How Verbal Thinking Elevates Learning

student working on mathThe notion of talking to oneself, often dismissed as a mere quirky habit or a sign of preoccupation, is, in fact, a powerful, evidence-based cognitive tool essential for learning, problem-solving, and achieving self-regulation. For educators, understanding and deliberately integrating this "verbal thinking"—known in psychological literature as private speech, self-talk, or self-explanation—into pedagogical practice can unlock deeper comprehension and foster truly independent learners. 

The psychological roots of verbal thinking's benefit trace back most prominently to the work of Soviet psychologist Lev Vygotsky. His socio-cultural theory identifies a critical stage in a child's cognitive development where social communication turns inward to become a robust tool for thinking. Vygotsky outlined a three-stage developmental framework for language: beginning with Social Speech in young children, where language is purely external and used for communicating with others; progressing to Private Speech during the preschool years (ages 3-7), where the child begins to speak aloud to themselves, often in a whisper or mumble, utilizing this overt language as a self-guiding tool for planning, regulating, and controlling their own behavior and problem-solving attempts.

For example, a child engaged in a puzzle might audibly walk themselves through the steps: "First, put the red block here, then the blue block goes on top." This transitional phase ultimately leads to Inner Speech (age 7+), which is the fully internalized, silent verbal thought that most adults use for abstract reasoning, reflection, and sophisticated problem-solving. For educators, the key takeaway from Vygotsky’s work is that overt verbal thinking, or private speech, represents the crucial bridge from externally guided learning—where an adult or peer provides the instruction—to true self-regulation and independent, complex thought. By encouraging students to verbalize their process, teachers are helping them build the necessary internal scaffolding for later, silent, and more sophisticated thinking.

Crucially, verbal thinking doesn't just manage behavior; it fundamentally alters how information is encoded and understood by the brain, supporting both memory and comprehension. Research in memory retrieval highlights a phenomenon known as the Production Effect, which demonstrates that reading or generating information aloud significantly improves its memory retention compared to reading it silently. This memory boost occurs because speaking information aloud engages a greater number of sensory channels simultaneously. The learner uses visual input (seeing the text), verbal/motor input (the physical articulation of the words), and auditory input (hearing the words being spoken). This richer, multi-modal encoding creates a more distinctive and robust memory trace in the brain, making the information much easier to recall later. This distinctiveness is vital: when a learner produces a word aloud, it stands out against the background of other silently read words, making the item unique in memory. Therefore, simply having students read key definitions, summaries, or steps aloud in a low-stakes environment is a simple, yet highly effective, way for educators to leverage this proven physiological mechanism to strengthen long-term memory.

Perhaps the most powerful cognitive benefit, particularly for complex material, is the deep processing that occurs through self-explanation. This process is not mere repetition; it is the active, conscious act of trying to explain new information by relating it to what one already knows, making necessary inferences, and proactively clarifying any ambiguities. The first benefit here is powerful metacognitive monitoring: when a learner verbalizes a concept, the very act of articulation immediately exposes areas of confusion or "knowledge gaps." If a student struggles to explain a step in a math proof or a scientific concept, the flaw in their understanding is instantly revealed, prompting them to go back and refine their knowledge. This is a critical act of metacognition—the vital process of thinking about one's own thinking. Secondly, self-explanation drives coherence building. Verbalizing forces the student to translate disparate, often fragmented, pieces of information into a coherent, logical structure. They are not just recalling isolated facts but actively constructing a unified mental model of how the concepts interact. This principle is famously embodied by the Feynman Technique—explaining a concept simply as if teaching it to a novice—which serves as a form of high-level, deliberate verbal thinking that ruthlessly exposes the limits of a learner's comprehension.

The idea that talking to yourself out loud is not only "okay" but also an excellent learning technique is satisfying, but as I dug into this research, I recognized things from my college and grad school education courses. Other than the idea that it's not abnormal behavior to talk to yourself, this research is not completely new. I used several of these pedagogies in my teaching.

