The digital transformation of education necessitates the development of innovative AI tools that cater to personalized and adaptive learning. Despite advances like large language models (LLMs) and Retrieval-Augmented Generation (RAG) systems that offer customized educational experiences, there remains a pressing need to address the complex interplay of emotion, motivation, personality, and socio-cultural factors influencing learner pathways. Intelligent tutoring systems (ITS) exemplify the progress in utilizing AI to analyze and respond to individual student performances dynamically, thereby promoting enriched engagement and comprehension.
This Research Topic aims to highlight significant AI-driven advancements and methodologies in the educational sphere, focusing on their practical applications and theoretical underpinnings. An emphasis is placed on systems like ITS and multimodal learning environments, which not only adapt to diverse student needs but also enrich learning through tailored feedback and content adjustments based on real-time analysis, taking into account learners’ individual profiles also in terms of personality, emotions and cognitive abilities. Furthermore, foundational research into AI’s capacity to discern and adapt to individual learning motivations will also be explored to inform future technological enhancements.
To gather further insights in enhancing educational practices through AI, we welcome articles addressing, but not limited to, the following themes:
- Development and effectiveness of AI-driven adaptive learning systems.
- Use of AI in multimodal educational settings incorporating diverse media types.
- Innovations in AI for real-time assessments and feedback in educational contexts.
- Ethical considerations and privacy implications of AI in education.
- AI applications in Special Education and their role in fostering inclusive learning environments.
- Predictive AI tools for identifying at-risk students and improving retention rates.
- Case studies on AI-based interventions that adapt to psychological and emotional student needs.
- Studies and theoretical contributions on psychological factors (e.g., personality, emotions, cognitive abilities) of students and how they should be analyzed in the context of AI for education to promote personalized learning
- Theoretical explorations of AI’s potential impacts on educational outcomes and processes.
This call for papers seeks contributions that bridge theoretical concepts with practical implementations, thus advancing our understanding of how AI can be leveraged to create more inclusive, adaptive, and engaging educational experiences.
The digital transformation of education necessitates the development of innovative AI tools that cater to personalized and adaptive learning. Despite advances like large language models (LLMs) and Retrieval-Augmented Generation (RAG) systems that offer customized educational experiences, there remains a pressing need to address the complex interplay of emotion, motivation, personality, and socio-cultural factors influencing learner pathways. Intelligent tutoring systems (ITS) exemplify the progress in utilizing AI to analyze and respond to individual student performances dynamically, thereby promoting enriched engagement and comprehension.
This Research Topic aims to highlight significant AI-driven advancements and methodologies in the educational sphere, focusing on their practical applications and theoretical underpinnings. An emphasis is placed on systems like ITS and multimodal learning environments, which not only adapt to diverse student needs but also enrich learning through tailored feedback and content adjustments based on real-time analysis, taking into account learners’ individual profiles also in terms of personality, emotions and cognitive abilities. Furthermore, foundational research into AI’s capacity to discern and adapt to individual learning motivations will also be explored to inform future technological enhancements.
To gather further insights in enhancing educational practices through AI, we welcome articles addressing, but not limited to, the following themes:
- Development and effectiveness of AI-driven adaptive learning systems.
- Use of AI in multimodal educational settings incorporating diverse media types.
- Innovations in AI for real-time assessments and feedback in educational contexts.
- Ethical considerations and privacy implications of AI in education.
- AI applications in Special Education and their role in fostering inclusive learning environments.
- Predictive AI tools for identifying at-risk students and improving retention rates.
- Case studies on AI-based interventions that adapt to psychological and emotional student needs.
- Studies and theoretical contributions on psychological factors (e.g., personality, emotions, cognitive abilities) of students and how they should be analyzed in the context of AI for education to promote personalized learning
- Theoretical explorations of AI’s potential impacts on educational outcomes and processes.
This call for papers seeks contributions that bridge theoretical concepts with practical implementations, thus advancing our understanding of how AI can be leveraged to create more inclusive, adaptive, and engaging educational experiences.