationship‑building. Attendees will leave with practical tools for integrating AI in ways that uplift community voice, cultivate student empathy, and sustain meaningful CBL partnerships.

Abstract: Many barriers to student learning are not about ability, but about design. This session reframes accessibility as a set of everyday teaching decisions, from how assignments are structured to how course materials are organized and communicated. Participants will explore practical strategies for reducing cognitive overload, increasing clarity, and creating flexible pathways for engagement. Through concrete examples and applied activities, attendees will leave with actionable techniques they can immediately implement in their own courses.