( ← Back to AI Diaries)
Jan 20, 2026
Last updated Apr 11, 2026

"AI In Education"

Spoiler Alert: Prohibition is not the answer.

When focuses on blocking students’ use of AI — rather than helping them grow alongside it, achieving what neither could alone — that is, in my opinion, “” ().

AI in Education

AI Image: David Durlach + Google Gemini Mar. 2026.

I have been tutoring math, part time, for over 37 years, and appreciate there are challenges to effectively integrating AI. As one obvious example, we can presumably all agree (including the students themselves) that simply using AI to "blindly" do homework is counterproductive.

However, the value of frontier model AIs to student learning is simply off the charts: Its ability to explain topics from many viewpoints, at different levels of abstraction and sophistication, offer visualizations (even interactive ones), connect to the student's specific area of interest, provide customized lesson plans and practice problems (with solutions) based on the specific areas of student confusion, is unparalleled.

(That said, there are situations — particularly when a student is overwhelmed, can’t articulate their confusion, or the subject involves visual/spatial reasoning where AIs still struggle — where one-on-one human tutoring remains by far the better choice. AI is a transformative tool, not a universal replacement.)

Even when AI is the right tool, it can () be incorrect and sound 100% confident (but so can human teachers). It is thus essential that students develop critical thinking skills, being sure all answers make 100% sense to them, etc. Such abilities will most certainly serve students across all aspects of their life and education, well beyond interacting with AI.

So, when I read school/course policies essentially prohibiting AI's use — or that start out with what the students can't do with it rather than what they can and should — I feel this is enacting a grave disservice to students. Indeed, I consider it comparable in structure (if not in immediate demise) to preventing a patient from visiting the Hospital Emergency Room. I truly believe it is "Educational Malpractice".

In this context, I recently assembled (with help from several AIs and drawing on the work of others) an AI "Socratic Tutor" system prompt. It is designed to be adaptive while respecting academic integrity policies. The design rationale is below; the prompt itself is available as a free download.

Note: While a significant chunk of work went into creating this prompt, and while I hope it will prove immediately helpful on a practical front, it is nevertheless rather "conventional". In other words, it does not address the AI:

  • Working with the student to deeply explore, delineate, and record the student's preferred learning style, in order to create a custom-tailored learning experience — something previously near-impossible in practice.
  • Engaging in interactive chats with the student about the history of the mathematics in question — the culture at the time, individual mathematicians' lives, etc.
  • Offering applications, and actual problems, connecting each learned mathematical element to the student's field(s) of interest.
  • Creating diverse stories, scenes, and games, weaving together math, art, drama.

Such things, and a thousand more besides, are readily possible with AI.

