Evaluation of the Implementation Effectiveness of Digital Scenario-based Teaching in University-level English Conversation Instruction: A Study Based on Artificial Intelligence Generated Content (AIGC)

Evaluation of the Implementation Effectiveness of Digital Scenario-based Teaching in University-level English Conversation Instruction: A Study Based on Artificial Intelligence Generated Content (AIGC) | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Evaluation of the Implementation Effectiveness of Digital Scenario-based Teaching in University-level English Conversation Instruction: A Study Based on Artificial Intelligence Generated Content (AIGC) Qilin Xuan This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6437895/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract In recent years, the rapid development of artificial intelligence (AI) technology has highlighted the growing potential of Artificial Intelligence Generated Content (AIGC) in the field of education. In university-level English instruction, traditional teaching models often fail to meet the increasing demand for oral practice and contextual communication skills among students. This study, grounded in the theoretical framework of digital scenario-based teaching and leveraging AIGC technology, designed and implemented a teaching model tailored for English conversation instruction in higher education. Through an empirical investigation into teaching outcomes—including dimensions such as student learning performance, communicative competence improvement, and instructional satisfaction—the findings demonstrate that AIGC-driven digital scenario-based teaching significantly enhances students' comprehensive language application skills while stimulating their interest and active engagement in learning. Moreover, this study identifies technical bottlenecks and pedagogical challenges encountered during implementation, proposing optimization strategies and providing valuable insights for the intelligent evolution of university English teaching. Digital scenario-based teaching University English instruction Artificial Intelligence Generated Content (AIGC) Conversation teaching Instructional effectiveness evaluation Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 1. Introduction 1.1 Research Background and Significance With the rapid advancement of educational informatization, the application of digital technologies in higher education has deepened, and digital scenario-based teaching has emerged as a pivotal approach to enhancing learning outcomes (Kugurakova et al., 2023 ; Qasim, 2024 ). In university English instruction, traditional teacher-centered classrooms provide limited opportunities for students’ language output, particularly in conversation teaching, where the development of communicative competence faces significant challenges (Ly, 2024 ; Nazim, 2024). Simultaneously, students increasingly demand diverse and personalized learning experiences, which traditional teaching methods struggle to fulfill (Ayeni, 2024). In recent years, the advent of Artificial Intelligence Generated Content (AIGC) has injected new vitality into the education sector (Chen, 2024; Sun, 2024 ). AIGC technologies, leveraging natural language processing, can generate high-quality linguistic materials and dynamic scenarios, enriching digital teaching with enhanced interactivity and contextuality (Korenčić et al, 2024 ). By integrating AIGC with digital scenario-based teaching, authentic linguistic communication contexts can be created, effectively improving students’ English proficiency and practical application skills (Sun & Han, 2023; Kong & Yang, 2024 ). This technology holds vast potential for application in university English instruction. It not only addresses students’ individualized learning needs but also significantly enhances instructional efficiency and quality (Ortikoy, 2024). Therefore, researching AIGC-based digital scenario teaching models and evaluating their effectiveness is of critical theoretical and practical significance for advancing the intelligent transformation of university English education. 1.2 Research Objectives and Questions This study aims to explore the application of AIGC-driven digital scenario-based teaching models in university-level English conversation instruction. Specifically, it seeks to achieve the following objectives: (1). To construct and design a digital scenario-based teaching model centered on AIGC technologies. (2). To evaluate the effectiveness of the AIGC-based digital scenario teaching model in enhancing students' communicative abilities (e.g., oral expression, interactive skills) and learning experiences (e.g., interest, engagement). (3). To identify potential technical bottlenecks (e.g., content generation quality, platform compatibility) and pedagogical challenges (e.g., teacher technical proficiency, student adaptability) during the implementation of the teaching model. Based on these objectives, the study addresses the following research questions: (1). How can an AIGC-based digital scenario teaching model be designed and implemented? (2). What is the impact of this teaching model on students’ communicative competence and learning experience? (3). Are there technical or pedagogical challenges in implementing this teaching model? By addressing these questions, the study aims to provide an innovative instructional model for university-level English teaching and offer empirical evidence for the application of AI technologies in education. 1.3 Research Innovations and Contributions This study contributes to theoretical, practical, and empirical dimensions in significant ways: (1). Theoretical Innovation: For the first time, this research integrates Artificial Intelligence Generated Content (AIGC) into the domain of digital scenario-based teaching, constructing a novel instructional framework. By combining AIGC’s dynamic content generation capabilities with the immersive nature of scenario-based teaching, the study expands the boundaries of digital teaching theory and provides fresh theoretical support for university English education research. (2). Practical Innovation: The study designs and implements an AIGC-based teaching solution for university-level English conversation instruction, exploring the complete process from scenario design and task generation to feedback mechanisms. This solution offers practical value for frontline educators and identifies key considerations and critical stages in model application through case studies. (3). Empirical Contribution: Through rigorous empirical analysis, the study comprehensively evaluates the teaching model's effectiveness, including improvements in students’ communicative competence, optimization of learning experiences, and enhancement of teaching efficiency. Furthermore, it investigates the technical and pedagogical challenges encountered during implementation, providing valuable insights and recommendations for the future integration of AIGC technologies in education. 2. Literature Review 2.1 Theories and Practices of Digital Scenario-Based Teaching Digital scenario-based teaching is an instructional approach that leverages information technology to create virtual or augmented teaching environments, promoting knowledge acquisition and skill development (Ahmad, 2020 ; Tiago & Mitchell, 2024 ). Rooted in constructivist learning theory, this method emphasizes guiding students to actively construct knowledge through exploration and practice in authentic or simulated scenarios (Maria, 2020 ; Macleod et al. 2022 ). In the context of language learning, digital scenario-based teaching often integrates multimodal resources—such as videos, audio, virtual reality, and interactive tasks—to create immersive learning experiences that spark learners’ interest and enhance their linguistic proficiency (Kim & Namkung, 2024 ; Wen & Castek, 2022 ; Clapp, 2024; Procel et al., 2024 ). In recent years, the application of digital scenario-based teaching in university-level English instruction has grown significantly. Research indicates that this approach allows students to practice language output and interaction within simulated real-life contexts, fostering improvements in their oral expression and communicative competence (Perez & Poole, 2019 ; Tiu et al, 2023 ). However, existing instructional designs often encounter challenges such as limited dynamism and interactivity of scenarios, which fail to fully meet students’ needs for realistic communication environments (Yang, 2023 ). Therefore, optimizing scenario design to enhance its generative and interactive qualities has become a key focus in the research on digital scenario-based teaching (Bai et al, 2024 ). 