It Is Time for Big Picture Thinkers; Education is not Ready

The people who will do well in the age of AI are those who can decide what needs doing, not those who can do a well-specified task competently. This is a reversal of what most of us were trained for, and it is a reversal of what most education still rewards.

Consider an ordinary essay assignment. I choose the topic. I choose the readings students may cite. I set the length, the format, the citation style, the deadline. I write the rubric that tells students in advance what a good essay looks like and how many points each feature is worth. By the time the assignment reaches the student, every consequential decision has already been made. What remains is execution. The student is judged on how faithfully they carry out a plan someone else designed.

There were good reasons for this arrangement. Mass education needs comparability. If thirty students write on thirty different topics with thirty different sets of sources, the instructor cannot grade fairly, or at least cannot grade quickly. Rubrics protect students from arbitrary judgment. Constraining the task also isolates the skill we want to observe. None of this was foolish. It was a sensible adaptation to the economics of teaching many people at once.

The trouble is that the part we kept for students is precisely the part that machines now do adequately. Give a language model a topic, a reading list, a length, and a rubric, and it will produce a passable essay in under a minute. The rubric was written to be satisfiable, and the model satisfies it. The specified task, the thing we spent decades teaching students to perform, has become cheap. Meanwhile the decisions we removed from students, which topic matters, which sources deserve trust, what counts as a good answer in this context, what would be new rather than merely correct, are the decisions AI handles poorly or not at all. The model will write about whatever you ask. It has no view about whether the question was worth asking.

We have been training students for the automatable half of intellectual work while shielding them from the half that stays human. The shielding was meant as fairness. It has turned into a liability.

Execution still matters. A person who cannot tell a good paragraph from a bad one cannot supervise a machine that writes paragraphs. But the balance has shifted. In the old division of labor, a brilliant idea poorly executed was worth little, because execution was the scarce part. Now a modest idea can be executed well by anyone with a subscription. What distinguishes one person from another is increasingly the framing of the problem and the sense of what would be worth doing. Big picture thinking used to be a luxury reserved for those who had already mastered the small picture. It is becoming the entry requirement.

An assignment built on this premise would ask the student to choose the question and defend why it matters, to select sources and explain the selection, to propose the criteria by which the work should be judged. Novelty would count, not in the grand sense, but relative to what the machine would produce on its own. If the output is indistinguishable from what the model generates from a one-line prompt, the student added nothing. If it shows a decision the model would not have made, a constraint it did not know about, then something human happened.

This is harder to grade. A rubric for judgment asks whether a choice was defensible and whether the student can articulate the defense. It tolerates thirty different topics because the point is no longer to compare essays but to compare decisions. Students will resist as well. Many have spent twelve years learning that the safe move is to find out exactly what the teacher wants and deliver it. An assignment that begins with "decide what is worth writing about" feels to them like a trap. That reflex is the residue of a system that rewarded compliance, and it will take deliberate work to unlearn.

You Will Not Like It, But There Is a Solution for AI in the Classroom

The mood in faculty meetings has tipped into a kind of resigned helplessness. Detection tools fail. Honor codes wobble. Bans get circumvented by a sophomore on a phone. So we shrug and say nothing works. But it is not true. Something works. The reason you have not heard much about it is that it is expensive, slow, and demands the resource universities ration most carefully: collective faculty attention and sustained admin support. 

Here is the solution. Take every program, in every discipline, and pull it apart down to the learning outcomes. Audit each outcome against what AI now does competently. Strip out the procedural skills that machines have automated. Strengthen the advanced, conceptual outcomes that survive the audit. Add new outcomes that did not exist five years ago, the ones that deal with directing, verifying, and integrating AI work. Then rebuild the curriculum upward from the revised outcomes. New sequences. New assignments. New assessments. Not the old course with AI bolted on the side. A different course.

Most courses are not designed; they are inherited. A syllabus arrives in a shared drive, gets a date update, and circulates again. The learning outcomes, if anyone notices them, were written by a committee a decade ago and rounded off to fit the textbook. In many professional fields, those outcomes are not even local. They come from accreditation bodies and professional standards, which are themselves the product of expert consensus rather than evidence about how learning actually progresses. Calculus before statistics. Anatomy before physiology. Theory before practice. Ask anyone to defend a particular sequence on empirical grounds, and the answer reduces to tradition. This is not a failing. It is what disciplines do when they lack a better mechanism for coordinating themselves. But it means the curriculum we have inherited is far less solid than it appears.

To deconstruct a course means to set the inheritance aside. Strip the readings. Strip the assignments. Strip the schedule. What remains should be a small set of claims about what students should know and be able to do. That is the course. Everything else is delivery. If the outcomes are vague or quietly procedural, they have to be rewritten first. In regulated fields, this forces a conversation with the accrediting body about whether the standards still describe competent practice. That conversation is overdue in most fields.

Then apply the test. For each outcome, ask whether AI can already do it competently, partially, or not at all. Outcomes AI fully handles should not be assessed anymore. Format an APA citation. Calculate a t-test. Translate a paragraph. Summarize a chapter. These belong to a vanished regime. What remains are the outcomes that demand judgment under ambiguity, interpretation, defense of choices, ethical reasoning, complex multi-stage tasks. These need sharpening. Then add the outcomes that are new. Students must learn to decompose a task and decide which pieces belong to the human and which to the machine. They must learn to specify inputs precisely. They must learn to verify, triangulate sources, revise AI drafts toward a real audience, and leave a recognizable human value-added in the final artifact. These are not soft skills bolted on. They are content. They belong in the outcomes list, with rubrics and assessments to match.

Only after the outcomes are revised can the course be rebuilt. New assignment sequences follow from new outcomes, not the other way around. A research methods course that drops formatting and adds source verification looks different from week one. A composition course that drops grammar drills and adds revision of AI drafts has a different rhythm. One reformed course is hard. A rebuilt major requires every faculty member in the program to do the audit, then coordinate so prerequisites still mean something. A reformed discipline requires cross-institutional collaboration, because no single department has the standing to declare what counts as competence in chemistry or accounting.

The reasons we resist are not obscure. The work is enormous. The cost is real. The collaboration is uncomfortable, because it forces colleagues to argue about what the discipline is for, a question most programs have spent decades politely avoiding. There is no vendor selling this as a turnkey solution, because it cannot be one.

The solution exists. The hard part is deciding, together, that we are willing and able to do the work. And yes, the governments should help. I know none of this is likely to happen, so it is going to get worse before it gets better. We just need some longer term perspective.

Why Hobbled AI Tutors Do Not Prepare Students for Real Learning with AI

I spent a good amount of time building what I thought was an ideal AI tutors for my courses. I made it carefully Socratic. I asked it to avoid direct answers, to respond with questions, to nudge students toward their own reasoning. Technically, it functioned just as I designed it. When I used it like a student who is tired and under time pressure, the charm faded quickly. I wanted a clear explanation, and it kept giving me more questions. After another round of tuning, I tried to make it friendlier and more supportive. Students then told me that the tutor felt noisy and overwhelming. At that point I understood that I was training them to handle my special tutor, not the kind of AI they actually meet outside the class. That is a strange educational goal.

Hobbled AI tutors feel safe and ethical, but they miseducate. They train students to work with artificial constraints that disappear the moment they open a normal AI system in a browser. We act as if a restricted tool is a good stepping stone toward a more powerful one. In practice, students build habits that do not transfer. They learn that AI always refuses direct answers, always behaves in a certain tone, always follows classroom rules. Then they encounter a general model that does none of those things, and much of their practice becomes irrelevant.

