Professional Development of ESP Teachers for Digital Transformation: Approaches, Challenges, and Policy Implications

2.1 Literature Search Strategy

To ensure the comprehensiveness and credibility of the literature review, the study draws on three authoritative academic databases: the Science Citation Index Expanded (SCIE), the Social Sciences Citation Index (SSCI), and the Arts & Humanities Citation Index (A&HCI). These databases include high-impact journals across diverse interdisciplinary domains such as educational technology, organizational management, policy analysis, and the digital economy. Their inclusion guarantees that the selected literature is both academically rigorous and practically relevant.

The search strategy focuses on keywords that capture the intersection of digital transformation and intelligent technology application. The core search terms include “ESP teacher professional development,” “ESP teacher training,” “interdisciplinary teaching,” “educational technology support,” “industry-embedded training,” and “data-driven feedback.” These terms encompass both the traditional dimensions of teacher development and the latest innovation trends driven by artificial intelligence and big data. The search scope is restricted to literature published from 2020 onward, ensuring the inclusion of the most recent research and policy developments related to ESP teacher professional development in the digital economy era.

During the search process, particular attention was given to studies examining how data-driven teaching models and performance evaluation mechanisms enhance teacher competencies and improve educational system efficiency. The reviewed literature also investigates the influence of policy environments and governance structures on teacher development, identifying key barriers such as institutional fragmentation, the disconnection between disciplinary knowledge and language pedagogy, and inequalities in access to educational technology. Based on these insights, this study underscores the need for data-driven policy design, sustainable talent development frameworks, and equitable access to digital infrastructure to promote inclusive and future-oriented teacher professional growth.

2.2 Literature Selection and Analysis

During the literature selection process, a large number of relevant studies were initially retrieved from the selected databases. To ensure accuracy, a deduplication procedure was first conducted so that each study was counted only once. Because the search results originated from multiple databases, some studies appeared more than once; these redundant entries were removed using reference management software. After deduplication, the screening phase began. The first step involved a preliminary review of the titles and abstracts. Studies that were not relevant to the topic – such as those focusing solely on general English teaching or language learning theories rather than the professional development of ESP teachers – were excluded. This step ensured that only studies directly related to ESP teachers’ career development, teaching methodologies, and educational technologies were retained.

Subsequently, a detailed full-text review was conducted for the filtered studies. At this stage, particular attention was paid to research subjects, study design, and methodological rigor to ensure that the selected papers met the predefined inclusion criteria. Eligible studies were required to satisfy the following conditions: The participants were ESP teachers or educators engaged in ESP professional development. The study focused on ESP teachers’ career growth, training models, teaching strategies, or the challenges they face. Valid empirical research methods were employed, ensuring scientific rigor and reliability. The content was relevant to modern educational technology, interdisciplinary teaching, or industry-integrated training. Studies that did not meet these criteria were excluded to guarantee the quality and relevance of the final selection.

To ensure transparency and methodological rigor, this study adhered strictly to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. The detailed process is illustrated in Figure 1, which depicts the sequential stages of identification, screening, inclusion, and exclusion. A total of 217 articles were initially retrieved based on the defined keywords and the time frame of 2020–2025. After duplicates were removed in the first stage, titles and abstracts were screened in the second stage, and studies unrelated to ESP teacher professional development or those limited to theoretical discussions of language learning were excluded. The remaining studies underwent a full-text review in the final stage. Based on the established inclusion criteria – focusing on ESP teachers, professional development, and digital transformation; employing empirical methodologies; and being published from 2020 onward – and the exclusion criteria (non-empirical studies, incomplete data, or duplicate publications), 50 high-quality articles were ultimately included for analysis. The reasons for exclusion at each stage are clearly presented in Figure 1.

Figure 1:

Literature selection process.

To systematically classify and comprehensively analyze the included studies, content analysis was conducted to establish a coding framework encompassing research themes, methods, study populations, and key findings. Keyword extraction and cluster analysis were further applied to support thematic categorization, ensuring consistency between qualitative interpretations and quantitative patterns. Barriers to professional development were identified and prioritized through frequency analysis and weighted evaluation, clarifying the major bottlenecks affecting ESP teacher development and their relative significance. For quality assessment, a dual independent review system was implemented. The evaluation criteria covered the rigor of research design, the adequacy of sample size, and the scientific validity of data collection and analytical methods. Any disagreements between reviewers were resolved through discussion, and when necessary, adjudicated by a third reviewer to ensure objectivity and fairness. Inter-rater reliability was calculated using Cohen’s kappa, yielding a coefficient of 0.82, which indicates a high level of agreement and reinforces the transparency and reliability of the methodological process. The results of the quality assessment informed both study selection and weighting, ensuring that the final analysis was grounded in high-quality evidence and thereby enhancing the credibility and generalizability of the findings.

The data extraction process was also carried out independently by two researchers. A pilot extraction was first performed on a subset of studies to test and refine the data extraction form and variable definitions. Subsequently, both researchers independently extracted core information from all included studies, including research design, sample characteristics, intervention content, and evaluation indicators. The extracted data were then compared, and any discrepancies were resolved through discussion or, when necessary, through review by a third expert. This process ensured the accuracy, consistency, and completeness of the extracted data.

In the literature analysis, both qualitative content analysis and quantitative bibliometric analysis were conducted to ensure a comprehensive and systematic interpretation of the selected studies. In the quantitative phase, bibliometric analysis was employed to identify the most frequent research themes and examine their interrelationships. CiteSpace was used to perform keyword co-occurrence and clustering analyses on the included studies. All keywords were first extracted and standardized by merging synonyms (e.g., “AI feedback” and “artificial intelligence feedback”) and unifying term variations. To ensure statistical reliability, only keywords appearing at least three times in the dataset were retained for analysis. A co-occurrence matrix was then generated to quantify the frequency with which two terms appeared together within the same study. The resulting keyword co-occurrence network (Figure 2) visualizes the relationships among themes: larger font sizes indicate higher frequency and centrality within the research corpus. Notably, keywords such as motivation, instruction, impact, primary education, and adaptive learning appear prominently, representing the main areas of research focus. By mapping keyword distributions from 2023 to 2025, temporal evolution in research priorities can be observed. In particular, the growing prominence of motivation and adaptive learning reflects an increasing emphasis on teacher learning motivation and personalized digital teaching technologies, which have become core themes in current and emerging ESP teacher development research.

Figure 2:

Keyword co-occurrence network. (a) Highlighted in 2025. (b) Highlighted in 2024. (c) Highlighted in 2023.

