A NEURO-DIGITAL LEARNING MODEL FOR AI DRIVEN DIGITAL TRANSFORMATION IN HIGHER EDUCATION

Abstract

The rapid development of artificial intelligence (AI) is transforming higher education from a traditional mode of information distribution to an intelligent, adaptive, and self-renewing system. In this paper, we analyze the evolution of paradigms of education in the so-called “presingularity era” when AI has attained a high level of capability and when final decisions are still made by humans. The goal is to construct a digital learning model for higher education using the theoretical bases of neuroeducation, brain-based learning and AI-based cognition. The methodology of the work is based on a methodical analysis of scientific publications, international strategic documents and domestic experience, integrated with a qualitative approach and grounded Theory. The analysis considers the evolution of educational models from “Education 1.0” to “Education 6.0”, emphasizing a transition from a teacher-centered system to an anthropo-digital synergy where the learner is a subject of coevolution and the teacher acts as a cognitive partner in collaboration with AI. This study also provides insights into the relationships among neuroeducation, brain-based learning and AI-based recognition. Cognitive load theory is used to explain how AI reduces the intrinsic load, limits the extraneous load and increases the germane load. The risks of AI cognition, such as weakening cognitive activity, fragmented attention and AI dependence, are also identified. The suggested paradigm combines neuroplasticity, brain-based learning, cognitive load optimization and AI recognition into a single model in which AI supports human thinking but does not replace it. The results have theoretical and practical consequences for educational science and the digital transformation of higher and postgraduate education in Kazakhstan (Smart.EDU).