LibraOpen

LibraOpen is UVA's home for open access scholarly articles, books, presentations, and other creative works. Learn more about LibraOpen here. Want to submit? Review our LibraOpen Checklist or contact libra@virginia.edu for deposit help.

Communities in DSpace

Select a community to browse its collections.

Recent Submissions

  • Thumbnail Image
    Item type:Publication, Access status: Open Access ,
    2026 Innovations in Pedagogy Summit
    (University of Virginia, 2026-04-29) Kristin Sloane
    The 2026 Innovations in Pedagogy Summit invited us to explore the role of trust in creating conditions where students and instructors can both thrive, through our theme of A Pedagogy of Trust. This site contains materials from the event's presentations and workshops.
  • Thumbnail Image
    Item type:Publication, Access status: Open Access ,
    The Critical AI Literacy Cultivation Framework
    (2026-05-13) Yi, Fang; Taggart, Jessica
    The Critical AI Literacy Cultivation Framework provides a structured yet flexible approach to developing educators’ and students’ AI literacy in higher education. Visualized as a flower, the framework organizes AI literacy into three interconnected and mutually reinforcing domains: Understanding AI, Engaging with AI, and Shaping AI's Role. Each domain cultivates distinct competencies essential for navigating AI in academic, professional, and societal contexts and builds capacity for critical, ethical, and reflective engagement with AI. Importantly, ethical reasoning, responsibility, and critical reflection are not supplementary components, but foundational elements woven throughout all three domains. The framework is designed to span students’ higher education experience at the curricular level — across programs, departments, or institutions — recognizing that AI literacy is cultivated through continuous growth rather than achieved through one-time mastery in a single course. At the institutional, departmental, or program level, leaders can use the framework to design a curriculum that ensures comprehensive AI literacy development among students. At the same time, the framework is not a linear hierarchical sequence that must be strictly adhered to, or a checklist of purely technical skills. Individual educators contribute one piece of this larger curricular puzzle: they can integrate AI literacy into their courses through any domain based on their course objectives, disciplinary needs, and students' prior knowledge, which gives them the flexibility and freedom to make choices that are right for their course.
  • Thumbnail Image
    Item type:Publication, Access status: Metadata only ,
    SKEPTICS at the Table: Rebuilding Trust in Student Learning in the Age of AI
    (2026-04-29) Natasha Heny; Fang Yi
    As generative AI reshapes teaching and learning, many instructors are asking not only what students can do with AI, but how we can sustain trust in the learning process. This interactive session introduces the SKEPTICS framework as a tool for helping students reflect critically on how AI affects their thinking, decision-making, and growth. Participants will explore ways to design assignments that promote transparency, honest self-assessment, and meaningful engagement rather than concealment or overreliance. Through hands-on activities, attendees will adapt the framework to their own courses and leave with practical strategies for building trust in student learning.
  • Thumbnail Image
    Item type:Publication, Access status: Open Access ,
    The Complexities of Love
    (2026-04-16) Camacho, Giovanna
    Love is a complex and multifaceted phenomenon that has been examined through biological, psychological, and relational perspectives. While some theories emphasize the role of neurochemical processes—such as dopamine, oxytocin, and adrenaline—in driving attraction and attachment, these explanations alone do not fully account for the long-term maintenance or dissolution of relationships. This gap has led to the development of alternative frameworks, such as Dr. Gary Chapman’s Five Love Languages, which propose that relational satisfaction depends on how individuals express and receive affection. Despite its widespread popularity, this model lacks strong empirical validation, with prior studies yielding inconclusive support for its underlying structure. This paper critically evaluates the scientific validity of Chapman’s framework, with particular emphasis on limitations in content validity and sample representation in existing research. A review of Egbert and Polk’s (2006) study highlights concerns related to sample size, demographic homogeneity, and insufficient statistical power. In response, a preliminary study was conducted with a more diverse participant pool, though it similarly faced constraints in sample size. Findings suggest that while the concept of relational maintenance remains valuable, more rigorous and inclusive research is needed to establish valid and generalizable constructs for understanding love and relationship dynamics.
  • Thumbnail Image
    Item type:Publication, Access status: Open Access ,
    Can Love Be Defined
    (2026-04-16) Camacho, Giovanna
    Numerous theories have sought to explain the nature of love and its influence on emotional well-being, relational dynamics, and interpersonal communication. Among these, Dr. Gary Chapman’s Five Love Languages model has gained widespread popularity, proposing that individuals express and receive love through five primary modalities: words of affirmation, quality time, gifts, acts of service, and physical touch. Despite its broad public appeal and global dissemination, the model was developed from clinical observations rather than empirical validation. This paper critically examines the theoretical foundations and scientific support for Chapman’s framework, situating it within existing literature on relational maintenance and communication. A review of prior research reveals that, although aspects of the model resonate with established constructs, empirical evidence supporting the validity and reliability of the five love languages remains limited and inconclusive. Notably, studies often cited as validation do not fully substantiate the model’s structure. This analysis highlights the gap between the model’s popularity and its scientific grounding, underscoring the need for more rigorous, evidence-based evaluation of widely adopted relationship frameworks.