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.
Instructional Material Critique and Redesign for Multilingual Learners: Integrating AI, WIDA, and New York State Standards
As AI becomes increasingly integrated into educational settings, teacher preparation programs such as Touro University’s GSE face an important challenge: helping aspiring educators use AI productively while preserving the professional judgment at the heart of teaching. The Instructional Material Critique and Redesign for Multilingual Learners: Integrating AI, WIDA, and New York State Standards assignment was designed with this goal in mind.
The assignment requires our TESOL candidates to engage first in the intellectual work that defines effective teaching. They analyze authentic instructional materials using the ESL Textbook Evaluation Checklist, the WIDA PRIME framework, New York State Next Generation Learning Standards, and TESOL research. Candidates identify strengths, limitations, linguistic barriers, and opportunities for instructional improvement. These decisions require disciplinary knowledge, critical analysis, and an understanding of multilingual learner development.
AI enters the process after candidates have completed this professional evaluation. Using Gemini and NotebookLM through the Touro student portal, candidates develop instructional artifacts such as leveled reading passages, vocabulary supports, comic strips, and infographics. The generated materials are not submitted as finished products. Candidates must verify sources, evaluate the quality of the output, revise it for accuracy and appropriateness, and explain how the redesigned material supports multilingual learners. The assignment places responsibility for instructional decision-making with the candidate.
Recent implementation of New York State guidance aligned with the WIDA English Language Development Standards makes this work particularly relevant. Candidates are expected to consider proficiency-level expectations, language features, communication demands, and content-language integration when redesigning instructional materials. The assignment requires them to translate WIDA principles into classroom practice, creating supports that maintain grade-level rigor while improving access for multilingual learners.
NotebookLM contributes an additional dimension of professional learning through its source-grounded design. Candidates are encouraged to build materials from selected curriculum texts, chapters, articles, and instructional resources rather than relying on generalized AI responses. This process strengthens habits of evidence-based practice, source verification, and curriculum alignment that are essential in PK-12 settings.
The project avoids cognitive offshoring and develops skills that candidates will use throughout their careers. Schools increasingly expect educators to evaluate AI-generated content, create multimodal learning resources, adapt materials for diverse student populations, and document standards alignment. Through this assignment, candidates gain experience with each of these responsibilities while maintaining ownership of the pedagogical decisions that shape student learning.
Kalee Fan is an early childhood special education teacher in NYCPS. She is currently pursuing a Bilingual Education Extension to better support multilingual learners and create more equitable learning opportunities for all students. She is passionate about fostering an inclusive classroom where every child feels valued, confident, and empowered to succeed.
“This assignment taught me that effective instructional materials are not simply selected. They are thoughtfully evaluated and adapted so multilingual learners can fully access, engage with, and contribute to learning.”
Kalee Fan, Bilingual Education Extension Candidate, Touro University
This comprehensive course builds upon the foundation of curriculum development and classroom management in the context of teaching English language learners. Expanding its horizons to embrace the digital age, the course adeptly weaves innovative technology integration into the domain of Teaching English to Speakers of Other Languages (TESOL). Crafted to empower prospective TESOL/BLE educators, the course hones in on fostering competence in designing, implementing, assessing, and reflecting within diverse language learning environments, all while capitalizing on the potentials of cutting-edge technology. With a strong focus on practical application and discerning appraisal of technological tools, this course primes upcoming educators to excel amidst the ever-evolving educational landscape. Includes 10 hours of field work.
The Curriculum Design Techtool resource project for EDDN 635 provides a strong model for meaningful AI integration in teacher education because it requires candidates to produce and document evidence of their decision-making process. Rather than submitting a finished curriculum map alone, candidates must include screenshots, exact prompts, complete AI outputs, links to artifacts, and explanations of how they revised or improved the generated material. These requirements make the candidate’s thinking visible and create accountability for the quality of the final product. The proof-of-work component also supports academic integrity because instructors can see how the candidate interacted with the technology, what recommendations the AI generated, and which professional decisions the candidate made independently. In curriculum design, where standards alignment and language progression are essential, this transparency is particularly valuable because it allows both the process and the product to be evaluated.