The cluster analysis (Figure 3) employed the log-likelihood ratio (LLR) algorithm in CiteSpace to group related keywords into thematic clusters, each representing a distinct research domain. Cluster #0 centers on ESP, underscoring its central role in the research field and highlighting the dominant focus on ESP teaching. Cluster #1 is organized around the teacher, encompassing studies on teacher training, professional development, and skill enhancement. Cluster #2 focuses on digital transformation, educational technology, and online learning, reflecting research on the application of digital tools in ESP instruction – such as online learning platforms, autonomous learning systems, and data-driven feedback mechanisms. This cluster illustrates the deep integration of technology and pedagogy, exploring how technological tools can enhance instructional effectiveness and promote teachers’ digital competence. Cluster #3 centers on artificial intelligence (AI), emphasizing its innovative applications in ESP teacher training and instruction. Representative topics include intelligent feedback systems, learning analytics, and personalized teaching solutions. This cluster highlights the pivotal role of AI in facilitating teachers’ digital transformation and advancing AI-driven educational reform. Overall, the clustering results reveal the multidimensional research emphases within the field, spanning language instruction, teacher professional growth, and the integration of intelligent technologies. The high modularity (Q > 0.7) and mean silhouette score (>0.7) indicate that the clustering structure exhibits strong reliability and clear topic separation.

Figure 3:

Keyword clustering analysis.

The timeline analysis (Figure 4) illustrates the evolutionary trajectory of research themes from 2020 to 2025, capturing both the sustained prominence of established topics and the rapid emergence of new areas such as adaptive learning in recent years. These temporal dynamics provide valuable insights into the developmental patterns and shifting priorities within the field. They also offer empirical evidence to support strategic planning for promoting digital transformation in ESP teacher professional development, highlighting the progressive integration of technology and pedagogy over time.

Figure 4:

Timeline analysis.

3.1 University–Industry Collaborative Training Models and Digital Transformation

The digital transformation of teaching content primarily refers to the adoption of digital technologies to optimize and innovate classroom materials and instructional methods, including online courses, intelligent teaching platforms, and adaptive learning tools. In contrast, the digital transformation of teacher training systems focuses on leveraging digital platforms and data-driven technologies to enhance professional competencies, reform training models, and advance overall career development frameworks. Among these approaches, university–industry collaborative training models have been widely recognized as an effective strategy for promoting ESP teachers’ professional development (Mao and Zhou 2024, e0305210). With the accelerating pace of globalization and the growing interdisciplinarity of education, ESP teachers are increasingly expected to possess strong language teaching skills and to integrate industry-specific knowledge into classroom instruction to meet the evolving needs of education and the labor market (Tang 2023, 101914).

Recent studies have emphasized the importance of university–industry partnerships in helping teachers update professional knowledge, strengthen practical competencies, and refine instructional content (Prasongko 2023, 162–173). This model underscores the synergy between theory and practice: universities contribute language pedagogy, disciplinary theory, and methodological training, while industry partners provide practical experience, case-based learning opportunities, and real-world applications to deepen teachers’ industry expertise. Figure 5 presents the structure and interaction mechanisms of this collaborative framework.

Figure 5:

Hierarchical structure of university-industry collaborative training (Note: This figure is drawn by the author to illustrate the hierarchical structure of university-industry cooperation training).

Figure 5 illustrates the overall framework of university–industry collaborative training, spanning from policy support to the implementation of concrete training activities. At the top of the framework, policy support – including educational regulations and industry standards – provides the institutional foundation and directional guidance for collaborative initiatives. Guided by these policies, universities focus on theoretical training, covering curriculum design, language instruction, and related research, with an emphasis on disciplinary knowledge and effective teaching methodologies. In contrast, industry partners are responsible for practical training, addressing real-world industry requirements through on-site internships, professional certifications, and hands-on experiences that develop teachers’ domain-specific skills. These two components are integrated during the collaborative training phase. The hybrid model combines industry case studies, practical guidance, and project-based cooperation, facilitating reciprocal enhancement of theoretical understanding and practical competence. This framework underscores the importance of aligning theory with practice and coordinating policy with implementation, ultimately enhancing the comprehensive professional capabilities of ESP teachers. However, the model demands strong coordination and substantial resource investment from both universities and industry partners. Without effective communication and institutional mechanisms, the outcomes of collaborative training may be limited, potentially affecting its long-term sustainability and impact.

Current research emphasizes the diverse forms of university–industry collaboration, including corporate internships, industry lectures, and expert seminars. Among these, corporate internships are widely regarded as particularly effective. They provide ESP teachers with hands-on experience in industry settings, enabling them to translate real-world professional requirements into classroom teaching.

For example, Liang et al. (2025) reported that medical English ESP teachers enhanced their mastery of professional terminology and practical skills through short-term medical internships. These experiences enriched teaching content and promoted teacher–industry interaction. Nevertheless, short-term internships have inherent limitations, as their brief duration may not fully capture industry complexity, leaving teachers’ deep understanding of practical work relatively underdeveloped (Liang et al. 2025, 114–135).

In addition to corporate internships, industry lectures and expert seminars provide teachers with up-to-date technological insights that can enhance instructional expertise. However, this passive mode of knowledge acquisition does not guarantee that teachers internalize or effectively apply what they have learned. For example, Avcı and Engin-Demir (2021) proposed that industry lectures improved vocational high school teachers’ understanding of professional English listening and speaking requirements and enhanced students’ communication skills. Nevertheless, the study was limited to a single school, restricting its generalizability and leaving unexplored how the lectures could be systematically translated into teaching practice. Overall, these findings underscore the importance of practical experience and industry knowledge for ESP teacher professional development. Yet, most programs rely on short-term initiatives or single-case studies, lacking systematic empirical analysis and longitudinal validation of training outcomes. Future research should pay greater attention to the long-term effectiveness of training and the mechanisms by which industry experience can be transformed into sustainable classroom practice (Avcı and Engin-Demir 2021, 125–139).

Aish and Tomlinson (2022) conducted a case study in which academic English teachers participated in collaborative training with subject-matter instructors, enabling their teaching methods to better align with professional requirements (Aish and Tomlinson 2022, 101145). Unlike corporate internships, this model emphasizes interdisciplinary collaboration within the educational context. Through recorded lectures, group discussions, and reflective analysis, teachers received targeted feedback for instructional improvement. The study demonstrated that such collaborative training is applicable to ESP teachers in industry contexts and facilitates integration between language instructors and subject-matter experts, thereby enhancing teaching quality and enabling teachers to more effectively merge professional knowledge with language instruction.

According to the literature, the implementation of university–industry collaborative training models varies across disciplines and regions. To ensure comparability, this study evaluates several major models reported in prior research based on six criteria: applicable discipline/field, participation frequency, course development cycle, intensity of resource investment, teacher satisfaction, and representative studies (Table 2).

