A four-module course that gives faculty a working mental model of how generative AI actually produces its outputs — and turns that model into principled judgment for teaching, research, and everyday academic work.
Faculty are being asked to set AI policies, redesign assessments, referee student AI use, and fold AI into their own research — often without ever being shown how generative AI actually works or why it fails.
This course starts there. Before any application, faculty build a working mental model of the generation mechanism itself — why inaccuracy, bias, and inappropriate output aren't glitches to wait out, but structural properties to manage. Every later decision about teaching, research, and workflow stands on that foundation.
The result isn't a list of tools or prohibitions. It's durable professional judgment — conceptual grounding built through inquiry, not prescription — that holds up as the AI landscape keeps shifting underneath it.
Syllabus AI policies and assessment rules are being written under deadline — often without principled grounding to make them defensible.
AI accelerates drafting and searching — then hands back a truth-checking obligation at every stage of the scholarly workflow.
Tool-specific training expires fast. A reflexive, deliberate practice gives faculty a posture that outlasts any single model or product.
The course moves deliberately — first the mental model, then its application to the teaching role, then the research role, and finally integration into a durable personal practice.
Identify indicators of inaccuracy, bias, and inappropriate output in AI-generated content — and explain why those failure modes arise from the underlying generation mechanism itself.
Establishes the conceptual foundation every faculty member needs before applying AI in any professional role. Faculty leave with a working mental model of how generative AI produces outputs — not as a technical explainer, but as the basis for professional judgment about reliability, productivity, and verification.
Describe the principles behind a defensible AI policy framework, and identify the features of assessment design that make AI use appropriate — or inappropriate — in a specific learning context.
Applies the Module 1 mental model to the teaching role. Faculty face mounting pressure to set AI policies, redesign assessments, and respond to student AI use — often without principled frameworks. This module builds conceptual grounding through inquiry, not prescription.
Recognize how AI tools both support and complicate research workflows, and identify the scholarly and ethical considerations involved in AI-assisted literature review, manuscript writing, and administrative work.
Extends the foundational mental model into the research and professional-practice dimensions of faculty work. The defining phenomenon is the Verification Burden — the labor-intensive truth-checking layer AI has added to every stage of the scholarly workflow.
Identify the characteristics of reflexive AI practice, and describe concrete strategies for integrating AI deliberately into teaching, research, and service workflows.
Having built conceptual frameworks for AI's mechanics, teaching applications, and research applications, faculty integrate those frameworks into deliberate personal practice. The Reflexive AI model gives faculty a durable posture toward a rapidly evolving landscape.
Every module ends in a capability faculty can name, use, and defend — to students, colleagues, and review committees.
A working mental model of generative AI — able to explain where outputs come from and why inaccuracy, bias, and inappropriate content arise from the generation mechanism itself.
A defensible basis for AI course policy — grounded principles for deciding when AI use is appropriate or inappropriate in a specific learning context.
Assessment-design judgment — the ability to identify which features of an assessment make it robust, or vulnerable, to student AI use.
Research-workflow awareness — recognition of how AI supports and complicates literature review, manuscript writing, and administrative work.
Verification discipline — a clear-eyed account of the truth-checking burden AI adds to scholarly work, and where it must be paid.
A reflexive personal practice — concrete strategies for integrating AI deliberately into teaching, research, and service, with a posture built to outlast today's tools.
Designed for busy academic calendars — no synchronous sessions, no software installs, no prerequisites beyond curiosity.
Each module builds directly on the last — from mental model to daily practice.
Start anytime and move at your own pace — designed around teaching and research obligations.
Delivered through QuantHub's learning platform — accessible from any browser.
Conceptual grounding faculty can adapt to their own discipline, courses, and institutional context.
Talk with the QuantHub team about rolling this course out to your faculty — as professional development, new-faculty onboarding, or part of an institution-wide AI readiness initiative.