Breaking the Expertise Bottleneck: How AI Partnerships Democratize Master-Level Teaching: The Crisis of Disappearing Expertise

In my first six years teaching I was working on my own in charter schools, then, once I started teaching in district schools, for one reason or another, I ended up still teaching on my own for another six years. As an intelligent, reflective, and creative person I taught myself how to teach. I did use textbooks in my first couple of years as a starter framework, however, it very quickly became obvious that textbooks were not sufficient for the students that I was working with. Fortunately my teaching career coincided with the internet explosion of materials and ideas to inspire my self-developed curriculum. The strength of the model in which I was operating was that I had total creative freedom with only the learning outcomes–standardized test scores as the only bar that I had to rise to. In my first year teaching it didn’t take me long to realize that this was an incredibly low bar for me to surmount. Passing the standardized tests are merely the minimum level that students are expected to get to. As my teaching career grew, along with the state standardized tests, since I began teaching in the first year that No Child Left Behind was implemented, I never really had to worry about teaching to the test, as we were advised every year, because I was teaching well above the test. This became obvious to my administrators who quickly noticed that my students’ grades closely matched the scores that they were getting on the state tests. Students who received A’s in my classes were getting “Masters” on their state reading and writing tests.

However, the drawback of my solitary endeavors was that I never needed to articulate what I was doing to anyone but my students–and this through trial, error, and iteration. So, by the time I had to work with a grade level team of teachers I already had a 12 year career of building my craft through intuition, with very few artifacts, save for assignments to show for it. Fortunately, my final 8 years in the profession taught me how to communicate my ideas and to learn and gain inspiration from my teammates, an invaluable experience, especially for my transition toward instructional design. But, by this point I was such an intuitive teacher that in many ways it felt like my final eight years were my first years teaching because now I was teaching as a member of a curriculum design team. Because the novel artifacts that I was producing were only documented in our lesson plans and with assignments there is very thin formal documentation of the systems that I and, later, my team and I, developed. One of those teammates has also left the profession and the other is only teaching Advanced Placement classes now. So, in essence there is very little of my highly effective institutional knowledge that I have left behind.

Similarly, I’m sure that all of you have at least heard of, if not experienced, the huge dearth of institutional knowledge that we are in the process of losing as Baby Boomers retire. As they are continuing to leave the workplace, whether it be classrooms or corporations they are leaving with irreplaceable institutional knowledge due to working for decades with undocumented processes.

Why Traditional Knowledge Transfer Fails

For example, I had developed, for decades, a wide variety of bilingual curricula, culturally responsive teaching methods, and specialized programs. Often, when I had large percentages of Latino students I might have a poem, in our unit, by the Spanish poet Garcia Lorca. In this lesson the poem was originally written in Spanish. So, the Spanish and non-Spanish speakers would have the experience, often for the first time in their lives, of having a lesson that was originally in Spanish and translated into English. Bilingual students learned to see and articulate the nuances that they were able to get from the original Lorca poem–in essence they got to take a turn being the expert with the literature and the non-Spanish speakers got to learn from them, a concept that had only previously been an academic one to all of them. As adults know, oftentimes we learn how to be better students when we can be both the learners and the instructors. Similarly, I would create lessons that were culturally responsive to the milieux in which I was teaching. One year I had a Nigerian student in one of my classes (although at that point none of the students in the school knew that he was Nigerian) and, coincidentally, one of my teammates introduced us to a short story by a Nigerian author whom he enjoyed. The story was about a Nigerian woman’s experience migrating to a rural Maine town and the cultural challenges and dance that was necessary for both groups. My teammate had introduced it as a way to connect with the Latino students, as well as any students with experiences of family emigrating to the United States, such as, when we were having pre-learning discussions about the phenomenon, I talked about the experiences of my great grandparents emigrating to the US from Eastern Europe that have been passed down in our family. When we got into the discussion, due to the discussion being grounded in pre-learning for the story, one of my students, known publicly as “Divine” the football star, admitted to the class that he was Nigerian. After the incredulous noises and comments from classmates who said things like, “What? I just thought that you were a regular Black guy (Black American).” he told us that he grew up in a Black only community in Dallas and, as a part of integrating into the community just let everyone presume that he was a Black American, including taking a very non-Nigerian name “Divine”. He told us that he spoke Yoruba only at home with his family and English only outside of the home. Divine finished his story by saying that that moment in his 11th grade classroom was the first time in his 17 years that he talked about being Nigerian with classmates and felt like it was the first time in his life that he wasn’t two different people. It was on this finishing note that many of his Latino classmates (in that class I had Mexican, Nicaraguan, Salvadoran, and Honduran students) talked about having similar feelings in their code switching lives–only speaking Spanish at home and English at school. As early as my second year teaching, when I didn’t have access to class sets of textbooks I developed creative solutions: photocopying materials, securing review copies of textbooks from publishing companies, and building a curriculum that connected canonical texts to students’ lives. My students discovered themselves in Chaucer, Shakespeare, and Arthur Miller, finding universal themes in their own experiences — though discussions of gang life remained strictly off-limits, I’ve always been good at engaging teens in a liminal space that pushes the boundaries of the sacred and profane, but that ultimately remains appropriate for the setting and age range. In this year I also co-Directed an early college program. My role was to work with the students who would be first generation college students; who’s reading , writing, and math skills were not at a community college level. I would work with them on study skills and counseling–for example, I frequently heard things like, “No one in my family has ever gone to college. Everyone in my family is telling me that I’m a sellout for being a part of this program.”