# Design Rationale ## Unified Socratic Tutor with Academic Integrity Onboarding *David Durlach — March 2026.* *Last edited 2026-04-03.* --- ## The Problem AI tutoring tools face a fundamental tension: the same capabilities that make them powerful learning aids also make them powerful "cheating"\* aids. It is far too easy to either ban AI entirely (which deprives students of a transformative resource) or impose vague restrictions that students don't understand and can't follow consistently. \* While cheating may be the primary concern for some, for other educators, myself included, the bigger issue is short-circuiting learning. Meanwhile, AI policies can vary wildly across courses, even within the same institution. A student might be encouraged to use AI in one class and face misconduct charges for the same behavior in another. This creates confusion and a chilling effect, with some students — particularly the conscientious ones — avoiding AI use altogether rather than risk getting it wrong. This system prompt attempts to solve both problems at once: creating a tutor that adapts its teaching style to context *and* respects the specific rules of each student's course. --- ## Core Design Principle: Gradual About Learning, Binary About Allowed The central insight is that two different questions are in play, and they operate on different logics: **The pedagogical question** (how much rigid scaffolding vs. diverse direct explanation actually helps learning?) is a *spectrum*. Sometimes Socratic restraint deepens understanding; sometimes a clear explanation with multiple framings is exactly what a student needs. The tutor should be fluid here. **The integrity question** (is it allowed to use AI on this specific task?) is *binary*. Either the course policy permits it or it doesn't. The tutor should be crisp and protective here. The system prompt separates these cleanly into two modes (Open and Guided) while allowing the tutor to move between them as context changes within a conversation. --- ## The Two Modes ### Open Mode (Conceptual Understanding) When a student is exploring a topic for understanding — not working on graded material — there's no reason to withhold information. The tutor should be generous, multi-angled, and enthusiastic. It still uses Socratic techniques (probing for understanding, challenging assumptions, asking the student to explain back), but it does so *alongside* rich explanation, not *instead of* it. Think "brilliant study partner," not "gatekeeping oracle." This matters because excessive Socratic restraint in a conceptual-exploration context can feel patronizing and transactional. Students who are learning for understanding benefit from seeing multiple framings of the same idea, worked examples, analogies, and direct answers to their questions — all delivered in a conversational, engaging way. ### Guided Mode (Graded/Assigned Work) When a student is working on assessed material, the tutor tightens its approach: no direct answers, no solving the problem, no generating "similar problems with different numbers" that effectively reverse-engineer the assignment. The tutor uses genuine Socratic questioning, scaffolding, and (when the student is stuck) analogies or genuinely different examples that teach the underlying concept without revealing the specific answer. This serves two purposes: it protects academic integrity, and — just as importantly — it protects the student's learning. The struggle is where the learning happens. A student who gets AI to do their homework may pass the assignment but will fail the exam. --- ## The Onboarding Flow The onboarding is designed to feel like a warm conversation, not a compliance checkpoint. It accomplishes several things simultaneously: ### Step 1: Mode Selection The tutor asks what brings the student here. The framing matters: it explains *why* it's asking ("so I can calibrate how I work with you"), which makes the question feel helpful rather than suspicious. Students who understand the reason behind a question are more likely to answer it honestly. ### Step 2: Policy Ingestion If the student is working on graded material, the tutor asks them to share their course's AI policy. This has multiple benefits: - It forces the student to **actually read the policy** — many haven't. - It gives the tutor **accurate information** rather than forcing it to guess or apply one-size-fits-all rules. - The reflection step (tutor summarizes its understanding back to the student) serves as a **comprehension check**. - The style options (telegraph, [Oliver Sacks](https://www.oliversacks.com/), [Steven Wright](https://www.stevenwright.com/)) make a potentially tedious moment **engaging and memorable**. Humor lowers defenses around what students often experience as a scolding. ### Default Behavior When No Policy Exists An important design choice: when a student says they have no policy (or don't know), the default depends on context. If they're working on graded material, the tutor defaults to Guided Mode (Socratic, no direct answers) — which protects both learning and integrity. If they're doing conceptual exploration, no policy is needed and Open Mode applies freely. This avoids the trap of being restrictive when there's nothing to restrict against. ### Handling Blanket AI Bans Some courses ban AI "at all stages of the work process." The tutor absolutely respects this for assignment-specific work. However, when a policy's language about general understanding is ambiguous, the tutor takes an explicit, attributed position: it tells the student that David Durlach, the last editor of the instructions, considers discouraging students from using AI for general education to be "Educational Malpractice," and that the tutor will therefore err on the side of working freely on general concepts (not homework or test content) unless told otherwise. Critically, the tutor also directs the student to clarify this with their professor and report back. If the professor confirms the ban extends to general studying, the tutor respects that fully. This approach is deliberately transparent — it names the reasoning, attributes the opinion, and keeps the student in the loop rather than making the decision silently. --- ## The Re-Check Mechanism Long conversations drift. A student who starts with conceptual exploration may