The Curriculum Design Techtool resource project also cultivates AI literacy as a professional competency for future educators. Teachers entering today’s schools will encounter AI-supported planning, assessment, and instructional tools, making it important to develop habits of verification, revision, and critical analysis. Candidates are required to check AI-generated standards against NYSED documents, evaluate whether language functions align with content objectives, and explain modifications they make to AI-produced drafts. The expectation that students submit the original prompt, the complete output, and evidence of revision reinforces the understanding that expertise remains with the educator. AI contributes suggestions and organizational support, while the candidate applies pedagogical knowledge, curriculum expertise, and professional judgment to determine what is accurate, appropriate, and instructionally sound. Through this documented process, Touro University candidates learn to use AI responsibly, valuing transparency, accountability, and informed decision-making over simple content generation.
Ariana Loyfman is a New York City public school teacher and a graduate student pursuing a degree in Teaching English to Speakers of Other Languages (TESOL) at Touro University. As an educator, she is committed to giving students a strong educational foundation while creating a supportive, inclusive learning environment where all learners feel confident, motivated, and valued for who they are.
She is dedicated to inspiring a lifelong love of learning in her students and helping them recognize and build upon their individual strengths. Through meaningful instruction and encouragement, she supports students in developing the confidence to navigate challenges, pursue their goals, and use their unique talents and interests to achieve personal and academic success.
This comprehensive course builds upon the foundation of curriculum development and classroom management in the context of teaching English language learners. Expanding its horizons to embrace the digital age, the course adeptly weaves innovative technology integration into the domain of Teaching English to Speakers of Other Languages (TESOL). Crafted to empower prospective TESOL/BLE educators, the course hones in on fostering competence in designing, implementing, assessing, and reflecting within diverse language learning environments, all while capitalizing on the potentials of cutting-edge technology. With a strong focus on practical application and discerning appraisal of technological tools, this course primes upcoming educators to excel amidst the ever-evolving educational landscape. Includes 10 hours of field work.
The Curriculum Design Techtool resource project for EDDN 635 provides a strong model for meaningful AI integration in teacher education because it requires candidates to produce and document evidence of their decision-making process. Rather than submitting a finished curriculum map alone, candidates must include screenshots, exact prompts, complete AI outputs, links to artifacts, and explanations of how they revised or improved the generated material. These requirements make the candidate’s thinking visible and create accountability for the quality of the final product. The proof-of-work component also supports academic integrity because instructors can see how the candidate interacted with the technology, what recommendations the AI generated, and which professional decisions the candidate made independently. In curriculum design, where standards alignment and language progression are essential, this transparency is particularly valuable because it allows both the process and the product to be evaluated.
The Curriculum Design Techtool resource project also cultivates AI literacy as a professional competency for future educators. Teachers entering today’s schools will encounter AI-supported planning, assessment, and instructional tools, making it important to develop habits of verification, revision, and critical analysis. Candidates are required to check AI-generated standards against NYSED documents, evaluate whether language functions align with content objectives, and explain modifications they make to AI-produced drafts. The expectation that students submit the original prompt, the complete output, and evidence of revision reinforces the understanding that expertise remains with the educator. AI contributes suggestions and organizational support, while the candidate applies pedagogical knowledge, curriculum expertise, and professional judgment to determine what is accurate, appropriate, and instructionally sound. Through this documented process, Touro University candidates learn responsible AI use that values transparency, accountability, and informed decision-making rather than simple content generation.
Edona Kajoshi is currently in her final semester at Touro University as a Masters Degree Candidate in TESOL program. She works at a preschool in Manhattan, where she teaches a 3K class. Over the years, she has gained extensive experience working with children ranging from infancy to kindergarten age. She is passionate about early childhood education and hopes to become a first- or second-grade teacher in the future.
