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.
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
This assignment 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, Professor of Computer Science & Jamie Sundvall, Ph.D, Psy.D. LP, LCSW, Assistant Provost of Artificial Intelligence
My motto, ‘Education for 2060,’ focuses on shared spaces of competencies shaped by AI and large language models. As schools, districts, and our students adopt AI tools for learning, assessment, curriculum development, and multilingual support, teacher education programs must equip our candidates with the knowledge and nuanced judgment needed to integrate these technologies ethically, strategically, and in alignment with sound principles of pedagogy and instructional design. The goal is not technological substitution but pedagogical enhancement. Responsible AI use requires a clear understanding of when and why an AI-supported process strengthens instructional decisions, particularly for multilingual learners who interact with complex academic texts across content areas.
The work of analyzing text complexity offers an ideal entry point for developing AI literacy in teacher preparation. Examining linguistic, cognitive, and cultural demands requires careful reasoning and structured evaluation. These skills align with high-quality instructional design and can be augmented by transparent AI tools that assist candidates in organizing ideas, checking coherence, and strengthening linguistic analysis without taking over intellectual labor. Within this assignment, targeted use of AI support mirrors the professional responsibilities teachers will face when adapting curriculum materials, planning differentiated instruction, and selecting resources for English Language Learners and Multilingual Learners. Candidates learn to pair human expertise with AI-supported review processes that promote accuracy, clarity, and reflective practice.
The integration of Microsoft Copilot for final review models responsible AI use that complements, rather than replaces, analytical work. Candidates are required to complete their paper independently and then invite AI-supported critique, focusing on coherence, alignment with APA standards, and clarity of argumentation. This mirrors practical praxis where educators may use AI tools to refine instructional plans, check alignment to standards, and evaluate materials before implementation. By engaging in this structured workflow, Touro University GSE candidates experience a practical application of AI literacy that reinforces their ability to evaluate complex text for ELL and ML access while maintaining professional accountability.
The broader purpose of embedding AI-supported review is to help our Touro University TESOL teacher candidates develop habits of mind that pair rigorous analysis with reflective metacognition. Engaging in text complexity analysis, considering reader and task variables, and examining linguistic challenges for multilingual learners requires nuanced evaluative skills. When paired with transparent and ethical use of AI as a secondary tool for refinement, candidates learn how exponential technologies can support differentiated lesson planning and curriculum construction. This fosters a readiness to lead in classrooms where multilingual learners depend on teachers who can leverage digital resources while upholding principles of equity, clarity, and culturally responsive practice.
Angelica Marziliano: I have been an educator for ten years, starting my career as a paraprofessional before transitioning to a general education teacher. Over the years, I’ve had the privilege of teaching a large and diverse student population, including many English Language Learners. I am currently pursuing my graduate degree in TESOL at Touro University to further support all students in reaching their full potential.
At Touro University, I learned that effective teaching means meeting each learner where they are, differentiating instruction so every student can reach their full potential.
The GovAI Summit serves as the leading forum for implementing the White House’s 2025 AI Action Plan. It gathers federal decision-makers, policy analysts, and technical experts to examine how AI is transforming public service, from procurement and oversight to infrastructure, education, and workforce training. Discussions emphasize accountability and practical deployment, offering insight into how institutions and agencies adopt AI responsibly and effectively.
Running alongside, AGENTIC 2025 focuses on the practical application of autonomous AI in real-world enterprise settings. Attending are executives, developers, and innovation officers who drive AI adoption in large organizations. The sessions highlight strategies for integrating AI into daily operations, managing risks, and achieving measurable impact across teams and products.
In my session, Autonomous AI in U.S. Schools: Practical Realities, Policy Tensions, and Institutional Readiness, I drew on John Wyndham’s “The Kraken Wakes” as an allegory for systemic adaptation to new intelligence.
🏛️ As part of Touro University’s comprehensive initiative to introduce #AI#literacy to our students, I am engaged in a #Touro#University#grant focused on developing AI literacy in #TESOL candidates. My project-based approach empowers future educators to leverage AI as a strategic partner in curriculum design, bridging theoretical understanding with applied classroom practice.
Joyann Castilletti is a 7th–12th grade certified English teacher, currently working as a permanent substitute teacher while pursuing her TESOL degree at Touro University. She is passionate about creating learning environments where every student feels seen, heard, and loved, and where each learner is supported in achieving success. She continues to inspire a love of learning in every English learner while equipping them with the skills to communicate confidently and effectively.
Although my time at Touro has been brief, it has inspired me to reconnect with my cultural roots through my father and grandmother, celebrating the legacy that came before me. Through Touro’s TESOL graduate program, I’ve had the opportunity to engage with peers from diverse backgrounds, sharing experiences that have enriched me professionally and sparked conversations I might never have had otherwise.
Joyann Castilletti, Touro University TESOL Candidate
Joyann Castilletti, Touro University TESOL Candidate, on her experience working with structured prompt engineering and AI:
Using this prompt showed me a few things about designing rubrics. For starters, specifics are key to a solid rubric. When I first started student teaching, every assignment I gave had some sort of rubric mainly to protect myself in case a student didn’t do too well. Since student teaching, I have still utilized rubrics but have worked towards making them more specific and rooted in whatever standard I was working on. The rubric that CoPilot and ChatGPT provided is a great jumping point if my students were doing this presentation. My biggest negative with this rubric is that since CoPilot is primarily analytic based, it does not allow for a holistic view of my students (especially since all of my key domains were also analytical). When I make my rubrics, I try to include some element that allows my students that may struggle with the assignment a chance to achieve highly in one category. Additionally, since this rubric was generated from a prompt it did not allow me to have student insight which I like to do (unless I took this rubric to the students and had a discussion about it with them for recommendations or suggested changes). I do like that CoPilot clearly establishes the format of “you do exactly this– you get this score”. When I make my rubrics, I tend to struggle with the verbiage to express exactly what I am looking for and to separate between each score point. With this said, by utilizing this format, I can create more efficient rubrics and change them as needed to make my accommodations.
Dr. J's Education Blog 