Academic technology and responsible AI
Helping educators explore emerging tools with agency, privacy, verification, accessibility, and a clear connection to teaching and learning.
The question
How can academic technology improve faculty work and student learning without turning adoption into a technology sales pitch?
What I explored
- Commercial and open-source language models, local inference, model evaluation, retrieval, memory, and workflow automation.
- Privacy-conscious paths that make data control and tool boundaries visible.
- AI-assisted research and open textbook development, including organization, drafting, fact-checking, revision, and accessibility preparation.
How I work
- Start with a real instructional or faculty problem, not a tool.
- Explain what data is used, where it goes, what the system can and cannot do, and what remains human work.
- Offer local and cloud options when appropriate, with verification and accessibility built into the workflow.
- Invite small experiments, listen to faculty experience, and revise based on evidence.
What success looks like
Faculty can do meaningful work with less friction, students encounter clearer and more accessible learning environments, and the institution can explain why a tool belongs in the workflow.
Evidence and boundaries
This work grows from higher education teaching, curriculum, faculty governance, technology committee leadership, an open educational resources grant, and ongoing local and cloud tool evaluation. It is a practice of careful translation: making complex technology usable while protecting human judgment, privacy, academic integrity, and trust.
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