Abstract

Against the backdrop of the deep integration of artificial intelligence into higher foreign language education, this study selects undergraduate talent training programs for foreign language majors from four Chinese universities as research samples: Beijing Foreign Studies University, Shanghai International Studies University, East China Normal University, and Shanghai Jiao Tong University. And it conducts a comparative analysis of their 2024-2025 talent training programs. The findings reveal significant disparities among the four universities in the number of AI-related courses offered and their credit proportions, giving rise to three distinct technology integration models, namely Embedded, Parallel, and Integrated, which correspond to three training paradigms: the feature-oriented paradigm, the education-integrated paradigm, and the interdisciplinary cross-integration paradigm. Meanwhile, common issues are observed across all four institutions, including insufficient depth of AI-integrated courses, a shortage of interdisciplinary teaching staff, and inadequate evaluation systems. Based on these findings, this paper proposes targeted recommendations to inform the digital transformation of foreign language programs in higher education.

Keywords

Language Planning, Artificial Intelligence, Foreign Language Programs, Talent Training Programs, Comparative Study

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1. Introduction

The rapid advancement of artificial intelligence is profoundly reshaping the landscape of foreign language (FL) education in higher education, “AI + foreign language” (AIFL). From intelligent translation tools to personalized learning platforms, AI applications in foreign language instruction and proficiency assessment are becoming increasingly widespread. In 2025, the Ministry of Education and eight other departments jointly issued the Opinions on Accelerating the Digitalization of Education, explicitly calling for “promoting AI-assisted educational transformation” and “integrating artificial intelligence technologies throughout the entire process and all elements of education and teaching”.

Against this policy backdrop, the question of how foreign language programs can adapt to AI development, reform their talent cultivation models while preserving the distinctive strengths of the discipline, and achieve effective integration with AI has become an urgent issue in contemporary foreign language education reform.

The impact of AI on foreign language majors has moved beyond theoretical discussion to tangible reality. Liu (2026) [1] argued that the mission of foreign language education should expand from language skill training to encompass regional studies, international communication, and cross-cultural competence, thereby serving China’s broader strategy of high-level opening-up.

Existing research has begun to address the intersection of AI and foreign language education from various perspectives. Using bibliometric methods, Qu & Yan (2025) [2] analyzed 382 publications from both Chinese and international databases spanning 2014 to 2024, revealing that the field has formed relatively stable core journal clusters. Research hotspots cover multiple dimensions, including virtual reality applications, AI-enhanced learner engagement, computer-assisted language learning, teacher roles, and writing instruction.

Other studies have focused on the risks of AI intervention in language learning, including cognitive outsourcing, identity displacement, and emotional hollowing, emphasizing the need to maintain learner agency, cultural awareness, and emotional authenticity in human-AI collaborative frameworks Gu et al. (2025) [3]. Qian (2025) [4] further argued that generative AI should be understood not merely as a tool but as an active agent that fundamentally restructures the entire foreign language education ecosystem.

While these studies have contributed valuable insights, they predominantly concentrate on pedagogical processes, technological applications, or theoretical frameworks, leaving a notable gap in comparative analysis of how different types of Chinese universities integrate AI into foreign language talent training programs. To address this gap, this study conducts a comparative analysis of the 2024-2025 undergraduate talent training programs from four representative Chinese universities across three dimensions: course types and distribution, integration models, and curriculum articulation and continuity.

2. Literature Review

In recent years, the integration of artificial intelligence and foreign language education has emerged as a focal point of scholarly attention. Existing research predominantly concentrates on three major areas: foreign language education policy, AI-empowered foreign language talent cultivation, and intelligent teaching applications. Building upon prior studies, this paper further formulates its research questions.

2.1. Research on Foreign Language Education Policy

Language planning theory offers a valuable analytical lens for examining foreign language talent training programs. Shen (2018) [5] reanalyzed college foreign language teaching reform from the perspective of language policy and planning, pointing out that the longstanding debate between the instrumental and humanistic orientations of college foreign language teaching stems from an unclear definition of their respective values. His analytical framework provides a reference for this study’s examination of curriculum design and program positioning.

