In real life, it may seem self-evident that gaining mastery takes years of intense, deliberate, and guided work. Yet the perennial optimism of students cramming the night before an exam tells us that the pursuit of a cognitive shortcut may be an enduring human impulse.

It is unsurprising, then, that students—and many adults—increasingly use the swiftly advancing tools of AI and Large Language Models (LLMs) as a shortcut around deeper, more effortful cognitive work.

The Irreducible Nature of Effort and Mastery

In a previous post in my series on LLMs, we briefly explored Stephen Wolfram's concept of “computational irreducibility”—the idea that there are certain processes cannot be shortcut and that you have to run the entire process to get the result.

One of the provocations of LLMs has been the revelation that human language (and maybe, animal language?) is far more computationally reducible than we assumed. As AI advances, it demonstrates that other tasks and abilities previously thought to reside exclusively within the human province may also be more computationally tractable than we believed.

Actual learning by any human being—which we could operationally define as a discrete body of knowledge and skills internalized to automaticity—inevitably requires practice and effort. A student must replicate essential learning steps to genuinely own such knowledge. There is no shortcut to mastery.

That said, the great enterprise of education is to break down complex and difficult concepts and skills until they are pitched at the Goldilocks level of difficulty to accelerate a learner towards mastery. This is the work, as I've explored elsewhere of scaffolding and differentiation.

In a conversation on the Dwarkesh Podcast, Andrej Karpathy praises the “diagnostic acumen” of a human tutor who helped him learn Korean. She could “instantly... understand where I am as a student” and “probe... my world model” to serve content precisely at his “current sliver of capability.”

This is differentiation: aligning instruction to the individual's trajectory. It requires knowing exactly where a student stands and providing the necessary manner and time required for them to progress.

His tutor was then able to scaffold his learning, providing the content-aligned steps that lead to mastery, just as recruits learn the parachute landing fall in three weeks at the army jump school in Fort Benning, as described in Make It Stick.

“In my mind, education is the very difficult technical process of building ramps to knowledge. . . you have a tangle of understanding and you’re trying to lay it out in a way that creates a ramp where everything only depends on the thing before it.” — Andrej Karpathy

Crucially, neither differentiation nor scaffolding is about making learning easier in the sense of removing effort. They are both about ensuring the learner encounters the “desirable difficulty” necessary to move towards mastery.

Karpathy views a high quality human tutor as a “high bar” to set for any AI tutor, but seems to feel that though the achievement of such a tutor will take longer than expected, it is ultimately a tractable (i.e. “computationally reducible”) task. He notes that “we have machines for heavy lifting, but people still go to the gym. Education will be the same.” Just as computers can play chess better than humans, yet humans still enjoy playing chess, he imagines a future where we learn for the intrinsic joy of it, even if AI can do the thinking for us.

The Algorithmic Turn and Frictionless Design

As Carl Hendrick explored recently on “The Learning Dispatch,” there's a possibility that teaching and learning themselves are more computationally tractable than we had assumed:

“If teaching becomes demonstrably algorithmic, if learning is shown to be a process that machines can master . . . what does it mean for human expertise when the thing we most value about ourselves... turns out to be computable after all?””

The problem lies in the design of most AI tools — they are designed for user friendly efficiency and task completion. Yet such efficiency counters the friction needed for learning. The Harvard study on AI tutoring showed promise precisely because the system was engineered to resist the natural tendency of LLMs to be maximally helpful. It was constrained to scaffold rather than solve.

As Hendrick notes, the fact is that human pedagogical excellence does not scale well, while AI improvements can scale exponentially. If teaching is indeed computationally tractable, then a breakthrough in AI tutoring could be an actuality. But even with better design for learning, unless both teachers and students wield such powerful tools effectively, they could lead to a paradoxical situation in which we have the perfect tools for learning, but no learners capable of using them.

Brain Rot & the Trap of the Novice

The danger of AI, then, is that rather than leading us to the promised land of more learning, it may instead impair our ability—both individually and generationally—to learn over time. Rather than going to a gym to work out “for fun” or for perceived social status, many may elect to opt out of the rat race altogether. The power of AI thus misdirected as an avoidance strategy, deflecting as much thought and effort and care from our lives as conceivably possible.