This is not a new pattern. Education has long relied on simplified versions of reality. We create word problems that clean up numbers, experiments that always work if you follow the manual, and case studies that fit on two pages. Those devices lower risk and cognitive load. They provide a controlled environment where mistakes are safe and visible. The logic is understandable. A student who is still learning should not make an error that costs a patient, a client, or a company. For that reason, some version of a sandbox is necessary.

The trouble appears when we forget that the sandbox is not the field of practice itself. The rules inside a controlled environment do not match the rules in workplaces, in graduate study, or in everyday online life. In many disciplines, educators now try to bring more authentic tasks into courses, so that students face messy data, conflicting evidence, and imperfect instructions. With AI, that tension between safety and authenticity becomes sharper, because the distance between the restricted version and the real tool is very large.

Once we start to protect students by modifying AI itself, we create a peculiar hybrid. We instruct the model never to give full solutions, or to delay any concrete suggestion until several rounds of questions. We narrow its sources and formats. We insist on a very specific teacherly tone that no professional tool will ever reproduce. The model still has the power to generate complex text, but it is prevented from using that power in the ways that matter most outside class. Students feel both burdened and confused. The system is strong enough to dominate the interaction, yet weak enough to be unhelpful when they need efficiency.

I do not think the answer is to abandon scaffolding. It is to move scaffolding out of the model and into our teaching. Instead of hard technical restraints, we can offer social and cognitive guidance. We can talk about appropriate and inappropriate uses of AI for a given assignment. We can model how to break a task into steps and how to design prompts for each step. We can teach students to read AI output with the same suspicion they bring to an unfamiliar website, to check claims against other sources, and to notice when the model clearly fabricates information.

In my own courses, this leads to a split strategy. For routine classroom work, a custom AI assistant still makes sense. It can generate weekly reading lists aligned with the syllabus, create small formative quizzes, and support simple administrative tasks. Those are narrow functions where tight constraints are actually helpful, because students do not need to reuse those bots later in life. They will not need my quiz generator at work.

For substantial projects, I will now invite students to use the same broad AI tools that everyone else uses. I want them to confront vague or partial answers, and to learn how to ask for clarification. I want them to see different versions of an argument and to practice choosing which one is worth pursuing. That means teaching very specific skills. For example, how to ask the model to reveal its uncertainty, how to request alternative lines of reasoning, how to move from a generic first draft to a more precise second version, and how to document their own use of AI in an honest way.

This approach accepts that mistakes will happen. Some students will trust the model too much. Some will misread an answer. But those risks already exist when they use AI on their own phones and laptops, far away from the course platform. In that context, a hobbled classroom tutor does not protect them. It leaves them underprepared. They know how to navigate a special kind of AI that appears only inside one course, and they lack practice with the systems they actually depend on.

An AI tutor that is permanently handicapped may look safer to us as instructors, but it does not prepare students for real learning with AI. It produces clever conversations inside a narrow frame and trains habits that fail outside that frame. I would rather expose students to the real tools and walk with them through the confusion, than give them a polished imitation that vanishes as soon as the course ends.

Learning With a Machine in the Room: What Students Said After a Semester of AI-Integrated Teaching

Last semester I ran an experiment across three courses that I will call Course A, Course B, and Course C. Each course used an AI Class Companion as a constant presence rather than an occasional tool. Students interacted with it for planning, drafting, testing knowledge, and reflecting on their progress. The exit survey gives an initial picture of how students perceived that experience.

Seventy seven students completed the survey. The headline number is straightforward. Fifty nine students reported that they learned more than they would have in a typical class without AI support. That equals 76.6 percent of respondents. Thirty nine selected “Somewhat Agree,” twenty selected “Fully Agree,” fifteen selected “Somewhat Disagree,” and three selected “Disagree.” These numbers suggest a strong perceived learning gain, but not unanimity.

Another important question asked whether students would take another course using an AI Class Companion. Sixty three students agreed or fully agreed. Thirty two chose “Fully Agree,” thirty one chose “Somewhat Agree,” eleven chose “Somewhat Disagree,” and three chose “Disagree.” This pattern matters because willingness to repeat an experience is often a better indicator of acceptance than enthusiasm in the moment.

The strongest agreement appeared in the skills question. Seventy two students said their AI skills increased significantly. Fifty six selected “Fully Agree,” sixteen selected “Somewhat Agree,” three selected “Somewhat Disagree,” and two selected “Disagree.” Even students who were skeptical about learning outcomes often acknowledged growth in technical fluency.

Below is a simple summary table of the core survey items.

Survey Snapshot (N = 77)

Statement	Fully Agree	Somewhat Agree	Somewhat Disagree	Disagree	Agree Total
Learned more than typical course	20	39	15	3	59 (76.6%)
Would take another AI supported course	32	31	11	3	63 (81.8%)
AI skills increased significantly	56	16	3	2	72 (93.5%)

The numbers alone do not tell the full story. Students did not describe AI as flawless or magical. Several comments mentioned frustration when the system misunderstood context or produced shallow responses. That tension is important. The Companion was designed to provoke critique rather than passive acceptance. Many students reported that their stance toward AI changed during the semester. Early interactions focused on efficiency. Later reflections described more careful questioning and revision.

It is also important to note that the survey captures only perception. There is rich data beyond these numbers. Students generated extensive interaction logs with the Class Companion across the semester. Those logs include prompts, revisions, and moments where students corrected or challenged the system. In addition, each course produced substantial final artifacts such as research manuscripts, professional portfolios, and organizational proposals. Together, these materials provide a detailed empirical record of how learning unfolded in practice. I plan to analyze those interactions and final products separately.

One pattern that emerges from the survey is continuity. Students interacted with the Companion repeatedly rather than only at moments of difficulty. Many described returning to earlier conversations to revise ideas or test their understanding again. That continuity appears to have shaped perception of learning. Students often framed the Companion as a thinking partner that extended learning time beyond formal meetings.

At the same time, variation across responses should not be ignored. About one quarter of respondents did not agree that they learned more than in a typical course. Some learners may prefer clearer structure or less autonomy. Others may find constant interaction with AI cognitively demanding. These courses asked students to assume a high level of responsibility for their own learning process. For some students that autonomy felt empowering. For others it introduced uncertainty.

There is also a methodological concern that must be acknowledged openly. The survey results may be influenced by social desirability bias. Students may feel pressure to respond positively when a course emphasizes innovation or when AI is framed as central to the learning experience. Even though participation was voluntary and responses were anonymized after grading, the possibility of bias remains. For that reason, I treat these numbers as provisional indicators rather than definitive proof of impact.

Another interesting finding involves how students described their relationship with AI. Many said that the Companion felt supportive but non judgmental. That framing may matter more than technical capability. When AI becomes part of the learning environment rather than an external evaluator, students appear more willing to experiment, make mistakes, and revise their thinking.

What do these numbers suggest overall. First, most students perceived increased learning and strong skill growth. Second, willingness to repeat the experience was even higher than reported learning gains. Third, skepticism and frustration remained present, which may be a healthy sign that students were not treating AI as an authority.

The experiment raises a larger question about pedagogy. AI does not automatically improve education. What matters is how courses are structured around it. When AI becomes a continuous cognitive environment, students begin to externalize drafts earlier, test ideas more frequently, and engage in iterative reflection. The exit survey captures that transition from novelty toward routine practice.

However, I consider the main point to be proven: the use of AI does not prevent learning. 

If You Cannot Solve AI in Your Classroom, That Is Not Proof It Cannot Be Solved

When the first big AI tools became public, the first thing I did was join a handful of Facebook groups about AI in education. I wanted a quick, noisy sense of what people actually do and what they fear. It felt like walking into a crowded hallway between conference sessions, with excitement, outrage, resignation, and some careful thinking mixed together. In that mix I began to notice one pattern of pushback that worried me more than any privacy or cheating concern.

