Artificial Intelligence-Driven Transformation of Foreign Language Majors at Local Universities under China’s New Liberal Arts Initiative: Opportunities, Challenges, and Pathways ()
1. Introduction
China’s New Liberal Arts Initiative was placed on a formal national agenda at the New Liberal Arts Construction Conference held in November 2020. The conference declaration identifies moral education, interdisciplinary integration, technology-enabled teaching, cultural inheritance and innovation, and service to national and regional development as central priorities (Ministry of Education of the People’s Republic of China, 2020). Four priorities directly inform the framework used here. They are humanistic and ethical development, interdisciplinary integration, careful use of digital technology in teaching, and responsiveness to social and regional needs.
AI is now used across the teaching process. In foreign language majors, it is changing classroom work, language services, and the way students demonstrate competence. The risks are familiar but serious: inaccurate content, opaque authorship, privacy breaches, and academic misconduct. These changes force foreign language majors to reconsider the relationship among language proficiency, specialized knowledge, digital competence, and higher-order thinking.
The issue looks different at local universities. These institutions are expected to support regional economies and communicate local culture to wider audiences, but many have limited faculty capacity, digital infrastructure, and opportunities for sustained industry collaboration. Reform models designed for leading research universities may not travel well to such settings. Existing studies often discuss foreign language education across the higher education sector. Fewer place the regional responsibilities and resource constraints of local universities at the center of the analysis. This article, therefore, brings together the educational priorities of the New Liberal Arts Initiative, the changes associated with AI, and the conditions under which local universities operate.
Source selection followed the questions posed in the article. Policy evidence came from the official website of China’s Ministry of Education. The Chinese studies address at least one of four topics: the New Liberal Arts, reform of foreign language majors, the institutional conditions of local universities, or AI-supported teaching. Peer-reviewed work was preferred. A national graduate-employment report was also retained because it documents changes in undergraduate majors. International sources were limited to work published after the public release of ChatGPT. They had to address language teaching, second-language writing, teacher or learner competence, or classroom use. Review studies received priority; individual empirical studies serve only as bounded classroom examples. The problems discussed below recur in the selected sources. They should not be read as estimates of how often each problem occurs across all local universities.
2. Rationale for the Digital and AI-Enabled Transformation
of Foreign Language Majors at Local Universities
Digital and AI-enabled change is not simply a matter of replacing one set of tools with another. It reaches into educational aims, classroom organization, assessment, and the distribution of institutional resources. Three concerns shape the discussion: the educational priorities of the New Liberal Arts Initiative, the changes AI brings to teaching, and the practical conditions of local universities.
2.1. Educational Priorities under the New Liberal Arts Initiative
The policy agenda asks universities to reconsider how disciplinary knowledge is organized and what graduates should be able to do (Ministry of Education of the People’s Republic of China, 2020). Information technology in humanities education still needs to serve the broader task of fostering virtue through education. Its value should be judged partly by its contribution to humanistic development and social responsibility. Technology can also open space for inquiry across disciplinary boundaries and for the creation of new knowledge. Used with care, it may help humanities education respond more closely to national and social needs (Xia, 2022).
Language proficiency therefore remains fundamental to foreign language majors. Graduate outcomes, however, should also include cultural understanding, reflection on values, intercultural interpretation, and a sense of responsibility. These abilities develop through sustained language study and concrete tasks. They should not be added as isolated topics at the edge of the curriculum.
Interdisciplinary education should begin with a real problem rather than a loose collection of subjects. Language, cultural experience, and relevant disciplinary knowledge can then become part of the same process of inquiry (Dong & Ran, 2023). At local universities, course content may draw on regional industries, local cultural resources, the needs of primary and secondary education, and demand for international communication services. Students can use a foreign language to address identifiable problems in these settings.
Such changes require a stable foundation. Sustained language training and essential humanistic knowledge are not expendable. Cutting core language courses merely to make room for other subjects would be risky. Students may leave with neither a secure command of the language nor a systematic understanding of the new field (Zha, 2018). The central task is to define how the foreign language core relates to knowledge from other disciplines and then build that relationship into course assignments and assessment.
2.2. How AI Changes Foreign Language Teaching
AI does not affect every teaching activity in the same way. Basic translation, routine searches, and standardized language correction follow relatively stable procedures and involve substantial repetition. Machines can handle parts of this work. The teacher’s role then shifts toward setting standards for the task, checking the output, revising it where necessary, and remaining accountable for the result. AI can also create communicative settings and supply practice materials. Even so, teachers must decide whether the output is accurate, appropriate, and fit for its intended audience (Li, 2024).