Lu & Li (2021) [6], drawing on language planning goal theory (Kaplan & Baldauf, 1997), divided China’s foreign language education planning into three stages, analyzing policy changes from three dimensions: status planning, acquisition planning, and prestige planning. Their analytical framework can be extended to this study’s investigation of curriculum design and program positioning. Wang et al. (2024) [7] proposed six key elements involved in language education management, providing theoretical support for understanding the rationale behind the formulation of foreign language talent training programs in higher education institutions.

Research on foreign language education policy from the language planning perspective has developed relatively mature theoretical frameworks and analytical approaches. However, existing studies tend to focus predominantly on macro-level policies or historical changes, with comparative studies on the talent training programs of university foreign language majors in the context of artificial intelligence remaining relatively scarce.

2.2. Research on “AI + Foreign Language” Talent Training

Zhao & Wang (2024) [8] conducted a multi-case comparative study of foreign language programs at QS top-ten universities, analyzing their training objectives, curriculum design, and resource conditions. Their findings indicate that top-tier institutions generally prioritize interdisciplinary talent cultivation and continuously optimize their curricula and teaching resources.

From a domestic perspective, Gong (2026) [9] examined the new humanities and social sciences transformation of foreign language universities under the national strategy of building a leading country in education. He proposed a dual-track system combining “foreign languages +” and “+ foreign languages” models, aimed at cultivating talents with global competencies for addressing complex challenges. His analysis provides a valuable framework for understanding how Chinese foreign language universities are restructuring their cultivation systems in response to AI disruption and national strategic needs.

Hong & Zang (2025) [10] further extended the discussion to postgraduate level, proposing a “four-dimensional competency framework” (intercultural communication, professional knowledge application, interdisciplinary thinking, and digital technology application) and a “three-stage progressive” cultivation pathway. Although their study focuses on postgraduate education at science and engineering universities, its competency-based approach offers useful reference points for undergraduate programs.

Existing studies predominantly focus on top-tier universities and curriculum design, leaving a notable gap in systematic comparison of AI integration strategies across different types of domestic universities from a language planning perspective.

2.3. Research on the Integration of Artificial Intelligence and Foreign Language Education

Distinct from talent training model research, which emphasizes curriculum systems and program positioning, studies on the integration of AI and foreign language teaching are more concerned with the specific application modalities of technology in teaching practice. Wang and Chen (2026) [11] identified five major application scenarios, namely teaching assistance, language learning and skills training, interactive learning, personalized learning, and intelligent assessment, providing a categorical framework for understanding the ways AI technology is applied in foreign language instruction.

Yang & Wang (2025) [12], from the perspective of technology integration, proposed an analytical framework for AI integration into the foreign language teaching ecosystem, arguing that AI applications are progressively extending from mere tool use to a comprehensive transformation of teaching models and learning environments. Sun & Liu (2025) [13] systematically reviewed the application outcomes of cutting-edge technologies such as generative AI and intelligent assisted instruction systems in foreign language teaching, offering a reference for understanding the multifaceted integration of AI and foreign language education.

However, current research primarily concentrates on pedagogical processes or technological tools, and there remains a gap in comparative studies of the similarities and differences in “AI + foreign language” integration strategies among domestic university foreign language programs.

2.4. Research Questions

Synthesizing the existing literature, it can be observed that while research on artificial intelligence and foreign language education is relatively abundant, comparative studies of talent training programs in university foreign language majors remain limited. First, there is a lack of systematic comparison of talent training programs across domestic university foreign language majors. Second, there is insufficient research from a language planning perspective on AI integration strategies, including course type distribution, integration models, and curriculum articulation design. Third, a systematic investigation of the similarities and differences among universities in their “AI + foreign language” integration approaches has yet to be established.

Accordingly, this paper proposes the following two core research questions:

1) In the context of artificial intelligence, what common characteristics are exhibited by the foreign language talent training programs?

