Faculty of Computer Science and Engineering

Permanent URI for this communityhttps://repository.ukim.mk/handle/20.500.12188/5

The Faculty of Computer Science and Engineering (FCSE) within UKIM is the largest and most prestigious faculty in the field of computer science and technologies in Macedonia, and among the largest faculties in that field in the region. The FCSE teaching staff consists of 50 professors and 30 associates. These include many “best in field” personnel, such as the most referenced scientists in Macedonia and the most influential professors in the ICT industry in the Republic of Macedonia.

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    Item type:Publication,
    Assessing Personalized Engineering Learning Experience with a Multi-Modal AI Tutoring Framework
    (IEEE, 2025-06-02)
    Fetaji, Bekim
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    Fetaji, Majlinda
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    Fetaji, Fjolla
    This paper presents an innovative and novel multimodal AI tutoring framework in the effort to increase the effectiveness of personalized engineering learning experiences. Filling the gaps in adaptive tutoring systems and considering people's emotional engagement, the framework combines the cognitive load theory, indicators of emotional intelligence, and adaptive learning algorithms to develop an overarching, context-specific instructional landscape. The study makes use of a mixed-methods design, using machine learning driven tutoring interfaces, state of the art learning analytics, and sentiment analysis on a long-term study with 68 engineering students. Incorporating powerful affective computing techniques and intelligent intervention methods, this study presents an encompassing approach that can tune the scaffolding support to learners' abilities and adjust to everchanging competencies of learners while being proactive about detecting the indicators of cognitive overload. The uniqueness aspect of this research lies in its synergistic combination of various data streams (multimodal) - text, visual, and biometric data - combined with dynamic AI-based recommendation model, which maximizes personalized feedback loops. Theoretically, the study expands the insights into how the cognitive load and the emotional dynamics form learning outcomes. Practically, it provides a scalable, flexible approach to the integration of multimodal AI tutors in various educational environments. This work helps to bridge the gap between the cognitive science principles and educational technology solutions and offers new findings regarding designing effective, user-centric intelligent tutoring systems that increase knowledge retention, create motivation, and increase the engineering education outcomes.