The comparison highlights that each training model has distinct advantages and limitations, making it suitable for different academic disciplines. Regular industry participation ensures that teachers continuously update their industry knowledge and gain direct practical experience. However, this model requires strong time management and may offer uneven opportunities across fields. Intensive collaborative training emphasizes long-term university–industry interaction, allowing ESP teachers to better understand industry needs and integrate them into teaching practice. This model, however, demands stable cooperation mechanisms, significant resource investment, and careful coordination between universities and industry partners. The blended online–offline training model provides flexible learning opportunities, enabling teachers to access industry knowledge anytime and anywhere while combining offline practice to enhance applied skills. Nevertheless, the interactivity and depth of online training still require further optimization.

3.2 Autonomous Professional Development Based on Online Learning Platforms

With the rapid advancement of information technology, online learning platforms have become essential tools for the professional development of ESP teachers (Ramadhona et al. 2022, 35–41). These platforms allow teachers to overcome constraints of time, geography, and resources, enabling them to select learning content flexibly according to their individual needs and thereby enhance their teaching skills and professional competence (Oviedo and Charpentier 2022, 72–85).

For instance, Sofi-Karim et al. (2023) noted that modern online platforms offer a variety of learning methods, including video tutorials, virtual internships, online discussions, and real-time interactions, which break the temporal and spatial limitations of traditional training models (Sofi-Karim et al. 2023, 507–523). This flexible learning format is particularly suited to teachers who wish to engage in professional development during busy teaching schedules, using fragmented time efficiently and avoiding the constraints of fixed schedules and locations. ESP teachers can, for example, select course videos for independent learning or participate in global online discussions to deepen their understanding of teaching methods, course design, and industry requirements. In addition to flexibility, effective professional development requires attention to instructional strategies. Research indicates that strategies grounded in constructivist theory, task-based language teaching, and differentiated instruction can significantly enhance student engagement and learning outcomes in ESP classrooms (Gisbert Cervera and Caena 2022, 451–455). Online platforms support this process by providing video demonstrations, teaching case repositories, and teacher communities, allowing ESP teachers to master and apply diverse instructional methods flexibly. This enables them to better address students’ varied learning needs, thereby improving both the relevance and effectiveness of teaching.

Another notable advantage of online learning platforms is the customization and specialization of resources. Many platforms offer industry-related courses and training content, keeping teachers informed of the latest developments, particularly in fields with rapid technological change such as medicine, law, and engineering. For example, Hsiao et al. (2023) investigated the impact of the flipped classroom method on self-efficacy, learning processes, and English performance among non-English major students (Hsiao et al. 2023, 507–526). An 18-week flipped classroom intervention, comprising pre-class materials and in-class interactive activities, was implemented in an ESP course at a Taiwanese university. Results indicated that the experimental group demonstrated significantly higher self-efficacy and improved performance in assessments compared with the control group. This study illustrates that the flipped classroom promotes higher-order skills, enabling learners to critically evaluate new ideas, connect them to prior knowledge, and establish multiple conceptual links. Figure 6 depicts the learning pathway of the flipped classroom approach.

Figure 6:

Learning path of flipped classroom (Note: this figure is drawn by the author with reference to relevant literature on flipped classrooms, such as Katona and Gyonyoru (2025, 100392)’s study, to present the learning process and stages in the flipped classroom mode).

Figure 6 illustrates the learning pathway of the flipped classroom in ESP instruction and its underlying theoretical mechanism. The model comprises **three consecutive phases – pre-class, in-class, and post-class – **connected through a continuous feedback loop supported by AI-enabled learning platforms. In the pre-class phase, autonomous learning and motivation are enhanced through digital resources and self-paced content delivery. The in-class phase emphasizes interactive and task-based learning, where peer collaboration and contextualized application deepen conceptual understanding. During the post-class phase, a data-driven feedback mechanism is implemented, combining AI analytics and teacher scaffolding to inform adaptive learning adjustments. This iterative process forms a closed-loop feedback cycle, promoting learning efficiency and sustainable competency development. Compared with traditional linear models, this mechanism highlights the dynamic interaction between learner autonomy, technology support, and instructional feedback, providing a more analytically grounded framework for understanding the effectiveness of flipped classrooms in ESP education.

Gaffas (2023) emphasized that in virtual and blended industry-related courses, ESP teachers acquire the latest industry terminology, professional skills, and practical standards. (Gaffas 2023, 10329–10358). These courses also help teachers stay updated on industry trends and integrate new knowledge into their teaching content. Such customized training enables teachers to better align industry needs with language instruction, thereby enhancing the practicality and relevance of classroom teaching. For example, in medical ESP courses, teachers can learn current medical terminology, disease descriptions, and treatment plans through online platforms, without relying on traditional in-person instruction. Other studies have highlighted that online platforms provide discipline-specific courses and include interdisciplinary content, significantly broadening ESP teachers’ knowledge and deepening their professional expertise. This flexible learning approach helps teachers remain current with industry developments, ensuring that teaching content is aligned with industry requirements and responsive to students’ learning needs.

Compared with traditional face-to-face training, online learning platforms also offer notable interactivity and foster the formation of professional learning communities. Through these platforms, teachers can acquire professional knowledge while interacting with peers and industry experts worldwide. For instance, Gaffas (2024) implemented Weibo-based teaching in a specialized medical English ESP course (Gaffas 2024, 54). The platform’s interactive features, including online discussions, forum exchanges, and topic seminars, enriched the learning experience and provided opportunities for teachers to become more familiar with medical-specific terminology. In addition, teacher motivation was enhanced through visible achievements, personalized learning path recommendations, and sharing of instructional outcomes, promoting sustained engagement in professional development. Nonetheless, some platforms still face limitations, such as insufficient incentive mechanisms and limited personalized support, which may constrain long-term teacher engagement and growth. Improving incentive structures and enhancing personalized services are therefore critical directions for the future development of online platforms.

Online platforms are particularly valuable in interdisciplinary and distance education contexts, where the cultivation of intercultural competence is essential for ESP teacher development. Since ESP instruction often targets students from diverse cultural and international industry backgrounds, teachers must possess cultural sensitivity and cross-cultural communication skills (Mishra 2019, 76–78). Online platforms provide transnational interactive environments, enabling teachers to participate in international seminars, engage in online intercultural training, and communicate with peers globally. These experiences strengthen teachers’ understanding of industry-specific cultural differences, communication norms, and professional etiquette, thereby enhancing their ability to guide students in navigating complex cross-cultural professional environments.

A comprehensive comparison of online learning platforms and traditional training methods in the professional development of ESP teachers is presented in Table 3.

The application of online learning platforms in ESP teacher professional development offers flexible, personalized, and industry-relevant learning methods. These platforms enable teachers to continuously update their professional knowledge, adapt to rapidly evolving industry environments, and enhance both teaching effectiveness and industry adaptability. Despite their advantages, online learning platforms still face challenges, including variability in content quality and insufficient interactivity in certain teaching activities. Therefore, improving content quality and enhancing interactivity and engagement remain key areas for future research and practice.

