Higher Education Access
Doctoral participation, academic labor, institutional constraint, and support for scholars with episodic or invisible disabilities.
I am a full-stack software engineer exploring doctoral study at the intersection of learning sciences, critical disability studies, human-directed artificial intelligence, and higher education access.
My current self-directed project, Adaptive Digital Academic Practice, examines how chronically ill, invisibly disabled, and energy-limited scholars use AI tools, automation, and digital workflows to sustain research while preserving interpretive control.
My interests focus on how higher education might better support academic work under conditions of fluctuating capacity, chronic illness, invisible disability, and limited physical energy.
Adaptive Digital Academic Practice is a preliminary framework for thinking about AI-supported academic workflows as adaptive scaffolds for energy-limited research, writing, coding, and qualitative analysis. The framework is self-directed and unpublished; I hope to refine it through doctoral study.
Doctoral participation, academic labor, institutional constraint, and support for scholars with episodic or invisible disabilities.
Technology-mediated learning, adaptive scaffolding, distributed cognition, and knowledge production under constraint.
Chronic illness, fluctuating access needs, energy limitation, and accessibility beyond compliance-centered models.
AI-supported research workflows that preserve interpretation, authorship, accountability, and human control.
The following work is self-directed, unpublished, and not yet peer reviewed. I present it here as evidence of research preparation and as the foundation for the doctoral questions I hope to pursue.
This manuscript combines disability studies, higher education access, qualitative methodology, and human-AI workflow design. It began as a thought experiment and developed into a practice-based research agenda about how digital tools may support energy-limited academic work.
As part of the ADAP project, I built a modular Python pipeline that uses LLMs to extract structural themes from qualitative text while keeping human interpretation at the center of the process. In an internal comparison, the workflow suggested reduced procedural cognitive demand while preserving substantial agreement with traditional manual coding.
Completed 2 years of foundational computer science and advanced mathematics coursework as a non-traditional post-baccalaureate student.
Joint postgraduate programs focusing on strategic management, international trade frameworks, and data-driven operational logic.
My software background includes building and operating production web applications, including education administration, trade finance, and website monitoring systems. This practical systems-building experience informs my developing interest in human-AI collaboration and academic workflow design.
Full-stack development, production web applications, database-backed systems, and AI-assisted coding workflows.
Qualitative research interests, thematic analysis, literature synthesis, disability studies, and higher education access.