This is the third class I have taken with Professor Cowin! In each class, I have learned things that I can use in my classroom to best support my students, especially my English language learners! This semester this class has taught me how to support the students’ needs. It also gave me a deeper insight on how I can add technology into my classroom despite not always having the technology materials other schools may have!
Edona Kajoshi, TESOL Candidate at Touro University, GSE
Early summer 2026 I decided to apply for the competitive Stanford University’s Fall 2026 Ethics, Technology, and Public Policy for Practitioners course. Every year, I take one synchronous course to be part of my transdisciplinary quest on knowledge and personal connection building. My final reflection and report on my course experiences are structured through the lens of Star Trek’s Prime Directive.
Session one’s Opening Session and Moral Caffeination reading was Ursula K. Le Guin’s The Ones Who Walk Away from Omelas. I was unfamiliar with the short story of a prosperous society whose happiness rests on the deliberate and excruciating suffering of an imprisoned child. The reading troubled me, and I rejected the offered choices between accepting the child’s horrific suffering for the sake of communal happiness or walking away as cowardly solutions. Instead, I devised a third solution: an outsider who rescues the child. My inspiration: a personal hero of mine: Raoul Wallenberg, a Swedish rescuer in Budapest, who acted to save as many Jews as possible during a time when the Nazi regime normalized the persecution of its Jewish citizens.
Personally, I reject collective happiness as justification for a society’s deliberate disregard for individual suffering. A tyrant may experience genuine happiness while inflicting profound suffering on others. A society may celebrate its collective well-being while knowingly sacrificing the well-being of some of its members. But what does that happiness tell us about the ethical legitimacy of either? Nothing. Isn’t happiness a subjective experience? Aristotle’s eudaimonia and Kant’s conception of duty influence my ethical outlook. Society’s satisfaction cannot justify what it knowingly inflicts on someone powerless. Needless to say, I felt very righteous post-session and Dr. Beverly Crusher’s objection to Captain Picard supported my righteousness.: “It’s hard to be philosophical when faced with suffering.”
Dr. Beverly Crusher: “But aren’t we interfering either way? If we take no action, it’s a conscious decision to let the Boraalans die.” The Next Generation, “Homeward” (1994).
However, sessions 2: Algorithmic Decision-Making and Fairness; 3: Data Collection, Privacy, and AI; and 4; Political Economy of Technology and the Distribution of Power, especially the deeper exploration on automated hiring, gave Omelas a distinctive and contemporary institutional form. An algorithmic decision can be transparent and still produce unfair outcomes. I learned that disclosure does not confer accountability; neither does an audit remedy harm unless someone is empowered to act on its findings. My recurring question became: who possesses the authority and incentive to correct a system once its effects are visible? Our privacy session made this question more difficult to resolve – I was still looking for an Omelas closure. Lawrence Lessig’s account of law, markets, norms, and architecture helped me see technological defaults as a form of governance. I explored a related problem before the course in The Educational Data Ghost: AI, Learning Platforms, and European Sovereignty (July 2026). My article asked “whether students and faculty can know where their traces have gone” and argued for “the right not to be indefinitely reconstructed from one’s own traces.” Learning platforms retain evidence of student activity and faculty work long after an interaction ends. Failing to investigate those data practices is a decision with consequences, but intervention must respect consent, privacy, and legitimate educational purposes.
The lectures and fireplace presentations also made me reconsider the relationship between an AI system’s analytical moral competence and its behavioral moral acting. An LLM may identify a defensible action and explain Kantian duty without acting in accordance with either. When an agent has tools and permission to operate beyond a chat window, this gap becomes very dangerous as ethical fluency is not evidence of reliable behavior. I explored that topic in my October 1st Horasis’ article, Capabilities, Behaviors and Resistance to Oversight: How Much Autonomy Have We Already Given AI?, where I examined incidents involving autonomous agents and argued:
“Institutions need detailed activity records, clearly defined permissions, independent evaluations, and procedures for identifying and reporting unauthorized actions.”
