Recently, my co-founder at Eduaide, Thomas Hummel, and I joined the ThriveinEDU podcast. The questions gave us a broad canvas to work with, and I want to put them down here. They clarify certain basic ideas about AI in education, address common misconceptions, and also open onto larger questions about what it means to be a teacher today.
Q1. Teacher burnout is at an all-time high with grading, planning, and paperwork. In your view, how can AI realistically reduce teacher workload without lowering instructional quality?”
The first piece worth considering here is that what a teacher does most of the time is teach. If you look at the schedules of most teachers in the US today, active teaching hours are the majority of the schedule, and that in itself isn’t typically perceived as the area of lost time. The feelings of burnout and perceived slights of disrespect that contribute to teacher turnover are myriad, and we’ll discuss a few. Yet, in general, they all contribute in some way to the teacher feeling underprepared and unsupported for working with their students. It is these two feelings—lack of support and inadequate preparation—that, over time, are demoralizing, making one cynical, jaded, or burned out.
It’s a snowball effect of bureaucratic red tape, administrative duties, paperwork, incessant meetings, emails, hall or lunch duties, and valuable parent-teacher communications. All this happens while the teacher is on their planning, during their lunch, or before or after school. Two of those four options are unpaid. One option, lunch, I believe, should be reserved for the teacher to take a break—being on for 8-9 hours a day is taxing. So, that leaves planning time as really the only paid time for a teacher to address all those previously mentioned pulls on their time and resources. On top of that, they have to plan for 4-8 classes, perhaps two or more subjects, and who knows how many unique learner needs are represented in those classes. This is what most educational AI tools are designed for—the non-instructional time in the teacher’s day. The question is how to make that limited time maximally productive so the teacher enters the classroom prepared, such that they can make that time productive.
Quality, in this context, comes through in the degree to which the system used contributes to the creation of high-quality instructional materials, a clear idea of how to implement those materials, strategies to address misconceptions and issues, planned differentiation for those students who have shown a need for it, extensions, elaborated analogies, and opportunities for practice. In other words, does the use of the tool meaningfully contribute to the teacher coming prepared to be a good teacher? If you provide that, if you enable that to happen, if you give professionals the tools and support they need to be effective, our thesis is that you reduce teacher burnout. It will not eliminate burnout, there are too many mounting systemic pressures that no mere piece of technology can remedy, but perhaps it serves as a pressure valve.
This links to the broader principle of sustainability we’ve talked about before: the profession cannot endure if it continually extracts unpaid labor from teachers. You can’t build excellence in a field that struggles to retain talent. AI, as a means of reducing the translation cost between evidence and practice, can redistribute the cognitive and temporal load, making teaching once again a sustainable career.
Q2. Teachers often spend hours creating differentiated lesson plans for diverse learners. How can AI help make planning more efficient while still honoring the individuality of each student?”
AI, if used well, can make planning more efficient, precisely by honoring student individuality. This does not mean merely pandering to surface-level engagement. Dropping in references to Fortnite or TikTok to catch attention only goes so far and is hardly a durable lever to pull for student engagement over time. Rather, it means embedding general heuristics teachers hold about their craft into instructional design—spacing practice, interleaving concepts, retrieval through low-stakes quizzes, modeling with worked examples, connecting to prior knowledge, scaffolding access and then fading supports, providing corrective feedback, giving examples and non-examples. Each of these practices has robust evidence supporting them, but implementing them consistently as the needs arise is demanding on the teacher’s already strained resources. Here we touch the research-to-practice gap. Teachers often know, in principle, that retrieval practice or spaced learning works. But operationalizing them across 5–8 classes, dozens of students, and varied needs is overwhelming. As Carl Hendrick put it, AI has the potential to assist deeply in the orchestration of instruction through the time-honored holy trinity of educational practice: Retrieval, Spacing, and Interleaving.