All really important and valuable work. However, these innovations remained in my individual classroom and were never systematized. This is the challenge that schools and companies, nationwide, face. I have had multiple scenarios, both as a new teacher and a veteran with mentorship programs, whether it be a new teacher or in a student teacher program. The challenge here, though, is similar: this practice does not scale. Documenting these innovations is an option, although it’s not very practical with educators or any busy professional. We just tend not to have the time for such documentation, and if we did the information may not have been as thorough or complete as necessary.

The RAG Solution for Institutional Expertise

One of the best solutions for this old challenge is a new technology: Artificial Intelligence Large Language Models (AI LLM). Although AI is trained on an enormous amount of human culture and society an institution can also train the AI on in-house information. This in-house information can then be used as RAG (Retrieval Augmented Generation). RAGs are internal data bases of material to help the AI to tailor it’s responses to the needs of the organization that created them. In this system professionals are able to upload any notes, assignments, or any information at all to the RAG file. With the power of the LLMs the AI are able to extrapolate, very thoroughly, in order to fill in any gaps. For example, this year I wrote a teacher memoir with an LLM AI. I started by feeding it around 80 pages of my journals and previous attempts at writing a memoir and asked it to give me a list of themes that I then structured into a narrative arc. Then, as the number of pages grew to a level that I struggled to keep organized, without having to pour through the entire document, I had the AI help me to stay organized–such as when I’d write material for a section the AI could remind me which chapter to put it in.

As well, something that I’ve learned from working with an LLM AI is that, after it had been trained on my model of teaching, through my journals and ongoing writing, I was editing a section that I’d asked for it to let me know if I’d left out any important details, had awkward or weak transition (they seem clear inside my head) I noticed that it had added a detail about a teaching anecdote that I was pretty sure I hadn’t written. The thing is, though, that it was a practice that I did engage in with my students. I asked it about this and it said that it recognized that I’d omitted this detail and, based on the patterns of my teaching practice it decided that the odds were extremely high that I engaged in this teaching practice, which was correct. If you haven’t made the connection: this is exemplary of how, no matter the thin nature of the materials that a professional adds — of course more is better — the LLM AI is capable of filling in key gaps when necessary.

Implementation Framework

As we can see, once any available artefacts are uploaded to the AI: for educators that could be lesson plans, notes, exemplary student work, assessment rubrics, or even simple narrative the AI is able to crystalize and memorialize these invaluable materials, or create an iterative process for updating materials as it gains more onsite information, including making modifications with future professional’s work, as well as the changing needs and goals of the institution or stakeholders from the archaeological artifacts from the master professionals.

In this way institutions would be able to identify successful patterns in the materials, such as pedagogical success epiphanies. One year I had a metacognitive realization with two different students who’d been really struggling with my lessons on essay writing. In my final years of teaching, I could detect when a student was taking endless time not because they lacked understanding, but because their concept of perfection was paralyzing their progress. These students needed to learn trust in the iterative process rather than pursuit of an impossible first-draft perfection.

This intuitive assessment led to one of my most profound discoveries about learning patterns. I began recognizing how students approached writing through the lens of their extracurricular passions. One young man, who initially produced abysmal drafts but showed steady improvement through iterations, revealed that his grandfather had taught him to carve wooden figures from blocks of wood. When I asked him to describe his carving process, I realized he was applying that same methodical, iterative approach to writing — slowly revealing the form within the material through careful, patient work. Once I realized this I could picture in my head how his writing gradually carved away the unnecessary writing and smoothed out the rough parts.

Similarly, I had a young woman who was a violinist, practicing alone in her room. Her writing process mirrored her musical practice: quiet, reflective, building complexity through repetition and refinement. Both students had been labeled as poor writers for ten years because their non-standard, prolonged processes were misunderstood by previous teachers who focused on speed, immediate results, and following what they thought of as the ‘normal’ process rather than recognizing different pathways to mastery.