The term “brain rot” describes a measurable cognitive decline when people only passively process information.

A study on essay writing with and without ChatGPT found that “The ChatGPT users showed the lowest brain activity” and “The vast majority of ChatGPT users (83 percent) could not recall a single sentence” of the AI-generated text submitted in their name. By automating the difficult cognitive steps, the students lost ownership of the knowledge.

Such risk is highest for novices. A novice could be defined by a need to develop automatized internal knowledge in a domain. Whereas an expert can wield AI as a cognitive enhancement, extending their own expertise, a novice tends to use it as a cognitive shortcut, bypassing the process of learning needed to stand on their own judgment.

If we could plug a Matrix-style algorithm into our brains to master Kung Fu instantly, we all surely would. As consumers, we have been conditioned to expect the highest quality we can gain with minimal effort. So is it any surprise that our students are eager to take full advantage of a tool designed for the most frictionless task completion? Why think, when a free chatbot can produce output that plausibly looks like you thought about it?

Simas Kicinskas, in University education as we know it is over, details how “take-home assignments are dead . . .[because] AI now solves university assignments perfectly in minutes,” and that students use AI as a “crutch rather than as a tutor,” getting perfect answers without understanding because “AI makes thinking optional.”

But really, why should we place all the burden of betterness on the shoulders of our students, when they are defaulting to what is clearly human nature?

The Barbell Approach

Kicinskas suggests that despite the pervasive current use of AI to shortcut thinking, “Universities are uniquely positioned to become a cognitive gym, a place to train deep thinking in the age of AI.”

He proposes “a barbell strategy: pure fundamentals (no AI) on one end, full-on AI projects on the other, with no mushy middle. . . [because] you need cognitive friction to train your mental muscles.”

The NY Times article highlighted a similar dynamic in that MIT study cited earlier: students who initially used only their brains to write drafts recorded the highest brain activity once they were allowed to use ChatGPT later. Students who started with ChatGPT never reached parity with the former group.

“The students who had originally relied only on their brains recorded the highest brain activity once they were allowed to use ChatGPT. The students who had initially used ChatGPT, on the other hand, were never on a par with the former group when they were restricted to using their brains, Dr. Kosmyna said.”

In other words, AI can enhance our abilities, but only after we have already put in the cognitive effort and work for a first draft.

So Kicinskas is onto something with the barbell strategy. We start with real learning, the learning that requires desireable difficulty, friction, and effort that is pitched at the right level for where the learner is at that moment in order to gain greater fluency with that concept or skill.

Once some level of ability and knowledge has been acquired (determined by the success criteria set for that particular task, course, subject, and domain) adding AI can accelerate and enhance the exploration of that problem space.

Using AI for Cognitive Lift, Rather than Cognitive Crutch

We must therefore design and use AI in more alignment with the “barbell” strategy.

At the beginning of a student's journey, or at the beginning of the development of our own individual products, we need to double down on the fundamentals. We must carve out that space for independent thought as well as for the analog and social interaction we require to gain new insights.. This is how we build the inner scaffold required for true expertise.

On the other side of the barbell, we can more enthusiastically embrace the capacity of AI to scale our ability for processing and communicating information. Once we have done the heavy lifting to clarify our thinking, we can use these tools to extend our reach and traverse vast landscapes of data.

The danger lies in that “mushy middle,” wherein we can all too easily follow the path of least resistance and allow others, including AI, do all our thinking for us by taking our attention away from our own goals. We must choose to think for ourselves not because we have to for survival, but because the friction of generating our own thought is what gives us our agency.

In a previous post, I explored how both language and learning is a movement from fuzziness to greater precision. It is possible that AI can greatly accelerate us in that journey, even as it is possible that it could greatly stymie our growth. The key is that we must subject our fuzzy, half formed intuitions first to greater resistance until they crystallize into more precise and communicable thought. If we bypass this struggle, we doom ourselves to perpetual fuzziness, unable to distinguish between AI automated slop and AI assisted insight.

Postscript: How I used AI for this Post

I use AI extensively in both my personal and professional life, and writing this post was no exception. I thought it might be helpful to illustrate some of the arguments I made above by detailing exactly how AI both posed a risk to my own agency and served to enhance it during the creation of this essay.

