Ethical AI
in Academic
Practice.

Helping educators, researchers, and institutions engage AI responsibly, keeping critical judgment and scholarly integrity at the center.

AI is no longer a future challenge for higher education; it is reshaping how students write, how researchers synthesize, and how institutions define academic integrity in real time. The pressure to respond has led many institutions to reach first for detection tools and prohibition policies. These rarely hold, and they rarely teach.

I work at the intersection of ethics, pedagogy, and knowledge production. My formation as a doctoral researcher at the University of Toronto, combined with active course instruction in contemporary ethical issues, has given me a practical and philosophically grounded perspective on what responsible AI integration actually requires.

Experience

The programs, institutes, and engagements that inform this work are documented in one place.

See my work

In Practice

What this work looks like

AI-resilient curriculum and assignment design

Redesigning course structures and assignments to center process, voice, and reflection rather than outputs that AI can easily replicate.

Institutional AI ethics frameworks

Developing guidelines for academic departments that address attribution, intellectual ownership, equitable access to tools, and the ethical responsibilities of educators and learners.

Faculty and researcher toolkits

Training PhD candidates, faculty, and research staff on using AI tools for literature mapping, structural editing, and data organization without displacing scholarly judgment.

Critical AI literacy workshops

Equipping students and educators to interrogate AI, exploring algorithmic bias, the limitations of large language models, and how AI reproduces or challenges structures of knowledge authority.

AI in academic life is too often treated as a compliance problem, when it might be a pedagogical opportunity.