Research on the Innovative Model of College Public Foreign Language Oral Teaching Driven by Artificial Intelligence Technology ()
1. Introduction
As the core driving force of the new round of technological revolution and new productive forces, artificial intelligence technology has given rise to a large number of innovative products, cutting-edge technologies, new ideas, and new techniques. This has not only injected strong momentum into industrial transformation but also driven innovation and change in the education sector. The deep integration of artificial intelligence with university education has become a key direction for future development. In the field of college public foreign language oral teaching, artificial intelligence technology demonstrates broad application prospects. With continuous breakthroughs in deep learning, natural language processing, and human-computer interaction, the application and popularization of intelligent oral language teaching have been greatly advanced. The deep integration of artificial intelligence technology with foreign language oral instruction not only breaks through the limitations of traditional teaching methods to enrich instructional scenarios but also expands students’ learning resources and pathways. This enables the sharing of high-quality educational resources, thereby driving innovation in university public foreign language oral teaching and comprehensively enhancing teaching quality (Zhang, 2021).
2. Problems and Current Status of Traditional Public Foreign
Language Oral Instruction
College public foreign language oral teaching has long faced the problem of limited effectiveness, which is mainly reflected in five aspects: overly monotonous classroom teaching models, insufficient opportunities for oral practice, uneven distribution of teaching staff and resources, an evaluation system that focuses too much on form, and a lack of student motivation. Using college English oral teaching as an example, this study conducts an in-depth analysis of the root causes and impacts of these issues.
2.1. Classroom Teaching Methods Are Overly Monotonous
In traditional university public English speaking instruction, teachers typically dominate the classroom, with content primarily focused on explaining pronunciation rules, grammar knowledge, and vocabulary usage. This approach lacks simulation of real-life scenarios and interaction between teachers and students. Such a teaching model not only struggles to stimulate active student participation but also limits the development of their oral expression skills. Meanwhile, classroom interaction primarily revolves around group dialogues, where students typically recite and imitate predetermined content. Although students can apply their knowledge relatively proficiently in these exercises, their expressions often become halting, unnatural, or even hindered when entering real-life communication scenarios (Fu et al., 2021). This phenomenon not only indicates the limited effectiveness of traditional oral teaching methods but also impedes the improvement of students’ English speaking skills to a certain extent.
2.2. Insufficient Opportunities for Oral Practice
In university public English speaking instruction, insufficient opportunities for oral practice constitute a prominent issue hindering students’ expressive abilities. Constraints in course scheduling and teachers’ heavy teaching workloads often result in limited actual practice time for students during speaking classes. Meanwhile, universities lack well-developed platforms and resources for oral practice, and students have very limited opportunities to engage in English communication. The shortage of extracurricular speaking practice channels exacerbates this situation, leaving most students in a passive state of “listening more, speaking less”, which makes it difficult to effectively improve their practical communication skills.
2.3. Uneven Distribution of Teaching Staff and Educational
Resources
At present, most universities face common problems of insufficient faculty and resources in public English oral teaching. On the one hand, the number of teachers with extensive oral teaching experience is limited, and gaps still exist in foreign language teachers’ professional competence and teaching proficiency. On the other hand, universities lack well-developed oral practice platforms and supporting resources, making it difficult to create diverse training environments for students (Dashtestani, 2015). In such circumstances, oral language instruction often lacks systematic structure and targeted focus, failing to provide students with high-quality learning support and struggling to meet the personalized developmental needs of learners at different proficiency levels.
2.4. The Evaluation System Places Excessive Emphasis on
Formality
The one-sidedness of evaluation systems has become a significant factor hindering improvements in the quality of university English speaking instruction. Currently, oral assessment in many universities exhibits formalistic and exam-oriented tendencies, placing excessive emphasis on the accuracy of pronunciation and intonation as well as the mastery of test-taking techniques, while neglecting comprehensive evaluation of students’ practical communication skills, linguistic fluency, and cross-cultural understanding. Under this assessment model, students often focus more on linguistic form than on the effective delivery of content in their expressions, making it difficult to genuinely enhance their English oral communication abilities.
2.5. Students Lack Sufficient Motivation to Learn
Motivation is the core factor in mastering a foreign language. However, against the backdrop of deepening economic globalization and the growing importance of spoken English, many students still lack sufficient intrinsic drive. Their learning objectives are often limited to passing exams or meeting course requirements. Due to the disconnect between the learning process and practical application needs, students frequently find spoken language learning monotonous and tedious, making it difficult to sustain long-term enthusiasm. This results in learning outcomes that are hard to consolidate and improve.
