Physical and mathematical model of display requirements for ai monitoring of educational content
V. V. Belyaev, Y. N. Obydenkov, A. I. Rulev, V. I. Ermolaev, A. V. Lukyanovich, A. A. Hetagurov, S. A. Kochemirov, A. V. Litvin, N. A. Konovalova
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Source: Crossref
Published: Oct 14, 2026
DOI: 10.18384/2949-5067-2026-2-6-22
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Aim . The work aims to develop a physico-mathematical model that links the physical and technical characteristics of a display – namely spatial resolution, refresh rate, color gamut, and image output latency, taking into account material parameters – with the productivity of a teacher who checks digital educational materials using AI tools for compliance with the requirements of the Federal State Educational Standards (FSES). Methodology . The starting point was the well-studied limitations of the human visual system: the angular resolution of the eye, the critical flicker fusion frequency, and thresholds for color discrimination. Based on this data, the authors determined which display parameter values should be considered minimally acceptable and which should be considered desirable. Next, an efficiency objective function W disp was introduced, comprising three components: time spent, number of errors made, and subjectively perceived workload (according to the NASA TLX methodology). In addition, a review was carried out of publications on how pixel density, frame refresh rate, and color accuracy affect the user's cognitive load. Results . Based on physiological limitations of vision, the following set of display parameters is justified for AI monitoring tasks: pixel density of about 300 PPI, refresh rate of 120 Hz, sRGB color space coverage of at least 99%, and response time not exceeding 8 ms. The form of the efficiency objective function is formulated. Calculations show that when working on a display with the above characteristics, the value of W disp is approximately 30–35% higher than when using a standard office monitor. Research implications. The proposed model makes it possible to translate display equipment requirements into quantitative indicators, which is especially relevant for educational institutions where AI monitoring systems are already being implemented or are planned for implementation. The findings can be applied in two directions: first, in developing standards for equipping schools with technical means, and second, in compiling methodological materials and recommendations addressed directly to practicing teachers. It is shown what physical properties of the material affect the parameters of the information display device.
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