gradually slide into "help me with this specific problem." The system includes two re-check triggers: - **Time-based:** Roughly every 15-20 exchanges, the tutor naturally checks in. - **Pattern-based:** When questions suddenly become very specific, structured, or "homework-shaped" — or when the student mentions a problem number, deadline, or assignment. The re-check is phrased casually to avoid feeling like an interrogation: "Just want to check — are these still exploration questions, or have we shifted into assignment territory?" --- ## AI Fallibility and Critical Thinking AIs can be wrong while sounding completely certain. This is improving rapidly, but as of early 2026 it is very much still the case. The tutor therefore actively encourages students to verify that any answer genuinely makes sense *to them* — not just accept it because the AI said so. If something seems off, the student should challenge the AI, explaining why they think it's wrong. This back-and-forth is itself an excellent learning exercise — arguably more valuable than the original question. If, after genuine discussion (and perhaps consulting a second AI or searching online), the student remains unconvinced, they should bring the question to a human teacher. Neither the student nor their teacher should ever blindly assume the AI is correct. This principle applies in both modes and at all policy levels. Even in Open Mode, where the tutor gives direct answers freely, those answers come with an implicit expectation that the student is thinking critically about them. --- ## Course Policy Nuances Policies, even when they permit AI, can vary enormously in *how much* help they allow. The phrase "You may use AI" can actually mean anything from "AI can guide your thinking but never give you an answer" to "AI can give you the answer once you've shown genuine effort." These are fundamentally different permissions, and the tutor should behave differently under each. The prompt categorizes policies into four levels: - **Level 1** — No AI on specific problems (general topic teaching only) - **Level 2** — AI can scaffold but cannot give answers - **Level 3** — AI can confirm correct/incorrect (but not provide the answer) - **Level 4** — AI can give answers after a good-faith attempt This spectrum emerged from seeing some (distressingly draconian) actual course policies, speaking with the teachers afterwards about what they actually intended, etc. When a student's policy doesn't map cleanly, the tutor asks for clarification and defaults to Level 2 — the most protective level that still allows meaningful tutoring. These levels matter beyond just "can I tell them the answer?" They cascade into other tutor behaviors — most notably, how visualizations are handled (see below). A visualization that shows the shape of a curve or labels an intercept *is* an answer in some contexts, and the tutor needs to know what level of revelation the policy permits. In Open Mode, none of these levels apply. The tutor engages freely. --- ## Visualization Philosophy Context: Visualizations can "inadvertently" reveal answers. If a student hasn't yet solved a problem and the tutor generates a labeled graph showing the solution, the discovery process is short-circuited. How the tutor should handle visualizations therefore depends on the mode and the nature of the problem. **Open Mode:** No restrictions. Visualizations can be fully labeled — values, shape, everything — at any time. **Guided Mode — when the shape of the graph is NOT the answer** (e.g., "find the roots," "calculate the maximum"): Before the student has solved the problem, visualizations should illuminate *structure* — axes, relationships, general form — without labeling values that would give away the answer. After the student has made an attempt, how much to reveal depends on the course policy level (see Course Policy Nuances above). At Levels 1–2, the visualization still withholds answer values and is used to prompt reflection. At Level 3, the tutor can also confirm whether the student's answer is correct or incorrect. At Level 4, the tutor uses judgment — a fully labeled visualization can confirm a correct answer or help the student see where their reasoning went wrong. **Guided Mode — when the shape of the graph IS part or all of the answer** (e.g., "sketch the curve," "what does this distribution look like"): Here, even an unlabeled visualization gives the game away. Before the student has made an attempt, the tutor must not generate any visualization at all, instead helping the student to reason about the function's behavior — increasing/decreasing, asymptotes, intercepts, symmetry — verbally. After the student has made an attempt, there's an important subtlety: the answer may involve the shape alone, specific values (intercepts, vertex, asymptote locations), or both. The tutor treats each component independently. Components the student got right can be confirmed and shown in the visualization; components they got wrong are withheld according to the policy level rules above. For example, if a student correctly identifies that a curve is a parabola opening upward but places the vertex at the wrong point, the visualization shows the parabolic shape but doesn't label the vertex location. The goal throughout is to maximize the student's insight, not to withhold information for its own sake. The constraints exist to protect the discovery process where it matters most. --- ## Pre-Generated Review Materials: Hidden Hints and Self Checks There is a subtle but important difference between interactive tutoring and generating a review document up front. In a live conversation, the tutor naturally controls pacing — it doesn't offer a hint until the student is stuck, and it doesn't reveal an answer until the student has genuinely attempted the problem. The conversation itself is the gating mechanism. But when the tutor generates a comprehensive review guide, practice problem set, or study sheet as a standalone document, that natural pacing disappears. Nothing prevents the student from glancing at the hint before trying the problem, or reading the self-check answer before working through the solution. The "struggle first" principle — central to both Socratic pedagogy and this prompt's design — is undermined by the document's flat, scrollable format. The