The integration of Generative Artificial Intelligence (GenAI) into higher education represents a paradigm shift from static skill acquisition to dynamic, human–AI collaboration. However, the psychological mechanisms governing students’ adoption—specifically the interplay between pedagogical novelty, ethical awareness, and habit formation—remain underexplored. To address this, this study develops and implements a dynamic practical curriculum incorporating AI and ethical awareness, aiming to foster responsible behavioral patterns in computer programming education. Employing a quasi-experimental design, we implemented a 16-week dual-track instructional intervention (incorporating AI-integrated pedagogy and ethical scaffolding) for 148 computer science students. Structural Equation Modeling (SEM) was applied to test an extended UTAUT2 framework. The findings reveal three critical theoretical insights that redefine GenAI adoption: (1) The eclipse of utility: contrary to established models, traditional utilitarian drivers of performance expectancy (β = 0.076, p = 0.39) and effort expectancy (β = 0.125, p = 0.13) yielded non-significant effects on behavioral intention. This suggests that for digital natives, algorithmic efficiency has devolved into a baseline hygiene factor, losing its motivational power. (2) The dominance of pedagogical novelty: hedonic motivation emerged as the paramount predictor of both habit (β = 0.457, p < 0.001) and behavioral intention (β = 0.336, p = 0.001). This confirms that adoption is driven by the situational interest and interactional novelty inherent in the human–AI partnership. (3) The cognitive brake mechanism: ethical awareness exhibited a divergent regulatory role. While it significantly legitimized conscious behavioral intention (β = 0.166, p = 0.011), it showed a non-significant, negative association with habit (β = −0.032, p = 0.653). This demonstrates that ethical reasoning functions as a cognitive brake (system 2) and actively disrupts the formation of mindless, automated dependency (system 1). These results provide empirical evidence for a dual regulation model of AI adoption and suggest that sustainable education requires leveraging pedagogical novelty to drive engagement while utilizing ethical awareness to prevent blind habituation.

This paper reports a study conducting an in-depth analysis of the impacts of ethical training on the adoption of AI tools among computer science students in higher vocational colleges. These students will serve as the pivotal human factor for advancing the field of AI. Aiming to explore practical models for integrating AI ethics into computer science education, the research seeks to promote more responsible and effective AI application and therefore become a positive influence in the field. Employing a mixed-methods approach, the study included 105 students aged 20–24 from a vocational college in Guangdong Province, a developed region in China. Based on the Unified Theory of Acceptance and Use of Technology 2 (UTAUT2) model, a five-point Likert scale was used to evaluate the participants’ perceptions of AI tool usage based on ethical principles. The Structural Equation Modeling (SEM) results indicate that while participants are motivated to adopt AI technologies in certain aspects, performance expectancy negatively impacts their intention and actual usage. After systematically studying and understanding AI ethics, participants attribute a high proportion of responsibility (84.89%) to objective factors and prioritized safety (27.11%) among eight ethical principles. Statistical analysis shows that habit (β = 0.478, p < 0.001) and hedonic motivation (β = 0.239, p = 0.004) significantly influence behavioral intention. Additionally, social influence (β = 0.234, p = 0.008) affects use behavior. Findings regarding factors that influence AI usage can inform a strategic framework for the integration of ethical instruction in AI applications. These findings have significant implications for curriculum design, policy formulation, and the establishment of ethical guidelines for AI deployment in higher educational contexts.

This study presents WordMap, an integrated text mining application developed to enhance the efficiency and usability of text analysis over a network. As unstructured text data continues to grow across domains, effective tools for segmentation and topic modeling have become increasingly essential for extracting insightful information. However, most existing solutions depend on multiple disconnected tools, and these often compromise workflow efficiency and user experience. Unlike traditional tools, WordMap combines corpus segmentation, topic modeling, and result visualization into a unified workflow for both Chinese and English languages, thereby reducing workflow fragmentation and lowering the user threshold. To assess usability and user acceptance, this research adopts the Technology Acceptance Model (TAM). WordMap employs PKUSEG and NLTK for bilingual corpus segmentation, utilizes BERTopic for dynamic topic modeling, and integrates interactive visualization to enable intuitive analysis. The PLS-SEM result shows that the perceived ease of use (PEOU) has a significant impact on both perceived usefulness (PU) and user attitude (ATT), while ATT strongly predicts behavioral intention (BI) (β = 0.674, p < 0.001). The results indicate that integrating core text mining processes into a user-centered design significantly boosts user satisfaction and adoption. By combining key processes and empirically validating user perceptions, the proposed framework facilitates the development of efficient and accessible text mining tools. It offers both theoretical and practical insights for future advancement and deployment in the field of text mining.

With the rapid development in data mining and learning analytics, integrating predictive analytics into educational data has become increasingly critical for supporting students’ learning trajectories. In many schooling systems, the academic tracks (such as Liberal Arts or Science) and the performance of junior high school students can substantially shape their subsequent university pathways and career planning. Despite the long-term impact of these decisions, academic track selections and the evaluation of students’ potential are often made without systematic and evidence-based guidance. Predictive computer applications can assist, but the training of accurate models and the selection of adequate features remain key challenges. This paper details our process of engineering such an application comprising two tasks based on 1357 real-world junior high school academic performance records. The first task applies a classification approach to predict students’ academic track orientation, while the second task employs a multi-output regression model to forecast students’ future academic performance in senior high school. Our approach shows that the stacking ensemble model achieved a classification accuracy of 85.76%, whereas the Bi-LSTM model with multi-head attention attained an overall R² exceeding 82% in performance forecasting; both models demonstrated strong and reliable predictive capability. Moreover, the proposed approach provides inherent interpretability by decomposing predictions at the subject level. Feature importance analysis reveals how different academic subjects contribute variably to both academic track decisions and future academic performance, offering actionable insights for academic counselling and future study planning. By bridging predictive modelling with students’ educational and career planning needs, this study advances the practical application of educational data mining and provides support for evidence-based academic guidance and future career choices in real-world contexts.

This study investigates how parents and students interpret and form continued engagement intentions with a radar visualisation tool designed to present multi-subject academic performance. While data visualisation is increasingly used in education, limited empirical attention has been given to whether parents and students, who share the same performance information but hold distinct roles, respond to visualised reports through similar behaviours. To address this gap, an interactive radar visualisation was developed to present secondary school students’ achievement across subjects with peer reference points. Drawing on the Unified Theory of Acceptance and Use of Technology (UTAUT) as an analytical framework, this study examines the determinants of continued intention to use the visualisation tool. Questionnaire data were collected from 706 parents and 264 students in a Macao secondary school. Structural equation modelling (SEM) revealed fundamentally different ideas of continued engagement. For parents, continued intention was significantly associated with performance expectancy (PE) and effort expectancy (EE), social influence (SI) and facilitating conditions (FC), suggesting the tool functioned as a decision support system for academic planning. For students, only social influence (SI) and facilitating conditions (FC) emerged as significant predictors, indicating that peer comparison and external expectations may not fit their needs. Parents also reported significantly higher continued intention than students. The finding extended UTAUT by demonstrating that core acceptance relationships are moderated by different roles, reframing technology acceptance in educational visualisation from system adoption to information interpretation. The study provides empirical evidence that visualised performance reporting functions not merely as a data display but also as a communication medium whose meaning is actively constructed by users. These insights highlight the need for role-sensitive design, emphasising actionable planning support for parents and personally meaningful, agency-oriented feedback for students, in order to foster productive home–school communication and sustained engagement with learning information.