Order	Time	ID	Full information
1	13:00-13:15	IT2101	Studying Abroad in the Age of AI: Implications of ChatGPT for Language Load, Learning Strategies, and Educational Ecology Author(s): Shing Tsyr Wei∗, Ken Zen Chen, Tzu-Hua Wang Presenter: Shing Tsyr Wei, National Tsing Hua University Abstract: This study investigates how generative artificial intelligence (AI), particularly ChatGPT, is positioned and utilized by Taiwanese graduate students in cross-linguistic academic contexts, and explores its pedagogical implications in higher education. The participants were 25 master’s and doctoral students who attended a one-month overseas academic program in the United States in 2023. Data were collected through semi-structured interviews and participant observation, and ana-lyzed using grounded theory with open, axial, and selective coding. Findings reveal that ChatGPT was not merely perceived as a technical learning tool, but increasingly as a co-learning partner mediating translation, conceptual clarification, and language production. Its use significantly reduced students’ lan-guage anxiety and cognitive load, thereby enhancing classroom participation and learning confidence. However, concerns also emerged regarding cognitive of-floading, weakened deep processing, and blurred ethical boundaries in learning practices. At the same time, teachers’ roles were observed to shift from knowledge transmitters to learning designers and ethical facilitators guiding stu-dents toward responsible and reflective AI use. Based on these findings, this study proposes three key implications: (1) genera-tive AI should be positioned as a cognitive mediator rather than a substitute for human learning, (2) both teacher AI literacy and students’ critical verification abilities should be strengthened, and (3) institutional governance frameworks should be established to ensure ethical and sustainable AI integration. Overall, the study contributes to rethinking human–AI co-learning and cross-linguistic aca-demic practices in the emerging AI era.
2	13:15-13:30	IT2133	An LLM-Driven and BERT-Based Analysis of Industry Requirements for Software Project Managers Author(s): Junyu Zhang, Jingdong Jia Presenter: Junyu Zhang, Beihang University, China Abstract: Software project manager is a key role in a software development team, and her/his competence directly affects project success. Software engineering management course in universities aims to cultivate outstanding software project managers who meet industry requirements. Therefore, accurately understanding industry requirements for this role is an essential prerequisite for achieving excellence in talent development. In this context, this study uses job advertisements as the primary data source and proposes an industry requirement analysis approach that integrates large language models (LLMs) with deep learning. We first obtained lots of job ads for software project managers from a recruitment website. Then，we obtained valid “job requirements” text through automated data collection and rule-based identification. Next, we used LLMs to extract atomic-level requirements. Further, fine-tune BERT model was used to classify these requirements into three categories: knowledge, skill, and disposition. Finally, we employed normalization and statistical analysis to distill common industry requirements. In addition, we also investigated the common industry requirements of each category for three sub-roles: informationization construction, system integration, and digital application project managers, and conducted a comparative analysis among industry requirements for sub-roles and the overall. The findings provide data-driven guide for optimizing software engineering management course and supporting individual competency development of software project managers.
3	13:30-13:45	IT3197	Modeling Career Trajectories in the AI Era: A Thermodynamic and Cybernetic Approach Author(s): Xin Xie Presenter: Xin Xie, Chengdu Neusoft University, China Abstract: The rapid proliferation of Artificial Intelligence (AI) and the transition into a Volatile, Uncertain, Complex, and Ambiguous (VUCA) labor market have rendered traditional static career matching models—such as Holland’s RIASEC and the Myers-Briggs Type Indicator (MBTI)—increasingly obsolete. This paper introduces the LAVA framework, a novel, dynamic, and quantifiable career development model designed for the 21st-century technological landscape. LAVA synthesizes four critical dimensions: Lifecycle (L), representing endogenous biological and sociological capacity; Algorithmic Displacement (A), quantifying task-based automation risks and augmentation potential; Vocational Entropy (V), drawing from thermodynamics to model systemic disorder and industry-driven obsolescence cycles; and Active Inference (A), utilizing cognitive neuroscience principles (the Free Energy Principle) to model individual adaptive agency and negentropy injection. By integrating these exogenous and endogenous factors into a unified mathematical function—the Global Career Score (GCS)—the LAVA framework provides a predictive tool for career trajectory visualization and "Career Digital Twin" simulation. Preliminary validation through expert review suggests that LAVA offers superior explanatory power regarding career anxiety and lifelong learning motivations compared to traditional frameworks, shifting the vocational paradigm from static matching to dynamic thermodynamic regulation.
4	13:45-14:00	IT3234	The OBE-CDIO Dual-Driven Method for Artificial Intelligence General Education Author(s): Fayou Sun, Huifang Qu Presenter: Fayou Sun, Jining university, China Abstract: It is difficult for artificial intelligence(AI) general education to attract students with different academic backgrounds, resulting in poor learning outcomes. Instructional methods typically designed for science students bring significant barriers to students in the liberal arts (e.g., humanities, social sciences), which leads to poor understanding or be far from instructional goals. This study addresses these shortcomings by proposing and implementing a novel pedagogical method that integrates Outcomes-Based Education (OBE) and Concept-Design-Implement-Operate (CDIO). Simply, we arrange various contents for students with different backgrounds and execute them iteratively. Our approach involves detailed case studies and teamwork, and learning outcomes prove that Students increased cross-disciplinary engagement, improved conceptual understanding, and significantly improved their ability to apply AI tools in their fields. Qualitative and quantitative experiments demonstrate that our OBE-CDIO dual-driven approach can effectively cultivate comprehensive AI literacy in heterogeneous undergraduate students.