The challenge for educators, then, is to move verbal thinking from an accidental occurrence to a deliberate, scaffolded learning strategy within the classroom environment. One highly effective technique is the Think-Aloud Strategy, which focuses on teacher modeling. This strategy is used to make the invisible thought process of an expert visible and accessible to students, thereby explicitly teaching them how to engage in effective self-talk. To implement this, the teacher must first explicitly state the goal: "I’m going to show you how a skilled reader or problem-solver thinks by saying my thoughts out loud." Then, as the teacher reads a complex passage, works through a mathematical equation, or analyzes a primary source, they must stop frequently to verbalize their internal dialogue. This might involve using strategic planning language like, "I'm thinking I should use the quadratic formula here because the equation is set to zero," or demonstrating monitoring and correction by saying, "That word, 'ephemeral,' sounds like it means brief, so I’m going to pause and look that up to make sure I understand the context," or making connections: "The author just described the main character as restless. That connects to the idea I read earlier about his lack of a stable job. I wonder if this will lead to him leaving town." Once modeled, the teacher must transition students to practicing the strategy, perhaps through paired activities known as Reciprocal Think-Alouds, before expecting independent use.

A second practical technique is the Self-Explanation Prompt. This method strategically inserts verbalization breaks into a learning task to force metacognitive reflection and is particularly useful in technical subjects. Implementation begins by identifying key moments in a text, problem set, or lab procedure where a deeper understanding is absolutely necessary before the student can proceed. At these pause points, the teacher provides students with specific open-ended questions they must answer aloud to themselves or in a brief reflection journal. Prompts should be targeted to specific cognitive functions, such as focusing on rationale ("Why did I choose this variable to isolate?"), demanding synthesis ("What is the main idea of this section in my own words?"), or explicitly asking for a connection ("How does this new concept relate to what we learned last week?"). For maximum impact, teachers should then encourage a "Think-Pair-Share" approach where students must first explain their logic to a partner, which solidifies the idea and provides practice in articulation before the whole class moves on.

Finally, the "Teach It Back" Method is a form of high-stakes verbal thinking rooted in the pedagogical principle that to teach a concept is to truly master it. In this strategy, a student is assigned the role of briefly "teaching" a key concept, a section of the reading, or a part of the homework to a small group, to the class, or even to an imaginary audience. The critical instruction given to the student is to explain the topic as simply as possible, perhaps using an analogy, metaphor, or non-technical language if appropriate. The student must translate complex, academic language into straightforward, accessible terms, which serves as the ultimate test of their own comprehension. The teacher should provide specific feedback not only on the accuracy of the content but also on the clarity and logical structure of the explanation, reinforcing the importance of effective verbal articulation as a measure of understanding. By integrating these verbal thinking strategies—modeling, prompting, and teaching back—educators are not just improving a single study skill; they are building the core components of the resilient and self-regulated learner, equipping students with the tools for lifelong, independent cognitive growth.

SOURCES
Vygotsky, L. S. (1978). Mind in society: The development of higher psychological processes. Harvard University Press. (This source is foundational for the concepts of Private Speech and its role in Self-Regulation.)

MacLeod, C. M. (2011). The production effect: Better memory as a consequence of saying aloud during study. Applied Cognitive Psychology, 25(2), 195–204. (This research provides the physiological basis for the Production Effect and memory benefits.)

Chi, M. T. H. (2013). Self-explanation: The effects of talking aloud or writing on learning. Topics in Cognitive Science, 5(1), 1–4. (This source details the mechanism and benefits of Self-Explanation for deep comprehension.)

Berk, L. E. (1992). The role of private speech in the development of mental processes. Psychological Review, 99(4), 779–795. (This provides contemporary developmental research supporting and elaborating on Vygotsky’s observations of private speech.)

The IT Side of Blogging

I blog here about technology and education, and sometimes about how those two industries cross paths. I'm the blogger. Tim Kellers is on the IT side of this. Though he had done some posts in the past, he is more often updating something or fixing something broken on his server or fixing something in some code. That is not my area of expertise, and I don't really want to know much about it. I just want it to work

In October, it wasn't always working. Posts that I had spent time writing just disappeared. The blog went offline. People told me that they couldn't access it because of security warnings. I stopped posting.

A Substack Above

Tim was texting me messages about our .net domain. He created an alternate version at a .icu domain. I had to look up .icu, a top level domain I had never seen before. It means so logically that it is illogical, "I See You."

Tim told me, "That instance runs on different CPU architectures, so I want to do that manual sync first before I move the domain name over." Then he said, "I just synced your post to s35.net," and "I went through an SQL dump of the database and found a whole lot of image files with our very old nji.edu address prefixes. I changed them for a local test, and it looks like a whole lot of broken images are back online.  That string was replaced 554 times according to the log file." 

All of which makes little sense to me. And that's okay with me as long as Tim hangs around.

