AI innovations in education: adaptive learning and beyond

The digital transformation of education requires the development of innovative AI tools that respond to personalized and adaptive learning. Despite developments such as large language models (LLMs) and Retrieval-Augmented Generation (RAG) systems that provide tailored educational experiences, there remains a pressing need to address the complex interplay of emotions, motivation, personality and socio-cultural factors that influence the learning processes. Intelligent Tutoring Systems (ITS) illustrate advances in using AI to dynamically analyze and respond to individual student performance, promoting enriched engagement and understanding.

This research topic aims to highlight key AI-driven developments and methodologies in the field of education, focusing on their practical applications and theoretical underpinnings. Emphasis is placed on systems such as ITS and multimodal learning environments, which not only adapt to the diverse needs of learners, but also enrich the learning process through tailor-made feedback and content adjustments based on real-time analysis, taking into account the individual profiles of students, also in terms of personality. , emotions and cognitive skills. Additionally, basic research into AI’s ability to discern and adapt to individual learning motivations will also be explored to inform future technological improvements.

To gather further insights into improving educational practices through AI, we welcome articles that cover, but are not limited to, the following topics:

● Development and effectiveness of AI-driven adaptive learning systems.
● Use of AI in multimodal educational environments that integrate various media types.
● Innovations in AI for real-time assessment and feedback in educational contexts.
● Ethical considerations and privacy implications of AI in education.
● AI applications in special education and their role in promoting inclusive learning environments.
● Predictive AI tools for identifying at-risk students and improving retention rates.
● Case studies on AI-based interventions that adapt to students’ psychological and emotional needs.
● Studies and theoretical contributions on psychological factors (e.g. personality, emotions, cognitive skills) of learners and how to analyze them in the context of AI for education to promote personalized learning
● Theoretical explorations of the potential impact of AI on educational outcomes and processes.

This call for papers seeks contributions that bridge theoretical concepts and practical implementations, expanding our understanding of how AI can be used to create more inclusive, adaptive and engaging educational experiences.

Keywords: AI in education, personalized learning, adaptive learning systems, multimodal AI, intelligent guidance systems, human-AI collaboration

Important note: All contributions to this research topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to divert an out-of-scope manuscript to a more appropriate section or journal at any stage of peer review.

The digital transformation of education requires the development of innovative AI tools that respond to personalized and adaptive learning. Despite developments such as large language models (LLMs) and Retrieval-Augmented Generation (RAG) systems that provide tailored educational experiences, there remains a pressing need to address the complex interplay of emotions, motivation, personality and socio-cultural factors that influence the learning processes. Intelligent Tutoring Systems (ITS) illustrate advances in using AI to dynamically analyze and respond to individual student performance, promoting enriched engagement and understanding.

This research topic aims to highlight key AI-driven developments and methodologies in the field of education, focusing on their practical applications and theoretical underpinnings. Emphasis is placed on systems such as ITS and multimodal learning environments, which not only adapt to the diverse needs of learners, but also enrich the learning process through tailor-made feedback and content adjustments based on real-time analysis, taking into account the individual profiles of students, also in terms of personality. , emotions and cognitive skills. Additionally, basic research into AI’s ability to discern and adapt to individual learning motivations will also be explored to inform future technological improvements.

To gather further insights into improving educational practices through AI, we welcome articles that cover, but are not limited to, the following topics:

● Development and effectiveness of AI-driven adaptive learning systems.
● Use of AI in multimodal educational environments that integrate various media types.
● Innovations in AI for real-time assessment and feedback in educational contexts.
● Ethical considerations and privacy implications of AI in education.
● AI applications in special education and their role in promoting inclusive learning environments.
● Predictive AI tools for identifying at-risk students and improving retention rates.
● Case studies on AI-based interventions that adapt to students’ psychological and emotional needs.
● Studies and theoretical contributions on psychological factors (e.g. personality, emotions, cognitive skills) of learners and how to analyze them in the context of AI for education to promote personalized learning
● Theoretical explorations of the potential impact of AI on educational outcomes and processes.

This call for papers seeks contributions that bridge theoretical concepts and practical implementations, expanding our understanding of how AI can be used to create more inclusive, adaptive and engaging educational experiences.

Keywords: AI in education, personalized learning, adaptive learning systems, multimodal AI, intelligent guidance systems, human-AI collaboration

Important note: All contributions to this research topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to divert an out-of-scope manuscript to a more appropriate section or journal at any stage of peer review.