<b>Integration of Emerging Technologies into Instructional Design on Language Education</b>

<p dir="ltr">This dissertation investigates the integration of emerging technologies—particularly Virtual Reality (VR) and Artificial Intelligence (AI)—into the instructional design in the field of language education. The first paper presents a design-based research study which put forward and implemented three design principles grounded in task-based language teaching (TBLT) pedagogy and two key VR affordances—authenticity and interaction—to develop task-based role-play speaking activities. The design principles were put into practice in a workshop series offered to undergraduate Chinese students interested in improving their English speaking. Sixteen Chinese undergraduate students engaged in the workshop series, completing eight tasks over eight sessions within a VR application called Immerse. Data from semi-structured interviews were thematically analyzed to explore how the design of the speaking activities supported English as a foreign language (EFL) learners in enhancing their speaking proficiency within VR environments. The design principles created and implemented in this design case study can serve as guidelines for designing and assessing speaking activities in VR environments, ensuring that educational practices are pedagogically sound and technologically enhanced. </p><p dir="ltr">Building on these findings, the second paper adopts a convergent mixed-method study and examines the impact of the situated learning approach on learners’ English-speaking performance, specifically in areas of fluency, vocabulary, pronunciation, and grammar, and explores learners’ perception of the instruction based on the situated learning approach. The study involved pre- and post-assessments of speaking performance and semi-structured interviews with six participants. Paired samples t-tests were used to assess the difference in the speaking performance and respective areas, and a thematic analysis was adopted to explore learners’ perceptions of the instruction based on the situated learning approach. Quantitative findings show a significant improvement in learners’ speaking performance (t(15) = 7.41, p < .001, Cohen's d = 1.82), with notable progress in fluency, vocabulary, pronunciation, and grammar. Thematic analysis of the qualitative data indicated the authenticity of the context and activities, the collaborative nature of the tasks, the expert guidance, and the opportunities for reflection all contribute to a comprehensive learning experience that aligns well with the principles of situated learning. </p><p dir="ltr">In response to the rapid development of AI, the third paper is a systematic literature review which examines the integration of Artificial Intelligence (AI) and Extended Reality (XR) technologies in language education, synthesizing findings from 32 empirical studies published between 2017 and 2024. Guided by the PRISMA framework, we searched four databases—ERIC, Web of Science, Scopus, and IEEE Xplore—using rigorous inclusion criteria to identify studies that explicitly integrated both AI and XR to support language learning. The review explores publication trends, educational settings, target languages, language skills, and learning outcomes, and analyzes how AI-XR technologies have been pedagogically integrated. Key integration strategies include coupling XR with AI technologies such as automatic speech recognition, natural language processing, computer vision, and conversational agents to support skills like speaking, vocabulary, writing, and intercultural competence. Reported affordances include enhanced engagement, motivation, personalized feedback, and immersive, context-rich learning environments. However, challenges persist in terms of technical limitations, instructional design gaps, scalability, and ethical concerns. Design recommendations and future directions emphasize the need for adaptive AI dialogue systems, broader pedagogical applications, longitudinal studies, and scalable, inclusive design. This review offers a comprehensive synthesis to guide researchers, educators, and developers in designing effective AI-XR language learning experiences. </p><p dir="ltr">Drawing on the complementary strengths of design-based research, mixed-method inquiry, and systematic review methodology, this dissertation provides new insights into the design principles, instructional methods, and broader trends associated with the use of emerging technologies in language education.</p>