I began by collecting sources. I had come across several articles and a podcast that felt connected, sensing emerging themes that related to my previous posts on LLMs. I started sketching out some initial thoughts by hand, then uploaded my sources into Google's NotebookLM.

My first impulse was to pull on the thread of “computational irreducibility.” I knew there was an interesting tension in language between regularity and irregularity, so I used Deep Research to find more sources on the topic. This led me down a rabbit hole. By flooding my notebook with technical papers, the focus shifted to abstractions likeKolmogorov Complexity and NP-completeness—fascinating, but a distraction from the pedagogical argument I wanted to make. Realizing this, I had the AI summarize the concept of irreducibility and then deleted the technical source files to clear the noise.

I then used the notebook to explore patterns between my remaining sources. Key themes began coalescing. It was here that I made a classic mistake: I asked Google Gemini to draft a blog post based on those themes.

The result wasn't bad, but it wasn't mine. It completely missed the actual ideas that I was trying to unravel. I realized I was trying to shortcut the “irreducible” work of synthesis. To be fair to my intent at the time, however, I was really just interested in seeing whether the AI gave me any ideas I hadn't thought of, from a brainstorming stance. It wasn't very useful, however, so I discarded that approach, went back to my sources, and spent time thinking through the connections as I began drafting out something new.

I then began to draft the post in Joplin, which is what I now use for notes and blog drafts. I landed on the analogy of the Hollywood training montage as the way to begin, and I then pulled up Google Gemini in a split screen and began wordsmithing some of what I wanted to say. As I continued drafting, I used Gemini as an editorial support. It advised syntactical revisions and fixed a number of mispellings. I then used it to help me expand on a half-formed conclusion, as well as for cutting an extended naval-gazing section that was completely unnecessary.

Gemini tends to oversimplify in its recommendations, however, and I didn't take all of it's suggestions. I generated some images in NotebookLM based on all the sources, and also enhanced an image I had already made previously using Gemini. Finally, I did a few additional rounds of feedback between NotebookLM to reconsider my draft in relation to all the sources in my notebook, and then returned with that feedback in Gemini, and again went through my draft on a split screen. This additional process gave me some good suggestions for reorganization and enhancement of some of the content.

In the end, I almost misled myself by trying to automate the thinking process too early. It was only when I returned to the “gym”—drafting the core ideas myself—that the AI became useful. My experience writing this confirms the barbell strategy: draft what you want to say first to build the conceptual structure, then use AI to draw that out further, and to polish and enhance it. Be very cautious in the mushy middle.

#AI #LLMs #cognition #mastery #learning #education #tutoring #scaffolding #differentiation #barbell

While we meticulously map out and prepare for the activities we engage our students in, the specific linguistic structures and vocabulary we employ often remains implicit, almost accidental. This raises critical questions: which aspects of our classroom talk truly accelerate literacy – is it sheer volume, vocabulary precision, or syntactic complexity? And how can we become more deliberate and intentional architects of this vital linguistic environment for all students, including those developing multi-dialectalism and multilingualism?

My recent presentation at ResearchED in NYC ventured into this territory, examining the research on how the linguistic environment we curate can influence student literacy achievement.

The Power of Classroom Talk: More Than Just Words

Why this focus on classroom talk? Because literacy isn't built in a vacuum. While foundational skills like decoding and spelling are absolutely critical (and have for all too long been sidelined), the elementary ELA block at large all too often focuses on isolated skills.

Despite elementary schools in the U.S. dedicating significantly more time to ELA than any other subject, reading scores (like those from state ELA tests or the more nationally normed NAEP) often remain stubbornly flat, including here in NYC. This prompts a crucial question: is simply adding more ELA time the answer, or do we need to rethink how we build literacy—both within and beyond ELA?

This is where focusing on content-rich talk across the content areas becomes vital. Subjects like social studies, science, math, and the arts offer fertile ground for developing the academic language and background knowledge that underpin strong literacy. In fact, some research suggests this cross-curricular approach may be more effective for reading comprehension than simply adding more ELA time. For example, a 2020 study by the Fordham Institute found that increased instructional time in social studies—but not additional time in ELA—was associated with improved reading comprehension for elementary students (Tyner & Kabourek, 2020). Notably, the students who benefited most from additional social studies time included girls and those from lower-income and non-English-speaking homes. Tackling the challenge of building a strong foundation begins, fundamentally, with the language we choose to use and explicitly teach across all subjects.