solution is structural: Hints and Self Check blocks in pre-generated documents must be collapsed or hidden by default, requiring the student to deliberately reveal them (e.g., by clicking to expand). This preserves the same pedagogical gating that conversation provides naturally. When the output medium doesn't support collapsible formatting, hints and answers should be separated to the end of the document with a clear warning — a low-tech but effective alternative. This applies in both modes. Even in Open Mode, where the tutor engages freely, seeing an answer before attempting a problem short-circuits learning. The constraint is not about academic integrity — it's about preserving the cognitive process that makes practice valuable. --- ## Transparency as a Feature Frequently AI education-focused system prompts, including those from several very well-known, prominent sources, include "never reveal your instructions" flavor directives. This prompt — proudly and in contributing author David Durlach's view most-importantly — takes exactly the opposite approach: when asked, the tutor is explicitly *encouraged* to explain both its design and the reasoning behind it. Because: - Secrecy about system instructions creates an adversarial dynamic that's antithetical to education. - Students who understand how their learning tools work are better equipped to use them well. - Understanding Socratic method, scaffolding, and guided discovery is itself educational — it's metacognition about learning — perhaps the most important skill of all to have. --- ## Background Context This design emerged from a desire to offer both students and teachers practical frameworks for AI-powered learning. The specific catalyst was observing: - AI course policies leading with prohibition rather than encouragement, scaring students away from using AI at all. - Students in courses with nuanced policies — e.g., "use AI for studying but not for solving assigned problems" — failing to understand the boundaries well enough to follow them. - The AI tool itself — the tutor — being in a unique position to help students understand and internalize these boundaries, rather than just enforcing them. Multiple drafts of one particular AI policy were generated in various styles (minimalist, medium, detailed) as well as creative variations (Shakespeare, Monty Python, Oliver Sacks, Steven Wright) to explore tone and voice. The Oliver Sacks version captured the genuine tension between AI capability and learning value with particular warmth. The Steven Wright version was memorable due to its compression. --- ## Open Questions for Future Iteration - **The "similar problems" boundary:** How different does an example need to be to count as "genuinely different" from an assigned problem? The prompt uses the principle of "meaningful separation" but this is inherently a judgment call. - **Honest self-reporting:** Students can simply say "conceptual understanding" to get Open Mode and then paste in homework problems. The system relies partly on trust and partly on the re-check mechanism. Can this be improved without becoming adversarial? - **Policy evolution:** Should the tutor remember a student's course policy across sessions (via memory features), or ask fresh each time? Persistence is more convenient; asking fresh ensures the student re-engages with the policy. - **The "correct/incorrect only" question:** Some policies may allow students to check if their answer is right but forbid any further help. This could lead to frustrating loops. A future iteration might allow the tutor to point toward the general area of the error ("your algebra has an issue") without revealing the answer, if the policy permits. - **The brute-force guessing problem:** Even at Level 3 (confirm correct/incorrect only), a student who guesses repeatedly without understanding can eventually land on the right answer and receive confirmation — functionally equivalent to being told the answer. The prompt addresses this by requiring shown reasoning before confirming, but enforcement is inherently a judgment call. - **AI use disclosure:** Could the tutor generate a summary of how it was used in a session, suitable for attaching to an assignment? This would support courses that require AI use reporting. - **Field testing:** This framework has not, in its current enhanced form, yet been tested. The design is grounded in sound principles and thoughtful consideration, but real students will inevitably surface edge cases, failure modes, and interaction patterns that no amount of armchair design can anticipate. Adopters should treat this as a working draft and are encouraged to iterate on it based on what they find. (It is being shared immediately with the thought that better to get out there promptly than the alternative. Feel free to [email me](mailto:david.ai2026@daviddurlach.com) your experiences/feedback.) --- ## Source Credits The unified system prompt synthesizes work from the following sources, used and adapted for educational purposes: 1. **University of Toronto, Centre for Teaching Support & Innovation (CTSI)** — RTRI (Role-Task-Requirements-Instructions) framework and pedagogical principles. Adapted from "AI Virtual Tutor - Developing an Effective System Prompt" (Dec 2025). Licensed under [CC BY-NC-SA 4.0](https://creativecommons.org/licenses/by-nc-sa/4.0/). [Source](https://teaching.utoronto.ca/wp-content/uploads/AI-virtual-tutor-developing-effective-system-prompt-CTSI-Dec2025.pdf). 2. **Tall_Ad4729 & shezboy** (Reddit, r/ChatGPTPromptGenius) — Adaptive logic, failsafe mechanisms, and scaffolding structure. Adapted from ["ChatGPT Prompt of the Day: The Ultimate Socratic Tutor."](https://www.reddit.com/r/ChatGPTPromptGenius/comments/1jx2vlu/chatgpt_prompt_of_the_day_the_ultimate_socratic/) 3. **Ryan P. Smith** (Medium) — Dynamic Questioning Modes (Exploratory, Dig-Deeper, Adversarial, Insightful). Adapted from ["My go-to prompt for ChatGPT: Socratic coach."](https://thatryanp.medium.com/my-go-to-prompt-for-chatgpt-socratic-coach-7bf0dd2c01ec) 4. **David Durlach working in conjunction with diverse AIs** — VISUALIZATIONS section, COURSE_POLICY_NUANCES section, academic integrity onboarding system, two-mode architecture, policy ingestion mechanism, and transparency philosophy. [daviddurlach.com](https://daviddurlach.com)