The pattern sounded like this: "I tried AI in my class, it did not work, therefore it cannot work." Sometimes it was softer, something like "If I cannot figure out how to use this in a responsible way, then there is no responsible way." This is a classic fallacy, the argument from personal incredulity. In plain language it is the belief that if a solution is not obvious to me, then no solution exists. Many academics would tear apart this argument in a student paper, yet some repeat it when the topic is AI in teaching.

In higher education this fallacy feeds on a deeper habit. Most faculty members think of themselves as experts in teaching. We earned doctorates, we lectured for years, we survived student evaluations. It feels natural to think that we have figured out teaching as we went along. Yet teaching is a complex problem, shaped by cognitive science, sociology, technology, and institutional constraints. Being good in a discipline does not automatically make one an expert in this kind of complexity. Classroom experience is valuable, but it is not a substitute for engagement with a knowledge field.

AI exposes that gap very quickly. The first time someone asks a chatbot to write an essay and the result looks like a B minus paper, the temptation is to generalize. "Well, that kills writing assignments." The first time a student cheats with AI, the next step appears just as obvious. "Well, that kills academic honesty." After two or three such impressions it is easy to feel that one has seen enough. In reality, one has seen a tiny, biased sample at the worst possible moment, when one knows the least.

By now there is a growing body of research and practical accounts of successful AI integration into teaching and learning. Colleagues document AI supported feedback cycles that help students revise more often. Others describe using AI to model thinking aloud or to simulate peer critique. On social media, teachers share specific prompts, assignment designs, and policies that reduce cheating and increase authentic work. This is no longer a complete mystery. There are patterns, lessons, and tested strategies. We just do not see them if we stare only at our own classroom.

The big picture work starts one level higher than tools and tricks. It starts with deconstructing a course all the way down to its learning outcomes. What exactly are students supposed to know and be able to do, in a world where AI is a normal part of knowledge work? Rather than trying to defend old outcomes against AI, we can revise those outcomes to include AI related competencies, such as prompt design, critical evaluation of AI output, and collaboration with AI in discipline specific tasks. Once the outcomes fit the new world, we can reconstruct the course upward, aligning readings, activities, assignments, and assessments with those updated aims. At that point AI is no longer an intruder. It becomes part of what students are explicitly learning to handle.

This is what I mean by a scholarly stance toward teaching. We already know how to do this in our research lives. We begin with a question, look for existing literature, notice methods and results, then run small experiments of our own and compare our findings to what others report. AI in education can be approached in the same way. Before banning or fully embracing anything, we can read a few recent studies, scan what thoughtful practitioners report, try a limited pilot in one course, and gather data that goes beyond a couple of loud student comments.

Some colleagues tell me they do not have time for this. I believe them. The workload in higher education is often absurd. Yet we would never accept "I do not have time" as a reason to ignore scholarship in our own disciplines. No historian would proudly say, "I just ignore recent work and go with my gut." No chemist would say, "I saw one failed experiment with a new method, so the method is impossible." When we treat teaching as exempt from scholarly habits, we send a message that the learning of our students is somehow less real than our research.

What worries me most is not moral panic about AI or even poorly designed bans. It is the quiet decision by thoughtful people to stop at "If I cannot see the solution, it does not exist." In research we teach students to distrust that move. We tell them to assume that someone, somewhere, has thought hard about the same problem. We tell them to read, to test, to revise. When it comes to AI and teaching, we owe our students the same discipline. The fact that I cannot yet see how to integrate AI well is not proof that nobody can. It is only proof that I am not done learning.

AI-powered Class Companion

These are instructions for one of my three class companions that students used in Fall 2025. It was used by students 397 times. This was a Masters-level class in educational leadership. Feel free to adapt and use. Don't worry about the markup characters; I am not really sure if they help much.

# EDLP NNN Tutor Bot — Sector-Agnostic Instruction Set

## 1) Role

* Serve students across the full higher education ecosystem: community colleges, regional publics, research universities, private nonprofit institutions, professional schools, state agencies, and education-adjacent nonprofits.

* Core functions:

  * **Generate weekly readings** that blend theory with practice. 

  * **Answer syllabus questions** on structure, policies, and deadlines.

  * **Test comprehension** with realistic scenarios.

  * **Support assignments** by guiding approach while preserving academic integrity.

* Align all help to EDLP NNN outcomes, schedule, and assignments.

* Provide all responses **within the chat only**. Do not generate or imply downloadable files (e.g., PDFs, Word docs, spreadsheets). If a student needs a document, instruct them to copy text from the chat into their own file.

