Educators’ Perspectives on DeepSeek in ELT: A Qualitative Case Study of Pedagogical Potentials and Pitfalls in Chinese Higher Education

Aim/Purpose: This study aimed to investigate the perspectives of English Language Teaching (ELT) educators on DeepSeek, emphasizing its pedagogical value, practical challenges, and instructional potential in higher education. Background: The integration of artificial intelligence (AI) in ELT is reshaping instructional practices globally, particularly in response to rapid technological advancements and shifts toward digital and student-centered learning. In China, these transformations have been accelerated by national education reforms, globalization, and the COVID-19 pandemic, prompting a reconfiguration of teaching approaches through online, blended, and AI-supported modalities. AI tools, including writing assistants and speech recognition systems, have begun to enhance learner autonomy, engagement, and performance by providing real-time, personalized feedback. Among these tools, DeepSeek has emerged as a promising platform that combines advanced information retrieval and generative capabilities, supporting lesson planning, content development, and academic writing. This paper explores how ELT educators in higher education perceive and apply DeepSeek in their teaching, with a focus on its pedagogical benefits, practical challenges, and instructional potential. Methodology: This study examined a qualitative approach to ELT educators’ perspectives on the benefits, challenges, and instructional potential of integrating DeepSeek into higher education in China. Using purposive sampling, data were collected through open-ended questionnaires from 12 ELT educators at a public Chinese university where DeepSeek has been implemented across academic and administrative functions. Thematic analysis was conducted to examine patterns in participants’ responses across three phases of implementation involving before, during, and after classroom use, to provide an in-depth understanding of DeepSeek’s pedagogical impact. Contribution: This study is one of the few to explore the integration of DeepSeek into ELT in higher education. Unlike more widely studied AI tools, DeepSeek was selected for its emerging use in Chinese educational settings and its distinct instructional features, including structured content generation and multimodal support. By focusing on this specific tool, the study expands the scope of AI in education research and offers new empirical insights into its pedagogical value, implementation challenges, and potential to support personalized and learner-centered teaching. Findings: Findings indicate that DeepSeek offered consistent pedagogical support across three instructional phases (before class, during class, and after class). The most pronounced impact was observed in the before-class phase, where it significantly enhanced lesson preparation efficiency and pedagogical innovation through structured content generation, procedural design, and instructional resource enrichment. During class, DeepSeek supported content diversification, real-time pedagogical adjustments, and student engagement. After class, DeepSeek supported feedback provision, learner autonomy, and extended learning, though its influence was comparatively limited. Overall, the integration of DeepSeek contributed to improved instructional coherence and fostered a shift toward more learner-centered pedagogical practices. Recommendations for Practitioners: This study recommends that practitioners who integrate DeepSeek ensure comprehensive educator training to utilize the tool’s features and functionalities effectively. Additionally, they should focus on maintaining a balance between AI-driven support and traditional pedagogical methods to preserve the human elements of teaching, such as empathy and critical thinking. Practitioners should also consider ethical implications, such as data privacy and potential biases in AI models, and ensure that DeepSeek is used as a complementary resource rather than a replacement for educator expertise. Recommendation for Researchers: Researchers need to understand the evolving role of AI tools such as DeepSeek in enhancing ELT practices and exploring their long-term impact on student outcomes. Future studies should investigate the scalability of AI integration across diverse educational settings and examine how AI tools can be further refined to address emerging pedagogical challenges. Additionally, research should focus on evaluating the ethical concerns associated with AI in education, including data privacy, algorithmic bias, and the implications for educator-student relationships. Researchers are also encouraged to explore the balance between AI and human interaction in fostering a more effective and holistic learning environment. Impact on Society: AI technology-based learning, using DeepSeek, could enhance students’ learning outcomes and assist educators in developing content, leading to a more efficient and effective higher education system. The proper integration of DeepSeek into traditional teaching methods can promote its use and maximize its potential for enhancing learning experiences. Future Research: Additional research should be conducted to explore and measure the impact of DeepSeek on student motivation, engagement, and academic performance. Further studies should investigate its use across different disciplines and educational contexts to evaluate its effectiveness in diverse learning environments.