5	14:00-14:15	IT3249	The Development of Artificial Intelligence and the Reform of Higher Education Author(s): Xiaojing Lin Presenter: XiaoJing Lin, Guang Dong Peizheng College, China Abstract: The AI era is driving the development of new core competencies, with the education sector being significantly impacted. The integration of AI and education has emerged as a focal point, where technological advancements are fundamentally transforming teaching philosophies, methodologies, and models in higher education. Consequently, universities must proactively lead the application and advancement of AI to adapt to these changes. This study utilizes the Citespace tool to analyze global AI research progress and the current state of higher education, exploring the concept of AI education, the logic behind its transformation, the rationale for driving high-quality development in higher education, and implementation strategies. The findings aim to facilitate educational reforms in universities and provide actionable insights for integrating AI technologies to foster innovative educational integration.
6	14:15-14:30	IT3261	Mapping Artificial Intelligence in Higher Education with VOSviewer: Hotspots and Trends Author(s): Rong Geng, Xiaoshi Song, Ning Ye, Ce Ji Presenter: Rong Geng, Northeastern University Abstract: Artificial intelligence (AI) has become a major driver of transformation in higher education, enabling innovations in teaching, learning support, assessment, and institutional governance. However, the rapid expansion of related publications makes it difficult to obtain an evidence-based understanding of the field’s intellectual structure and emerging trajectories. This study proposes a bibliometric–reasoning framework that integrates VOSviewer-based science mapping with knowledge graph reasoning to synthesize and interpret global AI-in-higher-education research. A total of 1,527 records published between 2022 and 2026 were retrieved from the Web of Science Core Collection and analyzed using bibliographic coupling, co-authorship, and keyword co-occurrence techniques. The results reveal (i) a multidisciplinary source ecosystem connecting educational technology, computing-oriented venues, and health/medical education outlets; (ii) globally centralized collaboration patterns, with China and the USA acting as key production and collaboration hubs; and (iii) a thematic knowledge structure dominated by generative AI (e.g., ChatGPT and large language models) alongside evaluation- and governance-related topics. Hotspot and density analyses further suggest that future research will increasingly focus on generative-AI-enabled pedagogy, reliable assessment and learning analytics, and responsible AI governance addressing ethics, academic integrity, and trust. Overall, the proposed approach demonstrates how bibliometric networks can be transformed into reasoning-ready knowledge representations, offering a reproducible pathway for trend identification and research agenda setting in fast-evolving educational technology domains.
7	14:30-14:45	IT3321	Human–AI Collaboration and Creative Behavior among Chinese College Students Author(s): Xiao Zhou Presenter: Xiao Zhou, Guangdong University of Science and Technology, China Abstract: This study examined the levels of human–AI collaboration and creative behavior among Chinese college students and explored the relationship between the two constructs. Data were collected from 558 undergraduate students across five universities in Guangdong Province, China. Using descriptive statistics and partial least squares structural equation modeling (PLS-SEM), the results indicated that students demonstrated relatively high levels of both human–AI collaboration and creative behavior. furthermore, human–AI collaboration was found to have a significant and positive effect on creative behavior. The findings support augmentation-based perspectives, suggesting that AI can enhance higher-order cognitive outcomes when used collaboratively rather than substitutivity. This study contributes empirical evidence to the growing literature on AI-supported learning and provides insights into how AI integration may foster creativity in higher education contexts.
8	14:45-15:00	IT3326	AI-Driven EST Translation Teaching A Human-AI Synergy Framework for New Engineering Talents Author(s): Jing Xiao, Yong Luo Presenter: Jing Xiao, Centre of Foundational Courses, Beijing Institute of Technology, Zhuhai Zhuhai, China Abstract: Amid China’s “New Engineering” initiative and growing global technical communication needs, EST translation teaching demands enhanced efficiency and precision. This study proposes an AI-assisted teaching framework integrating core computing technologies, including NMT engines, context-aware terminology retrieval algorithms, and LLMs. Targeting bottlenecks in specialized translation instruction for engineering students, the framework combines these technologies with tailored pedagogical strategies. Experimental results indicate the model boosts translation accuracy by 26.5% and terminology standardization by 46.2% compared to traditional methods. This work provides a scalable technical solution for EST teaching optimization, offering empirical evidence for human-AI synergy in engineering-oriented language education.
9	15:00-15:15	IT4472	Critical Issues of Education Transition to Technical Singularity Author(s): Dimiter Velev, Plamena Zlateva, Georgi Kirov Presenter: Dimiter Velev, University of National and World Economy, Bulgaria Abstract: This article examines the fundamental challenges facing the education system in the context of the approaching technological singularity, where artificial intelligence (AI) surpasses human cognitive abilities and begins to improve itself exponentially. The traditional educational model, inherited from the industrial era and based on linearity, standardization, and reproduction of an established volume of knowledge, turns out to be incompatible with the dynamics of exponential technological progress. Education can no longer be conceived as a system designed merely to transmit established volumes of knowledge. It becomes a dynamic, adaptive, and meta-cognitive infrastructure for cultivating meaning, judgment, and human agency within environments characterized by exponential change. Education in the age of technological singularity must transition from an industrial-era model of standardization to a post-industrial, cognitively augmented model focused on human-AI symbiosis, ethical reasoning, creative synthesis, and lifelong adaptability.  The aim of the article is to identify and analyze the main dimensions and critical issues of the transition of education to technological singularity.