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1 Introduction

At present, the new generation of information technology represented by artificial intelligence is changing the educational ecology in an unprecedented depth and breadth and promoting all-round changes in teaching concepts, teaching modes, teaching methods and teaching evaluation. The core of the digital transformation of education is the transformation of human beings. Teachers are the main body of educational reform. Teachers’ digital literacy, especially the ability to control artificial intelligence technology to optimize and innovate teaching, directly determines the effectiveness and boundaries of technology-enabled education (Wang & Li, 2026). The report to the 20th National Congress of the Communist Party of China in 2022 clearly proposed to promote the digitalization of education, and the Plan for Overall Layout of the Construction of Digital China Development in 2023 proposed to vigorously implement the strategic action of national education digitalization. In this context, the Ministry of Education and eight other departments jointly issued the Plan for Strengthening Teachers in Basic Education in the New Era, which calls for promoting the informationization of teachers’ team construction (Huang, 2026). In response to the national strategic action on digitally empowered teacher development launched by the Ministry of Education in 2025, strengthening teachers’ digital literacy has become a core priority in educational development.

However, the challenges in reality are still serious. The connotation of teachers’ digital literacy has also expanded rapidly from the traditional information technology application ability to the understanding, evaluation, ethical review and innovative integrated application of artificial intelligence technology (Zou & Gong, 2026). On the other hand, the traditional evaluation of teachers’ digital literacy mostly relies on questionnaires with strong subjectivity, and there are some problems, such as “low reliability, low effectiveness and lack of dynamics” (Wu & Liu, 2026). At the same time, the systematic evaluation tools and training system of AI teaching application ability are not perfect, the training content is separated from the actual needs, and the application transformation is not sufficient. Therefore, how to scientifically evaluate teachers’ artificial intelligence teaching application ability and how to create an effective training mode have become the core issues to be solved in educational theory and practice.

This paper attempts to respond to the needs of the times and systematically discuss the evaluation model and training mode of teachers’ digital literacy and artificial intelligence teaching application ability. Through theoretical analysis, model construction and path design, it is expected to provide a set of operable theoretical framework and practical guidelines for regions and schools to carry out precise teacher ability diagnosis and personalized training, to help teachers cross the ability gap from “using technology” to “making good use of technology” to “innovative application”, and to inject core momentum into the high-quality development of education.

2 Theoretical Basis of Teachers’ Digital Literacy and Artificial Intelligence Teaching Application Ability

2.1 Definition of Core Concepts

2.1.1 Connotation of Teachers’ Digital Literacy

Teachers’ digital literacy refers to the consciousness, ability and responsibility of teachers when they use digital technology appropriately to acquire, process, use, manage and evaluate digital information and resources and discover, analyze and solve educational and teaching problems in educational and teaching activities. In terms of connotation definition, in 2010, the British Future Laboratory released the research report “Digital Literacy in the Curriculum”, pointing out that teachers’ digital literacy consists of digital technology application skills, the ability to find and select information, creativity, critical thinking, collaboration, communication, security and other core elements (Hague & Payton, 2010). The industry standard of Teachers’ Digital Literacy issued by the Ministry of Education in 2022 defines it as digital awareness, digital technology knowledge and skills, digital application, digital social responsibility and professional development. Teachers’ digital literacy is not skilled in technical operation, but comprehensive literacy, such as critical thinking, ethical safety and continuous learning, which lays a foundation for teachers to carry out effective teaching and promote students’ development in the era of intelligence
(Liu, 2026).

2.1.2 Composition of Artificial Intelligence Teaching Application Ability

Artificial intelligence teaching application ability is a comprehensive ability for teachers to organically integrate artificial intelligence technology into the whole process of teaching design (Sun, 2026), teaching implementation, teaching evaluation and teaching reflection, so as to improve teaching effect and promote students’ learning and development. This capability is established on the basis of traditional technology integration capability and has obvious characteristics of intelligent technology. Scholars Xu Chunmei and others put forward in 2024 that AI teaching application ability includes five main aspects: AI concept cognition, AI knowledge and skills, AI education and teaching application, AI promoting professional development, and AI social responsibility. The AI-TPACK framework divides it into artificial intelligence technology knowledge, artificial intelligence technology content knowledge, artificial intelligence technology teaching method knowledge and the integration knowledge of the three, highlighting the deep integration of technology, teaching method and subject content (Guo, 2026). This ability requires teachers not only to be able to use tools, but also to understand the principles of intelligent technology, and to have high-level abilities such as human-computer collaborative teaching design, data-driven decision-making, and intelligent educational ethics research and judgment.

