This assignment, Instructional Method Assignment – Teaching a Mini-Lesson to an ML Audience, required creating a simulated teaching video that demonstrates one specific language teaching method from our course readings. This is a pretend lesson where you act as the teacher presenting to an imaginary multilingual learner audience for EDPN 673 Methods and Materials for Teaching English as a Second Language. This course provides a historical overview of second language acquisition theories and teaching methods. Students learn how to apply current approaches, methods and techniques, with attention to the effective use of materials, in teaching English as a second language. Students will engage in the planning and implementation of standards-based ESL instruction, which includes differentiated learning experiences geared to students’ needs. Emphasis is placed on creating culturally responsive learning environments. Includes 15 hours of field work.
The assignment was designed to deepen TESOL candidates’ methodological expertise while positioning them to engage with artificial intelligence in purposeful and pedagogically sound ways. It reflects Touro University’s broader initiative to strengthen AI literacy across its programs through a Touro Faculty AI Grant headed and supported by Shlomo Engelson Argamon, Associate Provost for Artificial Intelligence and Professor of Computer Science, and Jamie Sundvall, Ph.D, Psy.D, LP, LCSW, Assistant Provost of Artificial Intelligence. Within this institutional landscape, the assignment serves as a structured model for preparing educators to work in learning environments where AI is increasingly integrated into curriculum, assessment, and multilingual support.
My motto, Education for 2060, emphasizes the development of shared spaces of competencies influenced by AI and large language models. As schools and districts integrate AI into core instructional processes, teacher education programs must develop candidates who can navigate these systems with ethical judgment and instructional precision. This assignment, therefore, balances two essential design principles: strong safeguards against unverified AI substitution and intentional guidance for targeted AI use.
The AI-resistant component centers on a six to seven-minute simulated teaching video that requires candidates to embody a single method from the course readings. By performing the method in a real physical space with realia, gesture, classroom presence, and teacher talk, candidates demonstrate the translation of theory into practice. This performance reveals decision-making, sequencing, and pedagogical rationale that cannot be delegated to AI, ensuring that candidates are evaluated on their own instructional competence.
Targeted AI use is built into the assignment through Copilot-supported planning and reflection. Copilot is positioned as a thinking partner that helps candidates examine the structural logic of the method, refine the flow of the activity, and interrogate their own understanding. Proof of work in the form of screenshots and reflective commentary ensures transparency and allows candidates to analyze the accuracy, limitations, and pedagogical value of AI-generated suggestions. In this way, the assignment teaches AI literacy as a reflective and evaluative process rather than a generative shortcut.
The written analysis links the performance to course theories, identifies the method features demonstrated in the video, and articulates how Copilot contributed to planning choices. This component reinforces conceptual understanding while modeling a professional stance toward responsible AI use.
By combining embodied demonstration with documented AI-supported thinking, the assignment prepares candidates for a future in which educators and AI systems occupy interconnected roles. It brings the work full circle by returning to the idea of shared spaces of competencies. Candidates learn to inhabit these spaces with confidence, contributing their own pedagogical judgment while engaging with AI in ways that enhance, rather than replace, their professional expertise.
Rachel Melamed is a high school teacher in Brooklyn, New York. She earned her bachelor’s degree in Inclusive Education from SUNY Cortland and is a first-generation graduate student pursuing her master’s in TESOL at Touro University. Growing up in a Russian-speaking household helped her develop a passion for teaching multilingual learners and shaped her approach to connecting with them in the classroom.
Using Copilot helped me rework a lesson I had taught before and make it more accessible for English language learners. I learned how small adjustments and simplified, repetitive language can make a big difference when designing lessons.
Rachel Melamed master’s degree candidate in TESOL at Touro University
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