https://aurora.auburn.edu/handle/11200/3980 2026-04-05T17:06:58Z 2026-04-05T17:06:58Z https://aurora.auburn.edu/handle/11200/50770 2026-03-07T14:57:21Z

The 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.

https://aurora.auburn.edu/handle/11200/50769 2026-03-07T15:02:32Z

Collaborative 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.

https://aurora.auburn.edu/handle/11200/50768 2026-03-07T16:04:22Z

Systemic Architectures for Agentic Learning Integration: Strategic Directives for Human Capital Development This architecture establishes a normative framework for systemic knowledge management within specialized technical learning systems. By transitioning from fragmented tools to a collaborative learning architecture, we investigate the gap between technical innovation and educational mandates. This approach ensures that specialized learning communities prioritize practical utility and systemic integrity in developing human capital. Data evaluation was conducted through a dual-mode human-machine synthesis protocol, ensuring that directions align with global educational quality standards (Φ = .89). This framework identifies agentic learning platforms as the dominant shift for 2026, requiring a federated architecture for localized innovation.

https://aurora.auburn.edu/handle/11200/50767 2026-03-07T16:08:32Z

The Architecture of Truth: Replacing Prompt Engineering with Semantic Verification Protocols and Socratic Friction Current AI discourse focuses on prompt engineering; teaching humans to optimize linguistic inputs for better results. This investigation identifies this model as a contributor to epistemic fragility, where the validity of generated information is assumed rather than architected. We propose a system-centric orchestration architecture to replace passive user trust with active cognitive evaluation matrices. Utilizing semantic verification, the framework enforces strict disciplinary boundaries to prevent context drift and hallucination across specialized knowledge domains. Central to this architecture is the integration of Socratic friction. Unlike standard models designed for velocity, our system utilizes real-time interventions to trigger dialectical warnings. When a heuristic gap is detected, the system acts as an intellectual sparring partner, forcing the user into a state of disciplinary discernment. By intentionally slowing the generative process through automated verification, the framework restores the human researcher as the final epistemic governor. This shift from prompt engineering to verification architecture provides a scalable blueprint for institutional policies that prioritize professional stewardship and scholarly validity over algorithmic ease.