3.3 AI-Driven Teaching Feedback and Data Analytics Mechanisms

With the advancement of online education technologies, data-driven feedback mechanisms have become increasingly central to the professional development of ESP teachers (Coşgun and Savaş 2023, e3394). Compared with traditional classroom-based feedback, intelligent systems can collect and analyze learning behavior data in real time, enabling targeted instructional interventions and personalized adjustments. This approach significantly enhances teaching effectiveness and supports more efficient educational quality management.

These mechanisms typically comprise three core components:

1. Data Collection: Learning behaviors and engagement levels are continuously recorded through online learning platforms, learning management systems (LMS), and educational applications, generating a multidimensional data foundation for analysis.

2. Data Analysis and Pattern Recognition: AI and big data technologies are applied to mine learning data, identifying students’ learning habits, cognitive bottlenecks, and areas of strength. These insights support evidence-based decision-making for teachers.

3. Feedback Generation and Instructional Intervention: Based on the analytical results, the system automatically generates personalized teaching recommendations and delivers targeted learning resources. This capability facilitates precision teaching and learner autonomy, enabling the continuous optimization of the instructional process.

By leveraging such data-driven feedback mechanisms, ESP courses can be designed and delivered more effectively, while simultaneously enhancing teachers’ digital competencies and career development. These mechanisms also contribute to the broader objectives of educational digital transformation and innovative governance. Figure 7 illustrates the role of data-driven feedback in ESP instruction.

Figure 7:

Data-driven teaching feedback mechanism (Note: The figure is drawn by the author based on research results related to teaching feedback mechanisms, such as Qiu et al. (2024, 103188), to demonstrate the data-driven teaching improvement process).

Figure 7 illustrates the theoretical model of data-driven feedback mechanisms in ESP instruction. The mechanism begins with the collection of student data, including learning behavior and assessment data, which are then processed through data mining, trend analysis, and learning outcome evaluation to generate actionable instructional insights. The analytical results support teacher decision-making, such as optimizing teaching methods, providing personalized guidance, and adjusting course structures, thereby addressing students’ diverse learning needs. This process forms a closed-loop feedback cycle, in which the outcomes of instructional improvements are continuously fed back into the data collection and analysis stages, enabling dynamic optimization. Within this loop, AI and learning analytics platforms provide technical support, facilitating data analysis, personalized feedback, and evidence-based decision-making. Simultaneously, the teacher competency and professional development module reflects the enhancement of teachers’ digital literacy and professional skills through engagement with the data-driven feedback system. Through this dynamic interaction, data, technology, and teachers jointly contribute to improved ESP teaching quality and enhanced professional development. Compared with traditional linear feedback models, this mechanism emphasizes the dynamic interplay among learning data, technological platforms, and teacher competencies, offering a more analytically grounded and actionable framework to guide ESP instructional practice in the context of digital transformation. The data-driven feedback mechanism has various applications in the teaching practice of ESP teachers.

1. Intelligent Data Analysis Optimizing Teaching Decisions

Data mining technologies have been widely shown to optimize instructional strategies in ESP teaching (Weng 2024, 103374). For instance, Ghajarieh and Mirzabeigi (2024, 750–762) analyzed students’ assignment submission frequency, error types, and performance trends in an academic writing course. Their study revealed that the error rate in passive voice usage reached 30.3 % among some students, while overall writing proficiency was relatively low. Based on these findings, teachers adjusted lesson plans, reinforced grammar instruction, and provided personalized writing guidance, resulting in an average grade improvement of 12.4 %. Similarly, Hang et al. (2024, e2462) implemented an AI-based automatic error analysis system in a business English course. The system automatically identified grammatical, lexical, and formatting errors, offering personalized improvement suggestions. This approach significantly accelerated feedback compared with manual grading and recommended tailored learning resources based on error patterns. Martín-Monje and Barcena (2024, 22–42) evaluated the G-Rubric system for academic writing assessment. Results demonstrated that it effectively identified common issues and, through adaptive algorithms, suggested relevant reading materials, enabling precise teacher interventions and enhancing students’ academic writing skills.

Despite these advantages, several limitations remain. First, these systems are highly dependent on data quality and coverage; incomplete datasets or inaccurate labeling may lead to interventions that do not align with actual learning needs. Second, automated feedback has limited understanding of context, cross-cultural expression, and language creativity, particularly in ESP settings where subtle professional nuances significantly affect language appropriateness. Third, the effective use of these technologies requires ongoing resource investment and teacher training in digital literacy; without these, the systems’ full potential cannot be realized. Finally, overreliance on automated feedback may diminish students’ ability for self-correction and critical thinking, fostering dependence on external prompts rather than active reflection, which could pose long-term risks to language development.

1. AI-Driven Personalized Learning Feedback Systems

Within data-driven feedback mechanisms, personalized learning recommendation systems represent a pivotal application in ESP teaching. These systems dynamically adjust course content based on students’ learning status, enabling differentiated instruction (Liu 2024, 100719). For example, Fernández-Morante et al. (2022, 124) implemented an adaptive platform with learning analytics that automatically adjusted course difficulty according to student performance. In legal English courses, students at different proficiency levels received targeted terminology resources, resulting in significant improvements in exam outcomes. Xu et al. (2020, 779–794) developed an AI-driven personalized learning pathway system that analyzed assignments, classroom interactions, and quiz data to recommend literature, videos, and case-study tasks in medical English. This approach promoted student autonomy while enabling teachers to optimize courses more precisely. Similarly, Lolbi and Haroon (2025, 147–154.) applied a real-time feedback system in aviation English, dynamically adjusting classroom content to address high error rates in listening comprehension. The study also highlighted challenges with less common language forms, such as greetings, numeral pronunciation, and regional usage differences in Airspeak.

Despite their potential to enhance learning outcomes, several limitations exist. First, these systems are highly technology-dependent, requiring stable networks, adequate hardware, and robust data-processing capabilities, which may constrain feasibility for resource-limited institutions and exacerbate the digital divide. Second, limited algorithm transparency can hinder teachers’ understanding of recommendation logic, reducing trust in system outputs and potentially weakening instructional autonomy. Third, system performance relies heavily on the quality and completeness of input data; biased or incomplete data may distort recommendations and misguide teaching. Moreover, most existing studies are limited to short-term experiments and single-discipline contexts, lacking cross-disciplinary, cross-cultural, and longitudinal evidence, making it difficult to assess sustained applicability in complex educational ecosystems. Finally, the risk of over-personalization warrants attention. Excessive adaptation to individual learners may reduce students’ adaptability and critical thinking when confronting novel tasks, potentially hindering the development of comprehensive competencies.

