UNIVERSITIES · LANGUAGE CENTERS · BILINGUAL PROGRAMS
Your Faculty Are Ready to Teach with AI.
Your Institution Isn’t Ready to Support Them.
Method Mastery partners with English language institutions to close that gap — through structured needs assessment, differentiated instructional design, and evidence-based AI integration that produces measurable outcomes.
US State Dept English Language Fellow
Fulbright Distinguished Award in Teaching
NYS TESOL Teacher of the Year
ELP Specialist US State Dept
MSc Education TESOL, CUNY
THE PROBLEM
The Gap Is Widening Faster Than Most Institutions Realize
Students arrived with AI tools before institutions had policies to govern them — and faculty are now expected to redesign courses, assess AI-assisted work, and maintain academic integrity simultaneously, without structured support.
Most institutional responses have been reactive: a one-day workshop, a vendor demonstration, a policy document that contradicts the course catalog. The result is not AI integration. It is AI confusion, unevenly distributed across departments and cohorts.
The institutions gaining ground are not the ones that adopted the most tools. They are the ones that built the right sequence: understand the actual gap first, design instruction around it second, and introduce AI as infrastructure rather than novelty.
THE METHOD
A Framework Built for Institutional Contexts, Not Individual Classrooms
01 Needs Assessment
Every engagement begins here, without exception. We identify the specific gap between your institution’s current instructional practice and the AI-enhanced environment your students and faculty are already operating in — before any tool is selected or any training is designed.
02 Differentiated Design
Instruction is designed around the real profiles of your learners and faculty — not a generic cohort. This means differentiated lesson architecture, rubric-aligned materials, and curriculum structured around what your specific students need to produce, not what a generic AI tool assumes they need.
03 AI Integration
AI tools are introduced as infrastructure — embedded into faculty workflows and student tasks in ways that are measurable, institutionally defensible, and pedagogically grounded. Integration is the third step because it is the last step, not the first.
CURRENT WORK
Proof of Methodology, Applied in Instructional Contexts
The following projects represent active applications of the Method Mastery framework across three distinct institutional contexts. Each is designed to generate evidence, not just activity.
Project: Pilot — August 2026
HONG KONG POLYTECHNIC UNIVERSITY · ENGLISH LANGUAGE CENTRE
A Rubric-Matched Writing Coach for STEM-Track Undergraduates
In partnership with Professor Frankie Har at PolyU’s English Language Centre, Method Mastery is deploying a custom GPT — the Business Communication Rubric Coach — trained directly on the ELC3222 institutional assessment rubric. The tool gives STEM-track undergraduate students structured, rubric-aligned feedback on business writing tasks, reducing the gap between what students produce and what the rubric demands.
The pilot measures two outcomes: reduction in instructor feedback time and improvement in student alignment to rubric criteria across Assessment 1 and Assessment 2. Results will be documented and shared as a case study upon completion.
Project: Research Collaboration — In Development
NYC URBAN ELL CORPUS · MEIJI GAKUIN UNIVERSITY
Building the First L1-Differentiated Vocabulary Corpus for Urban English Language Learners
In collaboration with Dr. Charles M. Browne — Professor of Applied Linguistics at Meiji Gakuin University and creator of the New General Service List, the most widely used frequency-based vocabulary corpus in ESL education — Method Mastery is developing the NYC Urban ELL Corpus: a frequency-ranked vocabulary database differentiated by grade level and dominant L1, built from the texts New York City middle school English Language Learners actually encounter across ELA, Math, Science, and Social Studies.
The corpus addresses a documented gap: no existing vocabulary resource maps high-frequency acquisition targets by both grade band and dominant L1 simultaneously. The primary output is a corpus-backed graduated reader library for use by ENL teachers in urban US school contexts.
Project: Proof of Concept → MVP
ELLA AI · AI-ASSISTED LESSON PLANNING INFRASTRUCTURE
Reducing Teacher Planning Time Through Structured, Anonymized AI Lesson Architecture
ELLA AI is an applied AI lesson planning platform built on a five-constraint pedagogical framework derived from Krashen’s Input Hypothesis, Vygotsky’s Zone of Proximal Development, and Borg’s teacher cognition theory. The system guides teachers through a structured planning cycle — Interact → Revise → Reflect → Repeat — generating differentiated lesson plans built around anonymous learner profiles rather than identifiable student data.
The platform is designed for the institutional context: it reduces cognitive load on individual teachers, produces rubric-aligned lesson architecture, and generates planning outputs that are auditable, reproducible, and pedagogically defensible. ELLA AI is the operational layer through which the Method Mastery framework is delivered at scale.
WORK WITH METHOD MASTERY
If Your Institution Is Ready to Move Past the Workshop Model, Let’s Talk
An institutional diagnostic takes 30 minutes. It produces a clear picture of where your faculty and learners actually are, what the gap costs in real instructional terms, and what a structured AI integration program would look like for your specific context.
There is no obligation and no pitch deck. If the fit is right, we scope a pilot. If it is not, you leave with a clearer framework than you arrived with.
Or write directly: frederic@method-mastery.com