2.2 Current Status and Challenges in University-level English Conversation Instruction The primary objective of university-level English conversation instruction is to cultivate students’ communicative competence, addressing the demand for high-quality English professionals in a globalized context (Zhai & Wibowo, 2023 ). Traditional teaching methods typically rely on textbook-based dialogue materials, with teachers dominating classroom activities while students engage in brief group discussions or simulated dialogues (Badjadi, 2024 ). However, such approaches often yield limited progress in improving students’ practical communicative abilities. First, classroom time and resource constraints restrict students’ opportunities for language output, hindering rapid development of their oral expression skills (Musabal & Abdalgane, 2024). Second, textbook materials often lack contextualized designs, disconnecting classroom content from real-life communication scenarios and impeding students’ ability to apply language in authentic contexts (Ottu et al., 2024 ). Additionally, high student-to-teacher ratios and insufficient personalized teaching resources further constrain instructional effectiveness (Fuda & Mbangeleli, 2024). Addressing these shortcomings requires innovative teaching models and technological tools to provide students with richer and more authentic language practice scenarios. Such advancements can overcome the limitations of traditional teaching frameworks, thereby significantly enhancing instructional outcomes. 2.3 Applications of Artificial Intelligence Generated Content (AIGC) in Education Artificial Intelligence Generated Content (AIGC) has recently emerged as a prominent research focus in the education sector. AIGC utilizes large-scale language models (e.g., GPT) to generate high-quality content, including conversational materials, writing exemplars, and scenario-based tasks (Zhang et al., 2023 ). Its core strengths lie in the automation and customization of content generation. In language learning, AIGC can dynamically create personalized language tasks based on learners’ needs (Li et al., 2024 ). For instance, AIGC-generated interactive scenarios enable students to engage in near-authentic linguistic exchanges, thereby enhancing their practical language skills. Additionally, AIGC can provide real-time feedback—such as suggestions on grammar and vocabulary—helping students make self-directed adjustments during the learning process (Dai et al., 2023 ). Despite its promising potential in educational applications, AIGC also presents technical challenges that warrant attention, including the accuracy and relevance of generated content, cultural contextual sensitivity, and data privacy concerns (Wang et al., 2024 ; Fui-Hoon et al., 2023). Balancing technological capabilities with pedagogical requirements to maximize the educational value of AIGC remains a critical direction for future research. 3. Research Design and Methodology 3.1 Research Framework and Model The study follows a three-stage framework—theoretical foundation, model design, and outcome validation (Fig. 1 )—using a mixed-methods approach to explore the impact of AIGC-driven digital scenario-based teaching on university-level English conversation instruction. (1). Theoretical Foundation: This phase integrates the principles of digital scenario-based teaching with AIGC features to construct a theoretical model. The model consists of input (teaching resources and AIGC-generated tasks), process (task interaction and feedback mechanisms), and output (learning outcomes). (2). Model Design: This phase identifies key elements and implementation procedures for teaching scenario creation, dialogue task generation, and student interaction experiences. (3). Outcome Validation: This phase involves empirical research to verify the model’s efficacy through assessments of students’ language proficiency, learning experiences, and teacher feedback. The advantages and limitations of the model are also analyzed. The specific research framework is illustrated in Fig. 2 . (1) Input Step: Focuses on diversified teaching resources and task design, incorporating AIGC-generated dialogue tasks, multimodal tools (e.g., speech recognition, real-time translation), and defined learning objectives. (2) Process Step: Centers on task-driven scenario-based teaching practice. AIGC dynamically generates dialogue scenarios, enabling students to engage in interactive tasks. Real-time feedback provides personalized suggestions for improvement. (3) Output Step: Emphasizes learning outcome evaluation, including dimensions such as language proficiency (intonation, accuracy, fluency), learning interest, and adaptability to technology. This framework ensures systematic and targeted exploration, laying a robust foundation for practical application and theoretical advancement. 3.2 Teaching Model Design The AIGC-driven digital scenario-based teaching model (Fig. 3 ) comprises three core components: (1). Scenario Generation: AIGC technology generates diverse language scenarios aligned with teaching objectives and students’ proficiency levels. These scenarios encompass everyday conversations, professional dialogues, and cultural themes in dynamic formats (e.g., text, audio, video) to enhance immersion. (2). Task Design: Based on the generated scenarios, interactive tasks are developed, such as role-playing, problem-solving, and debate exercises, requiring students to produce language output in authentic contexts. Task difficulty is adjusted to progressively challenge students’ linguistic and cognitive abilities. (3). Feedback Mechanism: AIGC provides immediate feedback, analyzing students’ language output and offering personalized recommendations on vocabulary use, grammar, and pronunciation. Teacher-student interaction and peer evaluation further reinforce learning outcomes. The model emphasizes student-centered learning, fostering active engagement through dynamic scenarios and task-driven activities, aiming to enhance both communicative competence and learning experience. 3.3 Data Collection and Analysis Methods To comprehensively evaluate the effectiveness of the AIGC-based teaching model, mixed data collection and analysis methods are employed, including quantitative and qualitative approaches: (1). Quantitative Data Collection: Language Proficiency Tests: Standardized tests (e.g., IELTS speaking tests) assess students' oral and communicative skills before and after the intervention to measure improvement. Learning Experience Surveys: Surveys evaluate students' satisfaction and interaction experiences, covering dimensions such as interest enhancement, contextual adaptability, and task difficulty. (2). Qualitative Data Collection: Interviews: In-depth interviews with teachers and students capture insights into their experiences and identify areas for improvement. Classroom Observations: Observational data record students’ behavior, participation, and task execution details during teaching sessions. (3). Data Analysis: Quantitative Analysis: Descriptive statistics, t-tests, and correlation analysis assess the impact of the teaching model on language proficiency and learning experiences. Qualitative Analysis: Thematic analysis extracts key themes from qualitative data, revealing perceptions and feedback from students and teachers. 3.4 Research Participants and Experimental Setup The study involves 120 first-year English major students from Gongqing Institute of Science and Technology,, all with intermediate English proficiency. The experiment lasts for one semester (16 weeks), with participants divided into an experimental group and a control group (60 students each). (1). Grouping and Baseline Testing: A pre-test assesses language proficiency to ensure comparable skill levels between the groups. Background information, such as prior English performance and digital learning adaptability, is also collected. (2). Teaching Implementation: The experimental group participates in AIGC-based digital scenario teaching sessions twice weekly, involving simulated dialogues and interactive scenario experiences. The control group follows traditional teacher-led instruction, including textbook-based dialogues and group discussions. (3). Data Collection and Monitoring: Mid-term feedback is gathered every four weeks to track progress and experiences. Final tests and satisfaction surveys are conducted at the semester's end to evaluate outcomes. (4). Teaching Support: Teachers receive AIGC technology training, and the experimental environment is equipped with necessary resources (e.g., AI platforms, interactive tools). External variables are controlled to ensure scientific validity and reliability of results. 4. Empirical Research and Results Analysis 4.1 Evaluation Metrics for Teaching Effectiveness To scientifically assess the implementation of the AIGC-based digital scenario teaching model, a multi-dimensional evaluation framework was established. It encompasses three dimensions—language proficiency, learning experience, and technological adaptability—each with specific indicators (Table 1 ). Table 1 Teaching Effectiveness Evaluation Metrics Dimensions Indicator Specific Content Max Score Languages Proficiency Pronunciation and intonation Clarity and coherence of speech 10 Language accuracy Correctness of grammar and vocabulary 10 Communicative fluency Naturalness and logic of expression 10 Learning Experience Learning interest Active participation in tasks 10 Interaction quality Frequency and quality of teacher-student and peer interactions 10 Technology Adaptability Task acceptance Adaptability to AIGC-generated content 10 Technology satisfaction Evaluation of AIGC tools and applications 10 4.2 Analysis of Students’ Language Proficiency Improvement The comparison of pre-test and post-test results revealed significant improvements in the experimental group's language proficiency across all indicators (Table 2 & Fig. 4 ). Table 2 Statistical Data on Language Proficiency Tests Language Proficiency Dimension Experimental Group Pre-Test Mean Experimental Group Post-Test Mean Control Group Pre-Test Mean Control Group Post-Test Mean Pronunciation and Intonation 6.5 8.9 6.4 7.0 Language Accuracy 6.2 8.7 6.3 7.1 Communicative Fluency 6.0 8.5 6.1 6.8 The data indicates that the experimental group made significant progress in speech clarity, grammatical accuracy, and logical coherence of communication. This improvement underscores the effectiveness of AIGC-generated dynamic scenarios and personalized feedback in enhancing students’ practical language skills. The control group showed limited improvement due to traditional teaching methods. 