1. Data Visualization Technology Supporting Teaching Feedback

Data visualization technologies can greatly enhance ESP teachers’ understanding of students’ learning status and improve the precision of instructional adjustments (Tan et al. 2023, 159–175). For example, Maulida et al. (2024, 309–322) employed visual reports in an engineering English course to identify students’ weaknesses in reading technical documents. By increasing targeted terminology instruction, students’ performance improved significantly. Similarly, Roberts (2021, 110–121) implemented a real-time feedback dashboard in an aviation English course and discovered that some students’ accuracy in pronouncing professional terminology was below 70.5 %. Incorporating additional oral practice led to notable improvements in their performance. These cases demonstrate that visualization tools enhance both the timeliness and specificity of feedback, enabling teachers to make more informed instructional decisions. Despite these benefits, several limitations remain. First, most studies are single-course or small-sample investigations, lacking cross-disciplinary and longitudinal data. Second, reliance on data interpretation may increase teachers’ technical burden, requiring additional training and expertise. Third, implementation demands stable technical infrastructure and financial investment, which may limit feasibility for resource-constrained institutions.

Overall, while data visualization shows strong potential for enhancing teaching feedback, its effectiveness needs to be validated across broader and more diverse instructional contexts. By comparing traditional feedback methods with data-driven approaches in these cases, the role of data-driven models in improving teaching effectiveness and supporting teacher professional development becomes clearer. The detailed comparison is summarized in Table 4.

Data-driven feedback mechanisms play a critical role in enhancing ESP teachers’ instructional effectiveness and fostering their professional development. By analyzing student learning data in real time, teachers gain clearer insights into student progress, enabling them to adapt teaching strategies more flexibly and tailor course content to students’ specific needs. AI-powered tools, such as automated grading and error analysis systems, allow teachers to efficiently process large volumes of data, improving the accuracy, speed, and quality of feedback while simplifying instructional management. As the adoption of these tools increases, ESP teachers are steadily enhancing their digital competencies, leveraging data insights to drive targeted teaching improvements. Intelligent recommendation systems, which employ learning analytics, further support the creation of personalized learning plans for students across different proficiency levels. Such personalization has a significant impact on both student outcomes and satisfaction. Looking ahead, continued advances in AI and big data technologies are expected to further amplify the impact of data-driven feedback. These tools will enhance ESP teaching quality and support teacher professional development and provide valuable input for education governance and policy-making. Ultimately, data-driven feedback contributes to building a more efficient, adaptable, and innovative education system, bridging teaching, technology, and strategic decision-making in the digital era.

4.1 Balancing Domain Expertise and Language Teaching Skills: An Interdisciplinary Governance Approach

A major challenge in ESP teacher professional development lies in striking a balance between language teaching skills and domain-specific knowledge. This difficulty arises from the dual nature of ESP instruction: teachers must possess a solid foundation in language education while also understanding the specialized terminology, concepts, and real-world contexts of their target fields. For instance, in Legal English courses, teachers are required to teach grammar and syntax and to clearly explain legal terms, case reasoning, and judicial processes, enabling students to communicate accurately and appropriately in authentic legal environments (Rahmawati and Mar’an 2024, 11–22; Nasution et al. 2023, 121–130). Effectively combining language proficiency with subject expertise is therefore essential for high-quality ESP instruction.

Many ESP teachers have strong language teaching skills but lack domain-specific expertise, a gap particularly pronounced in technical courses such as engineering English and medical English. This deficiency limits teachers’ ability to explain specialized terminology or reconstruct realistic workplace scenarios, creating a disconnect between course content and real-world demands that hinders students’ professional readiness (Suri 2024, 98–106). For example, in medical English, some university instructors lack sufficient clinical experience, making it challenging to integrate teaching with practical medical practice (Huang et al. 2024, 103–116). Consequently, teachers struggle to guide students in applying professional terminology effectively, constraining students’ domain-specific language competence and overall teaching quality. These challenges underscore the urgent need for closer integration of industry expertise and language instruction, particularly in the context of digital transformation. Implementing interdisciplinary governance mechanisms that bridge language pedagogy with specialized knowledge is critical for enhancing both teacher development and instructional effectiveness in ESP education.

For example, Nasiri and Khojasteh (2024, 925) reported that teachers who participated in industry-specific certification programs for medical English courses significantly enhanced their understanding of medical terminology and successfully integrated this knowledge into their teaching. However, continuously updating teachers’ industry expertise and ensuring that course content keeps pace with rapidly evolving professional requirements remain persistent challenges in ESP teacher development. Similarly, Yang et al. (2025, 131–148) highlighted that aviation English teachers must master technical terminology and comprehend practical aspects such as flight operations and route management. Insufficient industry knowledge can hinder the integration of language instruction with professional content, negatively affecting course depth and instructional outcomes. In some cases, teachers’ lack of industry experience results in case studies and terminology instruction that fail to align with students’ real-world needs. To address this issue, some universities have begun recruiting dual-qualified teachers with both pedagogical and industry experience or providing industry internships and certification training to bridge knowledge gaps. For instance, as noted by Nasiri and Khojasteh (2024), medical English teachers who participated in certification programs were able to incorporate their enhanced professional knowledge into classroom instruction effectively. Nonetheless, maintaining teachers’ industry competence over time and keeping teaching content aligned with rapidly changing industry standards remain significant challenges.

These examples underscore the unique demands of ESP courses, where teachers must deliver language instruction while deeply integrating it with industry knowledge. Such integration enables students to apply language skills effectively in specific professional contexts. In advanced professional English courses, teachers’ industry expertise directly influences students’ ability to translate language competence into career readiness and professional competitiveness. Achieving this goal requires teachers to engage in interdisciplinary collaboration, participate in industry certification programs, and undertake authentic professional practice, subsequently incorporating acquired knowledge into classroom instruction. This approach enhances teaching quality and students’ practical skills and fosters teachers’ comprehensive professional competence. However, industry knowledge acquisition is an ongoing process. Without mechanisms for continuous updating and reflective practice, teachers risk knowledge obsolescence and rigid course content. Therefore, policies and institutional support – such as cross-industry learning initiatives, access to professional resources, and collaborative platforms – are essential to ensure that the integration of language and industry knowledge remains sustainable, scalable, and responsive to evolving industry demands.

4.2 Fragmentation of Career Development Paths and Its Impact on Organizational Performance

The professional development of ESP teachers has become increasingly nonlinear and fragmented due to the emergence of new engineering disciplines and structural reforms in higher education. This challenge primarily arises from the dual nature of ESP roles, which combine language instruction with industry-specific applications. However, current higher education systems often lack systematic support in critical areas such as teacher development frameworks, job evaluation standards, and interdisciplinary skill-building. Consequently, ESP teachers frequently encounter unclear career paths, ambiguous stage objectives, and fragmented resource allocation, which undermine their professional identity and sense of belonging within their institutions. From an educational governance perspective, fragmented career paths constitute a significant barrier to improving faculty performance. Such discontinuous trajectories weaken teachers’ motivation for professional growth and contribute to higher staff turnover and reduced job-role alignment. Over the long term, these structural issues compromise the stability and innovation capacity of the educational system, thereby hindering the attainment of high-quality education goals.

