The integration of generative artificial intelligence (GAI) into higher education is transforming students’ learning processes, academic performance, and psychological well-being. Despite the increasing adoption of GAI tools, the mechanisms through which students’ AI literacy and self-regulated learning (SRL) relate to their academic and emotional experiences remain underexplored. This study investigates how AI literacy and SRL are associated with writing performance and digital well-being among university students in GAI-supported higher learning contexts. A survey was administered to 257 students from universities in China, and structural equation modeling was used to examine the hypothesized relationships. Results show that both AI literacy and SRL significantly and positively predict students’ writing performance, with SRL having a stronger effect. Moreover, AI literacy shows a positive association with GAI-driven well-being, with writing performance serving as a partial mediator in this relationship. These findings suggest that fostering both technological competencies and effective learning strategies may support students’ academic outcomes while supporting their psychological well-being in AI-enriched educational environments. By integrating AI literacy and SRL into a unified model, this study contributes to the growing body of research on GAI-driven well-being in higher education and offers practical implications for cultivating balanced and sustainable learning experiences in the age of GAI.

Introduction: With the continuous expansion of higher education worldwide, the academic performance of first-generation college students has become an essential topic in the scope of international educational research. This study examines the impact of learning motivation (i.e., intrinsic and extrinsic motivation) and environmental support (i.e., instructional, relational, and campus support) on the academic outcomes of first-generation college students based on the Cultural Mismatch Theory and Self-Determination Theory from both individual and environmental perspectives.Methods: A two-stage stratified sampling strategy was adopted to guarantee that the samples were representative of the national populations of college students in mainland China. 87418 data were collected from the China College Student Survey (CCSS) 2020, among which 58,864 were first-generation college students. This study primarily employed descriptive statistical analysis and regression analysis methods.Results: Data analysis revealed that intrinsic learning motivation, relational support, campus support, and academic performance of first-generation college students are significantly lower than those of non-first-generation students. However, this study found no significant differences concerning external learning motivation and teaching support. Regression analysis showed that both learning motivation and environmental support positively predicted learning outcomes, with intrinsic learning motivation having the most substantial influence. Moreover, learning motivation and environmental support interact in jointly promoting the student’s academic success.Discussion: This study highlighted that the academic development of first-generation college students results from the interplay between individual characteristics and the surrounding environment. Postnatal factors, particularly individual learning motivation, and institutional environment support, play a crucial role in their academic achievements.

The rapid shift to online education, accelerated by the COVID-19 pandemic, has fundamentally altered the landscape of teaching, and learning worldwide, making the exploration of factors influencing online teaching experiences increasingly vital. While existing research has examined the impact of instructional support on teaching efficacy and experience, a critical gap remains systematically exploring how personal, social, and organizational factors shape online teaching practices. This study aims to address this gap by investigating the relationships among social and organizational support, technological efficacy, innovation awareness, and online teaching effectiveness. Data collected from 3,154 university instructors across China were analyzed using structural equation modeling (SEM). The findings reveal that both technological efficacy and innovation awareness significantly mediate the relationship between social and organizational support and the online teaching experience. From the perspective of the standard total effect values, technological efficacy played a pivotal role in shaping the online teaching experience. These results contribute to the field by deepening our understanding of how support structures influence online teaching outcomes, providing empirical insights for strengthening support mechanisms that empower educators in technology-enhanced teaching environments.

Intercultural Communicative Competence (ICC) is a crucial issue for the internationalization of higher education. In recent years, intercultural online learning has gradually become a new form of internationalization in higher education, which presents both challenges and opportunities for enhancing students' ICC. This study constructs an analytical framework for ICC within the context of online learning, drawing from existing research on ICC and classroom environment theory. Leveraging the "Global Integrated Classroom Learning Survey" by Tsinghua University's Global Integrated Classroom Teaching Research Group, 141 valid samples were collected and analyzed through descriptive statistics and regression analysis. Two main conclusions were drawn: (1) Students generally exhibit a relatively high level of ICC in online learning contexts, yet differences in ICC exist among different student groups; (2) Within online teaching, three contextual features—information literacy, instructional scaffolding, and technological support—significantly influence university students' ICC.

As artificial intelligence (AI) technologies become increasingly embedded in higher education, concerns have emerged regarding their psychological impact on university faculty. While existing research has largely focused on technological readiness and digital competencies, the social–psychological foundations of faculty well-being in AI-integrated teaching environments remain insufficiently explored. Drawing on social support theory and self-determination theory, this study proposes and tests a structural model of AI-related well-being among university faculty. A total of 523 faculty members in China participated in a cross-sectional survey measuring perceived social support, organizational support, AI literacy, technological self-efficacy, and AI well-being. Structural equation modeling (SEM) was used to examine the hypothesized pathways and mediating mechanisms. The results indicate that both social support and organizational support significantly and positively influence AI literacy and technological self-efficacy. In turn, AI literacy and technological self-efficacy significantly predict AI well-being and serve as key mediators. Among all factors, AI literacy exhibits the strongest total effect on AI well-being, followed by social support, organizational support, and technological self-efficacy. This study contributes to the theoretical understanding of faculty psychological adjustment during technological transitions and offers practical recommendations for institutions seeking to cultivate AI-ready and psychologically supportive academic environments.

Teachers often raise their vocal volume to improve intelligibility or capture students’ attention. While this practice is common in second language (L2) teaching, its effects on tone perception remain understudied. To fill this gap, this study explores the effects of loud speech on Mandarin tone perception for L2 learners. Twenty-two native Mandarin speakers and twenty-two Thai L2 learners were tested on their perceptual accuracy and reaction time in identifying Mandarin tones in loud and normal modes. Results revealed a significant between-group difference: native speakers consistently demonstrated a ceiling effect across all tones, while L2 learners exhibited lower accuracy, particularly for Tone 3, the falling-rising tone. The loud speech had different impacts on the two groups. For native speakers, tone perception accuracy remained stable across different speech modes. In contrast, for L2 learners, loud speech significantly reduced the accuracy of Tone 3 identification and increased confusion between Tones 2 and 3. Reaction times in milliseconds were prolonged for all tones in loud speech for both groups. When subtracting the length of the tones, the delay of RT was evident only for Tones 3 and 4. Therefore, raising the speaking volume negatively affects the Mandarin tone perception of L2 learners, especially in distinguishing Tone 2 and Tone 3. Our findings have implications for both theories of L2 tone perception and pedagogical practices.