Once AI becomes part of day-to-day teaching, the familiar division of responsibilities begins to change. Teachers and students need clearer roles, and assessment must change with them. Under teacher supervision, AI can assist with repetitive work such as explaining basic concepts, offering initial feedback, and simulating communicative situations. This does not make the teacher less important. It can leave more time for course design, work that develops higher-order thinking, and close guidance on when and how students should use AI tools.
Research conducted outside China also presents a mixed picture. A systematic review of 70 empirical studies in ESL and EFL education found additional learning opportunities, personalized support, and assistance for teachers. It also identified inaccurate information, privacy leakage, and academic dishonesty as recurring concerns. Most of the studies focused on writing, while rigorous evidence on longer-term performance and motivation remained limited (Lo et al., 2024). A broader scoping review likewise calls for further empirical work, clear ethical guidance, and continuing professional development for teachers (Law, 2024). Work written specifically for language educators also stresses that teachers and learners need the digital competence to use generative AI ethically and effectively (Kohnke et al., 2023).
2.3. Local Implementation Conditions and Competency Goals
AI-supported teaching at local universities cannot be planned apart from the regions they serve or the resources they actually have. Academic work in the humanities and social sciences should remain connected to the university’s regional service role. This may involve drawing on local resources and working across disciplines on issues related to distinctive local conditions and major regional industries (Tang, 2022).
Resources are limited, however. Foreign language majors can concentrate on one or two areas in which regional demand and existing strengths meet. Four considerations provide the test. These are stable regional demand, relevant faculty expertise, external partners able to support recurring projects or placements, and curriculum resources that the university can maintain. An area that depends on a single employer, one faculty member, or short-term funding is not yet a sustainable specialization. Once an area has met these tests, the university can develop it gradually through relevant courses, a better-balanced teaching team, and practice-based learning.
3. Current Problems in the Transformation of Foreign
Language Majors at Local Universities
The New Liberal Arts Initiative and the spread of digital technologies are advancing at the same time. Within the selected literature, four practical weaknesses recur. They are treated here as diagnostic categories, not as statistical estimates of their prevalence.
3.1. Major Homogeneity and Misalignment with Regional Needs
Many foreign language majors at local universities have similar educational objectives, curriculum structures, and models of practice-based learning. The distinctive profiles described in institutional plans are not always supported by coherent courses, dedicated faculty, or long-term partnerships. Regional economies differ. School education, foreign trade and manufacturing, cultural tourism, and international communication each make different demands on foreign language graduates. Majors need to draw systematically on local resources and respond to actual regional needs (Tang, 2022). A general call for “integrated competence” is not enough. Graduate outcomes need to state what students should know and be able to do in the occupational roles associated with the major’s chosen direction.
3.2. Overemphasis on Language Skills at the Expense of
Integrated Competencies
Listening, speaking, reading, writing, and translation remain the foundation of foreign language education. The concern is not that these abilities have lost their value. Rather, they do not by themselves define a distinctive graduate profile. Research on foreign-language undergraduates at local universities points to the importance of combining language ability with field-specific knowledge, practical experience, and sustained career support (Wei et al., 2018).
The rapid growth of AI as an undergraduate major between 2018 and 2022 shows that universities are changing their major portfolios in response to digital technologies. It does not demonstrate a decline in jobs that require foreign-language expertise (MyCOS Research Institute, 2023). The practical question for local universities is how to connect language study with relevant knowledge, substantial practicum work, and career guidance.
3.3. Limited Integration of Digital and AI Content into the
Curriculum
Many foreign language majors introduce AI through special lectures, short training sessions, or stand-alone electives. Core courses in writing, translation, intercultural communication, and professional practice have yet to integrate digital and AI content on a regular basis. Generative AI has already become part of many students’ day-to-day study. Yet regular use does not always mean informed use. Many students remain unsure about the rules they should follow. Few have received systematic training in checking generated material or keeping a complete record of how the tools were used.
In second-language writing, generated text should not be treated as a finished product. Students need to check its content and preserve enough of the process to explain how the tool shaped the work they submit (Barrot, 2023). These practices belong inside core courses and assessment, not only in general statements on academic integrity.
3.4. Gaps in Faculty Capacity, Platform Support, and Governance
Much of the existing course content was not designed for classrooms in which AI is routinely used. Software operation is only one part of the job. Teachers must also design learning tasks, examine machine-generated material, and deal with technical and ethical risks. As teaching changes, they need to reconsider how they define their professional expertise and their role as educators (Yu, 2026).
The same concern appears in international research. Teacher readiness involves more than familiarity with a tool. It includes pedagogical judgment, verification, task design, and the ability to set ethical limits on use (Kohnke et al., 2023; Law, 2024). These demands are difficult to meet when platforms, technical support, workload arrangements, and institutional rules remain underdeveloped.