Yet social studies—and other content areas—occupy an increasingly small portion of an elementary student’s learning (more recent RAND paper on this).

The Problem We Face

This focus is critical because many students, particularly in the K-5 grades, can encounter significant hurdles in developing robust literacy and language skills that are essential for academic success. These challenges can be particularly acute for multiidialectal or multilingual learners navigating academic language demands alongside or in addition to their home language(s). Key challenges include:

• Foundational Skills Gaps: Some students do not receive the focused instruction and practice they need in decoding and spelling to become fluent readers and writers.
• Knowledge and Language Gaps: Many students lack consistent and cohesive opportunities to build the background knowledge and language necessary to understand complex topics across different subjects, while building on and connecting to the cultures, schema, and languages they bring.
• Complex Language Exposure: The majority of students need more exposure to, and structured practice with, reading, writing, and talking using the complex language inherent in disciplinary discourse and texts.

What the Research Says: Listening In on Learning

I used the wonderful study by Jeanne Wanzek, Carla Wood, and Christopher Schatschneider, which I have highlighted in this blog before as the anchor for my presentation. Using LENA devices to record classroom instruction, they found:

• Teachers, on average, used relatively few academic or curriculum-specific vocabulary words.
• Crucially, teachers who did use more academic words had students demonstrating higher vocabulary achievement by the end of the school year. This held true even when controlling for the teachers' overall expressive vocabulary and across students with varying incoming abilities.
  • The takeaway: The specific words we choose during instruction have a measurable link to student vocabulary growth, a crucial component of academic success for all learners.

Correlation or Causation? Towards Stronger Links

Correlation, of course, isn't causation. Does using more academic language cause better outcomes, or do teachers with higher-achieving students simply use more academic language? While the Wanzek et al. study is correlational, a growing body of research points towards a causal link between targeted language exposure/instruction and improved outcomes. Here’s just a smattering:

• Conversational Turns: Interventions increasing parent-child conversational turns led to language skill improvements and predicted neurocognitive changes (Romeo et al., 2021).
• Mathematical Language: An RCT using dialogic reading to boost mathematical language positively impacted preschoolers' general math skills (Purpura et al., 2017).
• Classroom Math Talk: Teachers using more mathematical language were found to be more effective at raising student test scores in upper elementary grades (Himmelsbach et al., 2024).
• Content Literacy: A sustained literacy intervention grounded in science and social studies content led to lasting improvements in vocabulary, reading comprehension (across domains), and even math, demonstrating far transfer effects (Kim et al., 2024).
  • The takeaway: The pattern across these studies strongly suggests that actively improving the language environment through intentional instruction yields real results in student learning, with content-rich instruction showing particular promise for multilingual learners

Defining and Developing Academic Language

So, what is this “academic language” we're aiming for? It's the formal, complex, often abstract and decontextualized language common in school, texts, and professional settings (NYSED, Lesaux & Philips Galloway; Philips Galloway et al., 2019). Since this language isn't always prevalent outside school, the classroom becomes the primary place many students will learn it, making our role crucial, especially in fostering academic language development for multilingual learners.

Understanding how language typically develops—and recognizing that multilingual development adds further layers of complexity and potential cognitive benefits—helps us see where to intervene and build bridges for students:

1. Contextualized Interaction: Early conversational turns, rooted in the immediate environment.
2. Oral Storytelling: Moves towards abstraction, requiring inference and schema-building beyond the 'here and now'.
3. Shared Reading: Introduces more decontextualized language—denser vocabulary, complex sentences, formal structures typical of written text (I’ve rounded up a list of studies related to this).
4. Written Language: Characterized by rarer, more abstract words, complex syntax (like nominalizations, passive voice, relative clauses), and formal discourse structures

Our instruction aims to help students navigate this journey towards greater precision and abstraction. Leveraging students' home languages can serve as a powerful bridge along this continuum.

Explicit Teaching Meets Implicit Learning: Achieving “Escape Velocity”

So, how do we teach this complex language effectively?

