Developing a theoretical framework for AI interventions in education: cognitive offloading, cognitive debt, and learner agency
DOI: 10.23951/1609-624X-2026-5-46-55
This article addresses the problem of developing an integrative theoretical framework for the analysis and design of artificial intelligence (AI) interventions in educational environments. The aim of the study is to develop an integrative theoretical framework that enables the description and design of pedagogically meaningful AI interventions in education. Based on a systematic secondary analysis of literature reviews published between 2019 and 2026 on AI in education, cognitive offloading, learner agency, and AI ethics, as well as a conceptual comparison of existing theoretical frameworks, the article examines the “cognitive paradox” of AI interventions: technologies that reduce working memory load can simultaneously lead to cognitive offloading and the accumulation of “cognitive debt” – a hidden gap between demonstrated academic performance and the learner’s actual cognitive infrastructure. A two-axis “cognitive offloading – agency” model is proposed, distinguishing four typical configurations of learner – AI agent interaction. Among these configurations, the one termed “extended cognition” is identified as the most promising for the development of self-regulation. Furthermore, the article demonstrates how the SAMR (Substitution, Augmentation, Modification, Redefinition), TPACK (Technological Pedagogical Content Knowledge), JTBD (Jobs to Be Done), and SCOT (Social Construction of Technology) frameworks can be refined by incorporating the dimensions of cognitive offloading and agency. This refinement transforms them from descriptive taxonomies into pedagogical design tools that account for the risks of “cognitive erosion”. By integrating cognitive considerations with established technology integration models, the proposed framework offers a more nuanced understanding of how AI agents reshape learning processes. Finally, the article identifies research gaps and outlines prospects for empirical testing of the proposed framework in both Russian and international educational contexts, emphasizing the need for longitudinal and mixed-method studies that track the long-term effects of AI use on learner autonomy and cognitive skills.
Keywords: artificial intelligence in education, chatbots, cognitive load theory, self-regulated learning, Substitution – Augmentation – Modification – Redefinition (SAMR), Technological Pedagogical Content Knowledge, Social Construction of Technology
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Issue: 5, 2026
Series of issue: Issue 5
Rubric: GENERAL PEDAGOGY, HISTORY OF PEDAGOGY AND EDUCATION
Pages: 46 — 55
Downloads: 7