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This article focuses on developing an expert system applicable to the area of neurocognitive rehabilitation. The benefit of this interdisciplinary research is to propose an expert system that has been adapted based on real patients’ results from the Addenbrooke’s cognitive examination (ACE-R). One of this research’s main results is a unique proposal to transfer the ACE-R result to the CHC (Cattell–Horn–Carroll) intelligence model. This unique approach enables transforming the CHC model domains according to the modified ACE-R factor analysis, which has never been used before. The expert system inference results allow the automated optimized design of a neurorehabilitation plan to train patients’ cognitive functions according to the CHC model. A set of tasks in 6 difficulty levels (Level 1–Level 6) was proposed for each of the nine CHC model domains. For each patient, the ACE-R results helped determine specific CHC domains to be rehabilitated as well as the starting game level for the rehabilitation within each domain. The proposed expert system has been verified on real data of 705 patients and achieved an average error of 5.94% for all CHC model domains. The proposed system is to be included in the outcomes of the research project of the Technology Agency of the Czech Republic as a verified procedure for healthcare providers.

The article is devoted to the issue of the construction of an intelligent tutoring system which was created by our university for implementing distance learning and combined forms of studies. Significantly higher demand for such tools occurred during the COVID-19 pandemic when distance learning was used by students in their full-time studies. Current Learning Management Systems (LMS) do not address students' individuality regarding their various levels of input knowledge and skills or their different learning styles, which, in our case, are based on sensory preferences. Therefore, this article proposes a model of an intelligent tutoring system to control learning by accentuating the individual needs of a student. The foundation stones of this system are an expert system and adaptation mechanisms. The expert system acts as a tool for the identification of students’ needs from the point of view of input knowledge and sensory preferences. Sensory preferences influence the student’s learning style. The implemented adaptation mechanisms control the progress of the student through a study unit. The model was implemented in the LMS Moodle environment. Regarding the focus of the research content, our model is oriented on the study of the English language, where each student receives a unique study plan, which is continuously adapted based on achieved results. We consider the focus on the individuality of the student to be an innovative approach that can be achieved automatically on a mass scale.

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