Early overseas studies mainly focused on teachers’ ICT literacy, which refers to the capacity to integrate information and communication technologies into classroom teaching and highlights basic tool operation. A representative achievement is the TPACK framework proposed by [6]. As digital technologies penetrated every segment of education, the European Commission Joint Research Centre released the European Framework for the Digital Competence of Educators (DigCompEdu) in 2017. It divides educators’ digital competence into six domains and 22 specific capabilities: professional engagement, digital resources, teaching and learning, assessment, empowering learners and supporting learner development. Among these domains, it particularly emphasizes educators’ ability to apply technologies critically, creatively and responsibly to improve teaching practice ([7]). In recent years, with the rise of artificial intelligence, scholars have proposed incorporating AI literacy as an indispensable dimension into digital competence frameworks. For example, [1] constructed a three-dimensional framework for teachers’ digital competence under AI, covering cognition, technology and ethics. It indicates that foreign research on teacher competence is expanding from universal digital capabilities to new competency requirements brought by artificial intelligence.

In domestic research on teachers’ digital competence, frequently-occurring keywords include information literacy, information-based teaching ability, educational informatization, primary and secondary school teachers, information technology, teachers’ professional development, pre-service teachers/student teachers, promotion strategies, and TPACK. It can thus be found that digital competence has not yet become a mainstream term in Chinese academic circles. Most researchers focus on the development of capabilities at the micro level, such as the information-based teaching ability of in-service and pre-service teachers. In the initial stage, relevant domestic research was mainly driven by policies and centered on “primary and secondary school teachers’ information technology application ability”. Conceptually, research on teachers’ digital ability used to pay more attention to teachers’ technical skills in specific professional practices, with insufficient attention to teachers’ broader identity as digital citizens. The concept of “teachers’ digital competence” was first put forward by Ren Youqun and Yang Xiaozhe in 2017. They argued that teachers should possess corresponding capabilities to foster students’ digital competence and regarded digital competence as one of students’ core literacies ([8]). With the advancement of the Digital China Strategy, the concepts of “digital literacy” and “digital competence” have attracted growing academic attention. While drawing on international frameworks, Chinese scholars are actively exploring localized theoretical construction. For instance, Ren Youqun and Li Feng pointed out that “focusing on digital competence and enhancing citizens’ digital literacy” is an arduous mission of information technology education in primary and secondary schools in Shanghai. They embedded digital competence within digital literacy and clarified that it refers to comprehensive capabilities required for specific occupational roles ([4]). [11] established a teacher digital literacy model covering digital learning and innovation, digital instructional design and implementation, and digital social responsibility. Targeting higher education amid AI development, [13] systematically analyzed role transformation and competency demands for teachers in intelligent education at an early stage, pointing out that teachers ought to transform from knowledge transmitters to learning designers, data analysts and emotional tutors.

Overall, there is no unified consensus on the concept of teachers’ digital competence in domestic scholarship. However, the operational definitions formulated by Chinese scholars, which mirror the professional practice boundaries of specific teacher groups, provide references for investigating digital competence across different types of teachers.

Early studies on competency dimensions mostly centered on integrated theoretical frameworks. [6] put forward the TPACK (Technological Pedagogical Content Knowledge) framework, which holds that teachers’ digital competence lies in the interaction among technology, pedagogy and subject knowledge. The DigCompEdu framework released by the European Union defines six dimensions of educators’ digital competence: professional engagement, digital resources, teaching and learning, assessment, empowering learners and facilitating learner growth, stressing critical, creative and responsible technology application throughout the whole educational process ([7]). Entering the AI era, relevant dimensional research has made further progress, requiring data literacy and AI literacy to be added to traditional competency frameworks. [3] regarded data literacy as a core skill that enables teachers to collect, analyze and utilize data to optimize teaching decisions. [1] divided teachers’ AI literacy into cognitive, technological and ethical dimensions. Domestic scholars share the same viewpoint, arguing that data literacy and AI literacy are essential components of teacher competence. For instance, [13] stated that teachers working with AI need to possess multiple identities including learning designers, data analysts and emotional guides.

In summary, teachers’ digital competence in the AI era is built on traditional competency systems and supplemented with data literacy and AI literacy. Its core dimensions include cognition and operation of intelligent technologies, instructional design in smart environments, digital professional collaboration and digital leadership.

Three mainstream assessment methods are summarized in existing research: standardized self-rating scales, performance-based assessment and mixed-method evaluation.

