In open-world games, classical tools of dramatic analysis – the three-act structure, Freytag's pyramid, the hero's journey – fail to describe the actual gameplay experience: players gain freedom to choose the traversal order of modular content, making authorial control over pacing impossible. This paper introduces the concept of storytelling literacy – a player competency encompassing recognition of the emotional register of content, conscious pacing construction through activity sequencing, and narrative framing of one's own gameplay trajectory. Based on a formal model of player narrative state – an adaptation of the story-object/phase/facet/threshold-state formalism – we propose an architecture for an IT system supporting player-driven narrative. The system comprises a Narrative State Engine (feedback loop between action telemetry and the content subsystem), trajectory visualization tools for players, narrative coverage analysis tools for designers, and an adaptive content generation module based on large language models.

In the context of the expanding use of adaptive courses in higher education, the problem of early identification of underperforming students and automatic adjustment of their individual educational trajectory becomes increasingly relevant. Existing methods for identifying underperforming students do not provide an in‑depth analysis of the individualized learning approach employed by the student. The generalized adaptive learning algorithms currently in use do not directly address the task of identifying underperforming students, which may lead to their late detection – by which time it is no longer possible to adjust the students’ approach to learning to ensure successful course completion. The identified problem requires an immediate solution.

The aim of the study was to develop algorithms for identifying underperforming students in adaptive learning systems using a composite approach – based on analysing the individualized learning approach and enabling automatic adjustment of the individual educational trajectory to ensure successful course completion.

The developed algorithms detect student underperformance in mastering an academic discipline by predicting the outcomes of individualized learning using a domain model. In addition, algorithms for the automatic adjustment of the individual educational trajectory have been proposed. Unlike existing approaches, these algorithms generate an individualized educational strategy to ensure successful completion of the course. These algorithms can be integrated into existing adaptive learning systems to increase the proportion of students who successfully complete adaptive courses.