Parallels Between Prompt Engineering and the Methodology of Speech Competence Formation: A Neuro-Cognitive Approach
Author: Serhii Andrusenko
Publication date: 11.05.25
Why it matters:
Serhii Andrusenko is the creator of ANM™ (Andrusenko Neuro Method) and founder of the NeuroÉlan™ program. He is known for pioneering a neuro-cognitive approach to language acquisition that blends philosophy, artificial intelligence, and structured inner speech. His method reframes language as a deep architecture of consciousness.
In the context of the digital transformation of education, applied neuro-cognitive models are becoming increasingly in demand—models capable not only of knowledge reproduction but also of developing flexible, adaptive competences. Research in cognitive linguistics and neuropsychology (e.g., Tomasello, 2003; Pulvermüller, 2018) confirms that human speech activity is based on a system of neural predictions and is closely connected with the automated processing of patterns in the cerebral cortex.
This model functionally coincides with the operational principles of large language models (LLMs), allowing for the construction of a new methodological framework that unites human cognitive processes with machine learning algorithms.
1️LLM as a Cognitive Model: What Does It Share with the Human Brain?
As is well known, a large language model (LLM) is a predictive system trained on colossal volumes of textual data. Its operational principle lies in the incremental construction of text: the model receives a sequence of tokens and forecasts the next element, appending it to the chain and forming the context for further predictions. This cycle repeats, enabling the dynamic generation of meaningful linguistic output.
In an analogous manner, the human speech system functions as follows:
- Speech is constructed incrementally, integrating new elements based on preceding fragments and the situational context.
- Every new human utterance is conditioned by experience, cognitive memory, and the context of interaction—in other words, by a constant model of prediction and adaptation.
Thus, in the process of communication, the brain acts as a biological LLM, where every element of speech is constructed upon the foundation of preceding elements and accumulated experience.
2️ Prompt Engineering = Patterned Speech Engineering
LLMs represent a hierarchical prediction system trained on billions of text units to generate coherent linguistic sequences step by step. This process unfolds through several stages:
- Identification of the input context,
- Determination of the appropriate structure and length of the prompt,
- Prediction of the next token,
- Adjustment of style and format according to the task,
- Context updating through the incorporation of the predicted element,
- Optimization and testing to achieve maximum accuracy.
We observe the same mechanism in human speech production, where each new unit of speech relies on the preceding context, activating neural circuits of prediction and correction (Friston, 2010).
Thus, human communication constitutes a biological analogue of an LLM, where the “tokens” are morphological and syntactic elements, and learning is realized through the accumulation and automatization of patterns.
Within the context of a speech competence training methodology, the author’s hybrid approach—ANM™ (Andrusenko Neuro Method)—reproduces this very concept at a neuro-cognitive level:
- Students are trained to design their own “prompts”—pre-structured speech templates and formulations that become embedded in their long-term memory.
- The practice is built upon the principle of progressive complexity layering, where each new “input”—from the simplest everyday situations to complex business negotiations—forms a chain of skills in prediction and speech construction.
In essence, the ANM™ methodology and the NeuroÉlan™ training programs constitute prompt neuro-engineering for the brain:
the formation of cognitive trajectories that teach the student to launch the correct speech constructions with minimal effort.
3️ Comparative Table: Cognitive Engineering vs Machine Learning
| Prompt Engineering (AI) | ANM™ Methodology (Student’s Speech) |
|---|---|
| Prompt = structured input text | Speech task = contextual situation |
| Optimization of format and length | Selection of proper structure and intonation |
| Goal: high prediction accuracy | Goal: confident and adaptive communication |
| Repeated testing of prompts | Multi-level practice with increasing complexity |
| Adjustment to model specifics (GPT, etc.) | Individualization for personality and motivation |
4️ Neurocognitive Perspective: From Conscious Control to Automatism
Neuropsychological models of learning (e.g., Levelt, W. J. M., Roelofs, A., & Meyer, A. S. (1999). A theory of lexical access in speech production. Behavioral and Brain Sciences) emphasize the phase transition from conscious speech processing to automated motor function.
This transition fully mirrors the architecture of LLMs: first, the model undergoes a training phase (processing vast datasets), followed by an application mode (generation without human involvement).
In the ANM™ methodology and the NeuroÉlan™ training programs, skill transfer plays a pivotal role: students begin with a focus on conscious control (grammar, phraseology), but gradually shift these actions into an automatic regime. This ensures not merely mechanical fluency, but flexibility of response in real communicative situations.
5️ Practical Perspectives and Innovative Potential
The analogy outlined above establishes a solid foundation for the development of:
- New learning scenarios based on the principle “prompt → reaction → formation of new context”;
- Integration of AI assistants into the learning process as tools for diagnostics and self-training with instant feedback;
- Progress metrics analogous to “prediction accuracy” in LLMs, enabling objective monitoring of speech competence formation.
6️ Chomsky’s Speech Processor and the Rebooting of the Cognitive System in Second Language Acquisition
One of the key theoretical foundations for understanding the speech mechanism is the work of Noam Chomsky—Chomsky, N. (1965) Aspects of the Theory of Syntax. Cambridge, MA: MIT Press—who introduced the concept of a “speech processor”: an innate module capable of processing, structuring, and generating language based on universal grammar. In essence, this is the cognitive core that enables spontaneous mastery of a native language.
However, in second language acquisition, adult learners encounter the problem that their native speech processor continues to dominate, interfering with the processing and reproduction of foreign speech. This leads to phenomena of interference, slows down the formation of automatism, and hinders the attainment of genuine fluency.
The ANM™ methodology addresses this issue through a deliberate reboot of the speech processor:
thanks to a specific system of neural circuit training (“neuroprompts”), the student forms a new “cognitive configuration”, which enables them to:
-
- Reorient the speech motor system away from native language patterns toward foreign language structures;
- Reduce interference and accelerate the integration of grammatical and lexical patterns of the new language;
- Activate prediction processes at the level of the foreign language—something critically important for the development of confident, spontaneous communication.
- This neuro-cognitive reboot allows the foreign language to be perceived not as an external skill, but as a new “built-in system”, which dramatically accelerates the formation and automatization of speech competences.
Conclusion
Understanding the parallels between the architecture of LLMs and human cognitive cycles opens new horizons for neurolinguistic education.
The ANM™ methodology and the NeuroÉlan™ training programs demonstrate the potential of a hybrid model—merging neuroscience and AI into a unified learning system, where the teacher becomes an architect of neuroprompts, and the students acquire not merely knowledge, but robust cognitive tools for confident communication.