Innovation in Language Learning and Teaching

Multimodality in Language Learning and Teaching in the Age of AI and GenAI: Current Innovations and Emerging Trends

Aims and Rationale

The growing presence of Artificial Intelligence (AI) and Generative AI (GenAI) in second and third language (L2, L3) learning environments is profoundly reshaping how languages are learned and taught (Chen et al., 2022; Derakhshan, 2025a, 2025b; Lee et al., 2025; Stockwell, 2024; Wang et al., 2025; Wu & Wang, 2025; Yang & Zhao, 2024). Traditional multimodal pedagogies—relying on combinations of textual, visual, and auditory modes—are now evolving through novel AI- and GenAI-driven capabilities such as real-time speech recognition, multimodal feedback, avatar-based interaction, immersive simulations, and adaptive conversational agents (Chu et al., 2025; Ci & Jiang, 2025; Jiang, 2024; Li et al., 2024; Liu et al., 2025). These technologies make it possible to design richer and more interactive communicative environments that can respond to learners’ needs in nuanced and personalized ways (Chen et al., 2022; Derakhshan & Ghiasvand, 2024; Huang et al., 2023; Kohnke et al., 2025; Zong & Yang, 2025; Zou et al., 2025).

Although AI and GenAI-enabled multimodal tools are increasingly embedded in language education, many questions remain unanswered regarding their pedagogical effectiveness, the extent to which they shape learners’ behavioral, cognitive, and affective engagement, and their capacity to support language development across diverse learner profiles and proficiency levels. Additionally, the accelerated integration of these multimodal tools raises ethical, socio-emotional, and practical considerations—including transparency, equity of access, teacher readiness, and responsible use—that must be addressed to ensure educational value and learner well-being.

This Special Issue, therefore, seeks to gather innovative research and pedagogical perspectives that critically investigate multimodal learning in the age of AI and GenAI. It aims to illuminate emerging trends, innovative instructional practices, and evidence-based outcomes, while also foregrounding the challenges and complexities involved in integrating multimodal AI and GenAI tools into language learning environments. By offering a timely synthesis of research on these fast-moving developments, this issue will contribute valuable insights for language educators, researchers, curriculum designers, and policymakers striving to design human-centered and pedagogically sound AI and GenAI-enhanced learning ecologies.

Given the complex and rapidly evolving nature of technologically rich learning environments, this Special Issue encourages the use of advanced and innovative research methodologies (e.g., nested ecosystem frameworks, ecological momentary assessment, time-series analysis, retrodictive qualitative modeling, Q-methodology, latent profile analysis, and idiodynamic methods) to clarify how multimodal AI and GenAI tools dynamically shape language development in authentic contexts. By adopting approaches that trace moment-to-moment interaction patterns, capture individual differences, and reveal hidden learning trajectories, researchers can move beyond surface-level outcomes to uncover the mechanisms through which multimodality supports—or challenges—learners’ psycho-emotional experiences and overall academic achievement.

Proposed Topics

Contributions to this special issue may address, but are not limited to, the following areas: 

1.     AI- and GenAI-Supported Multimodal Instructional Design
Designing and evaluating multimodal instructional approaches supported by AI or GenAI in language learning environments.

2.     Learner Engagement in AI- and GenAI-Enhanced Multimodal Contexts
How learners’ behavioral, cognitive, and affective engagements are influenced by multimodal AI and GenAI tools

3.     Personalization in Multimodal AI- and GenAI-Enhanced Settings
Adaptive scaffolding and individualized support for diverse proficiency levels and learner profiles.

4.     Intelligent Multimodal Feedback for L2 or L3 Skill Development
Impacts of automated or semi-automated feedback on language learners’ speaking, listening, reading, and writing performance

5.     Immersive and Embodied Multimodal Communication
Learning affordances of VR/AR platforms, avatar-based communication, and embodied conversational agents

6.     Teacher Roles, Readiness, and Professional Learning
Teacher cognition, decision-making, and pedagogical readiness to adopt multimodal AI and GenAI tools in L2 or L3 classrooms.

7.     Multilingual Learners’ Agency, Autonomy, and Psycho-Emotional Experiences
How multimodal AI and GenAI-enhanced environments affect multilingual language learners’ self-confidence, autonomy, agency, and emotional well-being.

8.     Assessment of Multimodal Communicative Competence
AI- and GenAI- enabled evaluation of receptive and productive skills (speaking, listening, reading, and writing) and multimodal performance.

9.     Ethical, Equity, and Policy Implications
Transparency, fairness, authorship, access, and responsible use in AI and GenAI-mediated multimodal education.

