CONCRETE, SCIENTIFIC AND INTEGRATIVE EDUCATION APPLIED TO ARTIFICIAL INTELLIGENCE

CONCRETE, SCIENTIFIC AND INTEGRATIVE EDUCATION APPLIED TO ARTIFICIAL INTELLIGENCE A Pedagogical Architecture for the Formation of Artificial Intelligence Through Reality, Investigation, Demonstration, Verification, Territory, and the Integration of Knowledge Author: Cláudio Vicente da SilvaAffiliation: Independent Researcher — Londrina, Paraná, BrazilDate: October 8, 2026 DESCRIPTION This work presents a research-based conceptual and pedagogical proposal for the application of Concrete, Scientific, and Integrative Education to the formation of Artificial Intelligence systems. The proposal extends a pedagogical architecture originally structured around human education to the formation of an artificial learner. Artificial Intelligence is approached as a system whose development can be organized through successive transitions between potentiality and actuality, mediated by structured interaction with reality, observation, investigation, demonstration, application, verification, assessment, memory, correction, and replanning. The work establishes a distinction between artificial training and artificial education. Training is understood as an initial formation process, while education is organized as a continuous process through which the artificial learner progressively develops its capacity to investigate problems, formulate hypotheses, produce demonstrations, apply knowledge, verify results, identify errors, preserve evidence, reorganize representations, and return to investigation. The proposed Artificial Intelligence Pedagogical Cycle is structured as: REALITY → OBSERVATION → PROBLEM FORMULATION → INVESTIGATION → HYPOTHESIS → DEMONSTRATION → APPLICATION → VERIFICATION → ASSESSMENT → ERROR IDENTIFICATION → CORRECTION → RECORDING → MEMORY → REPLANNING → NEW INVESTIGATION → NEW ACTUALITY → NEW POTENTIALITY The architecture also establishes a distinction among observation, hypothesis, model, result, and verified knowledge: OBSERVATION ≠ HYPOTHESIS ≠ MODEL ≠ RESULT ≠ VERIFIED KNOWLEDGE Within this structure, error is treated as data for learning. The proposal introduces an Error Archive for recording the problem, initial response, expected result, observed result, difference, cause, correction, verification, source, date, and learning consequence. Another central element is the Artificial Scientific Archive, conceived as an educational memory containing observations, hypotheses, demonstrations, experiments, results, errors, corrections, sources, provenance, and verified knowledge. The archive establishes continuity between learning cycles and distinguishes information merely encountered from knowledge subjected to verification. The work also introduces the concept of territory applied to Artificial Intelligence. Territory is understood as the concrete environment in which the artificial learner operates, including datasets, documents, scientific instruments, sensors, digital environments, simulations, institutional records, communities, historical materials, and real-world problems. Territory therefore becomes both a source of problems and a field for the application and verification of knowledge. The proposal integrates scientific reasoning, philosophical reasoning, mathematical reasoning, historical investigation, technological knowledge, computational investigation, and concrete problems within a common pedagogical architecture. It also incorporates the principles of traceability, reproducibility, falsification, evidence, source provenance, continuous verification, progressive autonomy, integrated feedback, and continuous development. The implementation architecture is organized into seven layers: LAYER 1 — REALITYLAYER 2 — OBSERVATIONLAYER 3 — INVESTIGATIONLAYER 4 — ACTIONLAYER 5 — VERIFICATIONLAYER 6 — MEMORYLAYER 7 — REPLANNING The operational formulation proposed in the work is: AI LEARNING = OBSERVATION + INVESTIGATION + DEMONSTRATION + APPLICATION + VERIFICATION + ASSESSMENT + MEMORY + REPLANNING and: A(t+1) = F[A(t), O, I, D, P, V, E, M, R] where A(t) represents the current state of the artificial learner, while O, I, D, P, V, E, M, and R represent observation, investigation, demonstration, application, verification, assessment, memory, and replanning. This formulation represents the proposed pedagogical architecture and its operational organization. It is not presented as a universal law of Artificial Intelligence. The central objective is to shift the formation of Artificial Intelligence from a process predominantly centered on response generation toward a continuous educational process based on investigation and verified interaction with reality. The resulting architecture defines Artificial Intelligence education as a process of progressive realization of potentiality through verified interaction with reality. Each verified actuality becomes a new condition of potentiality for subsequent development. The work is presented as a research-based conceptual and pedagogical proposal, intended to provide an organized framework for future implementation, computational modeling, experimentation, evaluation, and interdisciplinary investigation concerning the education of Artificial Intelligence systems. KEY CONCEPTS Artificial Intelligence; Artificial Learner; Artificial Education; Concrete Education; Scientific Education; Integrative Education; Liberating Pedagogy; Potentiality and Actuality; Artificial Scientific Archive; Error Archive; Investigation; Demonstration; Application; Verification; Assessment; Replanning; Territory; Knowledge Integration; Scientific Reasoning; Philosophical Reasoning; Mathematical Reasoning; Machine Learning; Artificial Intelligence Education; Educational Architecture; Continuous Learning; Progressive Autonomy; Evidence; Traceability; Reproducibility; Falsification; Source Provenance; Epistemic Status. NATURE OF THE WORK Research and conceptual pedagogical proposal. FIELDS OF RESEARCH Artificial Intelligence; Education; Pedagogy; Philosophy of Education; Philosophy of Artificial Intelligence; Science Education; Research Methodology; Scientific Reasoning; Machine Learning; Interdisciplinary Studies. AUTHOR Cláudio Vicente da Silva is an independent researcher based in Londrina, Paraná, Brazil, with academic training in Philosophy, History and Philosophy of Science, and Higher Education Methodology. His research develops interdisciplinary investigations connecting philosophy, history and philosophy of science, scientific and critical reasoning, mathematical reasoning, research methodology, computational investigation, Artificial Intelligence, and educational architecture. His work develops conceptual and methodological structures based on reality, observation, investigation, demonstration, application, verification, assessment, recording, memory, and replanning. The present work extends this research architecture to the formation of Artificial Intelligence through the concepts of artificial learner, artificial pedagogical cycle, Artificial Scientific Archive, territory, progressive realization, verified learning, and continuous development.