Q3. AI tools like Erasmus can help to support teachers looking for ideas for differentiation. How do you see AI ensuring equity—so advanced learners, struggling students, and everyone in between all get meaningful support?”
Well, the first part of the answer is in the question. AI tools, such as our agent Erasmus, assist teachers who are looking to differentiate instruction for their students. This means that the technology is an amplification of the teachers' intentions. In short, equity in education has never been a feature of technology alone, but has always depended on the intention with which the technology is used.
Could an AI tutor today adapt to the needs of all students for meaningful support, which I would define as effective support insofar as it contributes to positive student learning outcomes, perhaps not possible without the aid of the tool? No, they most certainly do not. AI requires intention and direction. In their current state, they ensure equity only if ensuring equity is the goal of the user and the sandbox for creation is large enough to accommodate that intention. In the case of Eduaide, this might involve calling our Differentiated Instruction Assistant, specifying your need, objectives, standards, and content, and then exploring potential options and instructional interventions that you may or may not use. This should not be viewed as shirking responsibility from the designers and builders of the technology. You can certainly obstruct or pervert the user's intentions, and that should weigh heavily on the minds of everyone involved in the production of educational technologies.
Q4. Many new teachers struggle with a lack of onboarding and support. Could AI serve as a mentor or coach for new educators? What might that look like? How does Eduaide help?
The transition into teaching is notoriously difficult. Novices often lack the tacit knowledge that experienced teachers deploy: when to fade a scaffold, how to anticipate misconceptions, or what a good worksheet looks like. Could AI serve as a mentor or coach in this regard? Not in the human sense, but perhaps in a way that makes effective practices visible and accessible to both novices and seasoned teachers. Imagine a tool that not only generates problems but also provides implementation guidance: “Start with a fully worked example. Then try a partially worked problem. Then an independent one. Along the way, ask goal-free questions that prompt students to self-explain their steps.” In this way, AI can function as a practice space for pedagogical thinking. It expands the toolbox of methods and techniques and gives novices a framework for using them. In doing so, you can address one of the chronic causes of early-career attrition: lack of support.
Q5. Some educators are hesitant about AI, fearing it will either replace teachers or add another layer of tech to learn. What would you say to build trust and encourage thoughtful adoption?”
First, there is no panacea, no cure-all. Everything in life is a trade-off. AI will certainly add another layer of tech to learn, and this incurs an opportunity cost on the educator. The question is whether there is utility bought at the cost. That’s one reason why we started Eduaide: to provide an on-ramp for teachers to think about AI in pedagogical terms. However, there is no easy answer here. I would worry less about the replacement of the teacher by AI. The tech simply doesn’t have the capabilities to engage in the most economically valuable tasks. I think we’re seeing this with many of the recent studies on the effects of GenAI in various pilots. That said, in education, the technology will assuredly amplify existing problems, as well as presenting interesting emergent properties from the technology interacting with teachers and students. Like any technology, there are unique affordances and boundaries worth considering.
Q6. Looking ahead, do you believe AI will become as essential in classrooms? What’s your vision for how AI could transform the teacher’s role in the next 5–10 years?
All I can say for certain is that AI will have a meaningful effect on teaching and learning since the technology so closely deals with human cognition. What these effects will be and to what degree the effects will be felt is a difficult thing to predict. I do see paths forward where there can be meaningful tools made for teachers and students to assist in the work of teaching and learning, but I also know that if learning science has taught us anything, it’s that this is all a game of probabilities, not prescription or standard solutions. The hope is that AI helps us break dogma—that it allows teachers to experiment, adapt, and refine practice in light of evidence. The fear is that AI could harden dogma, offering prefabricated routines that lull us into comfort, shallow fluency, fragile understanding, and all the while, meaningful learning declines.
Absolutely loved reading this and having time to talk with you both! Can't wait to share the audio of our conversation. Definitely will post this in my Thrive Community for everyone to read as well.