The rapid digital transformation of higher education and the active integration of cloud-based artificial intelligence technologies into the educational process necessitate a fundamental rethinking of traditional models of knowledge transmission in favour of flexible adaptive support systems. In conditions where the development of learner autonomy and self-regulation has become a strategic goal of professional training, the methodology of digital scaffolding acquires particular pedagogical significance, determining not only the effectiveness of learning outcomes but also the quality of the cognitive independence formed in higher education students. A future specialist occupies a specific place in the system of professional development: he or she not only masters subject-matter competencies, but also develops the capacity for independent cognitive activity, critical thinking and metacognitive self-regulation. The quality of higher education training is therefore directly determined by the methodological design of the pedagogical support provided throughout the learning process. The purpose of the article is to provide a theoretical substantiation and to develop a methodology for implementing the digital scaffolding strategy in the educational process of higher education institutions through the application of cloud-based artificial intelligence services, with the aim of fostering learners' capacity for self-regulated academic activity. A definitional analysis of the concept of "digital scaffolding" requires, first and foremost, clarification of its constitutive elements. The concept of "scaffolding" in the psychological and pedagogical literature is interpreted on the basis of L. Vygotsky's theory of the zone of proximal development as a process that enables the learner to solve a problem, complete a task or achieve a goal that lies beyond the boundaries of their independent efforts. In the context of digital educational environments, this concept acquires new characteristics associated with the use of artificial intelligence technologies and automated didactic assistance tools. On the basis of scientific literature analysis, the essence of the concept of "digital scaffolding" is clarified as a flexible cognitive structure of learning support that functions through the continuous adjustment of assistance in accordance with the current state of the learner, with the distinguishing feature that management of support is realised not through the subjective perception of the educator but through prognostic analysis of behavioural data. A multidimensional classification of scaffolding technologies is proposed according to three interrelated criteria. According to the level of support detail, macro-level pedagogical support is distinguished, which involves the organisation of the general structure of learning activity and determines the pedagogical strategies that guide the resolution of complex educational tasks — realised through constructivist methods such as collaborative learning, project-based learning and problem-oriented learning – and micro-level pedagogical support, directed at assistance during the performance of specific learning actions within individual tasks or activities, realised through brief prompts, recommendations and individual consultations. According to the source of provision, socially conditioned support is identified, carried out by participants in the educational process — educators or more advanced students — and technologically conditioned support, ensured by digital educational environments and software tools, including learning simulators, interactive platforms and learning management systems. According to the mode of technological implementation, tool-embedded scaffolding is distinguished, which integrates assistance mechanisms directly into a specialised software tool, and tool-enveloped scaffolding, realised through universal digital educational platforms that structure the learning process as a whole and ensure the integration of various auxiliary instruments. The structural components of the digital scaffolding methodology are characterised on the basis of the analysis conducted. The diagnostic component provides for the determination of the learner's initial level of preparedness and the characteristics of their cognitive activity on the basis of analysis of task performance results. This component constitutes the foundation for the subsequent individualisation of support. The adaptive component ensures dynamic regulation of the volume of assistance during task performance: the system may expand or progressively reduce prompts depending on the learner's level of independence. The criterion for transition between support levels is constituted by quantitatively defined indicators of learner autonomy — in particular, the successful completion of a specified number of learning actions without recourse to auxiliary prompts, which serves as a signal for automatic increase in task complexity with a corresponding reduction in external support. The reflective component provides for the joint analysis of learning activity results by the learner and the educator on the basis of data generated by the artificial intelligence system, fostering the development of self-assessment skills and metacognitive awareness. The analytical component involves the consolidation of learners' cognitive activity results on the basis of autonomy data accumulated throughout all stages of support, and the evaluation of the effectiveness of the applied digital instruments. A three-level algorithm for the gradual reduction of pedagogical assistance is substantiated. The first level presupposes a maximum volume of assistance, when the system provides detailed step-by-step instructions and examples of task performance, ensuring the formation of basic understanding of problem-solving approaches. The second level is characterised by a transition to conceptual support: the system offers only orientating references to relevant rules, principles or activity strategies, stimulating independent search for solutions. The third level provides for metacognitive stimulation, whereby the system merely identifies errors or contradictions in task performance and proposes reflective questions that encourage the learner to independently analyse their own actions and decisions. The criterion for transition between levels is constituted by a stable reduction in decision-making time during task performance and a decrease in the frequency of recourse to auxiliary materials. It is substantiated that the formation of learners' capacity for self-regulated cognitive activity constitutes an independent goal of the pedagogical process in higher education institutions and requires an integral approach encompassing the content-related, procedural and environmental dimensions of the learning process. The content-related dimension presupposes the axiological enrichment of academic disciplines with tasks oriented towards independent analysis and reflection. The procedural dimension involves the application of adaptive methods and situational learning that place students in conditions of genuine cognitive choice. The environmental dimension encompasses the quality of the digital educational environment, the methodological competence of the educator, and the systemic design of artificial intelligence tools in accordance with pedagogical rather than purely technological logic. It is established that the effectiveness of digital scaffolding is determined primarily by the pedagogically substantiated design of transition conditions between support levels: a dependency is identified between the reduction of external assistance and the increasing frequency of independent analytical actions and reflective engagement with learning material. The technological capabilities of AI services are regarded as a necessary yet insufficient condition for the effectiveness of adaptive interaction — the methodological logic of scaffolding strategy design must precede the selection of digital tools, rather than be determined by them.