2) What are the major differences among these universities in their approaches to integrating AI into foreign language talent cultivation?

Through a comparative analysis of the training programs of the four universities, this paper aims to further elucidate the commonalities and differences in “AI + foreign language” talent cultivation among different institutions, thereby providing reference for curriculum reform and talent development in foreign language programs.

3. Research Design

3.1. Research Objects

This study balances specialized foreign language universities and comprehensive universities, selecting foreign language programs from four representative institutions: Beijing Foreign Studies University (BFSU), Shanghai International Studies University (SISU), East China Normal University (ECNU), and Shanghai Jiao Tong University (SJTU).

BFSU and SISU rank among China’s top specialized foreign language universities, whose disciplines of Foreign Languages and Literatures consistently occupy leading positions nationwide. BFSU devotes itself to cultivating elite foreign language talents, focusing on national international communication and foreign-related personnel training, and serves as a benchmark for the traditional foreign language education model. SISU took the lead in carrying out digital reform for foreign language majors and actively explored the integration approaches between artificial intelligence and foreign language programs, representing a typical case of transformative innovation among specialized foreign language universities.

As a key national normal university, ECNU boasts profound humanities and social science foundations, centers on the cultivation of foreign language education talents, and has accumulated rich reform experience in embedding AI into foreign language classroom teaching and innovating training models for normal foreign language majors, which aligns with the educational characteristics of normal universities. SJTU possesses world-class artificial intelligence and computer disciplines, fully leveraging its strengths in interdisciplinary integration of arts and sciences, and serves as a model for intelligent foreign language education in comprehensive universities.

Among various foreign-language majors, English boasts the broadest institutional coverage. It is widely offered in foreign-language universities, general universities and comprehensive universities alike. Sufficient public data are available concerning its enrollment scale, curriculum systems, faculty and graduate outcomes. It avoids the limitations of minority-language majors, such as limited host institutions, uneven sample distribution and highly divergent educational orientations. Consequently, confounding variables beyond major-specific attributes can be controlled. Supported by consistent observation indicators, it establishes a shared research foundation for cross-type comparative studies across the three categories of universities.

This paper takes the undergraduate foreign language programs, which mainly in the four universities as core texts of talent training programs. It focuses on analyzing core contents including AI-related training objectives, curriculum design, and course arrangements for comparative research.

3.2. Data Collection

To guarantee the authenticity, authority and completeness of research data, core data of this paper are official documents publicly released on the Academic Affairs Office and School of Foreign Languages websites of the four universities, including undergraduate talent training programs, teaching plans and course syllabi. All data have been screened, verified and sorted, with invalid information eliminated for subsequent analysis.

3.3. Data Analysis

This study mainly adopts comparative research method and content analysis method to compare and analyze the talent training programs of foreign language majors in the four universities. First, it sorts and categorizes curriculum types, course quantities, credit structures, and training objectives specified in the programs. Next, it compares disparities among the four universities in terms of AI-related curriculum provision, integration approaches, and talent training positioning.

This paper conducts analysis from three dimensions: training objectives, curriculum design, and integration pathways. It examines whether universities incorporate AI literacy and interdisciplinary application capabilities into their talent training systems, and whether such design conforms to the national development trend of cultivating interdisciplinary foreign language talents.

It also systematically compares the categories, contents, class hours, and credit allocation of AI-related courses in each university to analyze the characteristics and priorities of curriculum provision, and sorts out the integrated pathways and school-running features of AI and foreign language education adopted by each university (See Table 1).

4. A Comparative Analysis of Training Programs

This research selects the curriculum systems of English majors in four universities as research samples. The research is carried out from multiple dimensions including the quantity and types of AI-related courses, the integrated mode of “AI + Foreign Languages”, and typical case analysis. It aims to clearly demonstrate the current integration status, distinctive differences and application trends between English majors in universities and artificial intelligence technology.

Table 1. Study Sample based on analysis of “AI + Foreign language” courses.