During our session on AI governance under uncertainty, Seth Lazar’s concept of anticipatory AI ethics challenged me to consider when emerging AI capabilities and their foreseeable harms create a duty to intervene before those harms materialize, even when the evidence remains incomplete and the legitimacy of preventive action must still be established. His technological horizon asks us to examine plausible capabilities and their effects within existing institutions, without pretending to predict society’s entire future. Consistent with the Star Trek framing I used earlier, I applied this approach in my article Where No Worm Has Gone Before: AI That Self-Replicates onto Your $55 TV Box , looking at AI that replicates across inexpensive consumer devices. I observed that while laboratories can test and constrain models, they cannot by themselves secure household hardware, regulate device manufacturers, or protect networks. The duty to respond must be distributed among actors with different powers. An earlier article of mine, What the OpenAI Cyber Defense Letter Says, Who Signed It, and What Remains Unclear (August 2026), posed a related problem. Examining a public warning hosted by a company with commercial interests in the proposed response, I wrote that “a warning can be self-serving and accurate in the same breath.” That remains important to my thinking. Recognizing a genuine cyber threat does not exempt the proposed intervention from scrutiny, particularly when access, benefits, and authority are concentrated among the organizations promoting it.
Captain Kathryn Janeway: “You have no idea what the consequences might be once you involve yourself.” Voyager, “Time and Again” (1995).
Sessions 5. AI, Child Safety, and the Burden of Responsibility and 6. AI & the Future of Work continued my journey to a more nuanced thought process. I revisited my ‘heroic’ and, in hindsight, thoughtless Wagnerian rescue of the Omelas’ child. My original position was that no community can claim moral authority to perpetuate suffering simply because its members collectively accept or benefit from it. Communal consent does not legitimize the injustice. However, after session 5, I started asking myself:
Defensible aims: Ending the child’s suffering and simultaneously protecting Omelans’ rights.
Proportionate means: Using methods appropriate to the harm being prevented, without inflicting unnecessary additional harm.
Competence: Ensuring that those intervening possess the abilities and resources to do so effectively.
Accountability: Accepting responsibility for the consequences, including the child’s and Omelas’ welfare after the rescue.
The difficult to bear child-safety session brought these distinctions into sharper focus. My writing on New Mexico v. Meta had questioned leaving child protection to platforms whose commercial incentives favor engagement. Yet proposed client-side scanning of private communications creates a different risk: a protective unleveraging of privacy rights to justify disproportionate surveillance.
The future-of-work readings in session 6 extended the question of inaction. The Stanford Canaries in the Coal Mine research suggests a loss of the entry-level tasks. When employers automate those tasks, who assumes responsibility for creating alternative routes into skilled work? Will GenZ become the “useless” new class with total economic irrelevance? (Harari, 2022)
Finally, as a professor preparing future teachers, I find myself considering a much longer horizon: how do we educate teachers today to prepare children for the world of 2060? We cannot reliably predict the professions, technologies, or social conditions those children will encounter. We can, however, prepare teachers to cultivate intellectual independence, critical inquiry, disciplinary knowledge, ethical judgment, and the ability to learn beyond existing frameworks. My concern is that increasing reliance on AI may deprive my current candidates of the productive, intellectual struggles and practical (and sometimes painful) experiences through which judgment and expertise develop.
Cumulatively, the Stanford course gave me a distinct philosophical vocabulary with my final take-away: Non-intervention is not neutral in education either. The choices we make in preparing teachers today will influence the intellectual autonomy and economic opportunities available to their students (and our children’s children) – a future yet to be lived.
