Research & Frameworks
Each domain is independently rigorous and grounded in validated theory. Together, they represent a coherent research program built around one question: how do we design systems that work for the people inside them?
Domain I
This is my most developed research stream — a seven-layer framework ecosystem built over three years of teaching, independent scholarship, and classroom practice. Each layer addresses a distinct requirement for ethical, pedagogically sound AI integration in higher education. All are classroom-tested; most are published or in active submission.
The LTTR Framework (Literate, Transparent, Traceable, Responsible) and the Safe Ethical Zone — a psychophysics-informed model of how students navigate ethical decisions under pressure from AI tools.
Foundational Framework for GenAI Literacy & Deployment — five stages from Access through Adopt. Cited as a core framework in the Michigan SBDC's national AI guide for small businesses (Google.org initiative).
Student-Driven and AI-Driven interaction models. Ask–Review–Reflect–Follow-up cycle. Planned-Structured-Supervised (PSS) framework. Published as institutional resources at WMU.
PCTII cycle (Prepare–Create–Test–Improve–Integrate) — a practitioner-developed, code-free process for building custom educational AI agents. Educator-in-the-Loop (EITL) two-layer governance architecture.
14 storytelling characteristics for evaluating AI-generated instructional content. The Fact-O-Fictionist Story Problems GPT — a structured A–L intake interface constraining AI output within pedagogical boundaries.
Human-AI Collaboration Rubric — a 210-point instrument measuring interaction quality across nine criteria. 110 points for human tasks; 100 for AI tasks. Classroom-tested and published at ASEE 2024.
Strategies for preventing cognitive dependency and protecting independent student thinking in AI-integrated environments. Published as an institutional resource at WMU.
The AI portfolio includes detailed descriptions, framework visuals, deployed agents, and the complete research record for this stream.
Domain II
My PhD dissertation research at Western Michigan University applies machine learning and computer vision to a problem that has resisted easy measurement: how does fatigue develop in workers performing repetitive hand motions? Most current approaches rely on self-report or physiological sensors that are disruptive in real work settings. My approach uses video-based hand movement classification to detect fatigue signatures non-invasively — offering a practical alternative for occupational health monitoring.
This work is methodologically sophisticated and interdisciplinary, drawing on computer vision, signal processing, biomechanics, and ergonomics. It represents a meaningful technical and applied contribution to the field of occupational human factors.
This stream also connects to broader questions in my research about how technology can be designed to serve workers — not surveil them — and how we measure human performance in ways that preserve dignity and reflect how fatigue actually works in practice.
Domain III
Learning does not happen in a vacuum. Students carry full lives — including grief, trauma, instability, and stress — into classrooms that are often designed as if they do not. Trauma-informed pedagogy is not about lowering standards or avoiding difficulty. It is about designing courses that recognize the conditions under which learning is possible and that do not inadvertently harm the students they are meant to serve.
This stream includes published work on trauma-informed and student-centered pedagogy in higher education, policy recommendations for supporting women scholars navigating grief in academic institutions, and ongoing research into how course design can function as a protective structure rather than an additional stressor.
Aref, E., & Paul, D. (2026). Teaching through grief, designing for care: Toward trauma-informed and student-centered pedagogy. Higher Education Research and Development, 45(2), 366–373.
Aref, E., Wheeler, D., & Haggar, J. (2024). Navigating grief in academia: Prioritizing supports for women scholars through informed approaches. ASEE Annual Conference, Portland, Oregon.
Domain IV & V
My work in management education spans team dynamics, conflict resolution, project management, and the integration of AI as a thinking partner in organizational courses. I approach management not as a set of tools to memorize but as a design challenge: how do you build teams, run projects, and make decisions in conditions of uncertainty, with people who have competing interests and different strengths?
This stream is deeply connected to my teaching practice. The EM6000 GPT (Management and Organizational Behavior simulation agent) and Project Pal GPT were developed directly from this work — deployed in live courses before they became scholarship.
The scholarship of teaching and learning (SoTL) treats pedagogical practice as a site of inquiry. I publish from the classroom — not just about it. Course design decisions, assessment instruments, AI integration strategies, and student outcome data are all materials for research. This means that my teaching and my research are not parallel tracks; they are the same activity, described at different levels of abstraction.
Publications & presentations