In current TESOL practice, the question is no longer whether artificial intelligence belongs in the classroom, but how it can be integrated without displacing the intellectual and pedagogical labor that defines effective teaching. This Instructional Method Assignment – Teaching a Mini-Lesson to an ML Audience for the Touro University TESOL/BLE course EDPN 673 – Methods and Materials for Teaching English as a Second Language is designed as a deliberate response to that tension. It positions AI not as a substitute for thinking, but as a collaborator within a broader ecology of embodied teaching, disciplinary knowledge, and reflective practice.
At its core, the assignment asks Touro University TESOL/BLE teacher candidates to inhabit a methodological tradition not abstractly, but physically. The simulated teaching video foregrounds the body as a site of pedagogy: gesture, proximity, pacing, and the handling of realia become constitutive elements of meaning-making. In this sense, the “method-pure” requirement is not merely technical. It is epistemological. It asks candidates to test what it means for a theory of language learning to be enacted through voice and movement in space, rather than summarized in prose.
Evangelia Diakoumakos Method Teaching Simulation Video
The written analysis, by contrast, reclaims the domain of intellectual work. Here, candidates situate their chosen method historically and theoretically, interrogating its assumptions, affordances, and limitations. This component resists the reduction of teaching to performance alone. It insists that pedagogical action must be grounded in critical awareness, particularly when methods are transported into multilingual, contemporary classrooms that differ significantly from their original contexts.
Between these two domains lies the guided use of AI, specifically through structured co-creation with tools such as Microsoft Copilot. The reflective component makes visible an often invisible process: how ideas are iteratively shaped, challenged, and refined. In my view, this is where responsible AI use becomes pedagogically meaningful. Candidates are not rewarded for seamless outputs, but for evidencing discernment. They must demonstrate where AI supported clarity, where it introduced limitations, and where professional judgment required deviation from its suggestions.
The assignment, therefore, stages a productive dialectic. The physical performance of teaching resists abstraction; the analytical paper resists superficiality; and the AI collaboration resists passivity. Taken together, these elements model a form of teacher preparation that acknowledges technological change while maintaining a clear commitment to pedagogical intentionality.
Featured Touro University Candidate:
Evangelia Diakoumakos is an elementary school teacher in Brooklyn, who teaches a fourth-grade general education (ENL) class. As a teacher of a large multilingual learner population, she has developed an even stronger passion for language development and culturally responsive teaching. She is committed to creating an inclusive classroom where all students feel valued and supported in their learning.
“As a student nearing the completion of my master’s degree, one of the most rewarding experiences has been the ability to connect and apply concepts from my coursework at Touro directly to my classroom. My studies have not only transformed my instructional practices, but have also reaffirmed my love for language learning.”
Evangelia Diakoumakos, Touro University TESOL Candidate
Dr. J's Education Blog 