4.1. The Common Characteristics of “AI + Foreign Languages” Cultivation

The four universities share many commonalities in the construction of “AI + foreign language” talent cultivation. In response to the development needs of foreign language disciplines under the background of digital transformation, universities have proactively incorporated artificial intelligence into the overall professional construction plan. While persistently strengthening the foundation of students’ professional language abilities, they have further expanded the teaching content related to digital literacy, actively opened up interdisciplinary teaching tracks, and continuously explored a compound foreign language talent cultivation model that meets the dual demands of the translation industry and the digital market.

4.2. The Individual Differences of “AI + Foreign Language” Cultivation

Although the four universities have reached the above consensus on the goals of talent cultivation and construction ideas, due to the influence of factors such as educational positioning, disciplinary advantages, and development directions, the integration paths of “AI + foreign languages” in each university show obvious differences, forming different teaching and training models.

4.2.1. Analysis of the Current Situation of AI-Related Courses

Based on a statistical survey of course offerings, it was found that AI-related courses at these four universities are predominantly professional courses, supplemented by general education and compulsory public courses. This indicates that the application of AI technology in English language teaching has progressed beyond the basic popularization stage and entered a new phase of systematic cultivation that is deeply integrated with the discipline. The chart (Table 2) below presents the detailed distribution of course samples (English major) from the four universities.

Table 2. Distribution and proportion of course offerings by category across the four universities.

Based on the course distribution data in the table above, it can be seen that SISU ranks first among the four universities in the total number of AI-related courses and “AI + foreign language” courses combined. SJTU offers only a small number of pure AI courses, with a greater emphasis on interdisciplinary courses. ECNU and BFSU, however, have not yet established standalone compulsory AI courses and currently deliver AI education solely through integrated/embedded courses.

Overall, the four universities show obvious hierarchical differences in the number of AI-related courses offered for foreign language majors. Relying on their respective school-running characteristics, different types of universities have formed curriculum layouts with distinctive features.

4.2.2. Analysis of the Current Situation of AI-Related Courses Credits

To further analyze the specific pathways of “AI + Foreign Language” integration adopted by different universities, this paper compares the English program curricula of the four institutions in terms of course credit structure, course types, and talent cultivation orientation. Compared with course quantity statistics, credit proportion more directly reflects the actual weight and institutional positioning of AI-related courses within the overall curriculum framework.

Table 3. The distribution and proportion of credits for courses in the four universities.

As can be seen from the chart (Table 3) and the analysis of specific courses, SISU demonstrates the most comprehensive integration of AI and foreign language disciplines among the four institutions. Its curriculum covers multiple areas including translation technology and language data processing, with a relatively rich variety of AI-related course types and a high overall credit proportion, which points to a highly systematic approach.

Drawing on its strong engineering background, SJTU offers technology-oriented courses such as “Principles and Applications of Large Language Models” and “Natural Language Processing” as foundational support. By integrating technology with the foreign language discipline, the university pursues in-depth studies in hands-on areas including natural language processing and large-model applications, with a particular focus on technical operational capabilities and the translation of interdisciplinary knowledge into practice. Among the four universities, SJTU stands out most prominently in terms of both the depth of technology-discipline integration and its engineering-oriented characteristics.

ECNU takes “AI + Foreign Language Education” as its core focus, having established a comprehensive and coherent talent cultivation system within its professional programs. Built upon its distinctive pedagogical strengths, the university has developed a highly differentiated training model, making it a representative case in the field of “AI + Foreign Language Education”.

BFSU closely aligned with its core institutional characteristics of diplomatic translation and area/country studies, focuses on finely delineated application scenarios in its teaching while deeply integrating AI technologies to accommodate a diverse range of course content. For instance, courses such as AI Translation Technology and Introduction to Language Intelligence are designed to precisely address the translation needs of diplomatic affairs and the data processing challenges in area and country studies. This fully demonstrates the unique advantage that foreign language universities possess in empowering AI integration through their disciplinary strengths.

In summary, the overall differences among the four universities mainly lie in the fact that application-oriented institutions emphasize practical skills, normal universities focus on the implementation of educational scenarios, and foreign language characteristic institutions emphasize the integration of translation and digital humanities. As a result, a clear and distinctive talent cultivation model has been formed.

