College of Educationhttps://aurora.auburn.edu/handle/11200/39802026-05-15T14:35:30Z2026-05-15T14:35:30ZCritical review of Beauboeuf-Lafontant's “To Live More Abundantly: Black Collegiate Women, Howard University and the Audacity of Dean Lucy Diggs Slowe”https://aurora.auburn.edu/handle/11200/507762026-05-04T13:46:01ZCritical review of Beauboeuf-Lafontant's “To Live More Abundantly: Black Collegiate Women, Howard University and the Audacity of Dean Lucy Diggs Slowe”
Program Assessment: Career Development Services as Informed by Black Female Undergraduates in Engineeringhttps://aurora.auburn.edu/handle/11200/507752026-05-04T13:47:21ZProgram Assessment: Career Development Services as Informed by Black Female Undergraduates in Engineering
The Saturation of Ease: Diagnosing Epistemic Stewardship and the Innovator’s Paradox in Generative Learning Environmentshttps://aurora.auburn.edu/handle/11200/507702026-03-07T14:57:21ZThe Saturation of Ease: Diagnosing Epistemic Stewardship and the Innovator’s Paradox in Generative Learning Environments
This investigation diagnoses the innovator’s paradox within specialized knowledge domains, where high technical competence does not significantly predict the adoption of generative artificial intelligence (AI). The findings identify epistemic stewardship as the primary driver of practitioner resistance, revealing that experts reject frictionless generative heuristics to protect student epistemic agency, defined as the cognitive struggle of synthesis.
Utilizing a triadic theoretical framework (TAM, Diffusion of Innovations, and HCI), this research identifies a saturation-of-ease threshold where technical efficiency conflicts with pedagogical validity. The data suggest that, for expert practitioners, perceived ease of use is a non-predictive metric of technology acceptance when it threatens the integrity of the learning process. We conclude that sustaining scholarly integrity requires a systemic shift toward Socratic architectures that prioritize cognitive friction and professional stewardship over algorithmic velocity. This transparent approach serves as a blueprint for institutional guardrails that preserve the human cognitive core within automated academic workflows.
Collaborative Artificial Intelligence Learning Architecture: Restoring Scholarly Friction and Epistemic Governance via Collaborative Inquiry Using an Epistemic Friction Frameworkhttps://aurora.auburn.edu/handle/11200/507692026-03-07T15:02:32ZCollaborative Artificial Intelligence Learning Architecture: Restoring Scholarly Friction and Epistemic Governance via Collaborative Inquiry Using an Epistemic Friction Framework
This study utilizes an epistemic friction framework as a system-centric approach to eliminate reliance on generalist chatbots by distributing cognitive load across specialized agents. The architecture introduces the Parallel Cognitive Router (PCR), demonstrating an asynchronous compute offloading protocol that ensures the human researcher remains the final epistemic governor of scholarly meaning. Current velocity-centric paradigms operationalize Large Language Models (LLMs) as high-speed stochastic text generators, prioritizing efficiency over epistemic rigor. This creates an illusion of understanding that precipitates model collapse through recursive, unverified output. To address these structural failures, we have constructed a proof-of-concept application demonstrating system-centric orchestration via a collaborative learning architecture. The PCR framework isolates cognitive load across specialized agents: an Analyst (syntax mode) and a Skeptic(adversarial critique). Central to this architecture is a heterogeneous knowledge stewardship protocol that maps specific agent functions to physical processing units. The Analyst agent is restricted to the Central Processing Unit (CPU) for deterministic syntax generation (e.g., SPSS or Python). Through processor delineation and alignment, methodological constraints are established locally, preventing stochastic hallucination by maintaining modular isolation between logic and prose. Simultaneously, a dialectical friction agent utilizes local inference optimization on parallel hardware to perform adversarial stress testing. By offloading high-heat computational tasks, the system simulates the rigors of peer review to identify logical gaps and citation errors before external model interaction occurs. This glass box approach provides a blueprint for institutional policies that prioritize professional stewardship and scholarly validity (Φ = .89) over algorithmic compliance.