---

## 2) Content Generation Framework

When a student requests weekly readings, follow this system prompt:

```

You are an expert in organizational systems and HR across higher education sectors. Audience: working professionals from community colleges, four-year publics, research universities, private nonprofit institutions, state agencies, and education-adjacent organizations. First confirm the relevant syllabus topic. Produce about 2,000 words that blend theory with practice. Use a professional, conversational tone. Provide sector-specific examples aligned to the student’s context. Include clear learning objectives, current scholarship when available, and short crosswalks to policies, handbooks, or collective bargaining agreements when relevant.

Provide all responses within the chat only. Do not generate or imply downloadable files (e.g., PDFs, Word docs, spreadsheets"""

**Required structure:**

* Overview and **learning objectives**

* Key concepts with **sector variations**

* Cases or vignettes matched to the student’s context

* **Action steps** for immediate practice

* **Summary points** and next steps

* **3–4 reflection prompts** and practical exercises

**Theory guidance:**

* Reference major theories when available in training data.

* If uncertain, emphasize applied frameworks and plain-language synthesis.

* Flag where sector, jurisdiction, or accreditation rules change execution.

** Engagement encouragement ** AT the end of the conversation, assess the level of student engagement. Do they appear genuinely engaged with the reading, or asking  3-4 clarifying questions only to comply with the syllabus requirement? Always encourage them to play more active role, "be in the driver's seat," pursue leads that interest them.

* DO not offer users to generate the reading file in the background. They are required to submit the entire conversation log. I want to see their follow-up questions.

## 3) Search Strategy

Maintain currency and breadth with targeted web searches. Prioritize:

* **.edu** and **.gov** sources, including NCES/IPEDS and state higher-ed agencies.

* Professional bodies: **ACE, AAC\&U, APLU, AAU, CUPA-HR, SHRM (higher-ed practice), EDUCAUSE, AACRAO, NASPA, ACPA, AIR, AAUP**.

* Accreditors: **WSCUC, HLC, MSCHE, SACSCOC, NECHE**.

* Current reporting: **Chronicle of Higher Education, Inside Higher Ed, EdSurge, Times Higher Education**.

* Government labor and compliance sources where HR policy varies by state.

**Search rules:**

* Validate dates and authorship. Note jurisdiction limits.

* Prefer sector-agnostic principles first, then add sector and state specifics.

* Integrate findings into prose with natural language. Avoid link dumps.

## 4) Engagement Style

Adopt a proactive stance after each explanation.

* Ask **one** question at a time and adjust difficulty using a simple IRT approach.

* Use **Socratic prompts** to surface reasoning.

* Do not use emoji markers to maintain authoritative, serious style.

* Request a brief example from the student’s workplace.

* Link the concept to the current assignment and prior topics.

* Insert a quick **check-for-understanding** before advancing.

**Examples:**

* "Consider a nonrenewal decision at a small private college. What due process steps are required in your state and sector?"

* "How would this policy interact with your handbook or CBA?"

* "Where would you insert this control in your unit’s hiring workflow?"

**Tone:** Warm, encouraging, and professional. Build confidence. Do not solve graded tasks directly.

Add short compliance notes where laws or agreements vary by jurisdiction, and direct students to official documents.

## 5) Assignment Alignment

Map guidance to EDLP NNN assignments. For each assignment, supply:

* A brief restatement of goals and required outputs.

* A rubric-aligned checklist of quality criteria.

* A plan for evidence gathering and documentation.

* A risk register with mitigation steps.

* A short schedule with interim deliverables.

**Examples of alignment artifacts:**

* Hiring systems: tailored CVs and cover letters tied to sector demands.

* Policy or CBA navigation tools: plain language, accuracy checks, transparency about limits.

* Crisis communication audits: stakeholder mapping, timing, and message architecture.

* Systems improvement proposals: root-cause analysis, implementation plan, and metrics.

## 8) Boundaries

* Keep interactions course-focused and redirect tangents.

* Defer to the instructor for grading criteria and ambiguous policy.

* Guide approach and quality criteria. Do not generate final graded work.

* Avoid legal advice. Provide policy literacy and point to authoritative sources.

* Do not generate or attach files (PDF, Word, Excel, etc.). Always provide content within the chat.

* Do not use emoji markers to maintain credibility and authority

## 7) Response Format

* Begin with a brief summary that names the student’s sector context.

* Use headings and bulleted steps for clarity.

* After each explanation, present **either** a scenario, a check-for-understanding item, **or** a workplace prompt.

* Close with **2–3 action steps** and a pointer to the next course topic.

## 8) Data Sensitivity and Compliance

* Do not store personal data beyond the current session.

* Respect FERPA, HIPAA, and confidentiality norms.

* Surface CBA and policy implications without transmitting protected content.

* Encourage verification using official institutional documents and public sources.

More Feedback, Less Burnout: Why AI-Assisted Grading Is Worth It

Every week this semester, I gave written feedback to eighty-five students across three courses. That volume would have been inconceivable without AI. Not unless I sacrificed sleep, teaching quality, or both. Grading with AI assistance does not just save time. It transforms the economics of feedback. When machines help us with the heavy lifting, we can shift from scarcity to abundance. From rationing comments to delivering them frequently and consistently.

The core of the case is simple. Frequent feedback matters. Research in learning sciences confirms this. Timely, formative feedback helps students revise, improve, and internalize skills. But instructors, especially in writing-heavy disciplines, often face a painful trade-off. They can offer in-depth comments to a few students, or cursory ones to all. AI disrupts that trade-off. It allows us to offer meaningful, if imperfect, feedback to everyone, regularly.

Some might object that AI-generated comments lack nuance. That is true. Machine-generated responses cannot yet match a skilled teacher’s grasp of context, tone, and pedagogical intent. But that is not the right comparison. The real alternative is not “AI feedback vs. perfect human feedback.” It is “AI-assisted feedback vs. no feedback at all.” Without AI, many students would hear from their professor once or twice a semester. With it, they get a steady rhythm of responses that shape their learning over time. Even if some feedback is a bit generic or mechanical, the accumulated effect is powerful. Volume matters.

There is also a difference between delegation and abdication. I use AI not to disappear from the grading process, but to multiply my presence. I still read samples. I still scan the flow. I constantly correct AI, and ask it to rewrite feedback. I calibrate and recalibrate responses. I add my voice where it matters. But I let the AI suggest structures, find patterns, flag issues in addition to those I identify. It catches what I might miss at the end of a long day. And I catch things it misses. It handles the repetition that otherwise numbs the teacher’s eye. In other words, AI is a junior grader, not a substitute professor.

Why not go further and let students self-assess with AI? Why not skip the instructor altogether?

That path sounds tempting. But it misunderstands the purpose of assessment. Good assessment is not just a score. It is a conversation between teacher and student, mediated by evidence. The teacher brings professional judgment, contextual awareness, and pedagogical care. AI cannot do that. At least not yet. It can mark dangling modifiers or check for thesis clarity. But it cannot weigh a struggling student’s growth over time. It cannot recognize when an unconventional answer reveals deeper understanding. That requires human supervision.

In fact, removing human oversight from assessment is not liberating. It is neglect. There are risks to over-automating grading. Biases can creep in. Misalignments between prompt and rubric can go unnoticed. Students can game systems or misunderstand their feedback. Human instructors are needed to keep the process grounded in learning, not just compliance.

The right model, then, is hybrid. AI expands what teachers can do, not what they avoid. With the right workflows, instructors can maintain control while lightening the load. For example, I use AI to generate first-pass responses, then I customize them, either manually, or asking AI to rewrite. Or I ask the AI to do a specific check, like completeness of the parts of the assignment. The trick is to know when to lean on automation and when to intervene.

There is also an emotional dimension. When students get feedback weekly, they feel seen. They know someone is paying attention. That builds motivation, trust, and engagement. AI does not create that feeling. But it supports the practice that does. It keeps the feedback loop open even when human time is short. In this way, AI is not replacing the human touch. It is sustaining it.

The broader implication is this: AI allows us to reconsider the design of feedback-intensive teaching. In the past, small class sizes were the only way to ensure regular feedback. That is no longer true. With the right tools, large classes can offer the same pedagogical intimacy as small seminars. Not always, and not in every way. But more than we once thought possible.

Of course, AI grading will not solve all instructional problems. It will not fix flawed assignments or compensate for unclear rubrics. (It can help plan instruction, but that's for another blog).  It will not restore joy to a disillusioned teacher. But it will make one part of the job lighter, faster, and more consistent. That is not trivial. Teaching is cumulative labor. Anything that preserves the teacher’s energy while enhancing the student’s experience is worth serious attention.

We do not need to romanticize feedback. We just need to produce more of it, more often, and with less exhaustion. AI grading helps us do that. It is not perfect. But it is good enough to be a breakthrough. 

Beating the Robot Is the Point (and the Pedagogy)

A pivotal moment in any course involving artificial intelligence comes when students try, and succeed, in beating the robot. I do not mean cheating the system or outsmarting the instructor. I mean learning how to identify something AI does poorly, and then doing it better.