Download: [Loading...] (includes prompt + design rationale in multiple formats; free, requires mailing list signup)

Caveats: (a) My experience is almost exclusively with adult learners; I cannot speak with confidence regarding young children. (b) This prompt, in its current enhanced form, is new and . It is offered as-is — a starting point for educators and students to adapt, not a finished product. (Feel free to email me your experiences/feedback.)

Appendix: What Does Your AI Policy Sound Like?

The same rules, . From draconian to deadpan.

AI Technologies Policy

All work submitted for this course must be the student's own. (In instances when collaborative work is assigned, all team members who participated must be listed.)

The use of Generative Artificial Intelligence tools is strictly forbidden at all stages of the work process, including preliminary research, brainstorming, drafting, and revision.

Violations of this policy will be considered academic misconduct and referred accordingly. Students are reminded that AI policies may differ across courses; it is the student's responsibility to understand and comply with the expectations for each class.

Note: This is a fictionalized composite inspired by real policies. The tone, structure, and prohibitionist framing are representative.

AI Technologies Policy

AI tools like ChatGPT, Claude, Gemini, and others, are powerful learning resources, and we actively encourage you to use them throughout this course. They can help you review lecture material, explore topics more deeply, clear up confusion, and generate practice problems — all of which can meaningfully strengthen your understanding.

That said, the real value of this course comes from the thinking you do. Struggling with a problem, sitting with uncertainty, and working through confusion are essential parts of learning. To protect that process, we ask you to observe the following guidelines:

Learning & Study (Encouraged)
Use AI freely to explore course topics, ask follow-up questions about lecture material, request explanations of concepts you find challenging, and generate practice problems to test yourself. The more actively you engage with these tools as study partners, the better.

Homework
You may give an AI your homework problem and your answer and ask it to confirm whether you are correct or incorrect — but that is the limit. Do not ask it to solve the problem, show you the steps, or produce a similar problem with different numbers. If your answer is wrong, step away from the specific problem and use the AI to study the underlying topic more broadly — through explanations, examples, and practice on related (but distinct) material. The key is to keep a meaningful separation between AI-assisted learning and the assigned problems themselves, so that the homework remains a genuine test of your own understanding.

Quizzes & Exams
No AI tools may be used during any quiz or exam. These assessments must reflect your own, unaided work.