4. Opportunities and Challenges in AI-Driven Transformation
AI is changing the work done in foreign language education and the way foreign language majors are organized. It creates room for new courses and locally grounded teaching resources. At the same time, it forces universities to revisit language foundations, faculty preparedness, and governance.
4.1. Opportunities
When AI can handle basic language conversion, classroom time need not be spent on the same tasks as before. More attention can go to understanding context, interpreting culture, checking facts, and planning communication. These activities respond more directly to the wider demands of cross-linguistic communication (Cao & Zhao, 2022).
One possible course model combines a foreign language, a field of specialization, and AI. It can connect specialized study with research practice, although no single discipline can supply all the knowledge such a course requires (Dong & Ran, 2023; Zheng, 2024). Local industries and cultures can also provide material for corpora, terminology databases, and project repositories. Foreign language teachers, technical staff, and industry practitioners can develop these resources together for communication through text, images, audio, and video (Cao & Zhao, 2022).
Classroom evidence remains limited. In a four-week qualitative case study involving 13 university language learners in Turkey, students reported benefits for writing, grammar, vocabulary, motivation, and engagement. The small, single-site design does not support broad causal claims (Karataş et al., 2024). For local universities, such findings justify careful trials rather than wholesale adoption.
4.2. Challenges
When technology performs part of the routine work once used for language practice, universities need to distinguish between baseline proficiency and the wider ability to use language in complex settings. Language ability remains essential. The curricular question is how to connect it with specialized knowledge, intercultural judgment, and the capacity to evaluate AI-assisted work. Foreign language majors therefore need to choose specialized areas that fit their institutional circumstances while retaining a firm foundation in language and the humanities (Zha, 2018).
The sustainability of reform also depends on faculty capabilities and organizational support. Faculty need competence in platform operation, task design, output evaluation, differentiated guidance, and ethical decision-making. Factual inaccuracies, algorithmic bias, privacy breaches, and academic misconduct are related risks. Users need to verify AI output and judge whether it is fit for the intended purpose. Data collection and processing must also meet requirements for informed consent and privacy protection (Li, 2024; Lü, 2019). International reviews show that these risks are not confined to one national setting, while also noting that the evidence base remains uneven across language skills and study designs (Law, 2024; Lo et al., 2024).
5. Pathways for AI-Driven Transformation at Local
Universities
For local universities, transformation should be selective and feasible rather than led by technology alone. The proposed framework has five competency domains: foreign language proficiency, disciplinary or industry-specific knowledge, capacity for human-AI collaboration, intercultural communication competence, and humanistic and ethical judgment. These domains become useful for curriculum design only when departments translate them into work that students can demonstrate. Table 1 gives one observable graduate outcome for each domain. Individual departments can set performance levels that match the language taught, the chosen specialization, and local institutional conditions.
Table 1. Competency domains and observable graduate outcomes.
Competency domain |
Observable graduate outcome |
Foreign language proficiency |
Produce and orally present a coherent, audience-appropriate text at the level set by the major, then revise it in response to feedback. |
Disciplinary or industry-specific knowledge |
Use concepts and evidence from the selected specialization to analyze a documented regional problem in the foreign language. |
Capacity for human–AI collaboration |
Complete an approved AI-assisted task, retain the prompts, outputs, and source checks, and explain the revisions made before submission. |
Intercultural communication competence |
Adapt the same locally grounded message for two different audiences and explain changes in language, medium, and cultural framing. |
Humanistic and ethical judgment |
Identify factual, privacy, bias, and authorship risks in an AI-assisted product and propose a compliant revision. |
Six practical routes link the framework to major positioning, curriculum and instruction, faculty development, local resources, and ethical governance.
5.1. Align Major Positioning with Regional Needs
Major positioning should begin with evidence about regional demand and institutional capacity. Industry interviews and graduate-employment tracking can identify recurring occupational needs. An internal review should then establish whether the university has relevant faculty expertise, courses, data and technical resources, and partners able to provide sustained projects or placements.
These four considerations—regional demand, faculty expertise, partner capacity, and sustainable curriculum resources—provide the criteria for selecting one or two areas of specialization. A proposed area should be postponed when an essential form of support is absent. This is preferable to dispersing limited resources across several short-lived directions. The final choice should draw on local resources and leading regional industries while remaining broad enough to support graduates over time (Tang, 2022).
5.2. Structure the Curriculum in Modules
A modular curriculum should maintain a clear relationship among the disciplinary foundation, specialized study, and digital and AI-enabled practice. Language and humanities courses develop foundational abilities in comprehension, expression, and cultural understanding. Specialization modules provide knowledge consistent with the major’s stated focus. Digital and AI practice modules may cover AI fundamentals, corpus methods, machine translation evaluation, digital communication, and project-based training.