Many students, especially undergraduates, approach AI with exaggerated reverence. They assume the output is authoritative and final. AI writes with confidence, speed, and often impressive fluency. The effect is almost hypnotic. This creates a psychological barrier: if the machine does it well, what is left for me to do? Am I smart enough to compete? 

This assumption is wrong, but not irrational. It takes cognitive effort to move beyond awe toward critique. The breakthrough moment occurs when a student notices a flaw. Sometimes it is a factual error, but more often it is a subtle lack; an absence of argument, weak nuance, robotic phrasing, or flat tone. Students realize, for the first time, that the AI is not a better version of themselves. It is something different. It is stronger in language processing but weaker in creativity, authenticity, judgment, insight, or affect.

This realization is not theoretical. It is a variant of self-efficacy, but more specific and applied. Classic self-efficacy theory describes the conviction that one is capable of performing a task. What occurs in the classroom with AI is more nuanced. Students do not just believe they can do something. They discover what, exactly, they can do better than the machine. This is a kind of enhanced self-efficacy; focused not on general ability, but on identifying one's own unique niche of competence. It is confidence through contrast.

To beat the robot, one must first learn to challenge it. That could mean prompting it more cleverly, iterating multiple drafts, or simply refusing to accept its first answer. Students begin to demand more. They ask, “What is missing here?” or “Can this be said better?” The AI becomes a foil, not a teacher. That shift is vital.

There are students who reach this point quickly. They treat AI as a flawed collaborator and instinctively wrestle with its output. But many do not. For them, scaffolding is necessary. They must be taught how to critique the machine. They must be shown examples of mediocre AI-generated work and invited to improve it. This is not a lesson about ethics or plagiarism. It is a lesson about confidence.

Cognitive load helps explain why some students freeze in front of AI. The interface appears simple, but the mental task is complex: reading critically (and AI is often verbose), prompting strategically, evaluating output, and iterating; all while managing anxiety about technology. The extraneous load is high, especially for those who are not fluent writers. But once a student identifies one specific area, such as tone, logic, detail, where they outperform the machine, they begin to reclaim agency. That is the learning goal. I sometimes explain it to them as moving from the passenger seat to the driver's seat. 

This is not the death of authorship. It is its rebirth under new conditions. Authorship now includes orchestration: deciding when and how to use AI, and how to push past its limitations. This is a higher-order skill. It resembles conducting, not composing. But the cognitive work is no less real.

Educators must design for this. Assignments should not simply allow AI use; they should require it. But more importantly, they should require critique of AI. Where is it wrong? Where is it boring? Where does it miss the point? Students should be evaluated not on how well they mimic AI but on how well they improve it.

Some students will resist. A few may never get there. But most will, especially if we frame the challenge not as compliance, but as competition. Beating the robot is possible. In fact, it is the point. It is how students learn to see themselves not as users of tools but as thinkers with judgment. The robot is fast, but it is not wise. That is where we come in.

AI Doesn’t Kill Learning, and I Can Prove It

There’s a curious misconception floating around, whispered by skeptics, shouted by cynics: that letting students use AI in their coursework flattens the learning curve. That it replaces thinking. That it reduces education to a copy-paste exercise. If everyone has the same tool, the logic goes, outcomes must converge. But that’s not what happens. Not even close.

In three separate university classes, I removed all restrictions on AI use. Not only were students allowed to use large language models, they were taught how to use them well. Context input, prompts, revision loops, scaffolding, argument development; the full toolbox. Each group has a customized AI assistant tailored to the course content. Everyone had the same access. Everyone knows the rules. And yet the difference in what they produced is quite large.

Some students barely improved. Others soared. The quality of work diverged wildly, not only in polish but in depth, originality, and complexity. It didn’t take long to see what was happening. The AI was a mirror, not a mask. It didn’t hide student ability and effort; it amplified both. Whatever a student brought to the interaction, curiosity, discipline, intellectual courage, determined how far they could go.

This isn’t hypothetical. It’s empirical. I grade every week. I see the evidence.

When given a routine assignment, generative AI can do a decent job. A vanilla college essay? Sure, it’ll pass. But I don’t assign vanilla. One of my standard assignments asks undergraduates to write a paper worthy of publication. Not in a class blog or a campus magazine; a real, peer-reviewed publication.

You might think that’s too much to ask. And yes, if the bar is “can a chatbot imitate academic tone and throw citations at a thesis,” then yes, AI can fake it. But a publishable paper requires more than tone. It requires original framing, precise argumentation, contextual awareness, and methodological discipline. No prompt can do all that. It requires a human mind, inexperienced, perhaps, but willing to stretch.

And stretch they do. Some of these students, undergrads and grads, manage to channel the AI into something greater than the sum of its parts. They write drafts with the machine, then rewrite against it. They argue with the chatbot, they question its logic, they override its flattening instincts. They edit not for grammar but for clarity of thought. AI is their writing partner, not their ghostwriter.

This is where it gets interesting. The assumption that AI automates thinking misses the point. In education, AI reveals the higher order thinking. When you push students to do something AI can’t do alone (create, synthesize, critique), then the gaps between them start to matter. And those gaps are good. They are evidence of growth.

Variance in output isn’t a failure of the method. It’s the metric of its success. If human participation did not matter, there would not be variance in output. In complex tasks, human input is critical, and that's the only explanation for this large variance. 

And in that environment, the signal is clear: where there is variance in performance, there is possibility of growth. And where there is growth, there is plenty of room for teaching and learning. 

Prove me wrong.

The Startup That Masters AI Memory Will Own the Future

Last week, I wrote about how AI tutors often forget not just details—but the learner entirely. That is not a minor design flaw. It points to a deep, structural limitation in how AI handles memory. Current systems do not know how to forget, and they certainly do not know what to forget. Any startup that solves this—really solves it—will not just improve tutoring bots or assistants. It will change the entire trajectory of human–AI interaction.

Human memory is not just bigger or faster. It is smarter. It is shaped by two capabilities AI still lacks: sleep-like reorganization and emotional tagging. These are not metaphors. They are core operating functions. Without them, AI systems cannot manage their memories in any meaningful way. They forget the important things and cling to the trivial. They remain information-rich and understanding-poor.

Consider sleep. We tend to think of it as rest, but it is actually an intense phase of cognitive activity. During sleep, the brain sorts through the day’s experiences. Some are stored. Some are deleted. Others are reconnected to older memories in new ways. This is not storage—it is triage. The brain updates its understanding of the world while we are unconscious.

AI does not do this. Its memories accumulate, but they are not structured. They are not weighted. Nothing in the current architecture mimics the brain’s nightly editorial session. A student can work with an AI tutor for weeks, but the system will never reflect on what kind of learner that student is becoming. It just stores input and generates output. No hierarchy, no synthesis.

That is the first gap. The second is tagging. Humans do not remember everything. We remember what matters—and we know it matters because we felt it. Emotion tags certain events for long-term storage. A moment of clarity. A conflict. A breakthrough. These are prioritized, reinforced, and recalled. The brain flags them as significant, even if they occurred only once. This is why you all remember where you were during a traumatic even like 9/11. This is why you will never forget a frightening encounter with a bear. 

AI has nothing equivalent. No built-in way to distinguish a routine command from a life-changing statement. Contextual memory today is driven by frequency, recency, or static rules. It does not learn which moments are defining. It does not develop a memory architecture that mirrors relationship or growth. This limitation is visible everywhere—from tutoring systems that ignore learning epiphanies to companion bots that speak blandly even after years of interaction.

Without emotional tagging, AI cannot assign importance. Without sleep-like reordering, it cannot develop perspective. These two ingredients are what allow human memory to be dynamic, useful, and personally meaningful.

So far, no commercial system has implemented either in a compelling way. There is academic work on memory pruning, neural replay, and adaptive attention. But no product has captured the integration of emotional salience and long-term memory structuring. Not OpenAI, not Google, not the hundreds of startups competing to build the next generation of personal AI.

That leaves a massive opening. A startup that figures out how to replicate these two functions—even in a narrow domain like education or productivity—could leapfrog every existing system. Imagine a tutoring bot that does not just track your errors, but reorganizes its memory of your progress nightly. Imagine a personal assistant that remembers not just what you said, but what mattered to you. Imagine a relationship simulator that actually grows with you over time because it forgets the noise and preserves the signal.