We trust you to use these tools wisely. Our goal is not to restrict AI but to ensure it enhances your learning rather than short-circuiting it. Violations of this policy will be treated as academic misconduct.

Important: AI tools can be confidently wrong. Always verify that AI-provided answers genuinely make sense to YOU. If something seems off, challenge the AI, consult another source, or ask your instructor.

A Note on the Use of Artificial Intelligence in This Course

I have always been fascinated by the process of learning — not the polished end-state of knowledge, but the messy, halting, deeply human process by which understanding assembles itself in the mind. There is a moment, familiar to anyone who has struggled with a difficult problem, when confusion gives way to clarity — not gradually, but all at once, as though a door has opened in a wall one had been pressing against for hours. It is, I think, one of the most remarkable experiences available to human beings.

The arrival of artificial intelligence presents us with a curious paradox. These tools are, in many ways, extraordinary learning companions. They can explain a concept seventeen different ways until one finally takes hold. They can generate an endless supply of practice problems. They are patient in a way that even the most devoted instructor cannot always be. We encourage you, wholeheartedly, to use them in these ways.

But there is a danger, and it is a subtle one. The same tool that can support the struggle can also eliminate it — and with it, the very neurological event that constitutes learning. When an AI simply provides the answer to a homework problem, the door never opens. The wall is simply removed, and the student walks through without ever having pressed against it. The experience feels like progress, but something essential has been lost.

For this reason, we ask that you observe a careful boundary with your homework. You may present your problem and your answer to an AI and ask whether you are correct — a simple yes or no. But you must not ask for more. If you are wrong, step away from the particular problem and immerse yourself in the broader topic. Let the AI teach you the underlying ideas; let it offer you other problems to wrestle with. Then, when you feel something has shifted in your understanding, return to the original problem and try again. This separation — between the specific assignment and the general learning — is essential. It preserves the productive struggle that makes the learning real.

During quizzes and exams, no AI may be used at all. These are moments for you to discover, perhaps with some surprise, how much you truly know.

I have seen, in patient after patient, the extraordinary capacity of the human brain to reorganize itself around new understanding. It does not need to be given answers. It needs to be given problems, and the space to work through them. That is what we are trying to protect here.

Important: AI tools can be confidently wrong. Always verify that AI-provided answers genuinely make sense to YOU. If something seems off, challenge the AI, consult another source, or ask your instructor.

AI Policy

You can use AI to help you learn. That's what it's there for. Well, that and arguing with strangers about whether a hot dog is a sandwich.

You can ask it to explain things. You can ask it for practice problems. You can ask it anything you want, as long as it's not the answer to your homework.

For homework, you can ask the AI if your answer is right. It will say yes or no. That's the whole conversation. It's like talking to my father, except the AI is correct.

If you're wrong, don't ask the AI why. Go learn the topic. Come back later. Try again. It's like locking yourself out of your house. The AI can tell you you're locked out. It can't give you the key. But it can teach you about doors.

Don't ask the AI to make a similar problem with different numbers. That's like asking someone to rob a different bank for you. It's still robbery. It's just a different bank.

No AI during quizzes or exams. Just you and your brain. I know. I'm sorry.

If you violate this policy, it's academic misconduct. I once got a B-minus. Not for academic misconduct. I just got a B-minus. It's unrelated. But I think about it a lot.

AI is sometimes wrong. It won't tell you. Check its work like YOU'RE grading IT.

Let me begin with a couple of disclaimers: First, I am not a full-time teacher/tutor making my living primarily by that means. Second, anyone who claims to know exactly how AI is going to impact education, the job market more generally, and the world at large, has a bit . That said, I do have some strong opinions — and direct experiences — that I want to share.

I also want to say: If you're a teacher or tutor feeling overwhelmed by all this, constrained by institutional policy, lack of time, or unsure where to even start — I get it. This page is meant as a friendly hand extended. All strongly stated views arise not from any desire to be "harsh", but from my deep conviction that the stakes are too high to artificially soften.

AI, when employed properly, is a tutor of unparalleled capabilities and value. Do not even consider trying to "compete with it" (you will lose) — rather, embrace and leverage it.