4.3 Analysis of Student Satisfaction and Interaction Experience Results from satisfaction surveys and classroom observations (Table 3 & Fig. 5 ) revealed that the experimental group demonstrated higher satisfaction, particularly in task engagement and interaction experiences. Table 3 Student Satisfaction Survey Results Satisfaction Dimension Experimental Group Mean Control Group Mean Learning Interest 9.2 7.0 Task Adaptability 8.9 7.3 Teacher-Student Interaction 9.0 6.8 The experimental group appreciated the novelty and relevance of AIGC-generated tasks, which enhanced their interest in learning. Additionally, the interactive mechanisms improved engagement and the sense of collaboration during learning. In contrast, the control group reported monotony in traditional methods, with less classroom interaction, impacting motivation. 4.4 Adaptability of Different Student Groups to the Teaching Model The study analyzed the adaptability of different student groups to the AIGC teaching model based on variables such as gender, language proficiency, and technological familiarity (Table 4 & Fig. 6 ). Table 4 Adaptability of Different Student Groups Number Student Group Average Adaptability Score (Max: 10) 1 Male Students 8.7 2 Female Students 8.5 3 Low Proficiency Level 9.1 4 Intermediate Level 8.4 5 Advanced Level 8.2 6 Technologically Familiar Students 9.0 7 Technologically Unfamiliar Students 7.5 The data indicates that students with lower initial proficiency levels and higher technological familiarity adapted more readily to the AIGC teaching model. Conversely, students with limited exposure to technology faced initial challenges, highlighting the importance of providing additional technical training and support for such groups during implementation. These findings validate the effectiveness of the AIGC-based teaching model in improving language proficiency and learning experiences while emphasizing the need for tailored technical support to maximize adaptability across diverse student groups. 5. Discussion 5.1 Advantages and Disadvantages of AIGC in English Conversation Teaching This study highlights both the significant advantages and the limitations of AIGC technology in higher education English conversation teaching. Advantages: (1). Dynamic Scenario Generation: AIGC can generate diverse conversational scenarios in real-time based on teaching objectives and student needs, covering various topics and communication settings. This provides students with rich, authentic language practice environments. (2). Personalized Feedback: By analyzing students’ language output, AIGC offers immediate, specific feedback on grammar, vocabulary, and pronunciation, helping students identify and address areas for improvement. (3). High Resource Efficiency: AIGC significantly reduces teachers’ lesson preparation workload by automatically generating teaching content, offering students more opportunities for autonomous learning. Limitations: (1). Inconsistent Content Quality: Some generated language content may include grammatical or cultural inaccuracies, requiring manual intervention and correction by teachers. (2). Lack of Emotional Interaction: Tasks and feedback generated by AIGC may struggle to replicate the emotional interactions of real-life communication, potentially impacting students' emotional engagement and authentic communication skills development. (3). Technical Barriers: Teachers and students with limited familiarity with new technology may face a steep learning curve, particularly during initial implementation, which could hinder adoption and effectiveness. Despite these limitations, the flexibility and innovativeness of AIGC offer valuable support for English conversation teaching, warranting further exploration of its potential. 5.2 Challenges in Integrating Technology and Teaching Although AIGC demonstrates significant advantages, its integration into teaching still faces several challenges: (1). Technical Adaptability: Insufficient familiarity with AIGC technology among teachers and students, especially in the early stages, may necessitate additional training and support, increasing costs and time investment. (2). Content Accuracy: AIGC-generated content may occasionally reflect biases or limitations, leading to inaccuracies. In language learning, such errors might interfere with acquisition or foster misconceptions. (3). Overreliance on Technology: Excessive dependence on technology could reduce teachers' autonomy in instructional design, limiting their ability to leverage their expertise and creativity. Similarly, students may rely too heavily on technology, reducing their deeper engagement with language learning. (4). Data Privacy and Ethics: AIGC typically requires the collection and analysis of extensive student data, raising concerns about data security, privacy breaches, and ethical risks. Ensuring compliance with policies and regulations is essential. Addressing these challenges requires clear guidelines for technology use, enhanced technical training for educators and learners, and iterative optimization to ensure the effective integration of technology and teaching. 5.3 Prospects for Future Intelligent Teaching Models The development of intelligent teaching models will continue to drive educational innovation. Potential trends include: (1). Multimodal Integration: Future models may combine technologies like AIGC, virtual reality (VR), augmented reality (AR), and speech recognition to offer richer, multimodal learning experiences that enhance immersion and interactivity. (2). Highly Personalized Learning: Advances in AI will enable teaching models to dynamically adapt to individual students' learning habits, interests, and language proficiency, achieving true personalized instruction. (3). Data-Driven Optimization: By continuously analyzing student behavior and performance data, future teaching models can provide precise learning path recommendations and outcome predictions, supporting data-informed decision-making for both students and educators. (4). Redefining Teacher and Student Roles: In intelligent teaching environments, teachers will increasingly serve as facilitators and supervisors, while students leverage technology for more autonomous learning. This role shift will transform traditional classroom practices, fostering diverse teaching methods. Overall, intelligent teaching models will evolve toward greater flexibility, efficiency, and personalization, driving significant improvements in educational quality. 6. Conclusions and Recommendations 6.1 Research Conclusions This study explores the application effect of digital situational teaching based on artificial intelligence generation technology (AIGC) in college English dialogue teaching, and systematically evaluates it from multiple dimensions such as teaching design, implementation effect and student experience. The study shows that AIGC technology can generate rich and diverse situational content, significantly improve students' speech clarity, grammatical accuracy and communication fluency, and effectively enhance students' learning interest and classroom participation. However, the study also found that technology dependence and instability in content generation may have a certain impact on teaching effectiveness. In addition, different student groups have different adaptability to this model, and students with low technology familiarity need more support in the early stages. This study provides strong evidence for the theoretical development and practical promotion of digital situational teaching. 6.2 Practical Recommendations To effectively promote AIGC-based digital scenario teaching, higher education institutions can adopt the following measures: (1). Enhance Teacher Training: Regular workshops and demonstration lessons to help teachers master AIGC technology and its classroom applications. (2). Optimize Scenario Content: Establish professional review mechanisms to ensure accuracy and cultural appropriateness, mitigating the impact of technological errors. (3). Address Individual Differences: Offer tiered support for students with low technological proficiency or weak language foundations, including additional technical guidance and personalized instructional designs. (4). Adopt Diverse Evaluation Methods: Combine quantitative and qualitative assessments to comprehensively monitor student performance and inform instructional improvements. These measures will help fully realize AIGC technology’s potential, providing students with more efficient, interactive, and personalized learning experiences. 