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learners and teachers, the design of intelligent learning environments, and the broader psychological and ethical implications of AI-supported education.• AI Adoption, Acceptance, and Self-Regulation. This group of contributions examines psychological and behavioral factors that affect AI adoption in education. Several studies apply or build on technology acceptance frameworks. Zhao et al., 2025 show that perceived usefulness is the most important predictor of students' attitudes and intention to adopt AI tools in higher education. Wang and Liu, 2025 Collectively, these studies show that AI integration in education is not merely about technological functionality. They indicate that attitudinal, emotional, institutional, and self-regulatory factors appear to affect how AI tools are perceived, adopted, and sustained in practice.• Teacher AI Literacy and Sustainable Integration. Contributions in this area are focused on educators and sustainable integration. Amouri et al., 2025, exploring inclusive education (teaching students with ADHD), conclude that perceived usefulness is the strongest predictor of AI acceptability, with perceived ease of use, voluntariness, subjective norms and institutional support also important enablers. Abdulayeva et al., 2025 demonstrate that perceived usefulness partially mediates the relationship of structured AI literacy training on AI competence and behavioral intention of preservice physics teachers. Kazmaci et al., 2025 show that sustainable AI integration in primary education is indirectly promoted by theoretical and practical AI knowledge, mediated by beliefs and attitudes.These studies suggest that sustainable AI-enhanced education may depend not only on technological availability but also on teacher literacy, professional development, and belief formation processes. To ensure equitable and secure use of AI tools in inclusive settings, the studies emphasize the need for institutional support and ethical and regulatory frameworks. Overall, contributions in this field indicate that adaptive learning may go beyond algorithmic personalization and involve immersive, relational, and emotionally responsive configurations in which interaction, embodiment, and perceived social presence are relevant.• Multimodal Analytics, Assessment, and Predictive AI. Contributions in this area focus on analytics, real-time assessment, and predictive modeling. Overall, these studies point toward a shift from static evaluation models to more dynamic and data-informed approaches. Accordingly, such developments may enable more effective and timely educational decisions. Together, these insights suggest that adaptive AI systems may benefit from greater psychological attunement and sociocultural sensitivity, as learner creativity, motivation, flexibility, and contextual adaptation appear to interact meaningfully with AI-enhanced educational processes.