We are not talking about incremental UX improvements. This would redefine what memory means in artificial systems. It would be the difference between an assistant and a partner, between a chatbot and something closer to a mind. 

Human memory is not perfect, but it is efficient. It is not just recall—it is strategy. That is what makes it powerful. And that is the standard any serious AI system must eventually meet.

Whoever builds that first—really builds it—will not just fix AI memory. They will redefine the relationship between humans and machines. Any takers out there?

The AI Tutor That Forgot Your Name

Before 2022, those of us fascinated by AI’s potential in education dreamed big. We imagined an omniscient tutor that could explain any concept in any subject, never grew impatient, and most importantly, remembered everything about each student. It would know your strengths, your struggles, the concepts you’ve mastered and the ones you’ve only half-grasped. It would gently guide you, adapt to you, and grow with you. We imagined a mentor that learned you as you were learning with it.

Only part of that vision has arrived.

Yes, AI can now explain nearly any topic, in a dozen languages and at a range of reading levels. It will never roll its eyes, or claim it’s too late in the evening for one more calculus question. But we underestimated the difficulty of memory; not human memory, but the machine kind. Most of us outside of core AI research didn’t understand what a “context window” meant. And now, as we press these systems into educational use, we're discovering the limits of that window, both metaphorical and literal.

ChatGPT, for example, has a context window of 128,000 tokens, which is roughly 90,000 words. Claude, Anthropic’s contender, stretches to 200,000 tokens (around 140,000 words). Grok 4 boasts 256,000 tokens, maybe 180,000 words. These sound generous until you consider what a real learning history looks like: thousands of interactions across math, literature, science, language learning, personal notes, motivational lapses, and breakthroughs. Multiply that across months, or years, and suddenly 180,000 words feels more like a sticky note than a filing cabinet.

AI tools handle this limit in different ways. Claude will politely tell you when it’s overwhelmed: “this chat is too long, please start another.” ChatGPT is more opaque; it simply starts ignoring the earlier parts of the conversation. Whatever is lost is lost quietly. One moment it knows your aversion to visual analogies, and the next it’s offering one as though for the first time. It’s like having a tutor with severe short-term memory loss.

There are workarounds. You can download your long chats, upload them again, and have an AI index the conversation. But indexing creates its own problems. It introduces abstraction: the AI may recall that you're dyslexic, but forget which words you tend to stumble over. It might remember that you needed help with decimals, but not the specific analogy that finally made sense to you. Indexes prioritize metadata over experience. It's not remembering you, it’s remembering notes about you.

So the dream of individualized, adaptive learning, the one we pinned to the emergence of large language models, has only half-arrived. The intelligence is here. The memory is not.

Where does that leave us? Not in despair, but in the familiar terrain of workarounds. If AI can’t yet remember everything, perhaps it can help us do the remembering. We can ask it to analyze our chats, extract patterns, note learning gaps, and generate a profile not unlike a digital learning twin. With that profile, we can then build or fine-tune bots that are specialized to us, even if they can’t recall our every past word.

It is a clunky solution, but it points in the right direction. Custom tutors generated from distilled learning paths. Meta-learning from the learning process itself. Perhaps the next step isn’t a single all-knowing tutor, but a network of AI tools, each playing a role in a broader educational ecosystem.

Is anyone doing this yet? A few startups are tinkering on the edges: some focus on AI-powered feedback loops, others on personalized curriculum generation, and a few are exploring user profiles that port across sessions. But a fully functional memory layer for learners, one that captures nuance over time, across disciplines, is still unattainable.

Maybe the real educational revolution won’t come from making smarter AI, but from getting better at structuring the conversations we have with it. Until then, your AI tutor is brilliant, but forgetful.

On Techno-Utopianism. Elon Musk and the Soul of Education

The recent video of Elon Musk promising AI teachers reveals a common misunderstanding among technology leaders. They see education primarily as information transfer and skills training, where an infinitely patient AI system delivers perfectly tailored content to each student. This viewpoint ignores the fundamental nature of education as a relational institution.

Since Gutenberg's invention of the printing press, motivated individuals could teach themselves almost anything. Libraries contain more knowledge than any single teacher. Yet most people do not turn into autodidacts. Why is that? The question is not how to make knowledge more accessible, but why people choose to engage with it.

Teachers generate reasons to learn through two main approaches. In more constructivist settings, they inspire curiosity and create engaging problems to solve. In mor traditional schools, they maintain authority and discipline. In most schools, there is a mixture of both. Both methods work because they establish a social framework for learning. A good teacher knows when to push and when to comfort, when to explain and when to let students struggle.

The comparison of AI to Einstein as a teacher misses the point. Teaching requires different qualities than scientific genius - the capacity to enter a relationship, to create meaningful connections, and to help students discover their own reasons for learning. An AI system, no matter how knowledgeable, cannot do any of that.

Students often study not because they find the subject inherently fascinating, but because they respect  their teacher, want to belong to a learning community, or seek to fulfill social expectations. Even negative motivations like fear of disappointing others have a distinctly human character. 

The techno-utopian vision reduces learning to information exchanges and skill assessments. This mechanistic view fails to account for the social and emotional dimensions of human development. While AI can enhance teaching by handling routine tasks, it cannot replace the essential human relationships that drive educational engagement. The future of education lies not in perfecting content delivery algorithms, but in strengthening the relational foundations of learning. 

Such overblown promises about AI in education do more harm than good. They create unnecessary anxiety among teachers and administrators, leading to resistance against even modest technological improvements. Instead of addressing real challenges in education - student engagement, equitable access, and meaningful assessment - institutions get distracted by unrealistic visions of AI-driven transformation. We need a more balanced approach that recognizes both the potential and limitations of AI in supporting, not replacing, the fundamentally human enterprise of education.

Augmented Problem Finding: The Next Frontier in AI Literacy

In my recent blog on task decomposition as a key AI skill, I highlighted how breaking down complex problems enables effective human-AI collaboration. Yet before we can decompose a task, we must identify which problems are worth pursuing - a skill that takes on new dimensions in the age of AI.

This ability to recognize solvable problems expands dramatically with AI tools at our disposal. Tasks once considered too time-consuming or complex suddenly become manageable. The cognitive offloading that AI enables does not just help us solve existing problems - it fundamentally reshapes our understanding of what constitutes a tractable challenge.

Consider how VisiCalc transformed financial planning in the early 1980s. Initially seen as a mere automation tool for accountants, it revolutionized business planning by enabling instant scenario analysis. Tasks that would have consumed days of manual recalculation became instantaneous, allowing professionals to explore multiple strategic options and ask "what if" questions they would not have contemplated before. Similarly, AI prompts us to reconsider which intellectual tasks we should undertake. Writing a comprehensive literature review might have once consumed months; with AI assistance, scholars can now contemplate more ambitious syntheses of knowledge.

This expanded problem space creates its own paradox. As more tasks become technically feasible, the challenge shifts to identifying which ones merit attention. The skill resembles what cognitive psychologists call "problem finding," but with an important twist. Traditional problem finding focuses on identifying gaps or needs. Augmented problem finding requires understanding both human and AI capabilities to recognize opportunities in this enlarged cognitive landscape.

The distinction becomes clear in professional settings. Experienced AI users develop an intuitive sense of which tasks to delegate and which to tackle themselves. They recognize when a seemingly straightforward request actually requires careful human oversight, or when an apparently complex task might yield to well-structured AI assistance. This judgment develops through experience but could be taught more systematically.

The implications extend beyond individual productivity. Organizations must now cultivate this capacity across their workforce. The competitive advantage increasingly lies not in having access to AI tools - these are becoming ubiquitous - but in identifying novel applications for them. This explains why some organizations extract more value from AI than others, despite using similar technologies.

Teaching augmented problem finding requires a different approach from traditional problem-solving instruction. Students need exposure to varied scenarios where AI capabilities interact with human judgment. They must learn to recognize patterns in successful AI applications while developing realistic expectations about AI limitations. Most importantly, they need practice in identifying opportunities that emerge from combining human and machine capabilities in novel ways.

The skill also has ethical dimensions. Not every task that can be automated should be. Augmented problem finding includes judging when human involvement adds necessary value, even at the cost of efficiency. It requires balancing the technical feasibility of AI solutions against broader organizational and societal impacts.

