← Home / AI for Education
AI consulting
For schools, universities, and colleges of education.
I’ve worked with everyone from preschool teachers through twelfth grade, faculty across every discipline, and deans of colleges of education from across the country. The questions are surprisingly consistent: how do we limit dishonest use, and how do we make sure students use AI in ways that enhance their learning rather than shortcut it?
Choose your context
Three starting points.
Click any one to see what the work looks like.
PK‑12 districts & schools
Professional development that meets teachers wherever they are: already experimenting, quietly worried, or firmly unconvinced. We start with what you’re trying to teach, not with a tool demo.
- Building activities that support retrieval practice and formative checks
- Acceptable‑use and academic‑integrity policy teachers will actually follow
- Student AI literacy: what to teach, and at which grades
- Coaching for instructional coaches and technology leads
- Tool evaluation for privacy, accuracy, and instructional fit
Colleges & universities
Discipline‑specific work built from my own practice as a Human-First AI Faculty Fellow. A chemist and a composition instructor do not need the same session, and I don’t give them one.
- Course and syllabus AI policy, including the AI Stoplights framework
- Redesigning assignments so they stay meaningful
- Building course content, activities, and materials with AI
- Leadership sessions for deans and academic administrators
I presented on AI to deans of colleges of education from across the country at the AACTE Leadership Conference.
Teacher preparation programs
Preservice teachers need this most. They’ll walk into classrooms where students are already using these tools, and they should arrive with a stance, not a scramble.
- Ethical AI use in coursework and field placements
- Modeling transparent AI practice for their future students
- Evaluating tools for privacy, accuracy, and instructional fit
- Program‑level policy and curriculum integration
A sample series
What a PD series actually looks like.
A real four‑session series built for an education program, shared here as an example of the depth this work can go to. Sessions can be in person or virtual, with the option to record for faculty who can’t attend live. You keep all the materials afterward to refer back to.
Session 1: The Big Picture — Generative AI in Teacher Education
This opening session establishes a shared foundation on what generative AI tools can and cannot do, how they impact educator preparation, and where they already appear in P–12 and higher education settings. Rather than viewing AI as a threat or a cure‑all, the session frames it as a professional tool requiring active human oversight, positioning faculty as the essential decision‑makers. Participants explore ethical, privacy, and equity implications while identifying program‑level opportunities and pressure points.
What to bring: your curiosity, current questions, and initial concerns.
By the end of this session, you will be able to:
- Explain how generative AI tools produce output and the direct implications for reliability
- Identify core ethical, privacy, and equity considerations in educator preparation
- Outline emerging AI applications in P–12 classrooms that graduates will encounter
- Articulate one targeted question or focus area to investigate across the series
Session 2: Course Development — Re‑envisioning What You Already Teach
Faculty remain the experts on content, pedagogy, and student needs. This session demonstrates how AI can drastically reduce the friction of course redesign, whether updating units, scaffolding assignments, or developing alternative assessments. Participants configure a persistent workspace (e.g., ChatGPT or Claude Projects) pre‑loaded with course syllabi and standards to enable continuous, context‑aware iteration without repetitive prompting.
What to bring: existing syllabi, course materials, current AI policies (if any), and reflections from Session 1.
By the end of this session, you will be able to:
- Set up a persistent AI workspace pre‑loaded with your course standards, materials, and constraints
- Use AI to prototype and stress‑test alternative unit plans, assignments, or assessments
- Evaluate AI‑generated course materials against science‑of‑learning principles (retrieval, spaced practice, interleaving)
- Identify one concrete course revision to implement immediately
Session 3: Designing AI‑Integrated Assignments
Effective assignment design shifts the question from policing tool use to clarifying learning objectives: what must students learn, and does AI enhance or undermine that cognitive work? This session uses backward design to classify assignments along a prohibited, permitted, or required continuum. Participants explore transparent policy language, low‑fear disclosure protocols, and structural designs that naturally reinforce academic integrity and deeper inquiry.
What to bring: an assignment to revise and observations from your between‑session trial.
By the end of this session, you will be able to:
- Identify the essential cognitive tasks required by an existing assignment’s learning goals
- Classify assignments across a prohibited/permitted/required continuum with pedagogical justification
- Redesign an assignment so AI use either directly supports learning or is meaningfully constrained by task structure
- Draft clear, student‑facing AI guidelines that encourage transparent communication
Session 4: Preparing Preservice and In‑Service Teachers to Use AI Well
Graduates will enter classrooms where AI‑generated lesson plans, leveled texts, and IEP drafts are widespread. Teaching candidates must know how to critically evaluate fluent AI outputs against pedagogical standards and diverse student needs rather than accepting them at face value. This session focuses on practical evaluation protocols and digital literacy competencies to embed within existing educator preparation courses.
What to bring: refined assignment drafts and takeaways from the series.
By the end of this session, you will be able to:
- Articulate why deep content and pedagogical knowledge remains prerequisite for effective AI use in teaching
- Compare and critique AI‑generated instructional materials against evidence‑based practices and student needs
- Model a human‑in‑the‑loop evaluation protocol for teacher candidates to use independently
- Map where AI evaluation and literacy skills integrate into your program’s course sequence