Taeyeon Eom

This chapter examines how data analytics can be leveraged to enhance immersive teacher simulations, situating this inquiry within the broader learning sciences discourse on embodied cognition, data-informed feedback, and teacher professional learning. It explores both conceptual foundations and empirical cases to illustrate how analytics serve as mediational tools that connect immersive experiences with reflective teaching practice. The chapter unfolds in multiple sections: (1) The Innovation Journey: An Overview of Immersive Teacher Simulations outlines the evolution from traditional simulations to XR-based environments, highlighting the need for professional decision-making under realistic constraints. (2) Innovation in Existing Research and Practice situates teacher analytics within the trajectory from descriptive observation to multimodal and predictive modeling. (3) Study Approach and Design details how multimodal data-discourse, behavior, and gaze-from the TeacherGen@i simulation were collected and organized to reveal cognitive distribution of pedagogical discourse and interaction patterns. (4) Findings present the cognitive distribution of preservice teachers' pedagogical discourse and the sequential interaction patterns that emerge in exchange, illustrating how multimodal analytics make pedagogical reasoning processes visible within immersive simulations. (5) Understanding Innovative Practices in Teacher Education examines teaching analytics to enhance immersive teacher simulation based on the findings of the study. (6) Key Takeaways of the Innovation Journey identifies research challenges and design implications for scalable, analytics-enhanced teacher education. Together, these sections position immersive teacher simulations as a pivotal testbed for aligning learning analytics, professional learning, and next-generation immersive learning environment design.

The rapid digital transformation in education has necessitated an evaluation of the impact of technology-enhanced learning environments. This study examines the effects of one-to-one technology implementation on the digital literacy of middle school students in South Korea, focusing on the 2022 curriculum revision. The research employs a case study approach, analyzing data collected from two schools with varying levels of technological integration. Using pre- and post-surveys, interviews, and classroom observations, the study assesses changes in students’ digital literacy, including understanding and application of digital technology, digital mindset, digital thinking ability, and digital literacy practice. The findings indicate statistically significant improvements in digital thinking ability, particularly among students with higher academic achievement levels. However, disparities in teacher engagement and school infrastructure influenced the effectiveness of the one-to-one technology implementation. The study highlights the need for structured pedagogical strategies, teacher training, and curriculum integration to optimize the benefits of technology-enhanced education. Future research should explore best practices for technology-enhanced learning environments and strategies to bridge the digital literacy gap among students.

Teacher education faces persistent challenges, including limited access to authentic field experiences and a disconnect between theoretical instruction and classroom practice. While virtual reality (VR) simulations offer an alternative, most are constrained by inflexible design and lack scalability, failing to mirror the complexity of real teaching environments. This study introduces TeacherGen@i, a generative AI (GenAI)-enhanced VR simulation designed to provide pre-service teachers with immersive, adaptive teaching practice through realistic GenAI agents. Using an explanatory case study with a mixed-methods approach, the study examines the simulation’s usability, design challenges, and instructional utility within a university-based teacher preparation course. Data sources included usability surveys and reflective journals, analyzed through thematic coding and computational linguistic analysis using LIWC. Findings suggest that TeacherGen@i facilitates meaningful development of teaching competencies such as instructional decision-making, classroom communication, and student engagement, while also identifying notable design limitations related to cognitive load, user interface design, and instructional scaffolding. This exploratory research offers preliminary insights into the integration of generative AI in teacher simulations and its potential to support responsive and scalable simulation-based learning environments.