6.3 Future Research Directions Future research can be conducted in the following aspects: Quality optimization and cultural appropriateness of AIGC technology-generated content. Comparative study of AIGC teaching application models in different disciplines. Research on the integration effect of cross-cultural communication ability training and AIGC technology. Relevant norms and practices of technology ethics and student data privacy protection. Declarations Data Availability The raw data supporting the conclusions of this article will be made available by the authors without undue reservation. Ethics statement The studies involving humans were approved by Ethics Committee in Kunming University of Science and Technology. The studies were conducted in accordance with the local legislation and institutional requirements. Written informed consent for participation in this study was provided by the participants’ legal guardians/next of kin. Funding Sponsored by Ministry of Education's Industry-Academia Cooperation Program for Collaborative Education: "Research and Practice on the Development of Bilingual School-based Curriculum for Chinese Excellent Culture under Digital Intelligence Empowerment". Conflict interests All authors certify that they have no affiliations with or involvement in any organization or entity with any financial interest or non-financial interest in the subject matter or materials discussed in this manuscript. Generative AI statement The author(s) declare that no Gen AI was used in the creation of this manuscript. 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Association for the Advancement of Computing in Education (AACE) Yang H (2023) An Analysis of Innovative Teaching of English Phonetics for Non-English Major Students. Journal of Contemporary Educational Research , 7(8), 86–92. doi.10.26689/jcer.v7i8.5259 Zhai C, Wibowo S (2023) A systematic review on artificial intelligence dialogue systems for enhancing English as foreign language students’ interactional competence in the university. Computers Education: Artif Intell 4:100134. https://doi.org/10.1016/j.caeai.2023.100134 Zhang C, Zhang C, Zheng S, Qiao Y, Li C, Zhang M, Hong CS (2023) A complete survey on generative ai (aigc): Is chatgpt from gpt-4 to gpt-5 all you need? arXiv preprint arXiv 230311717. https://doi.org/10.48550/arXiv.2303.11717 Additional Declarations The authors declare no competing interests. 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Xuan","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAx0lEQVRIiWNgGAWjYBACxvbmAwYffkjU97M3EKmFuedYQuHMHgvGmT0HiNTCPiPH4DMPWwXjhhsJRGrhnZFjuHEGjwQzw83HG28w1NhEE9Qi2fOs2OCDhQQb4+y0YguGY2m5DYS0GLYnbzME2sLDLJ1jJsHYcJiwFvsDCea/edgkJNgkzxCphbEjxcAYqMUAaBGxWoCBbDizRyJBggfolwRi/AKNyroE++OHN974UGNDWAsyMJBIIEU5RAupOkbBKBgFo2BkAABKnUEPqhT4aQAAAABJRU5ErkJggg==","orcid":"https://orcid.org/0009-0004-0330-0479","institution":"Gongqing Institute of Science and Technology","correspondingAuthor":true,"prefix":"","firstName":"Qilin","middleName":"","lastName":"Xuan","suffix":""}],"badges":[],"createdAt":"2025-04-13 07:49:07","currentVersionCode":1,"declarations":{"humanSubjects":false,"vertebrateSubjects":false,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":false,"humanSubjectConsent":false,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-6437895/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6437895/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":80882695,"identity":"10a6bdf4-bb57-4953-bcf4-90e995231e1f","added_by":"auto","created_at":"2025-04-18 08:06:00","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":249569,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eOverall Research Framework\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-6437895/v1/e5520cf11a34ba8f0cea404a.png"},{"id":80883540,"identity":"f9185cd2-132d-4d13-b17e-81e5adbc9541","added_by":"auto","created_at":"2025-04-18 08:14:00","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":203217,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSpecific Research Framework\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-6437895/v1/f718b0ba2b3fb80d88cc8fbc.png"},{"id":80882696,"identity":"8e902ce9-5c34-4e5b-9cec-ae1afdc624b8","added_by":"auto","created_at":"2025-04-18 08:06:00","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":174125,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eAIGC-Based Digital Scenario Teaching Model\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-6437895/v1/da09ba2c6828ae009746d20c.png"},{"id":80882709,"identity":"e57c0cfb-efd8-43f4-b304-10a38c5be9ac","added_by":"auto","created_at":"2025-04-18 08:06:01","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":17184,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eLanguage Proficiency Test Results for Experimental and Control Groups\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-6437895/v1/f66346ce70bc72f6a1e07ed2.png"},{"id":80882712,"identity":"46f6f02a-93b2-48bf-8254-aa802612c2f5","added_by":"auto","created_at":"2025-04-18 08:06:01","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":15366,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eStudents Satisfaction Survey Results\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-6437895/v1/69525d4d7811fdf8d7b74374.png"},{"id":80882703,"identity":"33e9ec9e-0760-4cfb-8c3a-71b73b77d508","added_by":"auto","created_at":"2025-04-18 08:06:01","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":16040,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eAdaptability Scores for Different Student Groups\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-6437895/v1/3ce53eecfda4b8d6b1dbc12e.png"},{"id":80883778,"identity":"f5773b86-aacd-49e3-9de0-2709f79a76dc","added_by":"auto","created_at":"2025-04-18 08:22:01","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1837256,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6437895/v1/05a5e2dd-292a-4518-91eb-f3a7f5503230.pdf"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003e\u003cstrong\u003eEvaluation of the Implementation Effectiveness of Digital Scenario-based Teaching in University-level English Conversation Instruction: A Study Based on Artificial Intelligence Generated Content (AIGC)\u003c/strong\u003e\u003c/p\u003e","fulltext":[{"header":"1. Introduction","content":"\u003cdiv id=\"Sec2\" class=\"Section2\"\u003e \u003ch2\u003e1.1 Research Background and Significance\u003c/h2\u003e \u003cp\u003eWith the rapid advancement of educational informatization, the application of digital technologies in higher education has deepened, and digital scenario-based teaching has emerged as a pivotal approach to enhancing learning outcomes (Kugurakova et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Qasim, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). In university English instruction, traditional teacher-centered classrooms provide limited opportunities for students\u0026rsquo; language output, particularly in conversation teaching, where the development of communicative competence faces significant challenges (Ly, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Nazim, 2024). Simultaneously, students increasingly demand diverse and personalized learning experiences, which traditional teaching methods struggle to fulfill (Ayeni, 2024).\u003c/p\u003e \u003cp\u003eIn recent years, the advent of Artificial Intelligence Generated Content (AIGC) has injected new vitality into the education sector (Chen, 2024; Sun, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). AIGC technologies, leveraging natural language processing, can generate high-quality linguistic materials and dynamic scenarios, enriching digital teaching with enhanced interactivity and contextuality (Korenčić et al, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). By integrating AIGC with digital scenario-based teaching, authentic linguistic communication contexts can be created, effectively improving students\u0026rsquo; English proficiency and practical application skills (Sun \u0026amp; Han, 2023; Kong \u0026amp; Yang, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThis technology holds vast potential for application in university English instruction. It not only addresses students\u0026rsquo; individualized learning needs but also significantly enhances instructional efficiency and quality (Ortikoy, 2024). Therefore, researching AIGC-based digital scenario teaching models and evaluating their effectiveness is of critical theoretical and practical significance for advancing the intelligent transformation of university English education.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e1.2 Research Objectives and Questions\u003c/h2\u003e \u003cp\u003eThis study aims to explore the application of AIGC-driven digital scenario-based teaching models in university-level English conversation instruction. Specifically, it seeks to achieve the following objectives:\u003c/p\u003e \u003cp\u003e(1). To construct and design a digital scenario-based teaching model centered on AIGC technologies.