2.2 Intrinsic Relationship between the Two

There is a profound logical relationship between teachers’ digital literacy and artificial intelligence teaching application ability, and there is also a progressive relationship. Digital literacy is the basis and prerequisite for the development of AI teaching application ability, which provides teachers with the necessary cognitive framework, skill reserve and sense of responsibility to understand, accept and effectively use intelligent technology (Wang, 2026). Without solid digital literacy, teachers can not cross the technical threshold, let alone achieve the deep integration of artificial intelligence and education and teaching.

Artificial intelligence teaching application ability is the deepening, expansion and advancement of digital literacy in the context of intelligent technology. It requires teachers to master the unique attributes of intelligent technology, such as natural language processing, machine learning principles, generative content creation, and so on, on the basis of mastering general digital skills, and to translate them into specific teaching strategies and student support means. Both of them point to the new professional ability structure of teachers in the era of digital intelligence. The former focuses on universality and basic ability, while the latter focuses on particularity and frontier application. They support each other and evolve together, which constitutes the core competitiveness of teachers in the digital transformation of education.

2.3 Theoretical Basis for Evaluation and Training

The evaluation and cultivation of teachers’ digital literacy and artificial intelligence teaching application ability should be based on a solid theoretical foundation. Social cognitive theory holds that there is an interactive relationship among individual, behavior and environment, which provides a theoretical framework for the dynamic relationship among personal belief, teaching practice and organizational support in the process of teacher competence development. According to this theory, teachers’ self-efficacy, observational learning and outcome expectation will affect teachers’ behavior of using intelligent technology, and the school’s technological environment, policy incentives and cultural atmosphere are important external support conditions (Wang & Ye, 2026).

The ability structure theory and Spearman’s two-factor theory provide the basis for the establishment of the hierarchical and classified evaluation model. This theory divides ability into general factors and special factors, suggesting that evaluation should take into account teachers’ general digital literacy foundation and special AI teaching application expertise. The theory of teacher professional development stages holds that teachers have to go through different stages from novice to expert, and their needs and development paths are also different, so the training mode should reflect individualization and advancement. These theories together constitute the scientific basis of the evaluation model and the pertinence of the training mode.

3 Construction of the Evaluation Model of Teachers’ Artificial Intelligence Teaching Application Ability

3.1 Design Principles of Assessment Model

The design of the evaluation model adheres to the principles of systematicness, development, application orientation and scientificity. The principle of systematicness requires that the model should cover all aspects of consciousness, knowledge, application and innovation, and form an organic whole rather than an isolated indicator. The developmental principle holds that evaluation should focus on the dynamic development process of teachers’ abilities, rather than static evaluation results, and use process data to track the development trajectory of teachers’ abilities
(Zhang et al., 2026).

The principle of application orientation combines the evaluation index with the real teaching scene, highlights the actual ability of teachers to use artificial intelligence to solve teaching problems and improve teaching effect in practical work, and avoids the separation of theory from practice (Khoso et al., 2026). The scientific principle is that the evaluation method should use both quantitative and qualitative means and adopt multi-source data and multi-subject evaluation, that is, the combination of classroom observation, teaching work analysis, student feedback, peer review and self-report, to improve the reliability and validity of the evaluation. Together, these principles ensure that the assessment model can accurately diagnose the current situation and effectively guide future development.

3.2 Evaluation Index System

3.2.1 Consciousness and Attitude Dimension

The dimension of consciousness and attitude examines teachers’ recognition of the value of AI education, their willingness to apply it and their ethical vigilance. It is embodied in the forward-looking understanding of intelligent technology reform education, the internal motivation to actively explore and apply new tools, and the sense of responsibility in data privacy, algorithm fairness, human-computer relationship and other aspects. This dimension is the forerunner of ability development and plays a decisive role in teachers’ learning engagement and practice transformation.

3.2.2 Knowledge and Skills Dimension

The dimension of knowledge and skills examines teachers’ mastery of the basic principles of AI, the main tools and the special platform for education. Basic understanding of basic concepts such as machine learning and natural language processing, proficiency in the operation of common AI teaching tools (intelligent lesson preparation system, learning situation analysis platform, virtual teaching assistant, etc.) and basic skills of data collection, processing and interpretation. This is the technical basis for turning consciousness into practice.