While explicit teaching of vocabulary or grammar acts as a necessary accelerator, it works best when launching students into an environment rich with coherent and cohesive implicit learning opportunities. This explicit scaffolding is vital for all learners navigating complex academic language, and particularly crucial for multidialectal and multilingual students acquiring these structures in the more formal English used in school. Mark Seidenberg calls this synergy achieving “escape velocity”—where explicit instruction scaffolds and enables students to learn powerfully from the sheer volume of language they encounter through reading, writing, and discussion. Our goal is to engineer this velocity for all learners.

As we’ve also explored on this blog, part of building this velocity is about providing our kids with more texts and more talk—”textual feasts,” as Dr. Tatum calls it.

Putting Research into Practice: Classroom Strategies

How can we intentionally weave denser and more complex academic language into our daily practice, while valuing and leveraging the linguistic diversity of our students? It involves concrete, planned actions:

1. Plan to Amplify Knowledge & Language:

   • Identify core concepts in a unit/text.
   • Pinpoint the essential academic vocabulary used to explain these concepts.
   • Explore morphology and etymology (e.g., using tools like Etymonline) to deepen understanding, including potential cross-linguistic connections.
   • Analyze how these words function in different sentences and contexts.
   • Plan structured opportunities for students to practice reading, writing, and speaking with these words.

2. Leverage Multimodal Text Sets: Immerse students in a topic through various texts (articles, books, videos, images) and modalities. This creates multiple, varied exposures to related concepts and vocabulary.

3. Structured Supplements for Read-Alouds: Don't just read; enhance read-alouds by providing concise definitions, examples, asking stimulating questions that require using target vocabulary, connecting to prior knowledge, and using concept maps (Mosher & Kim,, 2025). Consider incorporating home language previews or connections where appropriate.

1. Explicitly Teach Morphology & Leverage Cross-Linguistic Connections: Build awareness of word parts (prefixes, suffixes, roots) and connections between words across languages. This is especially powerful for multilingual learners; recognizing shared roots and patterns (like transparent/transparente) and using contrastive analysis between languages (like comparing verb forms) can unlock meaning and build metalinguistic awareness. Use a consistent multisyllabic word decoding strategy. Use tools like concept/semantic maps to help visualize connections, including across languages.

1. Structure Reading Instruction (Before, During, After): Be intentional about the purpose of each read:
   • Before: Build background, preview text and vocabulary. Activate or build relevant background knowledge, connecting to diverse student experiences.
   • During (1st Read): Focus on flow and gist, model fluency, check basic comprehension.
   • During (2nd Read): Zoom in on specific words, sentences, author's craft. Practice paraphrasing key details.
   • During (3rd Read): Analyze structure and language more deeply. Ask inferential questions.
   • After: Review, engage with target vocabulary/language, summarize, practice speaking/writing using mentor sentences and target words.
2. Zoom In and Amplify: When revisiting texts, strategically select specific words or sentences to focus on. Use routines (echo/choral reading, dictation, sentence combining, contrastive analysis) to deepen understanding and usage. (See the Zoom In and Amplify Menu resource for ideas).

These routines can often be adapted using contrastive analysis or strategic invitations to use and connect to home language for multilingual learners.

Moving Forward: The Bottom Line

The research is increasingly clear: the language we choose to use and teach matters. By consciously choosing to immerse students in rich, academic language within and across content areas, providing both explicit instruction and ample opportunities for implicit learning through meaningful interaction with texts and topics, we can significantly enhance language development and overall literacy achievement, creating more equitable opportunities for all students, including multidialectal and multilingual learners. It requires intentional planning and a shift towards seeing every teacher as a teacher of language, but the potential payoff for our students is enormous.

To effectively address the challenges and leverage the power of classroom talk, the evidence points towards these key actions:

• Recognize the crucial role academic language plays in student literacy development across all subjects, recognizing its importance most especially for students developing multilingualism
• Understand the interplay between explicit language instruction (the accelerator) and the implicit learning that occurs through rich language exposure (the fuel).
• Actively implement strategies to intentionally increase the quantity and quality of academic language used in classroom instruction and student interactions daily, leveraging students' diverse linguistic resources as assets.