\u003c/p\u003e \u003cp\u003e(2). To evaluate the effectiveness of the AIGC-based digital scenario teaching model in enhancing students' communicative abilities (e.g., oral expression, interactive skills) and learning experiences (e.g., interest, engagement).\u003c/p\u003e \u003cp\u003e(3). To identify potential technical bottlenecks (e.g., content generation quality, platform compatibility) and pedagogical challenges (e.g., teacher technical proficiency, student adaptability) during the implementation of the teaching model.\u003c/p\u003e \u003cp\u003eBased on these objectives, the study addresses the following research questions:\u003c/p\u003e \u003cp\u003e(1). How can an AIGC-based digital scenario teaching model be designed and implemented?\u003c/p\u003e \u003cp\u003e(2). What is the impact of this teaching model on students\u0026rsquo; communicative competence and learning experience?\u003c/p\u003e \u003cp\u003e(3). Are there technical or pedagogical challenges in implementing this teaching model?\u003c/p\u003e \u003cp\u003eBy addressing these questions, the study aims to provide an innovative instructional model for university-level English teaching and offer empirical evidence for the application of AI technologies in education.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e1.3 Research Innovations and Contributions\u003c/h2\u003e \u003cp\u003eThis study contributes to theoretical, practical, and empirical dimensions in significant ways:\u003c/p\u003e \u003cp\u003e(1). Theoretical Innovation: For the first time, this research integrates Artificial Intelligence Generated Content (AIGC) into the domain of digital scenario-based teaching, constructing a novel instructional framework. By combining AIGC\u0026rsquo;s dynamic content generation capabilities with the immersive nature of scenario-based teaching, the study expands the boundaries of digital teaching theory and provides fresh theoretical support for university English education research.\u003c/p\u003e \u003cp\u003e(2). Practical Innovation: The study designs and implements an AIGC-based teaching solution for university-level English conversation instruction, exploring the complete process from scenario design and task generation to feedback mechanisms. This solution offers practical value for frontline educators and identifies key considerations and critical stages in model application through case studies.\u003c/p\u003e \u003cp\u003e(3). Empirical Contribution: Through rigorous empirical analysis, the study comprehensively evaluates the teaching model's effectiveness, including improvements in students\u0026rsquo; communicative competence, optimization of learning experiences, and enhancement of teaching efficiency. Furthermore, it investigates the technical and pedagogical challenges encountered during implementation, providing valuable insights and recommendations for the future integration of AIGC technologies in education.\u003c/p\u003e \u003c/div\u003e"},{"header":"2. Literature Review","content":"\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Theories and Practices of Digital Scenario-Based Teaching\u003c/h2\u003e \u003cp\u003eDigital scenario-based teaching is an instructional approach that leverages information technology to create virtual or augmented teaching environments, promoting knowledge acquisition and skill development (Ahmad, \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Tiago \u0026amp; Mitchell, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Rooted in constructivist learning theory, this method emphasizes guiding students to actively construct knowledge through exploration and practice in authentic or simulated scenarios (Maria, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Macleod et al. \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). In the context of language learning, digital scenario-based teaching often integrates multimodal resources\u0026mdash;such as videos, audio, virtual reality, and interactive tasks\u0026mdash;to create immersive learning experiences that spark learners\u0026rsquo; interest and enhance their linguistic proficiency (Kim \u0026amp; Namkung, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Wen \u0026amp; Castek, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Clapp, 2024; Procel et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn recent years, the application of digital scenario-based teaching in university-level English instruction has grown significantly. Research indicates that this approach allows students to practice language output and interaction within simulated real-life contexts, fostering improvements in their oral expression and communicative competence (Perez \u0026amp; Poole, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Tiu et al, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eHowever, existing instructional designs often encounter challenges such as limited dynamism and interactivity of scenarios, which fail to fully meet students\u0026rsquo; needs for realistic communication environments (Yang, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Therefore, optimizing scenario design to enhance its generative and interactive qualities has become a key focus in the research on digital scenario-based teaching (Bai et al, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Current Status and Challenges in University-level English Conversation Instruction\u003c/h2\u003e \u003cp\u003eThe primary objective of university-level English conversation instruction is to cultivate students\u0026rsquo; communicative competence, addressing the demand for high-quality English professionals in a globalized context (Zhai \u0026amp; Wibowo, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Traditional teaching methods typically rely on textbook-based dialogue materials, with teachers dominating classroom activities while students engage in brief group discussions or simulated dialogues (Badjadi, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). However, such approaches often yield limited progress in improving students\u0026rsquo; practical communicative abilities.\u003c/p\u003e \u003cp\u003eFirst, classroom time and resource constraints restrict students\u0026rsquo; opportunities for language output, hindering rapid development of their oral expression skills (Musabal \u0026amp; Abdalgane, 2024). Second, textbook materials often lack contextualized designs, disconnecting classroom content from real-life communication scenarios and impeding students\u0026rsquo; ability to apply language in authentic contexts (Ottu et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Additionally, high student-to-teacher ratios and insufficient personalized teaching resources further constrain instructional effectiveness (Fuda \u0026amp; Mbangeleli, 2024).\u003c/p\u003e \u003cp\u003eAddressing these shortcomings requires innovative teaching models and technological tools to provide students with richer and more authentic language practice scenarios. Such advancements can overcome the limitations of traditional teaching frameworks, thereby significantly enhancing instructional outcomes.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Applications of Artificial Intelligence Generated Content (AIGC) in Education\u003c/h2\u003e \u003cp\u003eArtificial Intelligence Generated Content (AIGC) has recently emerged as a prominent research focus in the education sector. AIGC utilizes large-scale language models (e.g., GPT) to generate high-quality content, including conversational materials, writing exemplars, and scenario-based tasks (Zhang et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Its core strengths lie in the automation and customization of content generation.\u003c/p\u003e \u003cp\u003eIn language learning, AIGC can dynamically create personalized language tasks based on learners\u0026rsquo; needs (Li et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). For instance, AIGC-generated interactive scenarios enable students to engage in near-authentic linguistic exchanges, thereby enhancing their practical language skills. Additionally, AIGC can provide real-time feedback\u0026mdash;such as suggestions on grammar and vocabulary\u0026mdash;helping students make self-directed adjustments during the learning process (Dai et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eDespite its promising potential in educational applications, AIGC also presents technical challenges that warrant attention, including the accuracy and relevance of generated content, cultural contextual sensitivity, and data privacy concerns (Wang et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Fui-Hoon et al., 2023). Balancing technological capabilities with pedagogical requirements to maximize the educational value of AIGC remains a critical direction for future research.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Research Design and Methodology","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Research Framework and Model\u003c/h2\u003e \u003cp\u003eThe study follows a three-stage framework\u0026mdash;theoretical foundation, model design, and outcome validation (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e)\u0026mdash;using a mixed-methods approach to explore the impact of AIGC-driven digital scenario-based teaching on university-level English conversation instruction.