3.2.3 Teaching Application Dimension

The dimension of teaching application is the ability of teachers to apply artificial intelligence technology to specific teaching links. It includes using AI to diagnose learning situations and design personalized learning paths, creating learning situations and activities supported by intelligent technology, using intelligent tools to assist classroom interaction, homework correction and feedback, and using data to drive teaching decision-making and adjustment. This dimension is directly related to the improvement of teaching effect.

3.2.4 Innovation and Development Dimension

The dimension of innovation and development reflects the high-level ability of teachers to jump out of conventional application, carry out creative integration, reflection and optimization. It is embodied in the use of AI tools to develop innovative teaching resources or models, to carry out teaching experiments based on intelligent technology, to critically evaluate the effectiveness and limitations of AI education applications, and to share experience and lead peer development in the teaching and research community. This is a sign of the transformation of teachers from technology users to educational innovators.

3.3 Assessment Methodologies and Tools

The assessment was conducted using a mixed method approach, combining subjective self-assessment with objective assessment, and using both process data and outcome evidence. Tools such as AI-TPACK, which have been tested for reliability and validity, are used to measure teachers’ knowledge and beliefs, performance tasks or situational simulation tests based on real teaching scenarios, to examine practical application abilities, classroom video analysis, teaching portfolio review, to capture the ability performance in teaching practice and to learn analysis techniques. The behavior log data of teachers in the intelligent teaching platform is collected, and multimodal analysis is carried out to construct a dynamic and three-dimensional portrait of teachers’ ability. Together, these tools form a multi-dimensional and accurate assessment toolbox.

3.4 Assessment Implementation Process

The assessment implementation adopts the cycle mode of “diagnosis-planning-supporting-reassessment” to ensure the developmental function of the assessment. The specific process is shown in Table 1.

Table 1 Implementation Flow Chart of Teachers’ Artificial Intelligence Teaching Application Ability Evaluation

The process considers that evaluation is not the end point, but the starting point of teacher professional development. Through systematic data collection, analysis and feedback, the evaluation is embedded in the work and learning cycle of teachers, forming a closed-loop of continuous improvement by evaluation to promote learning and reform. In the in-depth evaluation stage, special attention is paid to the actual performance of teachers in real or simulated teaching situations to ensure the ecological validity of the evaluation. In the stage of data analysis and feedback, intelligent technology is used to realize the automatic generation and personalized push of reports, so as to improve efficiency and pertinence.

4 Design of Training Mode for Teachers’ Artificial Intelligence Teaching Application Ability

4.1 Setting of Training Objectives

The training goal is to systematically improve teachers’ artificial intelligence in teaching application ability, so that they can be competent for the education and teaching work under the intelligent technology environment. There are four specific objectives, one is to deepen teachers’ understanding of the value of AI education, to establish a correct concept of technology application and ethics, the other is to master the core AI educational tools and resources, to have the practical ability to integrate AI educational tools and resources into teaching design, implementation and evaluation, and the third is to develop data literacy and evidence-based teaching. Intelligent technology can be used to carry out personalized teaching, precise teaching and research. Fourth, we should cultivate a group of key teachers who can innovate and apply AI technology and lead the teaching reform, so as to promote the ecological and intelligent transformation of school education.

4.2 Systematic Training Path

4.2.1 Stratified Training Content System

According to the professional development stage and ability baseline of teachers, the training content system of hierarchical progressive and classified guidance is designed, as shown in Table 2.

Table 2 Hierarchical Training Content System of Teachers’ Artificial Intelligence Teaching Application Ability

The system adheres to the principle of teaching students in accordance with their aptitude and provides a suitable learning ladder and development path for teachers with different starting points.

4.2.2 Diversified Training Methods

The training methods include the combination of online and offline, the combination of theory and practice, and the combination of individual learning and community collaboration. Specifically, it includes personalized online courses and a micro-certification system based on an intelligent platform, thematic workshops and case studies based on real teaching problems, action research and lesson study based on daily teaching, and the establishment of an inter-school or regional teacher learning community to promote experience exchange and collaborative innovation. Introduce enterprise experts and university researchers to carry out cross-border exchanges and project cooperation. Diversified ways are to meet the different learning needs of teachers and stimulate their internal motivation.