#literacy #education #research #AcademicLanguage #TeacherTalk #ReadingComprehension #Vocabulary #Instruction #ResearchED #MultilingualLearners #ENL #Biliteracy

1) LLMs gain their uncanny powers from the statistical nature of language itself; 2) the meaning and experiences of our world are more deeply entwined with the form and structure of our language than we previously imagined; 3) LLMs offer an opportunity for further convergence between human and machine language; and 4) LLMs can potentially extend our cognitive abilities, enabling us to process far more information.

In a previous series, “Innate vs. Developed,” we’ve also challenged the idea that language is entirely hardwired in our brains, highlighting the tension between our more recent linguistic innovations and our more ancient brain structures. Cormac McCarthy, the famed author of some of the most powerful literature ever written, did some fascinating pontificating on this very issue.

In this post, we’ll continue picking away at these tensions, considering implications for AI and LLMs.

Fuzziness and Precision in Language Development and Use

To start us off, I want to ground our exploration in two concepts we’ve covered previously in “An Ontogenesis Model of Word Learning in a Second Language”:

• Fuzziness: “inexact or ambiguous encoding of different components or dimensions of the lexical representation that can be caused by several linguistic, cognitive, and learning-induced factors. These factors include, among others, changes in neural plasticity, the complexity of mapping L2 semantic representations on the existing L1 semantic representations and of mapping L2 forms on the semantic representations, and problems with L2 phonological encoding”

• Optimum: “the ultimate attainment of a representation (or its individual components), i.e., the highest level of its acquisition, when the representation is properly encoded and no longer fuzzy”

I think these concepts are useful not only for thinking of learning new words in a language, but also for how we interact with LLMs and the language they are trained upon.

From Fuzziness → Optimum

When we first learn a language, whether while in the womb, in school, or after moving to a new community, what we hear and understand is fuzzy. The first thing we attune to is the prosody of the language: its tones, volume, and duration. We can’t yet fully distinguish words and sentences within a stream of speech, nor syllables from phonemes, nor vowels from consonants. Let alone connect those sounds (or signs) to meaning and communicate with them to others.

Yet as we gain greater discernment across hearing, vision, movement, and speaking, our representations of a language becomes more flexible and more precise. As I’ve written about elsewhere, connecting speech directly to its form in writing can enhance language and reading and writing development simultaneously. Oral and written language – and reading and writing – can develop reciprocally. Developing one supports refining the other.

Why would that be, given we didn’t invent the technology of writing until far down the timescale of human evolution?

Precision in Language and Cognition

Maybe it’s because the written form of a language requires greater precision in the representation in our minds. When greater precision is required, it takes more time and effort, at least initially, to produce.

As an example, you may have heard of the term “receptive bilinguals.” These are individuals who can understand the gist of an everyday conversation in another language, but may struggle to speak or produce it fluently. This is because they may have had fairly significant exposure to the language, especially in childhood, but their mental representations remain “fuzzy” because they rarely produce the language either orally or in written form.

The more that we hear and read AND produce a word – and particularly when we produce it both orally and in writing – the more likely and quickly we are to reach optimum.

We see this process play out in real time with babies. They listen to our sounds and watch our faces, then begin to babble, mimicking us. They begin connecting those sounds to things and ideas. And then they begin to gain a more precise understanding and use of a word, from there stringing multiple words together into sentences, again starting haphazardly and working towards greater flexibility and precision.

Fuzziness, Precision, and Specialization in Language, Cognition, Computation, and Literacy

LLMs have demonstrated that there is far more knowledge, meaning, and comprehension of the world embedded within the statistical relationships of the words and phrases we use than we previously suspected.

As we’ve also explored, there are fuzzier and more precise terms and concepts in a language. The more abstract and “decontextualized” an event or idea (meaning that the event or idea is not readily available in the context of that environment or moment) the more precise, vivid, or specialized our language becomes in the effort to describe it. This can lead us all the way to the extreme of computational language, which is highly precise, much harder for humans to learn, and quite alien in comparison to the general fuzziness of our everyday language used to communicate about everyday things.

The reason read-alouds are so very powerful in the beginning of childhood (and arguably, through adolescence, perhaps even beyond) is because they provide children with exposure to and immersion in this more decontextualized type of language and more abstract and broad understandings of the world. This helps prepare them for when they later engage with written forms of language and increasingly discipline-specific forms of discourse.

