Bo Sun

Within the framework of United Nations Sustainable Development Goal 4 (SDG 4: Quality Education), adult aesthetic education faces challenges related to the sustainability of access, personalisation, and humanistic values. This study examines levels of acceptance regarding generative AI-assisted aesthetic education in the Open University of China. Drawing on the Technology Acceptance Model (TAM) and lifelong learning theory, we surveyed 518 adult learners and used descriptive analysis (based on the full sample) alongside binary logistic regression and PLS-SEM (based on the 355 learners who had prior aesthetic education experience) to analyse adoption readiness, functional preferences, and influencing factors. Our main findings are as follows: (1) 91.5% of learners recognised the importance of aesthetic education, yet the actual participation rate was only 68.5%; (2) 50% of respondents expressed clear willingness to use AI-assisted aesthetic learning, with the most desired functions being “personalised art interpretation” and “creative inspiration support”; (3) no significant age-related differences were found in AI adoption readiness; (4) logistic regression identified aesthetic cognition and learning experience as significant predictors of AI adoption willingness; and (5) SEM showed that learning experience had the strongest direct effect on AI adoption readiness (β = 0.62) and partially mediated the relationship between aesthetic cognition and AI adoption readiness. We conclude that generative AI should be positioned not as a “substitute” but as a “personalised interpreter and creative partner” in adult aesthetic education. These findings provide practical implications for the digital transformation of The Open University of China and the design of AI-based educational products.

Effective procedure planning in instructional videos requires robust modeling of dynamic step sequences that adapt to contextual variations and diverse execution styles. Current single-modality approaches exhibit complementary limitations: text-based methods struggle to specify spatial relationships and quantitative action parameters, while video-based systems fail to establish task-level semantics and logical step dependencies. To address these challenges, we propose TeViJP, a dual modality framework that integrates textual abstraction with visual demonstration for adaptive workflow planning. Three key innovations in our work have been introduced: 1) A step-semantic aware prompting mechanism using contrastively aligned tokens to dynamically segment procedural phases in long videos, resolving boundary ambiguity under variable step orders; 2) A goal-conditioned hierarchical planner that injects multi scale task semantics through cross-modal attention, reducing error accumulation in long-horizon planning; 3) A Temporal Spatial-Semantic Fusion Transformer (TSSB) ensuring synthe sized previews maintain geometric consistency with historical states while aligning with textual constraints. Comprehensive evaluations across four benchmark datasets demonstrate superior performance, achieving 4.63% accuracy gains in step prediction and halving visual distortion metrics compared to state-of-the-art planning systems.

The students of the world face well-being-related issues due to tight competition among the students of higher educational institutions. The existing research suggests that sustainability education is helpful to enhance student well-being. To explore this relationship, the present study assesses the direct relationships between sustainability education and mindfulness, mindfulness and student well-being, and social support and student well-being. Moreover, this research examines the mediating role of mindfulness in the relationship between sustainability education and student well-being. Additionally, this research checks the moderating role of social support between mindfulness and student well-being. Through a purposive sampling technique, cross-sectional data were collected from 413 students studying in Beijing, China. This study uses SPSS v23 and SmartPLS v4.0.8 for data analysis. The results of this study show that all the direct relationships remain significant. Similarly, mindfulness significantly mediates the relationship between sustainability education and student well-being. However, the moderating relationship of social support remains non-significant. This study provides a unique theoretical combination of mindfulness-to-meaning theory and social support theory to assess the relationship among sustainability education, mindfulness, social support, and student well-being in the context of university education in Beijing, China. This research provides actionable insights for academicians and policymakers to design sustainability-focused curricula to enhance student love for the environment, which facilitates mindfulness and well-being, in the presence of social support.

Question continuation evaluation (QCE) is a branch task of dialogue act prediction (DAP) in the natural language processing area, which is aimed at predicting whether each question in a dialogue is worthy of being followed-up under a specific context. QCE is important for communication, education, and even entertainment. Regrettably, QCE has always been disregarded as an auxiliary task for conversational machine reading comprehension. QCE involves more information and relationships than the original DAP task, making it more complex. Moreover, the classification of QCE inherently renders the samples confusing. In this article, a transformer long short-term memory (LSTM)-based supervised contrastive learned model for QCE is proposed to automatically distribute QCE labels. This model is mainly constructed with transformer encoder blocks and LSTM modules, and supervised contrastive learning (SCL) is innovatively introduced to the training process. This model is good at extracting both information about corpora and the relationships among corpora, and SCL alleviates any confusion. With the only applicable dataset, i.e., Question Answering in Context (QuAC), experiments are conducted. This model is proven to perform well and is robust to missing data. The performance is 2.3% (accuracy) and 12.2% (macro-F1 score) higher than baselines from QuAC and only decreases by approximately 2.3% when 10% data remain.

Machine reading comprehension (MRC) is a crucial and challenging task in natural language processing (NLP). With the development of deep learning, language models have achieved excellent results. However, these models still cannot answer complex questions. Currently, researchers often utilize structured knowledge, such as knowledge bases (KBs), as external knowledge by directly extracting triples to enhance the results of machine reading. Although they can support certain background knowledge, the triples are limited to the interrelationships among entities or words. Unlike structured knowledge, unstructured knowledge is rich and extensive. However, these methods ignore unstructured knowledge resources, such as Wikipedia. In addition, the effect of combining the two types of knowledge is still not known. In this study, we first attempt to explore the usefulness of combining them. We introduce a fusion mechanism into a rich knowledge fusion layer (RKF) to obtain more useful and relevant knowledge from different external knowledge resources. Further to promote interaction among different types of knowledge, a bi-matching layer is added. We propose the RKF-NET framework based on BERT, and our experimental results demonstrate the effectiveness of two classic datasets: SQuAD1.1 and the Easy-Challenge (ARC).