Curriculum design
Start from the thinking a unit is meant to build. Then decide where tools can scaffold, where they can take on routine work, and where students need to work unassisted.
Teaching & learning portfolio
CETL, Ed.S. Ed.D. Candidate, Curriculum, Advocacy & Policy
Social studies teacher, coach, building and district leader, and curriculum researcher
This page gathers the instructional side of my work. I began in 2006 as a high school social studies teacher in Chicago Public Schools, served as a lead teacher and department chair, then moved into coaching, building leadership, and district curriculum and instructional technology. Across those roles the steady question has been what a lesson, task, or tool actually asks students to think and do. My doctorate in Curriculum, Advocacy, and Policy at National Louis University, now in data collection, studies that question as generative AI enters classrooms.
Four questions I bring to instructional decisions
Instructional philosophy
Most of my career has been spent close to instruction. I taught high school social studies in Chicago Public Schools, mentored new teachers as a lead teacher, chaired a department in an alternative school, coached teachers, supported a K–5 building as assistant principal, and led curriculum and instructional technology across a district. That work keeps returning me to one practical question. What is this lesson, task, or tool actually asking students to think and do?
Generative AI has made that question urgent. A student can now turn in polished work without doing the reading, reasoning, or revising the assignment was designed to build. My research calls this unproductive success. The response I work toward is instructional design that keeps productive friction, the effort that builds understanding and judgment, while removing exclusionary friction, the barriers that punish students without teaching them anything.
For school and district leaders, this means curriculum, assessment, professional learning, and policy have to be decided together. Phone rules, device choices, and AI procurement shape what students practice as much as any curriculum guide does.
How the approach shows up
Start from the thinking a unit is meant to build. Then decide where tools can scaffold, where they can take on routine work, and where students need to work unassisted.
Look for evidence of learning in the process as well as the product, so that assessment rewards the reasoning a generated output cannot supply.
Build educator learning around teachers' own classroom decisions and disciplines rather than tool tutorials, and design it with the same care expected of student learning.
Balanced, inquiry-driven history and civics instruction, supported by the C.O.R.E. and H.E.A.R.T. frameworks I developed for critical thinking and civic-minded reasoning.
Treat screens, devices, and AI features as instructional decisions. The useful question is what a screen asks students to practice, not only how long they use it.
Separate productive struggle from barriers tied to access, language, disability, or compliance burdens, so that rigor does not become exclusion.
Instructional leadership in practice
The instructional side of each role. The full resume covers technology operations, consulting, and policy work.
Doctoral work
I am completing an Ed.D. in Curriculum, Advocacy, and Policy at National Louis University. My dissertation studies pedagogical friction in the age of generative AI. The proposal was defended in August 2026, the study received IRB approval in October 2026, and data collection is underway. Completion is expected in 2027.
The study uses a qualitative-dominant convergent mixed methods design grounded in constructivist qualitative inquiry. It asks how classroom-facing educators, building-level administrators, district/system-level leaders, and adult university students make sense of the ways generative AI changes effort, dialogue, authorship, judgment, and participation. It also asks what language, assessment expectations, instructional practices, professional supports, and leadership approaches help preserve learning-supportive friction while reducing unnecessary barriers. The study is in progress, and no findings are reported here.
Noetic, rhetorical, existential, and infrastructural dimensions of educationally productive resistance, with a working distinction between productive and exclusionary friction.
An extension of Walter Ong's media ecology to generative AI, used to examine unproductive success, when polished output arrives without the learning it was meant to show.
Social studies frameworks for balanced history and civic reasoning. C.O.R.E.: critical thinking, openness, respect, and engagement. H.E.A.R.T.: honesty, empathy, accountability, responsibility, and thoughtfulness.
Pedagogical writing
Educational Leadership, ISTE+ASCD
Submitted October 2026 for the March 2027 issue
i.e.: inquiry in education, 18(1), Article 4
Open sourceCoSN, Consortium for School Networking
Open sourcemicahminer.com, October 10, 2026
Open sourcemicahminer.com, August 31, 2026
Open sourcemicahminer.com, July 25, 2026
Open sourceLTC Illinois Blog
Social Studies School Service Blog, November 7, 2024
Open sourceThe Leader, National Social Studies Leaders Association, Spring 2024
The Councilor: A National Journal of the Social Studies
Equity & Access Journal
Equity & Access Journal
IL ASCD Journal
Teach Plus
Times 10 Publications
Open sourceSecond book, on technoskepticism and the limits of AI in education.
Professional learning and speaking
ISTE+ASCD expert webinar
Open sessionDuPage ROE
Open sessionDisciplinary adaptation for social studies educators
Open sessionPreserving historical inquiry through pedagogical friction, C.O.R.E., and H.E.A.R.T.
Open sessionAAACS interactive scholarly session
Open sessionA simulation for calibrating productive challenge without raising exclusionary barriers.
Open toolLicensure and preparation