1 Introduction

This section discusses the importance of multimodal communication in socio-semiotic dissemination and the application of multimodal teaching in foreign language education. As sociocultural resources, modalities play a role in meaning construction through the interaction of various semiotic resources. Multimodal teaching integrates elements such as language, images, and sound, influencing teaching attitudes, methods, and learning outcomes. Scholars domestically and internationally have conducted in-depth research on multimodal discourse and teaching, exploring issues such as multimodal pedagogy, foreign language classroom instructional design, and multimodal textbook design. Technological advancements provide options for classroom multimodality, but challenges of underutilization persist. AIGC (AI-generated content) technology, which automates content creation through artificial intelligence algorithms, offers new potential for educational transformation. Large language models contribute to innovations in teaching methods, tools, and resources, yet practical applications remain underexplored. AI text-to-image models, as multimodal technologies within the AIGC framework, generate images from text and are applied in fields such as artistic creation and virtual reality. The prospects for AI text-to-image models in education are vast, offering new possibilities for transforming foreign language classroom instruction. This study aims to explore the potential of AI text-to-image models in foreign language teaching, addressing challenges in multimodal teaching through the development of teaching resources and activity design to enhance its effectiveness.

2 Potential Applications and Implementation Methods of AI Text-to-Image Models in Multimodal Foreign Language Teaching

This section examines the potential applications and implementation methods of AI text-to-image models in multimodal foreign language teaching. It emphasizes the importance of creating rich contextual environments in foreign language teaching and the role of multimodal learning scenarios in helping students overcome linguistic and cultural barriers. AI text-to-image models can generate explanatory images to aid discourse comprehension and design interactive multimodal teaching scenarios to enhance learners' understanding. Specific applications include: 1. Creating multimodal resources based on teaching contexts, using AI text-to-image models to automatically generate relevant images, such as inputting text prompts to guide the model in producing images aligned with teaching content; 2. Designing multimodal interactions integrated into the teaching process, such as live demonstrations of AI text-to-image model applications and picture-based speaking activities, to improve teaching quality and efficiency; 3. Strengthening multidimensional skills, such as critical thinking and cross-cultural competence, through participatory teaching interactions to cultivate learners' comprehension and production abilities; 4. Incorporating ideological and political elements into learners' experiences and evaluation processes to foster cognitive skills and value development.

3 The Potential of AI Text-to-Image Models in Assisting Multimodal Discourse Teaching

AIGC technology, by integrating and innovating modal resources, provides new opportunities for teaching model innovation and the restructuring of educational elements. AI text-to-image models demonstrate potential advantages in constructing multimodal discourse teaching resources, instructional design, and methodological innovation. These models can transcend thematic limitations, innovate resource generation methods, expand modal choices, and enrich resource presentation styles. In instructional design, AI text-to-image models facilitate multilateral interactions among teachers, students, and human-computer interfaces, enhancing sensory experiences and enabling more flexible, open, and interactive designs. These models also promote innovative improvements in teachers' instructional capabilities, enable personalized teaching, and cultivate students' ability to negotiate human-computer interactions, meeting the demands of talent development in the new era.

4 Conclusion

This section highlights the significance of AIGC technology in multimodal teaching, noting its ability to enhance learners' processing and analysis of multimodal data, deepen their understanding of discourse at deeper levels, and assist in predicting and interpreting patterns and trends in multimodality. Systemic functional linguistic principles provide support for understanding, evaluating, and applying AI-generated content. Leveraging the potential of AI in teaching can stimulate student initiative, improve teaching outcomes, and drive innovation in teaching models and methods. Future research should focus on the introduction and construction of multimodal resources and how digital and intelligent technologies can empower foreign language teaching. The application of AI text-to-image models in sociocultural discourse teaching represents an initial attempt to help students deepen their understanding of discourse. AIGC technology can support foreign language classroom instruction, encouraging students to explore discourse contexts and connotations more deeply. Future studies will further explore the systematic application of AIGC in foreign language education to meet the learning demands of the AI era.