Dacey, C. M., Cowin, J., & de los Reyes, J. (2026). Editorial: Artificial intelligence in educational and business ecosystems: Convergent perspectives on agency, ethics, and transformation. Frontiers in Education, 11, Article 1780854. https://doi.org/10.3389/feduc.2026.1780854
The contributions curated in this Research Topic resist the techno-solutionist narrative that frames AI as a neutral efficiency tool requiring only proper deployment. Instead, they capture tensions between automation and augmentation, between administrative rationalization and pedagogical transformation, and between predictive precision and educational equity. These tensions operate simultaneously across institutional, instructional, and individual levels, calling for analytical frameworks that draw on computer science, learning sciences, ethics, policy studies, and critical pedagogy. The authors invite readers to continue building collaborative and integrative approaches that match the scale and complexity of AI’s global impact. We can benefit from reconsidering our teaching methods and comparing the ethical challenges of AI in business education, as well as how AI can be effectively incorporated to help prepare workforce-ready graduates. The effects of using artificial intelligence in education are just beginning to be measured, and its use will likely have ripple effects for years to come (Dacey et al., 2023). As we look toward the horizon, we see the need for more flexible, decentralized, and inclusive governance and educational structures, much like a forest of diverse species adapting to changing conditions within a vibrant ecosystem (Dacey et al., 2025, p. 70). Throughout the production process of this Research Topic, we remained mindful that “companies like OpenAI stand at the forefront, pushing the boundaries of what generative pre-trained transformers or ‘GPTs’ can achieve. However, beneath this veneer of progress lies a complex web of epistemological and pedagogical quandaries that question the very foundation of AI’s trajectory in education” (Cowin, 2024). We invite continued dialogue on these fundamental questions as the field evolves. Author contributions CD: Conceptualization, Methodology, Project administration, Resources, Writing– original draft, Writing– review & editing. JC: Writing– original draft, Writing– review & editing. JdlR: Writing– review & editing. Conflict of interest The author(s) declared that this work was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
Dacey, C. (2022). Examining ethical tensions and the pursuit of care through an invitational lens. J. Invit. Theory Pract. 28, 5–30. doi: 10.26522/jitp.v28i.4208
This Curriculum Mapping for English Language Learners assignment is an important anchor for the Touro University TESOL Development and Classroom Management in the Technology Era – EDDN 635 curriculum course because it moves candidates beyond simply identifying standards toward critically examining how curriculum decisions affect multilingual learners’ access, participation, and academic achievement. By using and populating a curriculum map template, candidates learn to evaluate alignment among standards, learning objectives, instructional strategies, assessments, academic language demands, and appropriate scaffolds. The reflection process also requires candidates to justify what they retained, revised, or added after verifying AI-generated suggestions against NYSED standards, district documents, and established ESOL practices.
Working with the Touro University approved Microsoft Copilot AI provides an added professional benefit by helping candidates harness artificial intelligence for practical curriculum work. Candidates use carefully designed prompts to organize alignment checks, identify possible gaps, generate initial evidence statements, and consider accommodations for multilingual learners. However, they must also evaluate, verify, and revise Copilot’s output rather than accepting it uncritically. This process develops AI literacy alongside curriculum-design expertise and prepares candidates to use emerging technologies responsibly, efficiently, and thoughtfully in educational settings.
Jacqueline Shaffer is a 34‑year‑old educator with eleven years of teaching experience. She currently teaches middle school Spanish in Brooklyn. Jacqueline is dedicated to supporting diverse learners and creating an engaging, culturally responsive classroom environment.
Through this assignment, I learned how important it is to design a curriculum that includes clear supports for multilingual learners, so they can access grade‑level content, participate in discussions, and show their understanding through reading and writing. I realized that scaffolds like visuals, sentence frames, and bilingual resources aren’t optional, they’re essential for equity. This process helped me see how thoughtful planning can make learning more inclusive and meaningful for all students.
Jacqueline Shaffer, M.S. Candidate, TESOL, Touro University
Teaching the Teachers of 2060: The Embodied Method Simulation
Since the arrival of Large Language Models (LLMs) I experimented and worked on designing what I consider AI-proof assignments: tasks where candidates must demonstrate their understanding of theory through practical application, which no chatbot can do for them. The Embodied Method Simulation is a prime example. Each Touro University TESOL course participant selects a single language teaching method from our course readings, such as Total Physical Response, Suggestopedia, the Silent Way, or Communicative Language Teaching, and performs it in its pure, original form. In a 6 to 7-minute video, they teach an imaginary group of multilingual learners as if the class were real: on camera, in a physical space, surrounded by realia, using their voice, gestures, and movement to bring a method’s theory to life.