2.6. Challenges and Opportunities of AI for Innovative Public
Foreign Language Oral Teaching in Universities
Artificial intelligence technology is profoundly reshaping public foreign language oral teaching in universities, bringing both challenges and creating new opportunities. In terms of challenges, AI may weaken the value of real interpersonal interactions, overly rely on technology, or lead to a decline in students’ emotional communication abilities. At the same time, teachers need to adapt to the role transition from knowledge transmitters to learning guides and technical collaborators, which poses a challenge to traditional teaching models. The opportunities are even more significant, as AI can provide a highly personalized speaking practice environment, supporting students to engage in immersive training at any time, and effectively improving the accuracy and fluency of language expression through real-time feedback and error correction. For example, speech recognition and virtual dialogue systems can simulate diverse contexts, helping students overcome “speaking anxiety” and enhance learning motivation. In short, the combination of AI and foreign language oral teaching has become an irreversible trend. The key lies in balancing technology and humanistic education, empowering teaching innovation with AI, while preserving the emotional and cultural essence of language communication, and promoting the development of foreign language education towards a more efficient and humane direction.
3. Theoretical Foundations for Artificial Intelligence-Driven
Innovation in Foreign Language Oral Teaching
With the rapid advancement of artificial intelligence technology, it holds immense potential to revolutionize public foreign language oral instruction in universities. The application of artificial intelligence not only transforms traditional models of foreign language oral teaching but also drives innovation in teaching methodologies, learning assessment systems, and interactive formats. This study analyzes the implications from the perspectives of constructivist learning theory, sociocultural theory, and multiple intelligences theory.
3.1. Constructivist Learning Theory
Constructivist learning theory emphasizes that learning is an active process of knowledge construction, where students gradually refine their cognitive systems through continuous interaction with their environment, knowledge, and others. In university public foreign language oral instruction, artificial intelligence technology can serve as a vital tool to assist students in more efficiently understanding and mastering linguistic knowledge. Leveraging artificial intelligence systems to identify students’ actual learning needs, the platform simulates authentic language communication scenarios to create realistic oral practice environments (Steven et al., 2021). Simultaneously, it tracks students’ learning progress in real time, analyzes their oral expression proficiency and common challenges, and provides personalized learning plans and targeted training support for each student. This approach helps students rapidly enhance their practical communication skills.
3.2. Sociocultural Theory
Sociocultural theory posits that language acquisition occurs through interactive communication within specific sociocultural contexts. Artificial intelligence technology provides students with a virtual language community where they can engage in real-time exchanges with individuals from diverse cultural backgrounds. Through this process, students not only enhance their language proficiency but also deepen their understanding of cultural nuances through practical application. This facilitates rapid improvement in practical communication skills, thereby comprehensively elevating their spoken English proficiency.
3.3. Theory of Multiple Intelligences
Intelligence manifests in multiple dimensions, and learning should likewise unfold across these dimensions. Artificial intelligence technology integrates functions such as speech recognition, video analysis, and image recognition to provide students with multimodal learning support. Students can enhance their language skills through listening and speaking practice while reinforcing learning outcomes with visual and kinesthetic elements (Xu, 2025). Furthermore, this theory emphasizes that each student possesses distinct learning styles and unique strengths in intelligence.
4. Innovative Strategies for Public Foreign Language Oral
Instruction in Universities Driven by
Artificial Intelligence Technology
4.1. Building a Personalized Learning Platform
In traditional university public foreign language instruction, there is a widespread lack of qualified teaching staff and limited educational resources, resulting in oral language teaching that lacks systematic structure and targeted focus. Simultaneously, students exhibit significant disparities in vocabulary, oral expression, and listening comprehension. The traditional one-size-fits-all teaching model struggles to accommodate diverse learning needs across different proficiency levels, thereby hindering the oral language development of some students. With the support of artificial intelligence technology, university public foreign language oral instruction can leverage speech processing, facial expression recognition, and large language models to precisely analyze students’ weaknesses in oral expression. Based on the data results, personalized learning plans can be developed to effectively enhance students’ oral communication skills.
Using artificial intelligence technology to analyze students’ foreign language speaking proficiency provides precise data support for teaching, helping educators better understand students’ learning status and adjust instructional strategies accordingly. Educators can leverage a suite of artificial intelligence technologies—including the “Sewo Classroom Intelligent Feedback System”, “Smart Campus Teaching AI System”, “GPT”, and “DeepSeek”—to capture students’ facial expressions and body movements (Zhang, 2025). By analyzing their focus levels and emotional fluctuations during oral practice, these tools assess students’ emotional states, engagement, and learning motivation throughout the speaking process. Meanwhile, with the help of pronunciation standardization and speech recognition technology, it analyzes the differences between students’ pronunciation and standard pronunciation in real time, evaluates their speaking fluency, and identifies issues such as pauses, repetitions, and speaking speed. Combined with natural language processing technology, it can detect grammatical errors and inappropriate word usage in students’ spoken language.
Designing personalized spoken English learning paths based on artificial intelligence technology and tailored to students’ actual circumstances represents a key approach for implementing “teaching according to individual aptitude” in current university public English instruction. After teachers utilize AI technology to analyze students’ spoken English proficiency and conduct diagnostics, the intelligent platform automatically recommends textbooks, audio materials, videos, and interactive exercises that align with each student’s language level and learning objectives. Teachers can develop scientifically sound, personalized learning plans for students by integrating platform-recommended resources with instructional objectives. Simultaneously, teachers can use the platform to track students’ practice performance, learning content, and task completion, analyzing their progress and obstacles at different stages to identify strengths and weaknesses in vocabulary, grammar, and pronunciation. This enables teachers to promptly adjust teaching strategies and resource allocation, better meeting students’ diverse needs and effectively enhancing their oral communication skills.
