Recent advances in large language models have revitalized research on automated essay evaluation, yet critical concerns remain regarding their reliability, validity, and interpretability. This study presents a comparative analysis of five LLMs (GPT-4.1, LLaMA 4 Maverick, Gemini 2.5 Flash, Claude Sonnet 4, and DeepSeek R1) in the assessment of long English essays authored by non-native speakers in higher education. The analysis draws on LLM-generated scores for 60 essays to examine (a) intra-model reliability across repeated scoring runs, (b) the degree of alignment between model outputs and expert human ratings, and (c) causal feature dependencies that clarify how linguistic characteristics influence model scoring behavior. Findings reveal substantial variation: some models achieved near-perfect reproducibility and strong alignment with human raters, whereas others displayed inconsistency, score compression, or systematic underestimation. Causal discovery analysis further uncovered distinct evaluative heuristics, with most models prioritizing lexical precision and fluency, while others emphasized syntactic complexity or cross-domain integration. Collectively, these results establish model-specific reliability profiles and application contexts, providing empirical benchmarks and practical guidance for the responsible use of LLMs in educational writing assessment.

Despite the growing importance of English oral communication skills, traditional language learning approaches show limited effectiveness in simultaneously addressing psychological barriers and speaking proficiency among college students. While previous studies have explored anxiety reduction or speaking enhancement separately, a significant gap exists in research examining integrated approaches that tackle Public Speaking Anxiety (PSA), Interview Anxiety, and English-speaking proficiency improvement simultaneously. This study investigated whether an AI-integrated VR oral training application could effectively address these interconnected challenges. A quasi-experimental design was employed with 20 English major students from a mid-central university in Taiwan. Participants completed five training sessions using Meta Quest 2 headsets and an AI-integrated VR oral training application providing tailored feedback on pronunciation, grammar, and fluency based on IELTS standards. Pre- and post-intervention assessments utilized validated instruments including the Personal Report of Public Speaking Anxiety (PRPSA) and Measure of Anxiety in Selection Interviews (MASI), alongside comprehensive speaking proficiency measures. Results demonstrated significant improvements in English speaking proficiency, including increased sentence length and word count, with grammatical errors and incomplete sentences decreasing markedly (p < .001). Concurrently, significant reductions in both PRPSA and MASI scores (p < .05) were observed, though lexical diversity showed slight decline. VR-related motion-sickness symptoms were mildly alleviated, and participants' perceived control increased significantly (p < .05), while interest and attention levels remained stable. These findings suggest that AI-integrated VR oral training applications can effectively enhance English speaking proficiency while simultaneously reducing anxiety levels and improving self-efficacy among English learners. The study addresses a critical research gap by demonstrating the potential of integrated technological approaches to tackle multiple barriers to effective English oral communication, offering promising implications for language education and anxiety management in academic contexts.

Artificial Intelligence (AI) is changing and revolutionizing today’s economy and work life. A basic understanding of this technology is beneficial for even non-technical roles, highlighting the interdisciplinary nature of AI and its diverse applications. However, it is difficult to create significant, practically relevant learning experiences related to AI for students of different backgrounds, especially for students outside of computer science programs. To tackle this problem, we designed and evaluated an interdisciplinary, project-based course combined with creativity methods, where students from diverse study programs worked on an everyday challenge and tried to build AI prototypes to address it. The interdisciplinary nature of the course enabled students from diverse disciplines to collaborate and learn from one another. The course focused on project-based learning, providing students with hands-on experience in AI product design and implementation. It also incorporated teamwork and collaboration activities that enabled students to gain a better understanding of AI jointly. The course has been conducted and evaluated across two consecutive editions, involving a total of 32 students, providing a robust basis for the analysis presented in this study. Overall, the student feedback was favorable, indicating an enhanced sense of confidence in their AI abilities. We provide evidence that a project-based, interdisciplinary AI course incorporating creative methods can be an effective approach for students from diverse academic backgrounds to expand their knowledge and gain a more nuanced grasp of AI technologies. • A reusable university course design to teach AI and programming competencies • Encouraging students to apply creativity and innovation methods during project work • Fostering student collaboration, communication, and effective teamwork • Lessons learned and takeaways to support other AI educators

The integrative literature review addresses the conceptualization and implementation of AI Literacy (AIL) in Higher Education (HE) by examining recent research literature. Through an analysis of publications (2021–2024), we explore (1) how AIL is defined and conceptualized in current research, particularly in HE, and how it can be delineated from related concepts such as Data Literacy, Media Literacy, and Computational Literacy; (2) how various definitions can be synthesized into a comprehensive working definition, and (3) how scientific insights can be effectively translated into educational practice. Our analysis identifies seven central dimensions of AIL: technical, applicational, critical thinking, ethical, social, integrational, and legal. These are synthesized in the AI Literacy Heptagon, deepening conceptual understanding and supporting the structured development of AIL in HE. The study aims to bridge the gap between theoretical AIL conceptualizations and the practical implementation in academic curricula. • Development of a structured seven-dimensional framework (Heptagon) for AI Literacy in Higher Education. • Proposal of a working definition to capture and synthesize both recurring and emerging themes in AI Literacy conceptualizations. • Identification and integration of underrepresented AIL dimensions in recent literature (e.g. integration skills and legal and regulatory knowledge), and delineation from Media, Computational and Data Literacy.