4.3 Support and Safeguard Mechanism

Effective training needs to establish a support and guarantee system. In terms of system guarantee, schools should incorporate AI teaching application ability into teachers’ performance appraisal, professional title evaluation and professional development evaluation system and establish an incentive mechanism. From the perspective of resource guarantee, it is necessary to create an intelligent teaching environment, a stable and easy-to-use AI tool platform, and enrich the case base, curriculum resources, and training materials.

At the professional support level, a team composed of technical specialists, subject experts and researchers should be set up to provide teachers with timely technical questions, teaching design and research guidance. In terms of cultural guarantee, we should create an organizational culture that encourages exploration, tolerates failure, collaborates and shares, organize teaching innovation competitions, exhibition of achievements and other activities, commend advanced models, and create a positive application atmosphere. Together, these mechanisms form a safety net and booster for teachers to continue learning and practicing boldly.

5 Integration and Application of the Evaluation Model and Training Mode

5.1 Implementation Strategy of Promoting Construction by Evaluation

The key to the integrated application of the evaluation model and training mode is to create a linkage mechanism of “promoting construction by evaluation and combining evaluation with construction”. There are three implementation strategies, one is to regard the evaluation results as the access criteria for teachers to enter the corresponding level of training plan, so as to achieve accurate matching, the other is to integrate the formative evaluation (data analysis of learning process, assessment of the completion of practical tasks) into the training process, dynamically adjust the training program, and make the training program more effective. Thirdly, the training results (teaching innovation cases, research papers, evidence of students’ academic progress) are regarded as important observation points for evaluation, and the evidence chain of ability development is formed. Find out the short board through evaluation, make up the short board through training, and then test the effectiveness through evaluation to form a closed loop of continuous improvement.

5.2 Training Effect Evaluation and Feedback

The systematic evaluation of training effect is the key to optimizing the mode and ensuring the quality. The effect evaluation should not only focus on the simple satisfaction survey, but also pay attention to the change of teachers’ ability behavior and the improvement of teaching effect. Using the four-level evaluation model, the response layer evaluates the satisfaction and perceived value of the teachers participating in the training, the learning layer examines the mastery of knowledge and skills by testing and product evaluation, the behavior layer tracks the behavior change in teaching practice by classroom observation and peer review, and the result layer pays attention to the ultimate impact of students’ learning achievements and the innovative atmosphere of school teaching. Establish normal feedback channels, provide timely feedback on the evaluation results to training designers, training implementers and managers, so as to continuously optimize the training content, methods and support strategies.

5.3 Continuous Optimization of Development Mechanism

The development of teachers’ artificial intelligence teaching application ability is dynamic and continuous, and a long-term optimization mechanism needs to be established. There are four mechanisms, the first is to create a regional or school-level teacher competency development database to track the changing trend of teacher competency for a long time and provide a basis for macro-decision-making; the second is to regularly review and update the evaluation indicators and training content to keep up with the changes in technological development and educational policies; The third is to cultivate teachers as reflective practitioners and the core of learning community, encourage teachers to conduct action research, and transform personal practical experience into collective knowledge that can be shared. Fourthly, we should strengthen the cooperation between industry, university and research institutes, introduce the latest research results and practical wisdom and maintain the frontier and vitality of the training system. Depending on the institutionalized operation, we can ensure that the evaluation and training system can adapt to the changing educational ecology.

6 Conclusion

Establishing the evaluation model and training mode of teachers’ digital literacy and artificial intelligence teaching application ability is an epochal proposition to deal with the digital strategy of education and enable teachers’ professional development. This study clarifies the internal relationship between digital literacy and artificial intelligence teaching application ability through theoretical combining, shapes an evaluation system including many aspects and methods through model creation and formulates a hierarchical, classified and multi-supported training approach through model planning. The organic integration of evaluation and training is to break through the dilemma of traditional training and evaluation and to shape a support system integrating diagnosis, intervention and development. With the continuous updating of artificial intelligence technology and the deepening of educational scenarios, the model and model should be constantly tested, revised and improved in practice. Its successful implementation requires the coordination of policy, technology, culture and other aspects, and the ultimate goal is to promote every teacher to achieve professional growth in the era of intelligence, so as to better shoulder the responsibility of training future talents.

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