3.2.1. Standardized Self-Rating Scales

Standardized self-rating scales are the most widely adopted assessment tool. The SELFIE for TEACHERS instrument developed based on DigCompEdu can conduct large-scale measurements of teachers’ digital competence and greatly improve assessment efficiency. Learning from foreign assessment tools, Chinese scholars have developed localized measurement systems. A typical representative is the evaluation index system for university teachers’ teaching ability in the AI age constructed by [5], which is a classic domestic standardized self-assessment scale.

3.2.2. Performance-Based Assessment

The development of digital technology makes performance-based assessment feasible through analyzing massive digital traces. In the field of learning analytics, objective teaching data from learning management systems and forum interactions can accurately reflect teacher-student interaction patterns and students’ classroom engagement, forming a feasible paradigm for intelligent teaching effect evaluation. On this basis, Trust et al. integrated questionnaires, in-depth interviews, classroom observations and other qualitative approaches, and carried out empirical research via social network analysis. This mixed performance assessment approach enables more comprehensive and authentic measurement of teachers’ digital teaching behaviors and improves the validity of relevant evaluations ([10]).

3.2.3. Mixed-Method Evaluation

Mixed evaluation combines the strengths of quantitative and qualitative research. By integrating questionnaires, in-depth interviews and classroom observations, it avoids the one-sidedness of single assessment methods and realizes comprehensive, context-oriented evaluation of teachers’ digital competence ([10]).

Current academia has reached a basic consensus on competency dimensions and responded positively to new demands brought by artificial intelligence, while assessment methods have evolved from single to diversified forms. Nevertheless, several limitations remain. First, there is an obvious contradiction between universal competency frameworks and specific educational contexts; insufficient research explores differentiated competency structures and assessment standards for teachers from different school types and disciplines. Second, most assessment tools adopt static snapshot evaluation without longitudinal tracking of teachers’ competency growth trajectories. Future research can be advanced in two directions: first, enrich context-specific and differentiated research on competency dimensions, especially for special educational fields such as applied universities; second, realize intelligent upgrading of assessment tools and methods, and explore multi-modal data analysis driven by artificial intelligence to deliver more accurate and efficient performance evaluation ([12]).

A review of domestic and foreign literature reveals four key consensus points in research on teachers’ digital competence under artificial intelligence. First, consensus on connotation: Teachers’ digital competence has evolved from basic tool operation skills into high-order composite capabilities covering intelligent technology application, data-driven instruction, digital instructional design, AI ethics governance and sustainable professional development ([13]; [1]).Second, consensus on dimensional structure: Scholars universally recognize the multi-layered composite nature of teachers’ digital competence, whose core dimensions include intelligent technology operation, data literacy, integrated intelligent instructional design, digital ethics and security, and digital professional collaboration ([7]).

Third, consensus on assessment: Research abandons overreliance on subjective self-evaluation and forms mixed evaluation modes combining scale measurement, digital trace analysis, interviews and classroom observation, highlighting contextual authenticity of assessment ([10]). Fourth, consensus on developmental pathways: Short-term fragmented training generates limited effects on competency growth. Sustainable improvement of teachers’ digital competence relies on multi-stakeholder ecological training systems supported by school-based research, communities of practice and institutional digital leadership ([9]; [2]).

Despite fruitful research outcomes, prominent deficiencies still exist. Firstly, localized and differentiated research is inadequate. Mainstream competency frameworks are adapted from Western models including DigCompEdu and TPACK; most domestic studies merely interpret foreign theories without developing localized frameworks compatible with China’s educational system, diverse school types and disciplinary characteristics. Secondly, assessment systems have inherent flaws. Most evaluations adopt static cross-sectional measurement without longitudinal dynamic tracking of teachers’ competency growth. Specialized indicators for emerging competencies such as AI ethics and human-machine collaborative teaching remain incomplete, reducing evaluation accuracy ([5]). Thirdly, practical strategies lack operability. Most existing improvement plans are macro conceptual proposals without actionable implementation schemes and collaborative operating mechanisms. Fourth, frontier research lags behind technological development. Discussions on teaching transformation and new competency requirements brought by generative AI and educational large language models are insufficient, while in-depth research on ethical governance and mindset innovation is relatively scarce.

Based on the comprehensive review of connotation, constituent dimensions, assessment and improvement pathways of teachers’ digital competence in the AI era, future research can be carried out in the following directions. First, construct AI-oriented teacher digital competence models tailored to China’s digital education strategy, considering developmental characteristics of teachers across different educational stages, school types and disciplines. Second, optimize assessment indicators for teachers’ digital competence under artificial intelligence to improve the reliability, validity and efficiency of evaluation tools. Third, build a coordinated multi-level training system covering governments, schools and individual teachers. Fourth, keep pace with cutting-edge AI development to conduct in-depth research on human-machine collaborative teaching and digital ethical governance, so as to promote theoretical and practical upgrading of teachers’ digital competence.