The assignment asks for an embodied demonstration of pedagogy. Candidates show how a method actually works when they stand in a room with (imaginary) learners, handling materials, and moving through space. Theory earns its value there, in the moment of embodied instruction.
Why Copilot?
Alongside the performance, candidates co-create their teaching scripts with the Touro-approved Microsoft Copilot. Beginning with a person-centered, spatially grounded prompt, they engage in an iterative dialogue with AI, refining teacher talk, rehearsing simulated learner responses, and interrogating how their bodies and materials express the method’s principles. They then write a reflection analyzing what they accepted, adapted, or rejected from the AI’s suggestions.
Why build AI into an AI-proof assignment? Because the educators we certify today will still be teaching in 2060. They will spend their entire careers alongside artificial intelligence, and the essential career skill is directing these tools with professional judgment. When a candidate pushes back on Copilot’s suggestion because it violates the logic of TPR, or expands an AI-generated dialogue to better serve a newcomer learner, they are practicing exactly that skill: keeping the human teacher at the center while using AI as a thinking partner.
Kalee Fan is an early childhood special education teacher in a NYCPS. She is currently pursuing a Bilingual Education Extension to better support multilingual learners and create more equitable learning opportunities for all students. She is passionate about fostering an inclusive classroom where every child feels valued, confident, and empowered to succeed.
Quote about the Science of Reading:
“One of my biggest takeaways from studying the Science of Reading is that strong literacy instruction is both evidence-based and responsive to students’ individual needs. It has reinforced the importance of intentionally developing oral language alongside foundational reading skills so that multilingual learners can thrive academically.” Kalee Fan, Touro University TESOL Candidate
I used a few materials to support my mini-lesson recording
A Chair:
Cat Plushies:
Vocabulary Cards:
Sentence frame:
Part 3: Written Analysis and Copilot Co-Creation Reflection
Method Analysis
As an early childhood educator, I selected Total Physical Response (TPR) for my mini lesson because it aligns with the developmental needs of my kindergarten ENL students, who benefit from movement, hands-on experiences, and concrete language instruction. Developed by James Asher in the 1970s, TPR is based on the idea that language learning should resemble first language acquisition, in which children listen and respond physically long before they are expected to speak. The method creates a low-stress learning environment where learners develop comprehension through listening and movement. In a TPR classroom, the teacher gives commands and models actions while students demonstrate understanding through physical responses. The main principles of TPR include listening comprehension, physical movement, repetition, and delayed speech production. By connecting language to actions and engaging visual, auditory, and kinesthetic senses, learners gradually build understanding and confidence before producing language independently (Helbling English, n.d.).
TPR has several strengths. It lowers learners’ anxiety, increases engagement, and makes language comprehensible through concrete actions and real objects. These characteristics make it particularly effective for beginning English learners and young children (Cowin, n.d.). However, TPR also has limitations. As learners become more proficient, the method becomes less distinctive because advanced language functions and abstract concepts cannot always be represented through physical actions. In addition, reading and writing activities are often limited extensions of the oral activities in the classroom (Cowin, n.d.).
I selected TPR for my mini lesson on positional words as this topic is part of my school’s kindergarten math curriculum and provides a meaningful opportunity to connect language learning with content instruction. Positional words can be abstract for young English learners, but using TPR allows students to visualize and understand these concepts through physical movement and hands-on experiences. TPR emphasizes connecting language with actions and providing opportunities for learners to develop comprehension before producing language (Celce-Murcia et al., 2014). In my lesson, students first demonstrated their understanding by moving their bodies and a toy cat to different positions around a chair. After students had multiple opportunities to practice through physical responses, I incorporated a sentence frame, “The cat is _____ the chair,” to support oral language production. I selected a chair, cat plushies, vocabulary cards, and a sentence frame since these materials are concrete, visually accessible, and easy for young learners to manipulate. This lesson reflects the core principles of TPR by using repeated actions, multisensory experiences, and gradual language production to build students’ confidence and create an engaging, low-anxiety learning environment (Helbling English, n.d.).