On one hand, the absence of unified evaluation mechanisms and clear development channels makes it difficult for teachers to achieve balanced progress across teaching, research, and industry engagement, limiting the effectiveness of motivation and performance assessment. On the other hand, interruptions in career development reduce teachers’ ongoing learning momentum and hinder their ability to meet the evolving language demands of emerging industries (Meihami and Werbińska 2022, 31–43). These challenges affect individual career trajectories and impose structural constraints on universities’ efforts to integrate industry and education, ultimately limiting the quality of talent cultivation (Supunya 2023, 287–317).

From a career advancement perspective, promotion paths for many teachers are often influenced more by external factors than by improvements in teaching competencies (Diana 2022, 490–499). For instance, numerous universities and educational institutions maintain vague promotion requirements for ESP teachers, emphasizing academic qualifications, research output, or years of teaching experience while neglecting the comprehensive development of industry knowledge and instructional skills. As a result, teachers with strong teaching abilities may face obstacles in career progression due to insufficient academic research credentials or limited industry experience.

Regarding the unequal distribution of teaching resources and opportunities, many ESP teachers can improve specific skills in the short term through training programs or academic exchanges. However, the lack of long-term career development planning within the educational system often leaves teachers with limited opportunities for growth. They may be repeatedly assigned to similar teaching tasks with few chances for professional expansion, further exacerbating the fragmentation of their career trajectories.

In terms of teaching practice, many ESP teachers lack systematic career development support, often relying on isolated training sessions or short-term projects to enhance their skills. This fragmented approach is insufficient for sustaining long-term improvement in teaching quality and industry knowledge; instead, it can create confusion regarding career progression. Without structured career development planning, teachers struggle to consolidate the knowledge and skills they acquire, ultimately limiting their professional growth.

The fragmentation of ESP teachers’ career paths is influenced by both external factors and individual growth trajectories. Early in their careers, teachers often focus primarily on language instruction and improving student performance. Over time, however, they recognize that language teaching alone is insufficient for meeting the demands of advanced industry applications. At this stage, teachers need to deepen their expertise in specific industry domains. Yet, due to shifts in industry demands and changes within educational systems, they frequently face a precarious balance between industry knowledge and language teaching skills. The education system itself is a key contributor to fragmented career development (Ping 2022, 169–186). Many institutions fail to provide ongoing, systematic career planning for ESP teachers, offering unclear promotion standards and limited development pathways. As a result, teachers often lack long-term planning and motivation. For example, in some universities, ESP courses are assigned across disciplines in a simplistic, horizontal manner, without considering teachers’ personal career goals or development directions. This can leave teachers feeling “marginalized”, directly affecting their professional planning and growth opportunities.

Real-world examples further illustrate how the mismatch between industry demands and educational structures drives fragmented career paths. In China, for instance, many universities do not take teachers’ long-term professional development into account when designing ESP courses. Teachers may enhance their language teaching and industry knowledge through short-term training programs. However, the constant evolution of course content and the diversity and unpredictability of industry requirements make it challenging to achieve continuous growth through a fixed course system. Consequently, teachers struggle to maintain a coherent trajectory of professional development, limiting their ability to integrate language instruction effectively with practical industry applications.

4.3 Inequality in Educational Technology Access and Issues of Digital Inclusion

With the rapid development of educational technology, many higher education institutions have incorporated advanced technological tools into teaching to enhance educational quality and efficiency. Figure 8 illustrates the pathways through which educational technology impacts ESP teacher professional development, encompassing both positive and negative mechanisms. Along the positive pathway, adequate technological resources – such as online learning platforms, AI tools, and virtual laboratories – enhance teachers’ digital competencies. This enables the adoption of advanced instructional methods, including flipped classrooms and personalized learning, thereby improving teaching quality and student engagement. Ultimately, these improvements contribute to teachers’ professional development, manifested in career advancement, salary increases, and recognition for teaching innovation. Conversely, the negative pathway emerges when technological resources are insufficient or support is limited. In such cases, teachers’ ability to leverage technology is constrained, which restricts instructional effectiveness and limits professional growth opportunities. This reflects the unequal application of educational technology across institutions. Moreover, improvements in teaching quality and professional development can, in turn, positively reinforce teachers’ technological skills and resource acquisition, creating a closed-loop optimization mechanism. Policy and institutional support – such as school-based training and industry collaboration platforms – play a critical role in sustaining this positive pathway. Despite these opportunities, ESP teachers still face significant challenges in using educational technologies, particularly due to unequal access and application (Kakoulli-Constantinou and Papadima-Sophocleous 2020, 17–29). This inequality manifests in variations among teachers, schools, and regions and in differences in the depth, breadth, and sophistication of technology use.

Figure 8:

Impact path of educational technology on teacher professional development (Note: The figure is drawn by the author to analyze the key role path of educational technology in promoting teachers’ professional growth).

Although the widespread adoption of educational technology has provided teachers with a variety of instructional tools and methods, many ESP teachers still face challenges such as insufficient technical resources and limited access to technical training. These challenges are particularly acute in remote or resource-constrained schools, where teachers struggle to obtain high-quality technical support and training opportunities, often continuing to rely on traditional teaching methods (Keshtiarast et al. 2022, 444–472.). In such contexts, advanced educational technologies – including AI-assisted tools, learning management systems, and big data analytics – are underutilized, resulting in gaps in teaching quality and efficiency compared with more resource-rich regions (Budianto et al. 2022, 398–739).

In contrast, some major cities and high-level institutions have introduced a range of advanced educational technologies, including smart classrooms, AI-driven learning platforms, and online analytics tools. However, their use is often limited to a select group of well-equipped teachers or courses. While teachers in these well-resourced areas can leverage technology to enhance instructional effectiveness, educators in other regions lag behind, exacerbating disparities in both teaching quality and professional development opportunities.

For instance, Chen et al. (2020, 4046) examined higher education institutions in central and western China and found that ESP teachers in these regions, constrained by limited technical resources, were unable to fully exploit modern technological tools to improve teaching. Consequently, many relied on traditional methods, such as lectures and handwritten notes, which lacked interactivity and personalization. Although ESP courses in these institutions were content-rich, the absence of effective technological support restricted teaching outcomes, leading to slower student progress and lower achievement relative to courses in more resource-abundant regions.

On the other hand, some higher education institutions in major cities exhibit an excessive concentration in the application of educational technology. For instance, Fan (2023, e3410) reported that ESP teachers in universities in cities such as Beijing and Shanghai employ a range of advanced technologies, including AI-assisted teaching tools, adaptive learning platforms, and online assessment systems. While these technologies have the potential to enhance teaching efficiency, many teachers, due to insufficient technical training, are unable to fully leverage them, leading to a notable reduction in their effectiveness. This phenomenon highlights that, despite the sophistication of the technology itself, the lack of technical literacy may cause overreliance on tools to backfire, rather than improve teaching outcomes.

