Assessing Personalized Engineering Learning Experience with a Multi-Modal AI Tutoring Framework

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.