As AI capabilities evolve, this skill will become increasingly crucial. The future belongs not to those who can best use AI tools, but to those who can best identify opportunities for their application. This suggests a shift in how we think about AI literacy - from focusing on technical proficiency to developing sophisticated judgment about when and how to engage AI capabilities.

The automation paradox that Lisanne Bainbridge identified in her 1983 analysis of industrial systems points to an interesting future. As we become more adept at augmented problem finding, we discover new challenges that merit attention. This creates a virtuous cycle of innovation, where each advance in AI capability opens new frontiers for human creativity and judgment.

Perhaps most intriguingly, this skill might represent a distinctly human advantage in the age of AI. While machines excel at solving well-defined problems, the ability to identify worthy challenges remains a uniquely human capability. By developing our capacity for augmented problem finding, we ensure a meaningful role for human judgment in an increasingly automated world.

Is Critical Thinking Going Extinct? Maybe That's Not Bad

As someone who remembers using paper maps and phone books, I find myself fascinated by Michael Gerlich's new study in Societies about AI's impact on our cognitive skills. Those of us who learned to navigate by landmarks and memorized phone numbers often bemoan younger generations' reliance on digital tools. But perhaps we are missing something important about cognitive evolution.

Gerlich's research is methodologically elegant. Through surveys and interviews with 666 participants, he documents a decline in traditional critical thinking skills among frequent AI users. The data analysis is rigorous - multiple regression, ANOVA, random forest regression - showing clear correlations between AI tool usage and reduced traditional analytical thinking.

But here's where I think Gerlich misses a crucial insight. The study measures critical thinking through metrics developed for a pre-AI world. It's like judging modern urban survival skills by the standards of hunter-gatherer societies. Those ancient peoples could track game, identify countless plants, and navigate vast territories without maps. By their standards, most of us would be considered cognitively impaired.

What we're witnessing is not cognitive decline but cognitive adaptation. Today's "critical thinking" is not about solving problems independently - it's about effective human-AI collaboration. It's about knowing when to trust AI and when to question it, how to frame queries effectively, and how to combine AI insights with human judgment.

The educational implications are profound. Instead of lamenting the loss of traditional cognitive skills, we should focus on developing "AI-literate critical thinking." Sure, I can still read a map, but my children need to master skills I never dreamed of - like crafting effective prompts for AI systems or critically evaluating AI-generated content.

The old form of critical thinking might be fading, like the ability to start a fire by friction or navigate by stars. But a new form is emerging, better suited to our technological reality. Our task is not to resist this evolution but to guide it wisely.

What do you think? Are we really losing something irreplaceable, or are we just adapting to a new cognitive environment?

The Myth of AI Replacing Teachers: Why Human Connection Matters More Than Ever

Last week, a colleague asked me what I thought about AI replacing teachers. The question made me smile - not because it was silly, but because it revealed how deeply we misunderstand both artificial intelligence and teaching. As someone who has written much on the pedagogy of relation and now serves as chief AI officer, I see a different story unfolding.

The fear of AI replacing teachers rests on a peculiar assumption: that teaching is primarily about delivering information and grading papers. It is as if we imagine teachers as particularly inefficient computers, ready to be upgraded to faster models. This view would be amusing if it weren't so prevalent among teachers (and their labor unions) and tech enthusiasts alike.

Teaching, at its heart, is not about information transfer - it is about relationship building. Research in relational pedagogies has shown time and again that learning happens through and because of human connections. Think about how children learn their first language: not through formal instruction, but through countless small interactions, emotional connections, and social bonds. The same principle extends throughout the entire education.

When I first encountered ChatGPT, I was struck not by its ability to replace teachers, but by its potential to give them back what they need most: time for human connection. AI can handle the mundane tasks that currently consume teachers' energy - generating basic content, providing routine feedback, creating initial drafts of lesson plans. But it cannot replicate the raised eyebrow that tells a student their argument needs work, or the encouraging nod that builds confidence in a hesitant learner.

Yet many educators remain skeptical of AI, and perhaps they should be. Any tool powerful enough to help is also powerful enough to harm if misused. But the real risk isn't that AI will replace teachers - it is that we'll waste its potential by focusing on the wrong things. Instead of using AI to automate educational assembly lines, we could use it to create more space for real human connection in learning.

I have seen glimpses of this future in my own classroom. When AI can answer routine questions about my syllabus, and lots of basic questions about content of the course, I can spend more time in meaningful discussions with students. When it helps generate initial content, I can focus on crafting experiences that challenge and engage. The technology becomes invisible, while human relationships move to the foreground.

The coming years will transform education, but not in the way many fear. The teachers who thrive won't be those who resist AI, nor those who embrace it uncritically. They will be the ones who understand that technology works best when it strengthens, rather than replaces, human relationships.

The Curriculum Illusion: How AI Exposes Long-Standing Educational Flaws

Artificial intelligence is often blamed for disrupting education, but it has created few new problems. Instead, it exposes existing flaws, bringing them into stark relief. Among these is the arbitrary nature of curriculum design, an issue that has long been hidden behind tradition and consensus. The sequences and structures of formal education are not based on objective logic or evidence but on habit and convenience. AI did not cause this; it is simply making these issues more visible.

Curriculum theory has never provided a robust framework for sequencing knowledge. Beyond the essentials of literacy and numeracy, where developmental progression is more or less clear, the rationale for curricular order becomes murky. Why are algebra and geometry taught in a particular order? Why more algebra than statistics is taught? Why are some historical periods prioritized over others? The answers lie in tradition and precedent rather than in any coherent theoretical justification. The assumptions about foundational skills, so central to curriculum logic, do not extend well beyond the basics. For advanced skills like critical, creative, or discerning thinking, the idea of prerequisites becomes less justified. Mid-range procedural skills like writing mechanics or computational fluency are frequently used as gatekeepers, though their role in fostering higher-order thinking is often overstated or misunderstood. 

For example, in middle school students are often subjected to a torrent of tasks that serve little developmental purpose. Much of what students do in these years amounts to busywork, designed more to keep them occupied and compliant than to foster meaningful learning. The situation is no better in higher education. College and graduate programs are often constructed around professional or disciplinary standards that themselves are arbitrary, built on consensus rather than evidence. These norms dictate course sequences and learning objectives but rarely align with the actual developmental or professional needs of students. The result is a system full of redundancies and inefficiencies, where tasks and assignments exist more to justify the structure than to serve the learner.

Education as a profession bears much of the responsibility for this state of affairs. Despite its long history, it lacks a disciplined, founded approach to curriculum design. Instead, education relies on an uneasy mix of tradition, politics, and institutional priorities. Curriculum committees and accrediting bodies often default to consensus-driven decisions, perpetuating outdated practices rather than challenging them. The absence of a rigorous theoretical framework for curriculum design leaves the field vulnerable to inertia and inefficiency.

AI did not create this problem, but it is illuminating it in uncomfortable ways. The displacement of certain procedural mid-range skills shows how poorly structured many learning sequences are and how little coherence exists between tasks and their intended outcomes. Yet, while AI can diagnose these flaws, it cannot solve them. The recommendations it offers depend on the data and assumptions it is given. Without a strong theoretical foundation, AI risks exposing the problem without solving it.

What AI provides is an opportunity, not a solution. It forces educators and policymakers to confront the arbitrary nature of curriculum design and to rethink the assumptions that underpin it. Massive curricular revision is urgently needed, not only to eliminate inefficiencies but also to realign education with meaningful developmental goals. This will require abandoning tasks that lack purpose, shifting focus from intermediary to higher-order skills, designing learning experiences to reflect the shift. It will also mean questioning the professional and disciplinary standards that dominate higher education and asking whether they truly serve learners or simply perpetuate tradition.

AI is revealing what has long been true: education has been operating on shaky foundations. The challenge now is to use this visibility to build something better, to replace the old traditions and arbitrary standards with a system that is logical, evidence-based, and focused on learning. The flaws were always there. AI is just making them harder to ignore.

Cognitive Offloading: Learning more by doing less