4.2.3. Analysis of the “AI + Foreign Language” Integration Model

To further address the question of how artificial intelligence can be integrated into foreign language talent cultivation, this research conducts a comparative analysis of the integration models of the four universities, proceeding from two dimensions: curriculum organization and the degree of AI involvement.

Drawing on the characteristics of the English program curricula at the four institutions, and examining the logic of integration between AI technology and foreign language disciplines, the depth of integration, and the forms of curriculum organization, the “AI + Foreign Language” integration models can be categorized into three types: Embedded, Parallel, and Integrated. These three models differ significantly in their application scenarios, technological positioning, and institutional suitability, and are closely aligned with the distinctive features of each university’s educational mission and talent cultivation orientation.

The embedded model constitutes a shallow form of integration, primarily characterized by the incorporation of AI technology and technical methods into core disciplinary courses. Serving as pedagogical aids, these tools facilitate the enhancement of professional skills, including the use of corpus tools in translation practice and the application of natural language processing techniques in linguistic research. A case in point is ECNU’s course “Digital Intelligence-Supported Foreign Language Instructional Design”, which integrates intelligent teaching algorithms while preserving the essential structure of the original course.

The parallel model is a medium-level integration approach. It retains the intact structure of the language and literature curriculum while focusing on AI principles, foundational tools, and their operational logic. Institutions adopting this model offer separate AI-focused compulsory and elective courses that run parallel to traditional foreign language courses, each with its own teaching methodology. Though independently arranged, the two course streams collectively contribute to the development of interdisciplinary talent. A case in point is SJTU, where “AI and Localization Translation” is offered alongside “Comprehensive English”, creating a reciprocal reinforcement between technological and disciplinary learning.

The integrated model, in turn, represents a high-level integration that goes beyond the boundaries of tool-embedding or parallel course arrangements. Through interdisciplinary offerings such as “AI and Translation Studies” and “Literary Reading and Creative Writing”, it fosters a deep synergy between AI and the core capabilities of foreign language majors. This model combines both technological substance and disciplinary attributes, effecting a fundamental restructuring of the integration between AI and foreign language teaching.

5. Discussion

5.1. Optimization Schemes for AI-Assisted Foreign Language Teaching

Against the practical teaching drawbacks uncovered by the three AI integration models of the four universities, this subsection centers on frontline teaching scenarios and delivers actionable improvements covering in-class content design, faculty empowerment and formative classroom evaluation, targeting practical dilemmas such as superficial AI application, disjointed teaching content, teachers’ inadequate technical literacy and insufficient formative assessment.

To address superficial separation between AI tools and regular teaching sessions, institutional positioning shapes its specific path of embedding AI into classroom sessions. Specialized foreign language universities embed hands-on AI training within written translation, interpreting and international communication classes. Normal universities integrate intelligent student data analysis and digital lesson planning into regular foreign language pedagogy courses. Comprehensive engineering universities leverage campus computing resources to arrange large language model practices in linguistics and corpus analysis lectures. Intelligent tools run through in-class lectures, on-site drills and mini-class research tasks, enabling digital upgrading of courses.

Insufficient digital proficiency among frontline foreign language teachers forms the most immediate barrier to AI classroom implementation, which can be addressed through multi-party internal collaboration. Universities regularly host intelligent teaching workshops for course instructors to upgrade their digital teaching competencies. Interdisciplinary talents with dual expertise in foreign languages and AI are recruited to enrich teaching cohorts. Joint teaching with faculty from computer science departments is arranged for chapters concerning translation technology and linguistic big data, offering instant faculty support for integrated classes.

As a fundamental institutional safeguard for sustainable intelligent teaching, classroom evaluation criteria require two-way formative assessment rules covering both teaching and learning. For students, daily course grades incorporate in-class AI translation exercises, intelligent courseware design and small linguistic innovation outputs completed during lessons. For teachers, the quality of AI-integrated lesson design and practical intelligent teaching reforms are included in semester teaching performance reviews. Refined classroom assessment motivates teachers and students to apply intelligent technology in daily lectures, filling institutional gaps supporting single-course digital teaching.