New modules should not come at the expense of a coherent core curriculum in foreign language studies merely to make the major appear interdisciplinary (Zha, 2018). Tasks spanning several modules can instead address the same problem. Students then have to integrate language proficiency, specialized knowledge, and digital and AI methods.
5.3. Create an Integrated Cycle of Human–AI Instruction and
Assessment
Human–AI collaborative instruction should begin with clear lines of responsibility. Teachers define the problem, assessment criteria, and boundaries for tool use. AI can supply materials, create scenarios, and offer initial feedback. Students must select evidence, verify content, explain their revisions, and take responsibility for the final product.
Classroom work can follow a sequence of independent effort, tool-assisted work, comparison and verification, collaborative revision, and a short account of the process. Assessment should consider not only the final product but also how it was produced. Records of prompts, outputs, checks, and revisions make that process open to review. This approach is consistent with recommendations for second-language writing with generative AI (Barrot, 2023). Technology alone will not guarantee stronger self-monitoring. One small-sample study found that a smart learning community produced no significant change in indicators of learning-process monitoring and evaluation (Cai & Lin, 2022).
5.4. Build an Interdisciplinary Teaching Team
Faculty development should cover the principles and risks of AI tools, pedagogical integration, research innovation, and ethical governance. Foreign language faculty remain responsible for language and cultural objectives. Technical staff support tool use, while communication specialists and industry practitioners contribute field-specific contexts. In the AI era, teachers’ roles increasingly include instructional design, guidance in higher-order thinking, socio-emotional engagement, and ethical stewardship (Yu, 2026; Xu & Gu, 2025).
These roles can shape courses and projects only when they are translated into clear responsibilities within the team. International research also treats pedagogical judgment, verification, and ethical use as central to teacher readiness for generative AI (Kohnke et al., 2023; Law, 2024). Institutional arrangements matter as well. Curriculum development, resource development, joint lesson planning across departments, and supervision of student projects need to be recognized in faculty workload and performance evaluation.
5.5. Develop Locally Grounded Resources and Collaborative
Practice Projects
The value of local resources should be judged by their usefulness for teaching and regional service. Universities can build bilingual repositories based on local industries, cultures, and public services while keeping complete records of permissions, versions, and revisions. AI should be used only for the preliminary organization of source material. Faculty, students, and industry practitioners must jointly verify the content.
Collaborative projects with local partners need to be feasible and sustainable. Intercultural communication projects should address language, medium, and audience and must include procedures for information review (Cao & Zhao, 2022). A small, well-maintained repository tied to recurring courses is more useful than a large collection that cannot be checked or updated.
5.6. Strengthen Assessment and Ethical Governance for AI Tools
Universities should adopt tiered rules for AI use in different teaching contexts and clearly distinguish among permitted, restricted, and prohibited uses. Data collection and storage should follow standards for informed consent and privacy protection. Institutions also need procedures for dealing with inaccurate AI-generated content, undeclared assistance, and other forms of academic misconduct (Xu & Gu, 2025).
Written guidance, records of the learning process, and course-level academic integrity standards can turn general rules into routine practice. Students should know when disclosure is required, what evidence of tool use must be retained, and who is accountable for the submitted work. These rules need periodic review as tools, institutional platforms, and assessment practices change.
6. Conclusion
AI is changing decisions about major positioning, curriculum and instruction, faculty responsibilities, and quality governance. At local universities, those decisions should remain tied to the New Liberal Arts agenda and to the institution’s regional service mission. The five-domain framework in this article retains foreign language proficiency as its base. It adds specialized knowledge, human-AI collaboration, intercultural communication, and humanistic and ethical judgment. The outcomes in Table 1 give departments a starting point for course design and assessment.
The six pathways apply the framework to specialization, modular curricula, classroom responsibilities, teaching teams, local resources, and governance. There is no single model for every institution. A local university should proceed only where regional demand, faculty expertise, partner capacity, and sustainable curriculum resources can support continued work.
The limits of the argument are equally important. This article is a conceptual analysis of selected policy and research materials. It neither measures the prevalence of the problems identified nor tests the effectiveness of the proposed framework. Future research can examine the framework through course-based trials, graduate tracking, employer feedback, and comparisons among institutions with different language offerings and regional settings.
Acknowledgements
This article is an interim outcome of the Chongqing Municipal Education Commission educational reform project “Exploration and Practice of Aligning Foreign Language Majors at Local Universities with Advances in Language Intelligence under the New Liberal Arts Initiative” (Project No. 233398), led by Yubo Zhang.