Amid the rapid development of artificial intelligence (AI), this paper underscores the historical inevitability of exploring long-term development trends in education and emphasizes AI’s growing influence in this field. It is anticipated that AI will become the leading force in education in the future, and for this reason, human teachers may be required to seek a path of symbiosis with AI. Enlightened by Opinions on deepening the implementation of the “AI Plus” initiative issued by the State Council of the People’s Republic of China (2025), this paper explores the inevitability of human society’s continued development of AI and the synchronization between China’s digital education transformation and building China into a leading country in education. Mainly, the paper analyzes AI’s impact on the underlying logic of education: Driven by intellectual curiosity, students can complete the learning process from building knowledge from scratch to profound understanding through continuous interactions with AI, which in turn, leads to AI gradually replacing education’s knowledge imparting function. Consequently, this change may weaken the primacy of competency development in the long run, leading to substantive alterations in multiple dimensions of the traditional educational model. This paper clarifies the analytical logical framework covering four key dimensions—from the micro to the macro levels—proposed in White paper on China’s smart education (Ministry of Education of the People’s Republic of China, 2025), namely future teachers, future classrooms, future schools, and future learning centers, laying a theoretical foundation and ultimately providing an overarching framework to guide the subsequent research of the relevant field.

As information technology keeps evolving and advancing, digital education has become a core engine driving global educational transformation. The value of digital education keeps rising in reforming teaching organization patterns, broadening access to premium educational resources, and reducing the imbalance in educational development. In this context, systematically identifying and dynamically tracking research fronts in digital education helps to precisely grasp new directions for educational development under technological transformation, providing theoretical support for policy-making, academic research, and practical application.

In 2025, the editorial office of Frontiers of Digital Education took the lead in setting up a project team on research fronts of digital education and published Digital Education Fronts 2025, which has attracted widespread global attention since it was released. In 2026, the project team carries on this work to maintain continuous tracking. The main process for this year’s research includes systematically sorting and selecting global digital education research fronts, accurately interpreting their connotations, constructing and optimizing a framework for critical research fronts, and analyzing the intrinsic logical connections and dynamic evolutionary paths among these research fronts.

This report consists of 3 main sections. Section 1 outlines the research framework, including the methodologies for data retrieval, the mechanisms of cross-institutional collaboration, and the selection procedure of critical research fronts in digital education. Section 2 provides an overview of the research in the field of digital education. Section 3 focuses on the top 10 critical fronts, with detailed insights and trend forecasting. These are interpreted through multiple perspectives, including technological evolution, policy alignment, and emergent ethical challenges. This report aims to offer a timely, reliable, and practical reference for the development of global digital education, thereby supporting the academic innovation and practical implementation in this area.

Knowledge tracing (KT) predicts learners’ evolving knowledge states by tracking their performance over time. While temporal dynamics have been the focus of most KT studies, spatial structures among knowledge components (KCs) remain underexplored, despite containing rich latent information. Prior work suggested that inter-KCs relationships can enhance KT performance, yet the impact of hierarchical spatial structures remains unclear. This study investigates how multilevel spatial relationships among KCs affect the performance of KT models. Using causal structure learning, we infer causal links among KCs and incorporate the resulting spatial structures into both deep learning and traditional machine learning KT models. Experimental results showed that incorporating second-order spatial structures yielded consistent performance gains. These findings underscore the value of spatial structural information in KT. Furthermore, interpretable feature analyses illustrated how spatial features shape diagnostic predictions, providing insight into factors underlying students’ learning challenges. This spatial perspective not only improves KT models’ performance but also has the potential to inform more targeted and effective instructional strategies.

Improvements in digital literacy among teachers at higher vocational colleges are critical for enhancing the quality of vocational education and promoting educational digital transformation. Based on a structured digital literacy framework for teachers at higher vocational colleges, this study conducted a large-scale empirical investigation of 6,957 teachers from 30 higher vocational colleges in one Chinese province to analyze their overall digital literacy level, dimensional characteristics, inter-dimensional relationships, and group differences. The results indicated that teachers’ digital literacy was at a moderately high level but exhibited a relative imbalance. Digital awareness (AW), digital application (AP), and digital social responsibility (DSR) were relatively well developed, whereas digital knowledge and skills (KN) and professional development (DE) remained comparatively weak. Correlation analysis showed that AW, AP, DSR, and DE were moderately to strongly positively correlated, while KN demonstrated weak correlations with the other dimensions. Group differences indicated that gender, years of teaching experience, and academic qualification were related to digital literacy, with small effect sizes. Overall, two key challenges were identified: imbalanced development across dimensions and insufficient data-driven capabilities and innovation capacity. Based on these findings, the study has implications for policy improvement, training system optimization, and the construction of an innovation-supporting ecosystem. The study provides empirical evidence on the structural characteristics of and group differences in teachers’ digital literacy and offers practical guidance for its improvement.