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e(1). Theoretical Foundation: This phase integrates the principles of digital scenario-based teaching with AIGC features to construct a theoretical model. The model consists of input (teaching resources and AIGC-generated tasks), process (task interaction and feedback mechanisms), and output (learning outcomes).\u003c/p\u003e \u003cp\u003e(2). Model Design: This phase identifies key elements and implementation procedures for teaching scenario creation, dialogue task generation, and student interaction experiences.\u003c/p\u003e \u003cp\u003e(3). Outcome Validation: This phase involves empirical research to verify the model\u0026rsquo;s efficacy through assessments of students\u0026rsquo; language proficiency, learning experiences, and teacher feedback. The advantages and limitations of the model are also analyzed. The specific research framework is illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e(1) Input Step: Focuses on diversified teaching resources and task design, incorporating AIGC-generated dialogue tasks, multimodal tools (e.g., speech recognition, real-time translation), and defined learning objectives.\u003c/p\u003e \u003cp\u003e(2) Process Step: Centers on task-driven scenario-based teaching practice. AIGC dynamically generates dialogue scenarios, enabling students to engage in interactive tasks. Real-time feedback provides personalized suggestions for improvement.\u003c/p\u003e \u003cp\u003e(3) Output Step: Emphasizes learning outcome evaluation, including dimensions such as language proficiency (intonation, accuracy, fluency), learning interest, and adaptability to technology.\u003c/p\u003e \u003cp\u003eThis framework ensures systematic and targeted exploration, laying a robust foundation for practical application and theoretical advancement.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Teaching Model Design\u003c/h2\u003e \u003cp\u003eThe AIGC-driven digital scenario-based teaching model (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e) comprises three core components:\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e(1). Scenario Generation: AIGC technology generates diverse language scenarios aligned with teaching objectives and students\u0026rsquo; proficiency levels. These scenarios encompass everyday conversations, professional dialogues, and cultural themes in dynamic formats (e.g., text, audio, video) to enhance immersion.\u003c/p\u003e \u003cp\u003e(2). Task Design: Based on the generated scenarios, interactive tasks are developed, such as role-playing, problem-solving, and debate exercises, requiring students to produce language output in authentic contexts. Task difficulty is adjusted to progressively challenge students\u0026rsquo; linguistic and cognitive abilities.\u003c/p\u003e \u003cp\u003e(3). Feedback Mechanism: AIGC provides immediate feedback, analyzing students\u0026rsquo; language output and offering personalized recommendations on vocabulary use, grammar, and pronunciation. Teacher-student interaction and peer evaluation further reinforce learning outcomes.\u003c/p\u003e \u003cp\u003eThe model emphasizes student-centered learning, fostering active engagement through dynamic scenarios and task-driven activities, aiming to enhance both communicative competence and learning experience.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Data Collection and Analysis Methods\u003c/h2\u003e \u003cp\u003eTo comprehensively evaluate the effectiveness of the AIGC-based teaching model, mixed data collection and analysis methods are employed, including quantitative and qualitative approaches:\u003c/p\u003e \u003cp\u003e(1). Quantitative Data Collection:\u003c/p\u003e \u003cp\u003eLanguage Proficiency Tests: Standardized tests (e.g., IELTS speaking tests) assess students' oral and communicative skills before and after the intervention to measure improvement.\u003c/p\u003e \u003cp\u003eLearning Experience Surveys: Surveys evaluate students' satisfaction and interaction experiences, covering dimensions such as interest enhancement, contextual adaptability, and task difficulty.\u003c/p\u003e \u003cp\u003e(2). Qualitative Data Collection:\u003c/p\u003e \u003cp\u003eInterviews: In-depth interviews with teachers and students capture insights into their experiences and identify areas for improvement.\u003c/p\u003e \u003cp\u003eClassroom Observations: Observational data record students\u0026rsquo; behavior, participation, and task execution details during teaching sessions.\u003c/p\u003e \u003cp\u003e(3). Data Analysis:\u003c/p\u003e \u003cp\u003eQuantitative Analysis: Descriptive statistics, t-tests, and correlation analysis assess the impact of the teaching model on language proficiency and learning experiences.\u003c/p\u003e \u003cp\u003eQualitative Analysis: Thematic analysis extracts key themes from qualitative data, revealing perceptions and feedback from students and teachers.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e3.4 Research Participants and Experimental Setup\u003c/h2\u003e \u003cp\u003eThe study involves 120 first-year English major students from Gongqing Institute of Science and Technology,, all with intermediate English proficiency. The experiment lasts for one semester (16 weeks), with participants divided into an experimental group and a control group (60 students each).\u003c/p\u003e \u003cp\u003e(1). Grouping and Baseline Testing: A pre-test assesses language proficiency to ensure comparable skill levels between the groups. Background information, such as prior English performance and digital learning adaptability, is also collected.\u003c/p\u003e \u003cp\u003e(2). Teaching Implementation:\u003c/p\u003e \u003cp\u003eThe experimental group participates in AIGC-based digital scenario teaching sessions twice weekly, involving simulated dialogues and interactive scenario experiences.\u003c/p\u003e \u003cp\u003eThe control group follows traditional teacher-led instruction, including textbook-based dialogues and group discussions.\u003c/p\u003e \u003cp\u003e(3). Data Collection and Monitoring: Mid-term feedback is gathered every four weeks to track progress and experiences. Final tests and satisfaction surveys are conducted at the semester's end to evaluate outcomes.\u003c/p\u003e \u003cp\u003e(4). Teaching Support: Teachers receive AIGC technology training, and the experimental environment is equipped with necessary resources (e.g., AI platforms, interactive tools). External variables are controlled to ensure scientific validity and reliability of results.\u003c/p\u003e \u003c/div\u003e"},{"header":"4. Empirical Research and Results Analysis","content":"\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e4.1 Evaluation Metrics for Teaching Effectiveness\u003c/h2\u003e \u003cp\u003eTo scientifically assess the implementation of the AIGC-based digital scenario teaching model, a multi-dimensional evaluation framework was established. It encompasses three dimensions\u0026mdash;language proficiency, learning experience, and technological adaptability\u0026mdash;each with specific indicators (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eTeaching Effectiveness Evaluation Metrics\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDimensions\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIndicator\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSpecific Content\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMax Score\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eLanguages Proficiency\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePronunciation and intonation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eClarity and coherence of speech\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLanguage accuracy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCorrectness of grammar and vocabulary\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCommunicative fluency\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNaturalness and logic of expression\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eLearning Experience\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLearning interest\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eActive participation in tasks\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eInteraction quality\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFrequency and quality of teacher-student and peer interactions\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eTechnology Adaptability\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTask acceptance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAdaptability to AIGC-generated content\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTechnology satisfaction\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eEvaluation of AIGC tools and applications\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003e4.2 Analysis of Students\u0026rsquo; Language Proficiency Improvement\u003c/h2\u003e \u003cp\u003eThe comparison of pre-test and post-test results revealed significant improvements in the experimental group's language proficiency across all indicators (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e \u0026amp; Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eStatistical Data on Language Proficiency Tests\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLanguage Proficiency Dimension\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eExperimental Group Pre-Test Mean\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eExperimental Group Post-Test Mean\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eControl Group Pre-Test Mean\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eControl Group Post-Test Mean\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePronunciation and Intonation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e6.