Copilot Co-Creation Reflection
Before I began recording my videotaped mini lesson, I entered my initial prompt into Copilot to refine the structure and delivery of a six‑minute Total Physical Response lesson designed for Kindergarten ENL learners in a 12:1:1 bilingual special education setting. My prompt described the instructional context, the linguistic profiles of the students, and my goal of teaching spatial vocabulary using realia such as a chair and a cat plushie. Copilot generated a first draft that offered a clear introduction, a well‑sequenced modeling phase for positional vocabulary, and a strong closing routine. This initial script aligned closely with my own vision, and it helped me see how my ideas could be organized into a coherent, embodied lesson that foregrounded movement, gesture, and predictable routines. The process of reviewing this draft made me more aware of how intentional teacher talk and physical demonstration work together to support comprehension for Entering and Emerging learners.
As I continued refining the lesson, I entered a second prompt asking how to shift the rapid TPR practice from simple mimicry to a more hands‑on, high‑engagement activity. I asked whether providing each student with a miniature cat plushie and toy chair would be appropriate and how to transition them smoothly into this active phase. Copilot responded with a detailed one‑minute transition script that emphasized slow pacing, clear gestures, and explicit modeling of how students should manipulate their objects. This suggestion helped me clarify my embodied role in the method. I realized that TPR is not only about demonstrating vocabulary with my own body but also about orchestrating students’ physical responses in ways that reduce cognitive load and increase autonomy. I adapted Copilot’s script by simplifying some of the language and adjusting the pacing to fit the realities of a rug‑seated Kindergarten group. I kept the core idea of using manipulatives because it aligned with my professional judgment about sensory engagement and accessibility for multilingual learners.
My next prompt focused on the Checking for Understanding phase, where I planned to introduce the sentence starter “The cat is ___ the chair” to support expressive language production. I asked Copilot whether this was appropriate for students at Entering and Emerging proficiency levels and how to introduce the sentence frame smoothly within the TPR method. Copilot affirmed the approach and provided a scaffolded routine that paired each chunk of the sentence with a gesture, ensuring that the language remained grounded in physical action. This helped me better understand how my embodied role could support early expressive language without overwhelming students. I adjusted the script by reducing the verbal load even further and by ensuring that students demonstrated comprehension physically before attempting any spoken output. This adaptation reflected my professional judgment about developmental readiness and the importance of maintaining TPR principles even when introducing language frames.
Finally, I asked Copilot about an assessment idea involving vocabulary cards that students would place into the blank space of the sentence starter. I wanted to know whether this was feasible within the six‑to‑seven‑minute time limit and appropriate for a 12:1:1 Kindergarten group. Copilot explained that the idea could work only in a highly simplified form and recommended a tighter, more TPR‑aligned alternative that removed unnecessary cognitive steps. This feedback prompted me to reconsider the assessment entirely. I rejected the original card‑search idea because it introduced too many demands for this age group and time frame. Instead, I adopted the “Touch and Tell” approach, where students physically position their mini cat and chair and then echo the sentence starter. This alternative preserved the embodied nature of TPR, respected the developmental needs of my learners, and fit smoothly within the lesson’s pacing.
Through this process, I learned how valuable it is to refine instructional decisions through cycles of questioning, reflection, and revision. Copilot helped me clarify the embodied dimensions of TPR, especially the ways teacher movement, gesture clarity, and pacing shape multilingual learners’ access to meaning. At the same time, I exercised professional judgment by adapting suggestions to fit the specific needs of my classroom, rejecting ideas that were too complex, and expanding others to strengthen linguistic accessibility. This experience reinforced the importance of designing lessons that are intentional, responsive, and grounded in both pedagogical theory and the lived realities of young ENL learners.
