The 2024-2035 Master Plan on Building China into a Leading Country in Education calls for accelerating the development of a high-quality education system, and establishing an independent knowledge system of Chinese philosophy and social sciences. In line with the goal of building a leading country in education, the discipline of foreign languages and literatures and related programs should keep pace with the times, strengthen the guiding role of innovative theories, and promote interdisciplinary integration to construct an independent knowledge system for the discipline. They should adhere to a needs-based approach to enhance the relevance of foreign language programs, align with national development strategies to broaden both the scope and depth of cultivating internationally competent interdisciplinary talents, and implement political and moral education by developing a system of high-quality original teaching materials. At this new historical juncture, higher foreign language education should remain committed to national priorities, seize historical opportunities, rise to challenges, and break new ground in development.
With its rapid advancement, artificial intelligence (AI) is gradually replacing the instrumental value of the discipline of foreign languages and literatures, but not the ontological value. However, AI has prompted the discipline to engage in deep reflection on its shortcomings in substantive aspects of development, thereby accelerating its substantive and innovative development. To meet national strategic demands, the discipline should establish an independent system of global knowledge and scholarship while cultivating high-level talents equipped with interdisciplinary literacy and creative thinking. In the AI era, the discipline of foreign languages and literatures should aim to construct China's independent knowledge system and talent cultivation system, actively embrace and skillfully utilize AI technologies, and deepen the development of knowledge production, academic innovation, discourse construction and talent cultivation across its five major research areas under the concept of “New Humanism”, in order to demonstrate its irreplaceable value.
GenAI, as the core engine driving the digital transformation of education, is reshaping the practical paradigms of foreign language education through multidimensional synergistic mechanisms. This paper firstly addresses the limitations inherent in technology-enabled foreign language education, and clarifies the transformative significance of GenAI's technical features for forming a new education ecosystem. Subsequently, it examines the interaction of the four dimensions of teaching, learning, assessment and governance, elucidates the internal logic behind GenAI's empowerment of foreign language education, and thus proposes corresponding actionable approaches. In so doing, the paper aims to provide insights and references for the sustainable development of intelligent foreign language education.
The integration of Artificial Intelligence Generated Content (AIGC) into foreign language education is gradually reshaping pedagogical paradigms. Based on a quasi-experimental design and grounded theory analysis, this study introduces a production-oriented and interaction-rich teaching model, and examines the underlying mechanisms linking shifts in learning patterns with student engagement under AIGC-driven conditions.The results reveal that the dialogic, adaptive, and embodied learning modes intertwine and evolve through authentic learner-AI interaction. AIGC integration significantly enhances behavioral and cognitive engagement, while emotional engagement is stabilized through embodied multisensory participation. The learning modes jointly facilitate the deep integration of student engagement, blending into a coherent “interaction adaptation generation” paradigm.Beyond deepening the mechanic interpretation, the study offers both a theoretical framework and empirical evidence for understanding how AIGC can drive paradigm renewal in the digital transformation of language education.
Grounded in the self-regulated learning theory and blended learning theory, this study constructs an AI agent-assisted blended autonomous learning model of foreign languages, aiming to maintain the balance between the technological empowerment and development of autonomous learning abilities. This model emphasizes dynamic alignment of learning objectives and content, interactive design of learning methods and organization, and continuous improvement of learning assessment and feedback, and is implemented through a “planningexecutionreflection” cycle. An interpreting teaching experiment demonstrates that the model significantly improves students' interpreting performance, which is positively correlated with learning stages, while technology acceptance and feedback mechanisms reveal phased cognitive adaptation needs. Based on the experimental findings, the study provides recommendations for optimizing the model implementation, including collaboratively setting goals, explicitly teaching strategies and coordinating emotional support.
Based on the Unified Theory of Acceptance and Use of Technology (UTAUT) and teacher professional development theories, this study developed and validated a model of AI technology integration impacts on foreign language teachers' professional resilience. Analysis of the survey data from 329 Chinese university foreign language teachers revealed that AI technology integration (technological complexity, role transformation, work demands) had a significant impact on teachers' professional resilience (cognitive appraisal, emotional regulation, behavioral coping). Among these factors, role transformation exerted the most pronounced impact, and emotional regulation was most affected. Organizational support and individual adaptability not only significantly buffered the impacts of AI technology integration respectively, but also demonstrated a significant synergistic moderating effect. The study provides empirical evidence and implications for the development of related theories and practice of teacher professional development.
Implicature constitutes a central topic in L2 pragmatic research. The present study, using a self-paced reading task, explores the influence of different types of implicature on L2 implicature processing among Chinese English learners with varying English proficiencies. The linear mixed effect model results show that: (1) types of implicature significantly influence L2 processing, and the respective processing costs of indirect refusals, indirect opinions and ironies increase successively; (2) there are significant differences between advanced and intermediate learners in processing L2 implicature, the former coming out with higher accuracy and shorter response time, and the latter with lower accuracy and longer response time. These findings partially support the conventionality effect, and reveal the pragmatic inferencing mechanisms in L2 implicature processing across different levels of English proficiencies.
Informed by the expectancy value theory (EVT), this study investigates the predictive effects of motivational beliefs on foreign language (FL) learning engagement. Data collected from 356 senior high school students are analyzed using latent moderated structural equation modeling. The results are as follows: (1) expectancy for success, intrinsic value and extrinsic value significantly and positively predict FL learning engagement; (2) effort cost significantly and negatively predicts learning engagement; (3) emotional cost does not significantly predict learning engagement, but significantly moderates the relationship between expectancy for success and learning engagement. The EVT is applicable to studying FL learning engagement, with motivational beliefs as robust predictors of FL learning engagement. The study provides new insights into the influencing mechanism of FL learning engagement and ways to enhance FL learning engagement.
As artificial intelligence (AI) plays an increasingly significant role in the field of education, the issue of how to effectively empower the development and administration of English language tests has become a critical area of inquiry. This paper begins by outlining the current bottlenecks in the development and administration of language tests, and proceeds to examine the integration of AI across various stages of English test development and administration. Taking the Test for English Majors (TEM) as an example, the paper then explores potential pathways for optimizing assessment practices under the guiding principles of “human-centered AI” and “human-in-the-loop”. The paper also reflects on the ethical issues involved in applying AI to language testing, and proposes corresponding solutions and suggestions.
Views of language competence determine the paths and directions of foreign language education. As a significantly influential linguistic competence framework, the Communicative Language Ability (CLA) model has served as a theoretical foundation for developing language proficiency scales and formulating foreign language education policies worldwide. This paper clarifies that the CLA model proposed by Bachman & Palmer (2010) features dual-mode categorization (non-interactive and interactive), and integrates the dual framework of language competence and language use, thereby advancing the paradigm shift in applied linguistics toward sociocognitive approaches. The paper further explores the application of the CLA model to foreign language assessment, materials development and teaching, aiming to enhance the facilitating roles of language competence models in foreign language educational practice in China.