The inequality in technical training further affects the effective application of educational technology. Many ESP teachers, particularly in remote areas, have not received adequate training to utilize advanced tools effectively. For example, in medical English courses, Cheraghi and Motaharinejad (2023, 101250) observed that although some institutions implemented adaptive learning systems and AI-assisted teaching, many teachers lacked the necessary technical skills to apply these tools in practice. As a result, the potential advantages of these technologies were underutilized, limiting teachers’ ability to enhance teaching quality and advance their professional competencies.

The uneven adoption of educational technology has significant implications for both teaching practices and professional development of ESP teachers. First, disparities in technological resources contribute to regional differences in teaching quality. Schools and teachers with better technological support can leverage advanced tools to enrich instructional content and methods, thereby improving students’ learning experiences. In contrast, teachers in under-resourced regions remain reliant on traditional approaches, which restrict opportunities for innovation and limit teaching efficiency. Second, differences in technology adoption create unequal professional development opportunities. Teachers with access to advanced technologies can enhance their instructional skills, foster higher student engagement, and achieve improved learning outcomes. Conversely, those with limited access may remain confined to traditional teaching models, struggling to keep pace with evolving educational demands. This inequality exacerbates professional disparities, affecting job satisfaction and career progression. Therefore, narrowing the gap in educational technology application and strengthening teachers’ technical proficiency have become critical strategies for enhancing both the professional development and teaching effectiveness of ESP educators.

4.4 Policy Support and Governance Challenges: Promoting Educational Equity and Enhancing Organizational Performance

The professional development of ESP teachers relies on individual competency enhancement and on comprehensive policy and governance support. Current policies are often fragmented, and resource allocation is uneven, particularly in areas such as digital technology training and data-driven instructional support. This imbalance leaves teachers in some regions without adequate assistance, negatively impacting teaching quality and career advancement. To address these challenges, targeted policy measures should include the following:

1. Promote equitable and differentiated investment in digital resources.

Priority should be given to constructing high-speed network infrastructure in rural and remote areas, ensuring teachers can reliably access online teaching platforms and AI tools. In resource-rich urban universities, policies can focus on the deep integration of advanced AI tools and digital platforms, such as intelligent language assessment systems and adaptive learning platforms, to enhance instructional precision and improve teachers’ digital literacy. Collaborative projects between industry, academia, and research institutions should be encouraged to foster innovative applications of AI in course design and teacher training. For under-resourced rural institutions, policy efforts should emphasize basic network infrastructure and foundational digital training, ensuring the accessibility of teaching resources. For example, government and enterprise partnerships can develop cloud-based teaching platforms to provide online training and guidance for AI-assisted instruction, thereby narrowing the urban–rural digital divide and promoting educational equity and sustainable development.

1. Strengthen teacher digital skills training.

Regularly organized blended online and offline training programs should cover both fundamental digital literacy and professional instructional tools. In urban universities, training can emphasize data analytics, AI-assisted course design, and smart classroom applications. In rural institutions, the focus should be on developing basic information and communication technology (ICT) skills and promoting the use of open-source AI teaching resources. Establishing regional digital competency certification systems can further incentivize continuous professional growth.

1. Establish a scientific teacher evaluation system.

Teacher evaluation frameworks should incorporate digital teaching capabilities, interdisciplinary collaboration, and innovative practice. Data-driven teaching feedback can support personalized career development planning, while flexible standards reflecting the realities of different types of institutions ensure that the evaluation system remains inclusive and adaptable.

1. Promote deeper university–industry collaboration.

Policies should encourage universities to partner with enterprises to establish training bases that provide authentic industry cases and practical experiences. Industry experts should participate regularly in course design and offer feedback on instructional content, ensuring that curricula remain closely aligned with professional and industry needs.

1. Enhance data-driven educational governance mechanisms.

A unified educational data platform should be developed to facilitate resource sharing and real-time monitoring of teaching outcomes. Intelligent analytics can support evidence-based policy formulation and resource allocation, improving governance efficiency and transparency. Considering the differing characteristics of public and private institutions, targeted incentives and support policies should be designed to foster professional development and digital transformation for ESP teachers across all university types.

Through these concrete measures, policies can effectively strengthen ESP teachers’ digital teaching competencies, promote professional growth, reduce regional disparities, and foster educational equity, ultimately leading to sustained improvements in teaching quality.

5.1 Policy Support and Sustainable Development in Deep Industry-Education Integration

With the rapid evolution of industry demands and the accelerating digital transformation of education, the future development of ESP teachers must extend beyond enhancing professional skills and integrating educational technologies. It also requires the systematic advancement of governance structures, policy frameworks, and sustainability strategies (Al-Malki et al. 2022, 72–88). Achieving this goal involves modernizing the education system to ensure teaching quality and equity, inclusiveness, and governance capacity. In the context of deep industry–education integration, ESP teachers face dual demands: expertise in both domain-specific industry knowledge and language instruction. Promoting industry-embedded training emerges as a key pathway for professional growth (Kukulska-Hulme et al. 2023, 1081155). Through direct enterprise experience, teachers gain firsthand insights into professional contexts, better understand students’ career needs, and can effectively integrate authentic industry scenarios into classroom instruction. However, this model requires institutional and policy-level guarantees, including cross-departmental cooperation mechanisms, standards for school–enterprise collaborative education, and formalized teacher mobility systems.

The rapid evolution of industry demands, coupled with the accelerating digital transformation of education, presents new challenges for ESP teachers. Their future development must focus on enhancing professional skills and integrating educational technologies and on strengthening governance structures, policy frameworks, and sustainable development (Al-Malki et al. 2022, 72–88). This requires modernizing the education system to ensure quality, equity, inclusiveness, and effective governance. In the context of deep industry–education integration, ESP teachers face the dual challenge of mastering both industry knowledge and language instruction. Promoting industry-embedded training is therefore a critical pathway for future professional growth (Kukulska-Hulme et al. 2023, 1081155). This study proposes a theoretical four-dimensional interaction framework – “Industry Connection–Artificial Intelligence–Governance Mechanisms–ESG Principles” (Figure 9) – to conceptually illustrate how these four dimensions interact to support the digital transformation and professional development of ESP teachers. The model emphasizes that teachers’ digital transformation relies on technology adoption and on multi-stakeholder collaborative governance and the integration of sustainable development principles.

Figure 9:

Four-dimensional interactive model: industry connection–AI–governance mechanisms–ESG principles.