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e8.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e6.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e7.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLanguage Accuracy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e6.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e8.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e6.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e7.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCommunicative Fluency\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e6.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e8.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e6.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e6.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe data indicates that the experimental group made significant progress in speech clarity, grammatical accuracy, and logical coherence of communication. This improvement underscores the effectiveness of AIGC-generated dynamic scenarios and personalized feedback in enhancing students\u0026rsquo; practical language skills. The control group showed limited improvement due to traditional teaching methods.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003e4.3 Analysis of Student Satisfaction and Interaction Experience\u003c/h2\u003e \u003cp\u003eResults from satisfaction surveys and classroom observations (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e \u0026amp; Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e) revealed that the experimental group demonstrated higher satisfaction, particularly in task engagement and interaction experiences.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eStudent Satisfaction Survey Results\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSatisfaction Dimension\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eExperimental Group Mean\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eControl Group Mean\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLearning Interest\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e9.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e7.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTask Adaptability\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e8.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e7.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTeacher-Student Interaction\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e9.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe experimental group appreciated the novelty and relevance of AIGC-generated tasks, which enhanced their interest in learning. Additionally, the interactive mechanisms improved engagement and the sense of collaboration during learning. In contrast, the control group reported monotony in traditional methods, with less classroom interaction, impacting motivation.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003e4.4 Adaptability of Different Student Groups to the Teaching Model\u003c/h2\u003e \u003cp\u003eThe study analyzed the adaptability of different student groups to the AIGC teaching model based on variables such as gender, language proficiency, and technological familiarity (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e \u0026amp; Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eAdaptability of Different Student Groups\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNumber\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eStudent Group\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAverage Adaptability Score (Max: 10)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMale Students\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e8.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFemale Students\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e8.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLow Proficiency Level\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e9.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIntermediate Level\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e8.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAdvanced Level\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e8.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTechnologically Familiar Students\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e9.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTechnologically Unfamiliar Students\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e7.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe data indicates that students with lower initial proficiency levels and higher technological familiarity adapted more readily to the AIGC teaching model. Conversely, students with limited exposure to technology faced initial challenges, highlighting the importance of providing additional technical training and support for such groups during implementation.\u003c/p\u003e \u003cp\u003eThese findings validate the effectiveness of the AIGC-based teaching model in improving language proficiency and learning experiences while emphasizing the need for tailored technical support to maximize adaptability across diverse student groups.\u003c/p\u003e \u003c/div\u003e"},{"header":"5. Discussion","content":"\u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003e5.1 Advantages and Disadvantages of AIGC in English Conversation Teaching\u003c/h2\u003e \u003cp\u003eThis study highlights both the significant advantages and the limitations of AIGC technology in higher education English conversation teaching.\u003c/p\u003e \u003cp\u003eAdvantages:\u003c/p\u003e \u003cp\u003e(1). Dynamic Scenario Generation: AIGC can generate diverse conversational scenarios in real-time based on teaching objectives and student needs, covering various topics and communication settings. This provides students with rich, authentic language practice environments.\u003c/p\u003e \u003cp\u003e(2). Personalized Feedback: By analyzing students\u0026rsquo; language output, AIGC offers immediate, specific feedback on grammar, vocabulary, and pronunciation, helping students identify and address areas for improvement.\u003c/p\u003e \u003cp\u003e(3). High Resource Efficiency: AIGC significantly reduces teachers\u0026rsquo; lesson preparation workload by automatically generating teaching content, offering students more opportunities for autonomous learning.\u003c/p\u003e \u003cp\u003eLimitations:\u003c/p\u003e \u003cp\u003e(1). Inconsistent Content Quality: Some generated language content may include grammatical or cultural inaccuracies, requiring manual intervention and correction by teachers.\u003c/p\u003e \u003cp\u003e(2). Lack of Emotional Interaction: Tasks and feedback generated by AIGC may struggle to replicate the emotional interactions of real-life communication, potentially impacting students' emotional engagement and authentic communication skills development.\u003c/p\u003e \u003cp\u003e(3). Technical Barriers: Teachers and students with limited familiarity with new technology may face a steep learning curve, particularly during initial implementation, which could hinder adoption and effectiveness.\u003c/p\u003e \u003cp\u003eDespite these limitations, the flexibility and innovativeness of AIGC offer valuable support for English conversation teaching, warranting further exploration of its potential.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003e5.2 Challenges in Integrating Technology and Teaching\u003c/h2\u003e \u003cp\u003eAlthough AIGC demonstrates significant advantages, its integration into teaching still faces several challenges:\u003c/p\u003e \u003cp\u003e(1). Technical Adaptability: Insufficient familiarity with AIGC technology among teachers and students, especially in the early stages, may necessitate additional training and support, increasing costs and time investment.\u003c/p\u003e \u003cp\u003e(2). Content Accuracy: AIGC-generated content may occasionally reflect biases or limitations, leading to inaccuracies. In language learning, such errors might interfere with acquisition or foster misconceptions.\u003c/p\u003e \u003cp\u003e(3). Overreliance on Technology: Excessive dependence on technology could reduce teachers' autonomy in instructional design, limiting their ability to leverage their expertise and creativity. Similarly, students may rely too heavily on technology, reducing their deeper engagement with language learning.