5.2. Optimization Suggestions of Interdisciplinary Foreign Language Talent Cultivation in the AI Era

This subsection focuses on systematic optimization frameworks across three macro dimensions: full-cycle talent training chains, interdisciplinary resource mechanisms and graduation-oriented comprehensive evaluation systems, tackling systemic challenges including homogenized talent output, disjointed training phases, inadequate interdisciplinary resources and graduation assessments overemphasizing linguistic proficiency while ignoring digital literacy.

With a view to coherent four-year undergraduate education, a closed-loop three-tier talent training system spanning four undergraduate years is constructed to resolve long-term disconnection and insufficient progression across different training stages. Foundational modules deliver universal linguistic intelligence training for freshmen to consolidate basic digital capacities for all majors. Professional modules offer major-specific compulsory core courses aligned with institutional characteristics to shape unique interdisciplinary strengths. Advanced modules leverage school-enterprise joint training, long-term research projects and academic competitions to deliver high-level innovation training, forming a coherent four-year growth path and eliminating knowledge gaps across academic years.

Sustained supply of interdisciplinary educational resources underpins stable output of compound talents, yet persistent shortages of such resources constitute a systemic bottleneck limiting long-term talent cultivation, which requires permanent collaborative education mechanisms to guarantee stable resource supply. It focuses on long-term faculty echelon construction: regular cross-department joint faculty development programs are launched to renew foreign language teachers’ technical knowledge year by year. Long-term recruitment of leading liberal-engineering interdisciplinary scholars optimizes faculty structure. Fixed collaborative education mechanisms between foreign language and computer science universities are established to supervise students’ four-year internships and graduation theses, providing sustained resource support for full-cycle talent cultivation.

Centering on graduation outcomes and final training objectives, a comprehensive talent evaluation system covering the entire undergraduate study is reconstructed. This system targets final talent cultivation objectives, weighting for AI linguistic practice outputs are added to students’ comprehensive assessments, internship accreditation and graduation defenses. Such evaluation reshape the overall orientation of talent cultivation from top-level design to realize long-term coordination among institutional positioning, interdisciplinary major construction and supporting software and hardware resources.

6. Conclusions

This paper conducts a comparative analysis of AI and foreign language integration by taking talent training programs of foreign language majors in four universities as research objects. The findings reveal obvious disparities among universities in the quantity, types and training orientations of AI-related courses.

Specialized foreign language universities emphasize the application of AI tools in translation and language services. Normal universities focus on the combination of AI and foreign language teaching. Comprehensive universities highlight interdisciplinary integration and technical competency cultivation. Divergent training modes are closely associated with institutional positioning, disciplinary resources and talent training objectives. Meanwhile, the integration of AI and foreign languages in current universities still faces challenges including unbalanced curriculum provision, insufficient depth of technical courses and limited interdisciplinary coordination.

Generally speaking, artificial intelligence is gradually reshaping talent cultivation models for university foreign language majors, yet prominent disparities persist in integration pathways and training priorities across institutions. How to reasonably introduce AI curricula while preserving the distinctive strengths of foreign language disciplines remains a critical issue requiring continuous exploration in future foreign language education reform.

This paper has certain limitations, the sample scope is restricted, excluding local foreign language colleges, and the research only adopts textual analysis without empirical investigations of actual teaching effects. Subsequent plans include expanding the survey sample and conducting field empirical research to verify the practical implementation effect of the integration.

Fund Program

2026 East China University of Science and Technology Student Innovation and Entrepreneurship Project: Construction and Validation of an Innovative Training Model for Translation Talents—A Diachronic Study Based on the Interpreting Skills Course.

Supported by the Undergraduate Training Program on Innovation and Entrepreneurship grant 202610251115.

Conflicts of Interest

The authors declare no conflicts of interest.

References