Augmented Reality (AR), Virtual Reality (VR), and Mixed Reality (MR) technologies—collectively known as Extended Reality (XR)—have ushered in a new era of immersive and interactive instruction in applied sciences education. This systematic literature review aims to examine the application of XR technologies across various scientific and educational domains, evaluate their impact on learning outcomes, and identify the challenges hindering their broader integration. Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, a literature search was conducted using Web of Science, ScienceDirect, and IEEE Xplore, focusing on empirical studies published between 1 January 2010 and 1 November 2024, resulting in the inclusion of 56 studies. Among these, 32 studies (53%) employed VR, 25 studies (42%) utilized AR, and 3 studies (5%) adopted MR, with 4 studies exploring the combined application of VR and AR. The findings indicate that VR is primarily applied in higher education settings, such as universities and graduate programs, whereas AR is more prevalent in primary and secondary education; although MR is less frequently used, it exhibits distinct advantages in disciplines requiring high interactivity and realism. Overall, each XR modality can enhance learning motivation, efficiency, and immediate knowledge acquisition in short-term interventions, while long-term implementation may contribute to improved memory retention, increased learner confidence, and sustained engagement. Despite persistent challenges—including high equipment costs, spatial and temporal constraints, small sample sizes, and insufficient longitudinal evidence—these findings underscore the transformative potential of XR technologies in applied sciences education.

With the rapid development of Generative Artificial Intelligence (GAI) technology, programming education has emerged as a core application domain. Through a systematic literature review of 45 relevant studies from the Semantic Scholar database from 2023-2025, this study examined the current applications of GAI as an auxiliary learning tool in programming education, and its impact on learning outcomes. The findings reveal that GAI-assisted instruction demonstrates significant effectiveness across seven learning indicators: programming knowledge and skills, computational thinking and logical reasoning, problem-solving ability, programming self-efficacy, learning achievement, code quality, and learning behaviors and engagement. While the majority of studies confirm that GAI enhances student performance in various areas such as task completion, test performance, code structure and quality, and promoting self-directed learning, some studies indicate that GAI use may reduce learning depth and lead to over-dependence in specific tasks or complex reasoning contexts. From a pedagogical perspective, GAI prompts a transformation in teachers’ roles from knowledge transmitters to learning facilitators and guides, necessitating corresponding adjustments in curriculum design and assessment approaches. Based on the empirical findings, this study constructs an integrated conceptual model for GAI-assisted programming education integrating four core dimensions: implementation context factors, core influencing factors, learning performance indicators, and learning outcomes. The study identifies AI tool selection, students’ foundational abilities, and task complexity as key variables affecting learning effectiveness, and synthesizes seven patterns of student learning behavior changes under GAI assistance, providing concrete theoretical foundations and implementation guidelines for educational practice.

This study examined the effectiveness of Artificial Intelligence (AI)-powered Teaching Assistants (AITAs), developed through ChatGPT fine-tuning to support underperforming technical university students in Taiwan, in improving their Test of English for International Communication (TOEIC) reading comprehension. These students are required to meet the English proficiency graduation requirement of a minimum TOEIC score of 550. However, many struggle to attain even 200 out of 495 points in the reading section. Employing a quasi-experimental design, this study involved 226 undergraduate students from non-English major departments over a 12-week period. The experimental group utilized AITAs-driven materials, whereas the control group relied on conventional resources. Learning outcomes were evaluated through pre- and post-tests, and the extended Unified Theory of Acceptance and Use of Technology (UTAUT) was applied to identify the factors influencing students’ acceptance of AITAs-driven materials. An analysis of covariance revealed that students in the experimental group achieved significantly higher TOEIC reading scores than those in the control group. The extended UTAUT analysis highlighted that social influence, AI self-efficacy, performance and effort expectancy, and facilitating conditions significantly influenced behavioral intention. Task-technology fit moderated the relationship between behavioral intention and usage behavior. These findings provide robust empirical evidence for the efficacy of AI-driven personalized learning in enhancing TOEIC reading comprehension for university students. Furthermore, the research offers a comprehensive framework for integrating AI tools into language education practices. It addresses the specific challenges and unique educational needs of learners and leverages AI technologies to help language learners meet standardized testing requirements.