Artificial Intelligence for Education, Volume II: Recent AI Innovations and Their Implications for Teaching and Learning

In recent years, Artificial Intelligence has undergone a major transformation with the global emergence and rapid diffusion of Large Language Models (LLMs). This development represents a turning point in the evolution of AI, as advanced capabilities are now accessible to a wide range of users, including those without specialized technical expertise. As a result, AI is becoming an increasingly pervasive tool across multiple domains of human activity.

Education is among the sectors most profoundly affected by these developments. The integration of AI technologies into educational ecosystems is creating new opportunities for teaching, learning, and assessment, while also challenging established pedagogical practices. These transformations raise important questions about how emerging technologies can be meaningfully integrated into educational environments in ways that enhance learning outcomes and support diverse learners.

Unlike earlier research that focused primarily on traditional AI applications in education, this Research Topic specifically examines the transformative implications of generative AI and Large Language Models for digital learning design, pedagogy, and assessment. In particular, it seeks to explore how these technologies are reshaping the ways learning environments are designed, implemented, and evaluated.

One of the key challenges in this evolving landscape is identifying methodological and technological approaches capable of harnessing AI for the benefit of the entire educational ecosystem. This includes addressing organizational, cognitive, emotional, relational, and ethical dimensions that affect key stakeholders such as teachers, students, and families.

The objective of this Research Topic is, therefore, to gather contributions that go beyond simply reporting the use of AI tools in educational contexts. Instead, the focus is on exploring how technological innovation - particularly generative AI and LLMs - can help “open the black box of learning” by supporting teaching practices that are flexible, inclusive, personalized, engaging, and effective.

The emergence of generative AI requires a closer integration between pedagogical methodologies and technological innovation. While technology offers powerful new possibilities for educational design and delivery, its impact depends on how it is embedded within sound pedagogical frameworks. Bridging these two dimensions is essential for shaping the future of digital learning environments.

We welcome contributions providing empirical evidence, theoretical perspectives, design frameworks, and case studies on topics including, but not limited to:

- AI applications in real-world educational settings

- Generative AI in teaching and learning processes

- Human–AI collaboration in educational design

- Intelligent tutoring systems and adaptive learning environments

- AI-enhanced learning design and assessment

- Student profiling and learner modeling, including ethical implications

- Teachers’ competencies, perceptions, and attitudes toward AI

- Students’ interaction with generative AI tools

- Ethical, social, and pedagogical implications of AI in education

By bringing together interdisciplinary perspectives, this Research Topic aims to advance understanding of how AI-driven innovations can support effective, responsible, and inclusive digital learning ecosystems.

Article types and fees

This Research Topic accepts the following article types, unless otherwise specified in the Research Topic description:

• Data Report
• Editorial
• FAIR² Data
• General Commentary
• Hypothesis and Theory
• Methods
• Mini Review
• Opinion
• Original Research
• ... View all formats

Articles that are accepted for publication by our external editors following rigorous peer review incur a publishing fee charged to Authors, institutions, or funders.

Article types

This Research Topic accepts the following article types, unless otherwise specified in the Research Topic description:

• Data Report
• Editorial
• FAIR² Data
• General Commentary
• Hypothesis and Theory
• Methods
• Mini Review
• Opinion
• Original Research
• Perspective
• Policy and Practice Reviews
• Review
• Systematic Review
• Technology and Code

Keywords: Generative AI and Education, AI-Enhanced Teaching, Methodology-driven AI, Adaptive Learning, Large Language Models, Educational Technology, Learning Design, Human-AI Collaboration, Personalized Learning, Intelligent Tutoring Systems

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 guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.