When I was in Europe in September, I told Tim the site was not working and giving me odd errors. "Just added the Privacy/Cookies/GDPR thing to s35.icu.  Next time you are in Europe, see if the site connects," he texted.

Serendipty35 is back. Tonight is Mischief Night here in Serendipity35land, and I'm hoping no gremlins are out there that will prank Serendipity35.

OpenAI and Broadcom and Ten Gigawatts

This week, OpenAI made news with its new browser, Atlas. Wth all their plans and a new cloud-based AI browser, they need to scale their computing power. Here is a news summary (AI-generated, of course)

business dealsOpenAI has announced a strategic multiyear partnership with semiconductor giant Broadcom to co-develop custom-built chips and infrastructure. The collaboration aims to deploy 10 gigawatts of specialized AI accelerators by the end of 2029—a staggering amount of compute capacity equivalent to the power consumption of approximately 8 million U.S. households. (Markets Insider)

This deal marks OpenAI’s first venture into designing its own in-house processors, with Broadcom tasked with developing and deploying the systems. The chips—known as AI accelerators—are optimized for parallel processing, enabling them to execute billions of operations simultaneously. These accelerators will be deployed across OpenAI’s facilities and partner data centers, using Broadcom’s Ethernet-based networking solutions to ensure scalability and efficiency. (Broadcom Inc)

OpenAI CEO Sam Altman emphasized the significance of the partnership: “Developing our own accelerators adds to the broader ecosystem of partners all building the capacity required to push the frontier of AI to provide benefits to all humanity.” The Broadcom agreement is the latest in a series of megadeals OpenAI has struck in 2025 to secure the compute power needed for its rapidly expanding AI services. Earlier this year, OpenAI signed a $100 billion deal with Nvidia to deploy 10 gigawatts of Nvidia systems, beginning in the second half of 2026. (Reuters) In another major move, OpenAI partnered with AMD to deploy 6 gigawatts of chips and received a warrant for up to 160 million AMD shares, potentially making it one of AMD’s largest shareholders. (The Motley Fool)

These deals reflect a broader industry trend: major tech players are increasingly investing in custom silicon to reduce reliance on Nvidia’s dominant GPU offerings. Companies like Google, Amazon, and Microsoft have already begun developing their own AI chips, and OpenAI’s latest move places it firmly within this competitive landscape. While OpenAI has not disclosed how it plans to finance the Broadcom deal, analysts estimate that a single gigawatt-scale data center could cost between $50 billion and $60 billion. 

This latest partnership not only strengthens OpenAI’s technical capabilities but also signals a shift toward greater control over its hardware stack—an essential step as the race to develop next-generation AI systems accelerates.

Letting the Atlas Browser Shop for You

OpenAI's new Atlas browser has an Agent mode, which allows it to navigate sites, fill carts, and perform real-world tasks. Can it do it well for you - and will you trust it to do it for you?

Elyse Betters Picaro, a Senior Contributing Editor at zdnet.com, did a shopping test. For Plus and Pro users (only on Mac IOS for now), it includes a powerful Agent mode so that ChatGPT can take over your browser, click around, and perform tasks for you.

She tried having it do an order from Walmart and reported the process and results. You put n a prompt, just as you would for any chatbot request. Unsurprisingly, the more specific the prompt, the better the result. (GIGO still lives!) Initially, she asked, "Order me wood putty, paintable caulk, and 2-inch screws from Walmart." In a way out of some sci-fi story from the past, the Agent took over the cursor and all while she watched.

Her test is enough of a cautionary tale that I have not tried a similar test myself at this point. Privacy fears...

screenshot of order

Image by Elyse Betters Picaro via ZDNET

Walmart's site created a hurdle (a language-selection pop-up) that blocked Atlas' Agent from continuing, even though she had it access to her Chrome data and Keychain. That's frightening, but consider that she had already given the key data to those two other companies. It couldn't log in to Walmart, didn't know her location or default store, even though she has ordered from them before. She revised her prompt (as we have all done) and on the third try she prompted: "Order me 5 wood putty, 5 paintable caulk, and one pack of 2-inch screws. I want them delivered to my house from the Malone, NY, location in an hour. I've ordered these before, so use my past purchases to find the right products and brands I use."

It worked. The agent used her purchase history, searched for the products in past orders, and added them to her cart. It didn't complete the order but paused at the checkout screen so she could select a delivery window, adjust the tip, and confirm payment.

Does this amaze you, excite you, or frighten you? I'm glad that others are testing it. I've hit the pause button.