Artificial Intelligence Across Educational Contexts: Adoption, Experiences, and Responsible Practice

Artificial intelligence (AI) is transforming teaching, learning, assessment, student support, and institutional decision-making. Recent advances in generative AI, intelligent tutoring systems, automated feedback, adaptive learning, and learning analytics have created opportunities to personalise learning, improve accessibility, support educators, and enhance institutional efficiency. However, the rapid adoption of these technologies has also intensified concerns about bias, transparency, privacy, surveillance, academic integrity, unequal access, and the preservation of human agency. Evidence regarding the educational value of AI remains uneven, particularly across educational levels, disciplines, cultures, and stakeholder groups. Although emerging studies highlight the potential of AI-supported learning and assessment, ongoing debates concern reliability, explainability, teacher and learner agency, professional identity, and the appropriate balance between automation and human judgement. Shared evidence, policy frameworks, and pedagogical models for responsible implementation remain underdeveloped. Interdisciplinary research is therefore needed to examine not only what AI can do, but also how it is adopted, experienced, governed, and evaluated in diverse educational settings.



This Research Topic aims to advance an evidence-informed understanding of the adoption, use, governance, and impact of AI across educational contexts. It seeks to identify effective pedagogical and organisational practices, examine emerging risks and inequalities, and clarify the conditions required for ethical, transparent, inclusive, and sustainable implementation. Contributions may investigate how AI influences learning outcomes, assessment practices, educator workload, professional roles, learner autonomy, and institutional decision-making. The Research Topic also aims to explore how AI literacy can be developed among educators, students, leaders, and policymakers, and how human–AI collaboration can be designed to preserve professional judgement, meaningful educational relationships, and trust.



To gather further insights across schools, higher education, vocational education, teacher education, workplace learning, and lifelong learning, we welcome articles addressing, but not limited to, the following themes:



Generative AI and large language models in education

Intelligent tutoring and adaptive learning systems

Automated assessment, feedback, and grading

Learning analytics and AI-supported decision-making

AI literacy and digital competence

Teacher and student perceptions, attitudes, and experiences

Teacher agency, professional identity, and changing roles

Learner autonomy, engagement, and learning outcomes

Academic integrity and assessment redesign

Equity, inclusion, accessibility, and educational inequality

Bias, fairness, transparency, explainability, and trust

Privacy, data protection, surveillance, and cybersecurity

Institutional readiness, leadership, and organisational change

Policy development, regulation, and AI governance

Human–AI collaboration and the preservation of professional judgement

Resistance, unintended consequences, and implementation challenges

Cross-cultural, cross-disciplinary, and comparative perspectives

Participatory, design-based, and responsible innovation approaches

We invite original research articles, systematic and scoping reviews, conceptual and theoretical papers, methodological studies, case studies, policy analyses, perspectives, and brief research reports using qualitative, quantitative, mixed-methods, comparative, design-based, or interdisciplinary approaches.

Article types and fees

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

• Brief Research Report
• Conceptual Analysis
• Data Report
• Editorial
• FAIR² Data
• General Commentary
• Hypothesis and Theory
• Methods
• Mini Review
• ... 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:

• Brief Research Report
• Conceptual Analysis
• 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: Artificial intelligence, Education, Adoption, Knowledge perception

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.