References:
Celce-Murcia, M., Brinton, D. M., & Snow, M. A. (Eds.). (2014). Teaching English as a second or foreign language (4th ed.). Heinle ELT.
Cowin, J. B. (n.d.). Methods and techniques in teaching ENL: Total Physical Response.
Helbling English. (n.d.). Total Physical Response [Video]. YouTube.
Microsoft Copilot. (2026). Responses generated for lesson‑planning refinement. Microsoft.https://copilot.microsoft.com
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.
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
“Silicon Valley’s faith in technology as the savior of humanity echoes ancient myths of divine intervention.” Lanier (2013)
This essay investigates the Messiah Savior Complex in Big Tech, where artificial intelligence is presented as a redemptive force capable of solving humanity’s most urgent challenges. Using the historical analogy of the narwhal tusk trade, in which tusks were sold as unicorn horns to European elites, the analysis illustrates how myth-based narratives continue to influence technological realities. In contemporary discourse, these narratives take the form of hyperstitions, which are beliefs that become real through repetition, institutional reinforcement, and collective investment. Such dynamics obscure empirical scrutiny and displace critical engagement with the socio-technical realities of AI development. The essay argues that magical thinking and industry promotion often sustain these belief structures to deflect regulatory oversight and maintain public enthusiasm. Rather than rejecting technological progress, the paper calls for a transdisciplinary framework that treats AI as embedded in systems requiring accountability, transparency, and contextual awareness.
The unicorn horn deception was not merely a case of medieval gullibility but a sophisticated system that leveraged cultural symbols and created powerful incentives to maintain the existing illusion. Similarly, today’s AI narratives function as powerful mythologies that shape investment, policy, and public understanding. Cowin, J. (2025). Narwhals, unicorns, and Big Tech’s messiah complex: A transdisciplinary allegory for the age of AI. The Journal of Systemics, Cybernetics and Informatics, 23(7), 146–151. https://www.iiisci.org/journal/sci/Contents.asp?Previous=#/
The Touro University Copilot Grant supports my work as a faculty member in explicitly teaching teacher candidates how to use Copilot as an instructional design tool within a structured, standards-aligned pedagogical framework. In this course, Copilot is not introduced as an optional productivity aid. It is taught as a professional instructional resource whose use must be intentional, transparent, and grounded in TESOL theory, state standards, and multilingual learner pedagogy.
The instructional focus of this grant-funded work is on teaching candidates how to work with Copilot, rather than merely allowing its use. Candidates are guided through a faculty-modeled process that emphasizes instructional problem identification, constrained prompting, critical evaluation of AI-generated outputs, and revision based on professional judgment.
Instructional context and assignment purpose
The Copilot integration is based on a major assessment titled “Instructional Material Critique and Redesign with Infographic.” The assignment is designed to teach candidates how to critically analyze instructional materials and redesign them to improve accessibility and rigor for multilingual learners.
Materials may include complete texts or individual chapters from instructional resources commonly used in schools. The assignment explicitly teaches candidates how to engage in mastery-level material critique and redesign using established TESOL and multilingual education frameworks.
Explicit teaching of Copilot as an instructional design tool
Within this assignment, I explicitly teach candidates how Copilot can be used as a co-creative instructional design partner under faculty supervision and pedagogical constraints. Copilot is introduced through direct instruction and modeling, not discovery-based experimentation.
Generates draft instructional materials, not finished products
Requires human evaluation using research-based criteria
Must be revised to ensure linguistic accuracy, cultural responsiveness, and standards alignment
This explicit framing positions Copilot as part of the instructional design process, not as an authority or substitute for professional educators’ expertise.
Xavier Campoverde is a bilingual social studies teacher at the high school he attended growing up. He is passionate about ensuring that every student has the ability to learn based on their individual needs, building on what they already know, and establishing a safe learning environment for all. He is also a proud husband and father to two wonderful children.
I learned that being a TESOL educator means being an advocate, a designer, and a listener, using data, culture, and technology to ensure every multilingual learner can thrive. Xavier Campoverde, Touro University TESOL Candidate.