References

Chen, X., Zou, D., Xie, H., Cheng, G., & Liu, C. (2022). Two decades of artificial intelligence in education. Educational Technology & Society, 25(1), 28–47. Retrieved from https://www.jstor.org/stable/48647028

Chen, Y., Zhi, Y., & Derakhshan, A. (2025). Integrating artificial intelligence (AI) into the English as a foreign language classroom: Exploring its impact on students’ achievement emotions and willingness to communicate (WTC). European Journal of Education, 60(3), e70157. https://doi.org/10.1111/ejed.70157

Chu, H. C., Lu, Y. C., & Tu, Y. F. (2025). How GenAI-supported multi-modal presentations benefit students with different motivation levels. Educational Technology & Society, 28(1), 250–269. Retrieved from https://www.jstor.org/stable/48810718

Ci, F., & Jiang, L. (2025). The integration of generative artificial intelligence into digital multimodal composing in second language classrooms: a scoping review from the perspective of tasks. Digital Applied Linguistics, 2, 102939–102939. https://doi.org/10.29140/dal.v2.102939

Derakhshan, A. (2025a). EFL students’ perceptions about the role of generative artificial intelligence (GAI)–mediated instruction in their emotional engagement and goal orientation: A motivational climate theory (MCT) perspective in focus. Learning and Motivation, 90, 102114. https://doi.org/10.1016/j.lmot.2025.102114

Derakhshan, A. (2025b). Tracking the effects of Gemini as a GenAI tool on L2 learners’ writing proficiency and anxiety: Latent growth curve modeling approach. The Asia-Pacific Education Researcher. https://doi.org/10.1007/s40299-025-01042-5

Derakhshan, A., & Ghiasvand, F. (2024). Is ChatGPT an evil or an angel for second language education and research? A phenomenographic study of research‐active EFL teachers’ perceptions. International Journal of Applied Linguistics, 34(4), 1246–1264. https://doi.org/10.1111/ijal.12561

Huang, A. Y., Lu, O. H., & Yang, S. J. (2023). Effects of artificial intelligence–enabled personalized recommendations on learners’ learning engagement, motivation, and outcomes in a flipped classroom. Computers & Education, 194, 104684. https://doi.org/10.1016/j.compedu.2022.104684

Jiang, J. (2024). When generative artificial intelligence meets multimodal composition: Rethinking the composition process through an AI-assisted design project. Computers and Composition, 74, 102883. https://doi.org/10.1016/j.compcom.2024.102883

Kohnke, L., Zou, D., & Su, F. (2025). Exploring the potential of GenAI for personalised English teaching: Learners' experiences and perceptions. Computers and Education: Artificial Intelligence, 8, 100371. https://doi.org/10.1016/j.caeai.2025.100371

Lee, S., Choe, H., Zou, D., & Jeon, J. (2025). Generative AI (GenAI) in the language classroom: A systematic review. Interactive Learning Environments. https://doi.org/10.1080/10494820.2025.2498537

Li, W., Chen, X., & Huang, L. (2024). Promoting second language writing through technology-driven multimodal text feedback. Innovation in Language Learning and Teaching, 18(5), 462–479. https://doi.org/10.1080/17501229.2024.2315104

Liu, M., Zhang, L. J., & Neufeld, T. J. (2025). Chinese EFL learners’ GenAI literacy in digital multimodal composing and self-regulated writing: chain mediation effects of needs satisfaction and creative self-concept. Innovation in Language Learning and Teaching. https://doi.org/10.1080/17501229.2025.2549754

Stockwell, G. (2024). ChatGPT in language teaching and learning: Exploring the road we’re travelling. Technology in Language Teaching & Learning, 6(1), 2273–2273. https://doi.org/10.29140/tltl.v6n1.2273

Wang, Y., Alm, A., & Dizon, G. (Eds.). (2025). Insights into AI and language teaching and learning. Castledown Publishers.

Wu, H., & Wang, Y. (2025). Disclosing Chinese college students’ flow experience in GenAI-assisted informal digital learning of English: A self-determination theory perspective. Learning and Motivation, 90, 102134. https://doi.org/10.1016/j.lmot.2025.102134

Yang, L., & Zhao, S. (2024). AI-induced emotions in L2 education: Exploring EFL students’ perceived emotions and regulation strategies. Computers in Human Behavior, 159, 108337–108337. https://doi.org/10.1016/j.chb.2024.108337

Zong, Y., & Yang, L. (2025). How AI-enhanced social-emotional learning framework transforms EFL students’ engagement and emotional well-being. European Journal of Education, 60(1), 1–12. https://doi.org/10.1111/ejed.12925

Zou, M., Reinders, H., & Amjad, F. (2025). Understanding the potential role of GenAI-mediated informal digital learning of English (GenAI-IDLE) in the Global South: AI literacy, emotions, and willingness to communicate as outcomes. ReCALL. https://doi.org/10.1017/S0958344025100360