4.2. Simulating Real-Life Scenarios to Stimulate Students’ Initiative
In traditional public foreign language oral instruction, students often face insufficient opportunities for oral practice and a lack of authentic communication scenarios. Artificial intelligence technology can effectively overcome these limitations by utilizing intelligent platforms to simulate various real-life situations (Xu, 2023). This provides students with an immersive, highly interactive language learning environment that encourages active participation in oral exercises and exchanges, thereby continuously enhancing their spoken expression skills.
The intelligent platform utilizes technologies such as virtual reality to construct diverse, authentic communication scenarios, including tourism conversations, business negotiations, and job interviews. These simulated situations closely mirror the practical demands students may encounter in the future while dynamically adjusting to their learning proficiency and expressive abilities. The platform dynamically adjusts difficulty levels based on learning progress: At the beginner stage, students primarily practice greetings and daily conversations; as their speaking skills gradually improve, the system guides them into more challenging discussions, debates, and even speeches. This progressive training not only fosters a sense of accomplishment through continuous breakthroughs but also helps students master language application skills in near-real contexts (Lee & Kim, 2024). This approach further stimulates learning interest and effectively enhances foreign language speaking proficiency.
Teachers can leverage artificial intelligence technology to convert classroom content and learning materials into online resources while simultaneously curating and delivering high-quality online learning materials to students. The intelligent platform automatically transforms these resources into engaging audio or video content tailored to each student’s individual interests and learning habits, stimulating multiple senses to perceive and comprehend linguistic information (Waluyo et al., 2022).
4.3. Establishing a Multidimensional Evaluation Mechanism to
Promote Learning Optimization
In college public foreign language oral teaching, establishing a scientific and effective evaluation mechanism is a key step in promoting learning optimization. The introduction of artificial intelligence provides new support for building a multi-dimensional evaluation system. By leveraging technologies such as speech recognition, natural language processing, and sentiment analysis, intelligent platforms can comprehensively collect data on students’ pronunciation, intonation, speech rate, grammatical accuracy, and vocabulary richness during oral practice, and generate objective, quantitative feedback in real time, effectively overcoming the subjectivity and one-dimensional limitations of traditional manual evaluation. Meanwhile, artificial intelligence can conduct in-depth analysis of students’ historical learning data, generating personalized learning curves and competency development charts that visually track their progress and learning trajectories. Teachers can leverage these multidimensional assessment results to dynamically optimize instructional content and strategies, providing differentiated guidance tailored to students at various proficiency levels to better achieve personalized education (Wang, 2025). Additionally, the platform’s visual data comparison and progress tracking features enable students to clearly understand their growth trajectory, promptly identify and correct poor study habits and negative mindsets, thereby effectively enhancing their oral expression skills and self-directed learning abilities through continuous improvement.
4.4. Enriching Classroom Interaction Methods to Stimulate
Students’ Enthusiasm for Communication
Traditional classroom interaction methods often face challenges such as low student engagement and limited interaction formats. The integration of artificial intelligence technology can effectively optimize interaction models and stimulate students’ enthusiasm for participation. Teachers can leverage AI to design diverse interactive activities, thereby increasing the frequency of interactions between teachers and students as well as among students themselves, and invigorating the classroom atmosphere. Meanwhile, interactive tasks tailored to students’ interests and preferences can be designed using speech recognition and natural language processing technologies. For instance, the system can push suitable conversation topics based on students’ language proficiency or select discussion themes aligned with their interests, thereby enhancing engagement and motivation. Additionally, group collaboration tasks combined with virtual partners can foster students’ cooperative awareness and team spirit. Through task progress tracking, interactive feedback, and data visualization provided by the intelligent platform, students gain a clear view of their learning progress and performance. This further motivates their active class participation, thereby enhancing the effectiveness of English speaking instruction and improving students’ language expression skills.
5. Conclusion
Artificial intelligence technology is driving innovation and transformation in education, particularly in university public foreign language oral instruction, injecting new momentum to overcome long-standing teaching bottlenecks. Through intelligent technology, instructors can precisely analyze students’ foreign language oral learning progress, develop personalized teaching plans, create immersive learning environments, optimize classroom interaction methods, and establish intelligent feedback mechanisms. These innovative measures not only stimulate students’ learning interest and enrich their learning experiences but also effectively enhance their English speaking proficiency and cross-cultural communication skills.
Future research should focus on several key directions: firstly, deepening multimodal interaction, integrating speech, expression, and body motion recognition, and enhancing the authenticity of AI feedback. The second is to explore emotional computing, enabling AI to recognize and adapt to learners’ emotional states such as anxiety and excitement, and achieve more humane and caring interactions. The third is to strengthen research on ethics and data privacy, and build a responsible AI teaching framework. The fourth is to promote the innovation of teacher AI collaborative teaching mode, clarify their respective advantageous roles, and achieve human-machine symbiotic teaching.