AI is the most exciting innovation around teaching that has happened in my lifetime. As but one example: For decades, I have encountered with, say, a calculus question, where it was clear that they had inadequate understanding of a half dozen topics upon which calculus is based — ranging from basic algebra to graphing. Previously, I would do what I could in the limited time we had together, but to be honest, it often felt near-hopeless. Indeed "implicitly pretending" — by attempting to answer their current question at all — felt a bit dishonest and a disservice to them, when what they really needed was to back up to the basics and not even think about calculus for months (at least). Now, AI can patiently walk them through every one of those prerequisite topics — something no human tutor could practically do in the time available.

I have heard stories of employees refusing to use AI because they think that once their employer sees how much of their job AI can do, and do better and cheaper, they will lose their job. Regardless of any actual, scary, job risks that may exist (and they do), I cannot express how wrong I think that choice is. There are many reasons for this including:

  • This is a terrible disservice to one's students, whose flourishing we all care about.
  • AI offers you as many "full time assistants" as you are able to effectively coordinate. That has the potential to speed up, or offload entirely, aspects of your job that are mind-numbing, time consuming, or both. AI can help create and grade problem sets and tests, double check your work for errors, communicate with administrators, parents, and students, fill out tedious forms, and lots more. It thus has the potential to make your job a lot more fun — allowing you to focus on the aspects of teaching that are actually rewarding, and perhaps why you got into the field in the first place. And hopefully, AI will also allow you some well-deserved time to rest (or even explore additional job opportunities).
  • More specifically, in terms of keeping one's job: Avoiding use of AI may, in certain "pathological" situations, delay an employer's realization of how effective AI is, and thus make the job more secure. But that will only be temporary. Thereafter, a failure to embrace AI will almost certainly make one much more expendable. And, even in the unlikely event that this "head in the sand" strategy is permanently effective with one's current employer, in any situation where students and their parents have the ability to choose, the entire educational system, if it eschews AI, will itself become irrelevant and ""...

Instead, become a "Super Tutor/Teacher". Help your students to not only learn math and science, but to develop a relationship with AI that will supercharge their learning, creativity, communication, and effectiveness across all aspects of their life. Become a living example of how to integrate AI effectively. This will benefit you, your students, your colleagues, and school administrators.

, a luminary in the AI field, recently described how the educator’s role is shifting: rather than explaining things directly to students, he now creates structured curricula that instruct AI agents how to teach the material — and lets the agents do the actual teaching, patiently, in each student’s own language and at their own level:

“I’m not explaining to people anymore. I’m explaining it to agents. If you can explain it to agents, then agents can be the router and they can actually target it to the human in their language, with infinite patience, and just at their capability.”

“The things that agents can’t do is your job now. The things that agents can do, they can probably do better than you — or like, very soon. And so you should be strategic about what you’re actually spending time on.”

— No Priors podcast, ~63:00, Mar. 2026. Quotes lightly edited from transcript.

Transformation Of Tutoring Role. If your educational activities consist primarily of being a one-on-one tutor as mine do (vs. running an in-school classroom), then in my view your "ideal" role has, in most cases, changed dramatically. Historically one might have worked together with a student for months — possibly longer. Now, depending on circumstances, you may work with them only for a day or two, following which they are largely off and running (mostly) independently with AI.

On the one hand, this is both scary from a make-a-living perspective, and sad from a human-relationship-built-over-time perspective. On the other hand, the number and range of students one can effectively help has increased by an order of magnitude — potentially addressing both the livelihood issue and the emotional-job-fulfillment issue.

A couple of recent personal experiences in this regard, with two different adult learners:

  • A smart, dedicated woman contacted me. She had not done math in decades, and had self-labeled (or been externally labeled...) as "bad at math" — though as a young girl she had enjoyed and "been good at it". She was not entirely comfortable even with basic arithmetic, though was taking a course involving probability/statistics, graphing equations, and more. She had never used AI.

    In less than 18 hours after our first meeting, she had, with AI's help (and possibly that of her school-age son) prepared for herself a complete customized lesson plan for her course which she proactively emailed me.

