Proceedings of the International scientific and practical conference ―Synergy of Modern Science and Education‖ (February 2-4, 2026) / Publisher website: www.naukainfo.com. – New York, USA, 2026. - 324 p.

71 to schema construction and understanding). The practical goal of learning design is to minimize extraneous load – not by ―simplifying knowledge,‖ but by reducing noise, unnecessary decisions, and non-obvious navigation [3]. In digital products, this logic naturally translates into UX tools: structuring, ―portioning‖ (chunking), hints, sequential steps, reduced visual density. Evidence suggests that minimally guided instruction can be ineffective due to increased extraneous cognitive load, particularly for novices [4]. From this follows the central idea of the paper: if we detect overload cues in time, we can automatically switch the interface into a mode that reduces extraneous load. Here the idea of the concept of moment-aware adaptivity appears. Most adaptive learning systems historically worked along the ―learner profile‖ axis: level, progress, typical errors, recommended pace. This is important, but insufficient. The proposed approach adds a second axis – state in the moment. We define this as follows: moment-aware adaptive e-learning is a system that dynamically changes the presentation of material and the interface based on cues of current load/fatigue/attention loss, with the goal of improving learning quality and supporting self-regulation. It supports the learning process through signals that correlate with overload, and through controlled UX interventions. Such parameters could serve as ―state sensors‖ without specialized sensors. First, ocular (eye-based) cues could play that role. Those include:  Fixation duration, which is widely used in educational research as an indicator of attention and cognitive processing; the metric has complex distributions and requires cautious interpretation, but it is useful as part of a multi-signal approach [5]. Prior work demonstrates that longer fixations generally correlate with higher cognitive load, although this relationship can be modulated by task type and user experience [2];  Pupillometry (pupil dilation). Eye-evoked pupil dilation has a strong basis as a cognitive-load-sensitive indicator (under controlled lighting). Classic