Autism, also known as autism spectrum disorder (ASD), is a neurodevelopmental condition associated with differences in emotional processing and social communication. Electroencephalogram (EEG) analysis presents a unique avenue for exploring its underlying neural evolutionary mechanisms. To this end, this study explored the similarities and differences in emotional processing between children with ASD (ASD group) and those without ASD (control group) using EEG. The final analysis included 45 children: 22 with ASD (mean age = 5.29, age range: 2–8) and 23 without ASD (mean age = 4.37, age range: 2–6). EEG signals were synchronously collected during stimulation with a series of emotional videos. The t-tests on the collected EEG data were performed to determine any statistical differences in power spectral density, sample entropy, and differential entropy values between the groups. A functional connectivity analysis was also performed for a more comprehensive understanding. SHapley Additive exPlanations (SHAP) were applied to validate the findings, ensuring their robustness and reliability. The results showed that the ASD group exhibited reduced beta-band activity in the frontal regions and enhanced delta-band activity in the temporo–occipital areas compared to the control group. Entropy analyses revealed lower brain complexity in the ASD group. Functional connectivity results showed increased high-frequency synchronization in the ASD group but more coordinated low-frequency connectivity patterns in the control group. Moreover, the application of SHAP-based analysis with XGBoost confirmed the significance and predictive value of beta- and delta-band features in the frontal and occipital regions, providing potential biomarkers for distinct emotional processing in individuals with ASD. Overall, this study holds potential to facilitate the understanding of the neuronal mechanisms underlying emotional processing in individuals with ASD and inform the development of targeted neurotherapeutic interventions.

The credit transfer system (CTS) is a complex learning and educational management system involving multiple entities such as learners, schools, and the government. Considering the external constraints, strategic assumptions, and payment assumption, and based on the benefit relationship among stakeholders, this paper attempts to construct a game-theoretic analysis framework of multi-stakeholder participation in the construction of a CTS, which focuses on the learning strategies of learners, the investment strategies of schools, and the management strategies of the government among the three entities considered in this study. Furthermore, the cost allocation and benefit demand of stakeholders to build a three-subject dynamic evolutionary game model have been analyzed. The authors also conducted a numerical simulation to analyze the decision-making mechanism of learners, schools, and the government under the CTS. The findings show that “strong participation” by learners in ability improvement, “active participation” by schools in building high-quality teaching resources, and “strong dominance” in supervision and support by the government together constitute the optimal strategy in constructing a suitable CTS.

The rapid development of artificial intelligence (AI) is accelerating the digital transformation of higher education. Today, “AI + Education” has become a key feature of Education Informatization 2.0 Action Plan in China. This study presents practical experiences in applying AI to programming courses. First, the global trends in AI-powered teaching and learning are analyzed. Key challenges in programming education that can be addressed by AI are then identified. Focusing on common teaching problems, an introductory programming course is used to demonstrate the construction of a course engine powered by large language models. This engine enables the creation of intelligent courses, driving innovation in teaching scenes, and transforming both teaching and learning methods. The exploration then extends to the design of AI-enhanced teaching and learning environments, featuring AI teaching assistants and AI learning companions. These tools provide scalable, differentiated, and personalized support for teachers. They also enable one-on-one, adaptive, and customized learning experiences for students. An integrated learning support system is proposed, which combines courses, training, competitions, testing, evaluation, and certification. The goal is to build a smart teaching ecosystem with knowledge services, personalized learning, and instructional support, as well as to realize the entire teaching process of “course–training–competition–testing–evaluation” empowered by AI for all elements and all time periods. Furthermore, the intelligent & interactive virtual massive open online courses (IMOOCs) for C programming is developed. A new hybrid teaching model based on IMOOC, which integrates virtual and real elements and promotes cross-domain collaboration, has also been explored. Potential risks of overreliance on AI tools are discussed, together with strategies to address them. Finally, future trends and challenges in “AI + Higher Education” are examined. The study argues that AI will unlock new possibilities for reshaping how higher education is delivered and experienced.

Transforming engineering education in the AI era requires an evaluation of new instructional tools and a reconceptualization of the division of labor among teachers, students, and intelligent learning companion systems (ILCSs). This work explores how a retrieval-augmented generation intelligent learning companion can be embedded within a human–AI collaborative teaching model by using an analog circuit laboratory instruction as a case study. A controlled experiment compared traditional teacher-led guidance with system-supported instruction, focusing on three core dimensions: knowledge acquisition, learning effect (cognition, skill, and emotion), and flow experience (cognitive control, immersion and time transformation, loss of self-consciousness, and autotelic experience). The results indicate that while the system showed a limited impact on knowledge acquisition and emotion, it significantly enhanced skills, immersion and time transformation, and autotelic experience. These findings suggest that ILCSs serve as effective complements in practice-oriented engineering education, particularly in terms of providing personalized support and instant feedback strengthening hands-on learning and student engagement. Such companions cannot fully serve as a substitute for teacher-led conceptual scaffolding or emotional guidance. The study’s theoretical contribution lies in emphasizing the importance of role allocation in human–AI collaborative education and offers practical implications for the design of learner-centered, practice-oriented instructional models in intelligent education.