As language learning develops towards greater precision, networks in the brain are forged and strengthened. One of the reasons why early childhood is so incredibly important to language and literacy and motor development is because the brain supercharges the neural connections it is forming in all directions. Dendrites spring up like fungus after a rain. But learning new things requires a bit more effort as we age because we work far more on pruning our existing connections for efficiency.

Yet no matter our age, developing these increasingly robust cross-brain connections, and then increasingly specializing and refining them for specific domains and uses, can increase our mental resilience.

We can see this process of specialization play out in real time with young children as they learn to read and write. As they gain greater precision with representations of language through spelling, writing, and volume of reading, their brains increasingly forge further connections between the architecture used with executive function, speech, vision, and motor control, while then specializing and refining them.

Developing language and literacy in multiple languages – to the point of optimum – even further connects, specializes, and refines those networks. And when one is bi- or multi-literate on disciplinary topics – with the specialized and precise language required for communicating flexibly about those topics – then those networks are yet further refined.

This is similar, arguably, as with the development of cognition. Cognition—a fancy way of saying “awareness, knowledge, and understanding”—includes the facets of executive function and memory that are also tapped into when developing language, yet are surprisingly separable from language in the brain, in terms of the processes identified through brain scans, at the same time.

I think a useful way to think of this distinction may be the difference between the unconsciousness or the lack of awareness we may have about something PRIOR to learning it, and the unconsciousness and lack of awareness we have AFTER learning it to optimum. When we have attained fluency with a skill or pushed our knowledge into long-term memory, we no longer need to apply much effort – nor thought – to drawing upon it. It is the degree of effort that is required in order to learn or use something that determines the level of cognition we need to initially draw upon. And while we can certainly expand our cognitive ability and other aspects of our learning potential, there are also hard upper limits – such as the bottlenecks of our working memory and our attention.

We overcome those bottlenecks by committing important information to long-term memory through regular use and communication, automatizing regularly used skills through practice, and leveraging the institutionalization of knowledge-based communities and the technologies of writing (texts) and digitization to process and communicate and further refine larger volumes of information.

The Limitations and Potential of LLMs

While human children rapidly develop language and literacy from comparably minimal amounts of input and interaction in their world, LLMs are trained on vast bodies of text, the majority in written form (thus far). Their training is developed to refine and make more precise their abilities to predict the concatenations of continued tokens and words from what we have fed them.

Similar to human brains, LLMs move from a fuzzy-to-precise spectrum as they refine the “weights” they assign to linguistic tokens across their many layers. Early or small models of LLMs, akin to our “receptive bilingual” example earlier, demonstrate some receptive capabilities, but their generated outputs are highly fuzzy, as they did not have sufficient neural layers, training, and feedback (i.e. sufficient input and production) to achieve something close to optimum in their generation of human-like language.

But to state the obvious, LLMs do not experience the world as we do. They have no bodies, no sensory input, no social interactions (unless you count the part of their training that requires humans to provide them with corrective feedback). As a reminder, the fact that they have the capabilities they do–derived merely from the accumulated statistical relationships of parts of words–is remarkable. They do not “think,” at least, not in the manner in which our own cognition functions, and they do not continuously build and further refine their knowledge–yet–from ongoing interactions and input from other AI and with us.

LLMs are like if we took away all the other parts of our brain—those more ancient parts that continue solving problems and help us steer our way home and keeps our hearts beating—and only left the parts dedicated to language. That they are able to do all they can from mere statistical relationships forged from language alone is–again–remarkable, but it also shows us their limitations.

To be frank, that the dialogue has been so singularly focused on the “intelligence” of LLMs, with the goal of forming “artificial general intelligence” (AGI) seems remarkably off base to me. What I am far more interested in is the potential of these models to teach us something about our own development of language and literacy–and thus, how we can better teach those abilities–and to extend our own cognitive abilities.

Enhancing Cognition with AI

Towards this end, I want to suggest some implications for education that takes us away from fears about AI making kids dumber or taking away jobs from teachers.

AI and LLMs can enhance our cognitive abilities by helping us to:

• Process Large Amounts of Information to Gain Knowledge: AI and LLMs are getting better and better (seemingly every week) in sifting through vast amounts of information, such as databases, research, transcripts, and other documents, to help us summarize, answer questions, paraphrase, and understand the relevant knowledge contained in them. Furthermore, they are getting better and better at translating across multiple languages and in reading multiple modalities. You can feed an LLM an image with text in another language and it can read it.