\u003c/p\u003e \u003cp\u003e(4). Data Privacy and Ethics: AIGC typically requires the collection and analysis of extensive student data, raising concerns about data security, privacy breaches, and ethical risks. Ensuring compliance with policies and regulations is essential.\u003c/p\u003e \u003cp\u003e Addressing these challenges requires clear guidelines for technology use, enhanced technical training for educators and learners, and iterative optimization to ensure the effective integration of technology and teaching.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec22\" class=\"Section2\"\u003e \u003ch2\u003e5.3 Prospects for Future Intelligent Teaching Models\u003c/h2\u003e \u003cp\u003eThe development of intelligent teaching models will continue to drive educational innovation. Potential trends include:\u003c/p\u003e \u003cp\u003e(1). Multimodal Integration: Future models may combine technologies like AIGC, virtual reality (VR), augmented reality (AR), and speech recognition to offer richer, multimodal learning experiences that enhance immersion and interactivity.\u003c/p\u003e \u003cp\u003e(2). Highly Personalized Learning: Advances in AI will enable teaching models to dynamically adapt to individual students' learning habits, interests, and language proficiency, achieving true personalized instruction.\u003c/p\u003e \u003cp\u003e(3). Data-Driven Optimization: By continuously analyzing student behavior and performance data, future teaching models can provide precise learning path recommendations and outcome predictions, supporting data-informed decision-making for both students and educators.\u003c/p\u003e \u003cp\u003e(4). Redefining Teacher and Student Roles: In intelligent teaching environments, teachers will increasingly serve as facilitators and supervisors, while students leverage technology for more autonomous learning. This role shift will transform traditional classroom practices, fostering diverse teaching methods.\u003c/p\u003e \u003cp\u003eOverall, intelligent teaching models will evolve toward greater flexibility, efficiency, and personalization, driving significant improvements in educational quality.\u003c/p\u003e \u003c/div\u003e"},{"header":"6. Conclusions and Recommendations","content":"\u003cdiv id=\"Sec24\" class=\"Section2\"\u003e \u003ch2\u003e6.1 Research Conclusions\u003c/h2\u003e \u003cp\u003eThis study explores the application effect of digital situational teaching based on artificial intelligence generation technology (AIGC) in college English dialogue teaching, and systematically evaluates it from multiple dimensions such as teaching design, implementation effect and student experience. The study shows that AIGC technology can generate rich and diverse situational content, significantly improve students' speech clarity, grammatical accuracy and communication fluency, and effectively enhance students' learning interest and classroom participation.\u003c/p\u003e \u003cp\u003eHowever, the study also found that technology dependence and instability in content generation may have a certain impact on teaching effectiveness. In addition, different student groups have different adaptability to this model, and students with low technology familiarity need more support in the early stages. This study provides strong evidence for the theoretical development and practical promotion of digital situational teaching.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec25\" class=\"Section2\"\u003e \u003ch2\u003e6.2 Practical Recommendations\u003c/h2\u003e \u003cp\u003eTo effectively promote AIGC-based digital scenario teaching, higher education institutions can adopt the following measures:\u003c/p\u003e \u003cp\u003e(1). Enhance Teacher Training: Regular workshops and demonstration lessons to help teachers master AIGC technology and its classroom applications.\u003c/p\u003e \u003cp\u003e(2). Optimize Scenario Content: Establish professional review mechanisms to ensure accuracy and cultural appropriateness, mitigating the impact of technological errors.\u003c/p\u003e \u003cp\u003e(3). Address Individual Differences: Offer tiered support for students with low technological proficiency or weak language foundations, including additional technical guidance and personalized instructional designs.\u003c/p\u003e \u003cp\u003e(4). Adopt Diverse Evaluation Methods: Combine quantitative and qualitative assessments to comprehensively monitor student performance and inform instructional improvements.\u003c/p\u003e \u003cp\u003eThese measures will help fully realize AIGC technology\u0026rsquo;s potential, providing students with more efficient, interactive, and personalized learning experiences.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec26\" class=\"Section2\"\u003e \u003ch2\u003e6.3 Future Research Directions\u003c/h2\u003e \u003cp\u003eFuture research can be conducted in the following aspects: Quality optimization and cultural appropriateness of AIGC technology-generated content. Comparative study of AIGC teaching application models in different disciplines. Research on the integration effect of cross-cultural communication ability training and AIGC technology. Relevant norms and practices of technology ethics and student data privacy protection.\u003c/p\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003col\u003e\n \u003cli\u003eData Availability The raw data supporting the conclusions of this article will be made available by the authors without undue reservation.\u003c/li\u003e\n \u003cli\u003eEthics statement The studies involving humans were approved by Ethics Committee in Kunming University of Science and Technology. The studies were conducted in accordance with the local legislation and institutional requirements. Written informed consent for participation in this study was provided by the participants\u0026rsquo; legal guardians/next of kin.\u003c/li\u003e\n \u003cli\u003eFunding Sponsored by Ministry of Education\u0026apos;s Industry-Academia Cooperation Program for Collaborative Education: \u0026quot;Research and Practice on the Development of Bilingual School-based Curriculum for Chinese Excellent Culture under Digital Intelligence Empowerment\u0026quot;.\u003c/li\u003e\n \u003cli\u003eConflict interests All authors certify that they have no affiliations with or involvement in any organization or entity with any financial interest or non-financial interest in the subject matter or materials discussed in this manuscript.\u003c/li\u003e\n \u003cli\u003eGenerative AI statement The author(s) declare that no Gen AI was used in the creation of this manuscript.\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAhmad T (2020) Scenario based approach to re-imagining future of higher education which prepares students for the future of work, Higher Education, Skills and Work-Based Learning, Vol. 10 No. 1, pp. 217\u0026ndash;238. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1108/HESWBL-12-2018-0136\u003c/span\u003e\u003cspan address=\"10.1108/HESWBL-12-2018-0136\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAyeni OO, Al Hamad NM, Chisom ON, Osawaru B, Adewusi OE (2024) AI in education: A review of personalized learning and educational technology. 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In university-level English instruction, traditional teaching models often fail to meet the increasing demand for oral practice and contextual communication skills among students. This study, grounded in the theoretical framework of digital scenario-based teaching and leveraging AIGC technology, designed and implemented a teaching model tailored for English conversation instruction in higher education. Through an empirical investigation into teaching outcomes\u0026mdash;including dimensions such as student learning performance, communicative competence improvement, and instructional satisfaction\u0026mdash;the findings demonstrate that AIGC-driven digital scenario-based teaching significantly enhances students' comprehensive language application skills while stimulating their interest and active engagement in learning. Moreover, this study identifies technical bottlenecks and pedagogical challenges encountered during implementation, proposing optimization strategies and providing valuable insights for the intelligent evolution of university English teaching.\u003c/p\u003e","manuscriptTitle":"Evaluation of the Implementation Effectiveness of Digital Scenario-based Teaching in University-level English Conversation Instruction: A Study Based on Artificial Intelligence Generated Content (AIGC)","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-04-18 08:05:56","doi":"10.21203/rs.3.rs-6437895/v1","editorialEvents":[{"type":"communityComments","content":1}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"5f14a838-a843-4b1f-a140-f5695b53c6b5","owner":[],"postedDate":"April 18th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-04-18T08:05:56+00:00","versionOfRecord":[],"versionCreatedAt":"2025-04-18 08:05:56","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6437895","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6437895","identity":"rs-6437895","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}