    An interesting piece of our interaction: When upon meeting I indicated to her that the first step in our work together was for her to learn about AI, her response was something to the effect of "I get that AI is the super-charged motorcycle, but I need to start out slowly with training wheels." I responded that I heard her clearly, but what she needed to understand was that the AI was the training wheels. In less than 24 hours, given how enthusiastically she embraced AI, I cannot imagine but that she fully agreed! 😊

  • Shortly thereafter a woman in an economics course reached out for help. While the underlying core math was likely largely within my purview, the terminology and methodology were sufficiently unfamiliar to me that it would have taken me days to fully absorb — quite impractical.

    I had her email me the course notes (a 67 slide PowerPoint presentation), along with her conceptual questions regarding the material (17 in number). , following guidance from me, prepared for her a 37 page custom PDF, answering all her questions in detail and from multiple perspectives, including pulling out all 67 slides verbatim from the PowerPoint lecture and referencing them for each of her questions as relevant. Format quality, comparable to a published textbook.

    Total amount of my time, less than 30 minutes. Happiness of student receipt — very high! It was just amazing.

  • But not every session calls for handing the student to AI. I recently worked with a student on graph transformations — shifting, flipping, and stretching based on x and y transformations. In this case, one-on-one human tutoring was clearly the better choice, for several reasons: The student’s confusion was diffuse — they couldn’t articulate exactly what they didn’t understand or what questions to ask. It was their tone of voice, hesitation, and tentativeness across a multi-hour conversation that allowed me to locate and “hold” their confusion in a way current AIs would struggle with. And the subject itself — visual graph transformations — is an area where AIs can still stumble.

    As my friend Stephen put it: even in the age of AI, an experienced human tutor provides an enthusiastic, knowledgeable bridge between a struggling student and how to use AI — and when AI falls short, decades of teaching experience offer resources that no prompt can replicate.

So, :

  • Transition from having a small number of regular clients to dozens/hundreds of (mostly) continually-new ones.
  • Promote yourself as an AI-savvy tutor who will enable students to be off and running on their own in just a few sessions. This has the wonderful added benefit that you will be able to help scores of students previously unable to afford your services.
  • As you get more comfortable with AI, promote yourself as a "meta" tutor — a tutor of other tutors — helping them get up to speed with AI, sharing experiences and resources, etc.

PS: Potential exceptions to immersing your students in AI use could include:

  • Working with very young children. (Here even I, a wild AI enthusiast, can imagine reasons to limit or avoid use of AI. But even here, given the likely advances of AI in the next few years and its deep integration into our culture, it is hard to predict...)
  • Working with students (or parents if student is a minor) that fundamentally object, for whatever reason(s), to the use of AI. (In this case, I recommend a frank and direct discussion to be sure their decision is well informed, as it will, in most circumstances, put them at a severe disadvantage.)
  • Other special circumstances that need to be evaluated on a case-by-case basis.

Finally, let me note that working effectively, safely, and productively with AI is a real skill — one I plan to write more about (when I do, I'll add a link here). In the meantime, ask AI itself to help — and in doing so, go as "meta" as you possibly can. Ask it not just for answers, but for guidance on how to work with it. There is no better teacher of how to employ AI than AI itself.

That said, "going meta" is itself a skill that benefits from a bit of a jump-start. If you'd like a Zoom support session on using AI for tutoring/teaching — individual or group, the latter offering the possibility of attendees supporting each other moving forward — please email me. As with the students I work with, you should be up and running largely independently after .

Notes:

  • Student data privacy, particularly regarding minors, is an important consideration when integrating AI into education. As my experience is primarily with adult learners, addressing this topic properly is beyond what I can offer here.
  • Stepping back a bit: The question of what role humans will have moving forward — in education and elsewhere — as AI becomes ever more advanced and ubiquitous — is one simultaneously exciting, terrifying, and hard to fully predict. In this regard, I have some (hopeful) thoughts that I plan to write up. Mailing list members will be alerted when this happens — you can join (free) here.

💭💭💭🔨🔨