• Augment Our Own Thinking and Writing: LLMs work really well in helping us spitball ideas or redraft our own writing. The fear that they will stop kids from being taught to write is misplaced – the writing produced by LLMs is only as good as what they are given. Yes, they are great at boilerplate forms of writing! But that’s the exact kind of writing that we do want to automate and reduce our own time and thinking on. When it comes to deeper writing and thinking like this series and post, it ain’t writing it for me. But I do find it really helpful when I get stuck or when I want to get suggestions for revision.

  In Sum

The effectiveness of our use of AI and LLMs hinges on the quality of our input.

As with previous tools like Google Search, the more precise and informed our prompts, the more powerful and accurate their responses.

Another way of framing this idea: LLMs can help us further widen or refine our own ideas and language. They are far less useful in just handing them to us. They mirror and leverage what we provide to them.

There is a lot of talk about the “hallucinations” of LLMs, but perhaps a better way to frame it is as “pixelation,” or grain size. There are larger and smaller grain sizes of pixels. The coarser the grain, the less clear it is. The finer the grainer, the sharper it becomes. The more vague and broad the grain size we feed them, the more BS they will spit. The more precise and narrow grain sizes we provide, the more accurate and useful their responses will be. They can then help us move into different grain sizes from there (either widen our lens, or narrow our lens).

This means that we need to keep teaching our kids stuff. The more knowledge they have, the more precise and flexible their ability to wield language, the better they can use powerful tools like AI.

We can help kids to use AI in this way, and we can create tech-free spaces in our schools where they need to put in the cognitive effort and time they need to build their fluency with language and literacy and read texts that build their knowledge. And then when we engage them with the tech, we teach them how to use it to extend, rather than diminish, their own potential.

There’s implications here for teachers too – in fact, I think the most exciting potential for AI is actually freeing teachers up to spend more time teaching, and less time marking up papers and analyzing data. But that’s for another post.

#AI #LLMs #cognition #language #literacy #learning #education Discuss...

Now that’s a “natural” ecosystem I’m discussing. Let’s explore the concept of a man-made ecosystem in order to better adapt that idea to schools. In a man-made ecosystem, such as a garden, the gardener works to recreate natural environments, but with a focus on a purpose that suits the gardener, such as food growth, or flower cultivation. Sometimes that focus is so monolithic that the gardener ends up in constant battle with nature, and must maintain their garden on life support infusions of toxic herbicides and pesticides. Fortunately, there are methods of deliberately harnessing natural processes and dynamics to best serve our own selfish interests. When the gardener best recreates the conditions that will foster interconnectivity and diversity of life adapted to their environment, their garden will thrive.

Now let’s bring that idea back to schools. In education, instead of growing food or flowers, our work is to grow our kids’ minds. A lot of times, this effort of increasing achievement is presented as a type of competition, which is furthered through the use of punitive grading systems and high stakes testing. Sometimes the way we talk about it makes it seem like all we want to do is pump steroids into the minds of our youth. But we know that’s not what it’s about. Education is about nurturing, developing, instilling, guiding. And in terms of an ecosystem, the big idea is that ultimately, no one is really competing, even if it looks like that on the surface. Ultimately, we work to counterbalance each other and create an environment that best harnesses the resources available within that given community.

This all sounds relatively banal, even to me, but the reason I keep pushing this analogy between gardening and education is because I’m seeking to apply permacultural principles to the ecosystems of schools. Permaculture is a philosophy of cultivating land grounded in holistic and sustainable design practices. The permacultural approach is a method for countering devastating ecological practices.

I believe that one of the critical issues underlying education reform is that we are all too often seeking superficial means of enhancing student performance. In a garden, we might temporarily achieve enhanced production through an arduous turning of topsoils and expensive input of chemicals. In a school, we might temporarily raise student test scores through test prep. But ultimately in both scenarios, we are only doing battle against nature and economy. In order to enhance productivity sustainably, we have to build up the foundations of our communities, our ecosystems. This requires targeted investments in the communities that most require it. There is no other way.

#ecosystems #schools #education #permaculture #interconnectivity #diversity #design