The school digital renewal process (SDRP) has evolved from adoption at the infrastructure level to deep pedagogical transformation centered on personalized, competence-based learning. Traditional indicators, such as device availability or connectivity, lose relevance at advanced SDRP stages. This paper proposes a novel, evidence-based approach to constructing indicators that capture shifts in learning content and organization through an automated analysis of schools’ digital footprints using AI tools, such as publicly available digital resources. Drawing on the Bloom’s revised taxonomy and empirical data from international schools, we demonstrate the feasibility of tracking second-order changes without relying on teacher surveys. The framework supports the comparative monitoring of digital transformation aligned with the demands of the age of AI. The paper introduces a groundbreaking innovation: the use of AI tools for gathering and analyzing indicators from publicly available digital resources in schools. This approach offers a scalable and cost-efficient method of tracking and evaluating SDRP at the later stages of development.

The integration of artificial intelligence (AI) into education marks a critical transition, not only through the adoption of new tools but by challenging the epistemological foundations of teaching and learning. AI reshapes how knowledge is produced, mediated, and evaluated, raising critical questions around equity, agency, and accountability in increasingly data-driven environments. Its emergence compels educators and policymakers to reconsider long-standing assumptions about what counts as learning, how it is measured, and who benefits from technological change. This paper examines how AI is transforming key structures and practices across the broader education landscape, with a particular focus on school education as a strategic and emblematic site for early intervention and pedagogical innovation. While many of the transformations discussed in the paper are relevant across educational levels, schools represent a crucial point where students first experience the cognitive, social, and ethical dimensions of AI, and where systems can act early to promote inclusion, reflection, and readiness. Drawing on major European frameworks, this paper analyzes how AI is reshaping four interdependent pillars of education: curricular content, teaching paradigms, assessment systems, and governance structures. International case-based insights illustrate diverse implementation strategies while also revealing persistent challenges, such as digital inequalities, gaps in teacher preparation, and limited availability of robust mechanisms for algorithmic accountability. Adopting a conceptual, policy-informed approach, this paper synthesizes scholarly literature, European regulatory frameworks, and implementation evidence to propose a systemic view of educational transformation. Rather than framing AI as a mere driver of automation, the paper argues for a transformative approach rooted in equity, human agency, and democratic values. In practical terms, it distills policy-relevant guidance on ethics-by-design, human-in-the-loop safeguards, and capacity building for teachers and school leaders to enable responsible, system-level implementation. The conclusions highlight that, when supported by coherent policy infrastructure and teacher empowerment, school education systems can align technological innovation with inclusive, ethical, and future-oriented learning, ensuring that AI contributes to social justice rather than reinforcing existing inequalities.

The rapid diffusion of AI in education is commonly framed as a pedagogical, ethical, or technological challenge. This paper argues that AI constitutes a fundamentally governance-related issue, as it reshapes how authority, responsibility, and accountability are distributed within education systems. Building on governance theory and critical scholarship on digitalisation, platformisation, and datafication, the paper conceptualises AI as a systemic and transversal actor that operates across boundaries between centralised regulation and decentralised educational practice. The paper develops an analytical framework grounded in reconfigured hybrid governance models and introduces a conceptual distinction between foundational AI infrastructures (AI models), AI content, and AI tutors. Through a structured literature review and conceptual analysis, it demonstrates how existing governance arrangements—designed for earlier phases of digitalisation—are increasingly misaligned with AI-mediated education systems. The analysis highlights four key governance risks, including the homogenisation of learning processes, intensified surveillance, blurred accountability, and the erosion of student and teacher agency. In response, the paper proposes a reconfigured hybrid governance approach that differentiates governance responsibilities across system levels and AI functions. It further advances concrete policy recommendations aimed at operationalising this approach through regulatory oversight, accountability mechanisms, and the protection of educational purpose and professional autonomy. By foregrounding governance as a central analytical and policy concern, the paper contributes to current debates on how education systems can harness the benefits of AI while safeguarding democratic values and human-centred education.

The integration of artificial intelligence (AI) in education is reshaping how learning is designed, delivered, and governed. This study examines five interrelated domains: integrating AI in education, learner development and personalization, curriculum and educational content, role of teachers, and governance and regulation. It argues for systemic, ethically grounded educational approaches that promote equity, lifelong learning, and collaborative policymaking.