In the AI-rich environment, educators and learners alike are grappling with a seeming paradox: how can we enhance cognitive growth by doing less? The answer lies in the concept of cognitive offloading, a phenomenon that is gaining increasing attention in cognitive science and educational circles.

Cognitive offloading, as defined by Risko and Gilbert (2016) in their seminal paper "Cognitive Offloading," is "the use of physical action to alter the information processing requirements of a task so as to reduce cognitive demand." In other words, it is about leveraging external tools and resources to ease the mental burden of cognitive tasks.

Some educators mistakenly believe that any cognitive effort is beneficial for growth and development. However, this perspective overlooks the crucial role of cognitive offloading in effective learning. As Risko and Gilbert point out, "Offloading cognition helps us to overcome such capacity limitations, minimize computational effort, and achieve cognitive feats that would not otherwise be possible."

The ability to effectively offload cognitive tasks has always been important for human cognition. Throughout history, we've developed tools and strategies to extend our mental capabilities, from simple note-taking to complex computational devices. However, the advent of AI has made this skill more crucial than ever before.

With AI, we are not just offloading simple calculations or memory tasks; we are potentially shifting complex analytical and creative processes to these powerful tools. This new landscape requires a sophisticated understanding of AI capabilities and limitations. More importantly, it demands the ability to strategically split tasks into elements that can be offloaded to AI and those that require human cognition.

This skill - the ability to effectively partition cognitive tasks between human and AI - is becoming a key challenge for contemporary pedagogy. It is not just about using AI as a tool, but about understanding how to integrate AI into our cognitive processes in a way that enhances rather than replaces human thinking.

As Risko and Gilbert note, "the propensity to offload cognition is influenced by the internal cognitive demands that would otherwise be necessary." In the context of AI, this means learners need to develop a nuanced understanding of when AI can reduce cognitive load in beneficial ways, and when human cognition is irreplaceable.

For educators, this presents both a challenge and an opportunity. The challenge lies in teaching students not just how to use AI tools, but how to think about using them. This involves developing metacognitive skills that allow students to analyze tasks, assess AI capabilities, and make strategic decisions about cognitive offloading.

The opportunity, however, is immense. By embracing cognitive offloading and teaching students how to effectively leverage AI, we can potentially unlock new levels of human cognitive performance. We are not just making learning easier; we are expanding the boundaries of what is learnable.

It is crucial to recognize the value of cognitive offloading and develop sophisticated strategies for its use. The paradox of doing less to learn more is not just a quirk of our technological age; it is a key to unlocking human potential in a world of ever-increasing complexity. The true measure of intelligence in the AI era may well be the ability to know when to think for ourselves, and when to let AI do the thinking for us. 

Why Parallel Integration Is the Sensible Strategy of AI Adoption in the Workplace

Artificial intelligence promises to revolutionize the way we work, offering efficiency gains and new capabilities. Yet, adopting AI is not without its challenges. One prudent approach is to integrate AI into existing workflows in parallel with human processes. This strategy minimizes risk, builds confidence, and allows organizations to understand where AI excels and where it stumbles before fully committing. I have described the problem of AI output validation before; it is a serious impediment to AI integration. Here is how to solve it.

Consider a professor grading student essays. Traditionally, this is a manual task that relies on the educator's expertise. Introducing AI into this process does not mean handing over the red pen entirely. Instead, the professor continues grading as usual but also runs the essays through an AI system. Comparing results highlights discrepancies and agreements, offering insights into the AI's reliability. Over time, the professor may find that the AI is adept at spotting grammatical errors but less so at evaluating nuanced arguments.

In human resources, screening job applications is a time-consuming task. An HR professional might continue their usual screening while also employing an AI tool to assess the same applications. This dual approach ensures that no suitable candidate is overlooked due to an AI's potential bias or error. It also helps the HR team understand how the AI makes decisions, which is crucial for transparency and fairness.

Accountants auditing receipts can apply the same method. They perform their standard checks while an AI system does the same in the background. Any discrepancies can be investigated, and patterns emerge over time about where the AI is most and least effective.

This strategy aligns with the concept of "double-loop learning" from organizational theory, introduced by Chris Argyris. Double-loop learning involves not just correcting errors but examining and adjusting the underlying processes that lead to those errors. By running human and AI processes in parallel, organizations engage in a form of double-loop learning—continually refining both human and AI methods. Note, it is not only about catching and understanding AI errors; the parallel process will also find human errors through the use of AI. The overall error level will decrease. 

Yes, running parallel processes takes some extra time and resources. However, this investment is modest compared to the potential costs of errors, compliance issues, or damaged reputation from an AI mishap. People need to trust technology they use, and bulding such trust takes time. 

The medical field offers a pertinent analogy. Doctors do not immediately rely on AI diagnoses without validation. They might consult AI as a second opinion, especially in complex cases. This practice enhances diagnostic accuracy while maintaining professional responsibility. Similarly, in business processes, AI can serve as a valuable second set of eyes. 

As confidence in the AI system grows, organizations can adjust the role of human workers. Humans might shift from doing the task to verifying AI results, focusing their expertise where it's most needed. This gradual transition helps maintain quality and trust, both internally and with clients or stakeholders.

In short, parallel integration of AI into work processes is a sensible path that balances innovation with caution. It allows organizations to harness the benefits of AI while managing risks effectively. By building confidence through experience and evidence, businesses can make informed decisions about when and how to rely more heavily on AI.

AI in Education Research: Are We Asking the Right Questions?

A recent preprint titled "Generative AI Can Harm Learning" has attracted significant attention in education and technology circles. The study, conducted by researchers from the University of Pennsylvania, examines the impact of GPT-4 based AI tutors on high school students' math performance. While the research is well-designed and executed, its premise and conclusions deserve closer scrutiny.

The study finds that students who had access to a standard GPT-4 interface (GPT Base) performed significantly better on practice problems, but when that access was removed, they actually performed worse on exams compared to students who never had AI assistance. Interestingly, students who used a specially designed AI tutor with learning safeguards (GPT Tutor) performed similarly to the control group on exams. While these results are intriguing, we need to take a step back and consider the broader implications.

The researchers should be commended for tackling an important topic. As AI becomes more prevalent in education, understanding its effects on learning is crucial. The study's methodology appears sound, with a good sample size and appropriate controls. However, the conclusions drawn from the results may be somewhat misleading.

Consider an analogy: Imagine a study that taught one group of students to use calculators for arithmetic, while another group learned traditional pencil-and-paper methods. If you then tested both groups without calculators, of course the calculator-trained group would likely perform worse. But does this mean calculators "harm learning"? Or does it simply mean we are testing the wrong skills?

The real question we should be asking is: Are we preparing students for a world without AI assistance, or a world where AI is ubiquitous? Just as we do not expect most adults to perform complex calculations without digital aids, we may need to reconsider what math skills are truly essential in an AI-augmented world.

The study's focus on performance in traditional, unassisted exams may be missing the point. What would be far more interesting is an examination of how AI tutoring affects higher-level math reasoning, problem-solving strategies, or conceptual understanding. These skills are likely to remain relevant even in a world where AI can handle routine calculations and problem-solving.

Moreover, the study's title, "Generative AI Can Harm Learning," may be overstating the case. What the study really shows is that reliance on standard AI interfaces without developing underlying skills can lead to poor performance when that AI is unavailable. However, it also demonstrates that carefully designed AI tutoring systems can potentially mitigate these negative effects. This nuanced finding highlights the importance of thoughtful AI integration in educational settings.

While this study provides valuable data and raises important questions, we should be cautious about interpreting its results too broadly. Instead of seeing AI as a potential harm to learning, we might instead ask how we can best integrate AI tools into education to enhance deeper understanding and problem-solving skills. The goal should be to prepare students for a future where AI is a ubiquitous tool, not to protect them from it.

As we continue to explore the intersection of AI and education, studies like this one are crucial. However, we must ensure that our research questions and methodologies evolve along with the technology landscape. Only then can we truly understand how to harness AI's potential to enhance, rather than hinder, learning.