Aside from those of you who are reading this, my metacognitive breakthrough may well go to the grave with me (hopefully far in the future). However, with a RAGs like AI system these stories would be systematized in the school, both for veteran teachers and new teachers. Even if a professional’s perception isn’t one that enables them to see these connections, this concept could provide an instructional designer or academic coach a conceptual foundation from which to build lessons that could help professionals to be able to utilize these concepts.

In education we often had administrators with very little experience in the classroom. Certainly my 20 years in the classroom is seldom, if ever, matched by an administrator. These kinds of professional insights and wisdom can be invaluable to administrators with less experience in a classroom, as well because they often have to oversee departments in which they’ve never had any experience. With this RAG AI model an administrator could get a quick and easy tutorial for what kinds of things to look for in a subject area teacher’s classrooms to make evaluations much more robust and on point. I’m sure that there is an Instructional Design correlation to this. So, in this way administrators, seasoned, and new professionals are able to get master level insights and proven practice wisdom.

Conclusion: From Intuition to Institution

The irony of writing about preserving educational expertise is that I’m doing exactly what I’m advocating for — using AI partnership to articulate and systematize insights that I developed over two decades but had never formally expressed. My breakthrough moments with the wood carver and violin player weren’t isolated victories; they represented a reproducible approach to recognizing and leveraging students’ existing knowledge frameworks. Yet without deliberate documentation and analysis, these insights would have remained locked in my classroom, benefiting only the students I happened to teach.

This is the expertise bottleneck in its most stark form: thousands of educators developing sophisticated, effective practices that never transcend their individual classrooms. RAG systems offer a pathway to break this cycle, but only if we approach them as archaeological tools for excavating wisdom rather than replacement systems for generating new content.

The technical framework is straightforward — feed AI systems with master educators’ lesson plans, student work samples, assessment strategies, and reflective practices. The transformative potential lies in the pattern recognition: AI can identify recurring themes across a master teacher’s career that even they might not consciously recognize. Just as my AI writing partner helped me understand my stream-of-consciousness style and its connections to authors I’d loved decades earlier, educational RAG systems could help teachers recognize and name their most effective practices.

Imagine if my breakthrough with culturally responsive teaching — connecting a Nigerian student’s experience of being an immigrant with the short story that we were reading — had been systematically analyzed and made available to other educators working with diverse populations. Or if my discovery that some students approach writing like craftspeople — methodically shaping raw material through patient iteration — had been documented as a framework for supporting non-traditional learning processes.

These insights didn’t emerge from educational theory or research studies. They came from the daily practice of paying attention, asking questions, and remaining curious about how learning actually happens in real classrooms with real students. RAG systems can preserve not just the insights themselves, but the questioning patterns and observational practices that generated them.

The scalability advantage becomes clear when we consider teacher preparation programs. Instead of relying solely on generic pedagogical theory, new educators could access AI systems trained on master teachers’ actual classroom decisions, assessment choices, and student interaction patterns. Rather than learning that “differentiated instruction is important,” they could see how master teachers actually recognize different learning approaches and adapt their methods accordingly.

This democratization of expertise addresses one of education’s most persistent challenges: the gap between research and practice. My most effective teaching strategies didn’t come from academic studies but from patient observation and iterative refinement. RAG systems can capture this practitioner wisdom at scale, making it accessible to educators who might never have the opportunity to work alongside master teachers.

The broader implications extend beyond individual classroom improvement. Educational institutions could develop RAG systems that preserve their most effective practices across generations of faculty. When master teachers retire, their wisdom wouldn’t disappear — it would become part of the institutional knowledge base, continuously available to support new educators.

Perhaps most importantly, this approach honors the sophistication of teaching expertise while making it more visible and transferable. Too often, effective teaching is dismissed as “just intuition” or “natural talent.” RAG systems can reveal the complex pattern recognition, adaptive decision-making, and contextual knowledge that characterizes master-level practice.

As I transition from classroom teaching to instructional design, I’m learning to translate these insights for adult learning contexts. The fundamental principles remain the same: effective learning happens when we recognize and build on learners’ existing knowledge, when we adapt our approaches to different learning preferences, and when we create environments where students feel safe to take intellectual risks.

The expertise bottleneck isn’t just an educational problem — it’s a human knowledge problem. Every profession loses irreplaceable wisdom when experienced practitioners retire without systematic ways to capture and transfer their insights. Education simply provides the clearest example because teaching expertise is so often invisible, undervalued, and undocumented.

By approaching AI as an archaeological tool for excavating and preserving human expertise, we can break the cycle of perpetual knowledge loss. The technology exists. The need is urgent. What we require now is the commitment to document, analyze, and democratize the wisdom that master practitioners have developed through decades of thoughtful work.

My memoir writing showed me that the experiences were always there — I just needed better tools to excavate and organize them. Educational expertise faces the same challenge. The wisdom exists in countless classrooms. We simply need the systematic approaches to make it visible, transferable, and enduring.