In this framework, the four dimensions collectively drive teacher development through dynamic feedback and collaborative mechanisms. Industry connection refers to teachers acquiring the latest professional knowledge, terminology, and industry standards by participating in industry-tailored courses, internships, and expert guidance. For example, medical English teachers can learn professional terminology and patient communication skills through hospital internships, while aviation English teachers can collaborate with pilots and ground staff to master practical operational procedures. Industry updates provide a real-world foundation for course design and instructional content. AI-driven learning analytics and feedback systems collect data on teachers’ and students’ learning behaviors to generate personalized recommendations and optimize course design. For instance, in a flipped classroom, AI can analyze students’ assignment completion and classroom interaction data, offering teachers guidance on adjusting teaching pace and focusing on key content. The integration of AI feedback with industry information enables teachers to dynamically adapt course design to meet the latest industry requirements.

Regarding governance mechanisms, schools and industries establish cross-departmental cooperation, funding allocation, and performance-based incentive systems to ensure resources are distributed effectively and to encourage teachers’ active participation in professional development. For example, including technology application and industry engagement indicators in teacher evaluation ensures the implementation of relevant policies. Governance mechanisms provide institutional support for AI applications and industry collaboration while reinforcing ESG principles.

ESG principles emphasize educational equity, resource optimization, and policy transparency, ensuring that teachers from different regions and backgrounds have equal access to training resources. For example, through policy and funding support, teachers in rural or remote areas can also participate in online industry courses and AI-assisted teaching. The feedback from the ESG dimension can further influence industry collaboration and governance mechanisms, creating a closed-loop optimization that promotes the sustainable professional development of teachers. For instance, in medical English instruction, a teacher may acquire the latest professional knowledge through hospital internships (industry connection), optimize course design using AI analysis of student learning data (AI feedback), receive dedicated funding and performance incentives from the school (governance mechanisms), and ensure that all teachers have access to training (ESG principles). This cycle forms a dynamic closed loop, making the model not only a static structure but also a representation of real-time interactions and actionable interconnections among the four dimensions.

To strengthen ESP teacher development within this framework, the following measures are recommended:

1. Customized Industry Courses

Develop industry-specific courses through university–industry collaboration, enabling teachers to enhance their language skills while gaining in-depth knowledge of industry terminology, workflows, and regulations. For example, medical English teachers can participate in hospital training to learn professional terms and patient communication techniques. This approach ensures that teaching content aligns closely with real-world professional requirements.

1. Industry Practice Opportunities

Provide short-term internships, on-site teaching experiences, and lectures from industry experts. These opportunities deepen teachers’ understanding of students’ professional contexts and enhance the practical relevance of courses.

1. Industry Mentorship Programs

Engage industry professionals to guide teachers in addressing real-world challenges. For instance, aviation English instructors can collaborate with pilots and ground staff, staying up-to-date with industry developments and adjusting teaching content accordingly.

1. Policy Support and Governance Incentives

Establish cross-departmental collaboration mechanisms, allocate dedicated funding, and implement performance-based incentives. Incorporate industry engagement and technology application into teacher evaluation systems. By integrating Environmental, Social, and Governance (ESG) principles, policies can promote educational equity, optimize resource allocation, and strengthen institutional governance. Specifically, ESG ensures equal access to professional development resources across regions (Equity), improves efficiency in the use of funds and technology (Resource Optimization), and enhances transparency and accountability in policy implementation (Governance). Collectively, these measures support the sustainable integration of industry expertise and language education.

5.2 AI-and Data-Driven Precision Teaching Strategies and Performance Optimization

Traditional teaching feedback methods are often limited by delays and subjectivity, reducing their effectiveness. To overcome these challenges, future ESP teachers should actively leverage AI and data-driven instructional systems to implement precision teaching. By collecting and analyzing student learning behavior data in real time, teachers can dynamically adjust teaching content and methods. This approach not only enhances teaching performance and student learning outcomes but also supports personalized learning and provides technological backing for teacher development and educational equity. The following strategies can help realize these benefits:

1. Real-Time Learning Analysis Tools: Intelligent platforms collect data on students’ homework completion, online participation, and learning trajectories, automatically generating individualized reports that enable teachers to identify knowledge gaps accurately. This facilitates a continuous teaching cycle of diagnosis, feedback, and targeted intervention. For example, visual learning trajectory charts allow teachers to quickly detect differences in students’ mastery of specialized English terminology or language structures, enabling focused instruction and practice.

2. Personalized Learning Paths: Big data analytics can dynamically recommend learning content and resources based on each student’s progress, test scores, and cognitive level. This level of personalization increases learning efficiency, reduces repetitive teacher workload, and improves the accuracy and fairness of lesson planning.

3. Adaptive Teaching Platforms: Leveraging machine learning and big data, adaptive platforms analyze students’ learning styles and mastery levels, adjusting teaching materials and methods in real time to better meet individual needs. This approach leads to improved teaching outcomes and more responsive instruction.

4. Cross-Disciplinary Data Integration: To provide ESP teachers with a comprehensive view of students’ overall academic performance, universities should develop systems that integrate data across multiple disciplines. By combining language learning data with results from professional courses and practical skill assessments, teachers gain a holistic understanding of students’ learning and can make more informed, systematic instructional decisions.

5.3 Intelligent Technology-Assisted Personalized Growth

To address the challenges posed by fragmented career development paths, intelligent technology-assisted personalized growth represents a critical direction for ESP teacher development. By leveraging intelligent tools to support career planning and enhance proficiency in educational technologies, future ESP teachers can pursue continuous, individualized professional growth. The following strategies are recommended:

1. Personalized Career Development Platform

Develop a dedicated career development platform for ESP teachers that utilizes AI and data analytics to provide tailored guidance. The system can generate personalized career enhancement plans based on data such as teaching performance, student feedback, and research output, while recommending relevant training programs, workshops, and professional certifications. This enables teachers to pursue targeted development paths and maintain continuous progress in their careers.

1. Continuous Learning and Skill Enhancement

Encourage teachers to engage in ongoing learning and skill development through intelligent technologies. Online learning platforms, massive open online courses (MOOCs), and other digital resources allow teachers to improve professional knowledge and skills anytime, anywhere. Emphasis should be placed on areas such as educational technology, data analytics, and domain-specific expertise, ensuring teachers remain competitive in a rapidly evolving academic and professional landscape.

1. AI-Based Teaching Reflection Tools

Integrate AI technologies to develop teaching reflection systems that assist teachers in post-class self-assessment and reflection. These systems can automatically generate reports based on classroom interaction data and student performance, helping teachers identify strengths and areas for improvement. Such tools foster self-directed growth and support the enhancement of teaching effectiveness through actionable, data-driven insights.

1. Collaborative and Shared Career Growth Communities

Establish professional growth communities for ESP teachers using intelligent platforms to facilitate collaboration and experience sharing. Teachers can exchange teaching strategies, learning resources, and success stories, receiving peer support and feedback. The platform can also recommend relevant industry experts, conferences, and professional activities, helping teachers broaden their perspectives, expand networks, and strengthen professional competencies.