ABSTRACT This study investigated the impact of Artificial Intelligence‐Generated Content (AIGC) tools, such as ChatGPT, on higher education students' intrinsic motivation, emotional learning, development of critical thinking and transformation of emotional learning. By designing a conceptual framework grounded in Self‐Determination Theory, the study explored how the use of AIGC tools shapes learning outcomes through both direct and moderating effects of intrinsic motivation. A survey was administered to 475 university‐level students across several disciplines in the Punjab province of Pakistan. Data analysis was conducted using Partial Least Squares Structural Equation Modelling (PLS‐SEM) to measure the relationships between AIGC usage, intrinsic motivation and learning outcomes. The results revealed that AIGC tools have positive effects on intrinsic motivation, which significantly influences the development of critical thinking and the transformation of emotional learning. Intrinsic motivation demonstrated a limited moderating role. A significant interaction was observed only between AIGC Impact and intrinsic motivation in predicting Emotional Learning Transformation ( p = 0.044), whereas the moderation effect for Critical Thinking Development was non‐significant ( p = 0.405), and interactions involving AIGC Tools for Learning were not significant. These findings suggest that the moderating role of intrinsic motivation is pathway‐specific rather than general. Furthermore, high‐frequency usage of AIGC tools was found to negatively predict ELT when controlling for motivation ( β = −0.069), highlighting risks of over‐dependency. The findings emphasize the importance of fostering intrinsic motivation to maximize the pedagogical benefits of AI‐powered tools. The study suggests that educational institutions must prioritize AI literacy and ethical guidelines to prevent cognitive passivity and ensure responsible AI integration in the digital era.
ABSTRACT Although artificial intelligence (AI) has become an integral component of language education, research has primarily emphasized technological factors influencing learning outcomes, with limited attention to the psychological attributes that enable students to benefit from AI‐supported instruction. Addressing this gap, the present study investigated whether EFL students' character strengths predict academic performance, resilience and psychological well‐being in AI‐integrated educational environments. A quantitative cross‐sectional design was employed with 773 undergraduate EFL students from Chinese universities. Participants completed validated measures of character strengths, resilience, psychological well‐being and academic performance. The data were analyzed using descriptive statistics, Pearson correlation and structural equation modelling (SEM). The findings demonstrated that character strengths were positively associated with all three outcomes. SEM further revealed that character strengths significantly predicted academic performance, resilience and psychological well‐being, accounting for substantial variance in each construct. These findings highlight character strengths as key psychological resources that support both academic achievement and psychological adaptation in AI‐supported language learning. The study extends current research by integrating positive psychology with AI‐enhanced education and underscores the importance of fostering students' psychological strengths alongside technological innovation.
ABSTRACT Teacher well‐being in challenging educational contexts has important implications for sustainable professional learning, yet less is known about how job demands and job resources operate among EFL teachers in underexamined regional settings. Drawing on Job Demands‐Resources theory, this mixed‐methods study examined the direct and indirect relationships among job demands, job resources, resilience, teacher efficacy, work engagement and emotional exhaustion among EFL teachers in Western China. A total of 256 valid survey responses were analyzed using Partial Least Squares Structural Equation Modelling, followed by semi‐structured interviews with eight teachers to interpret the quantitative patterns. The results showed that job demands significantly predicted emotional exhaustion, but not work engagement. Job resources showed no significant direct effects on either outcome. Instead, both resilience and teacher efficacy mediated the relationships between job demands, job resources and work engagement, whereas only resilience mediated the relationships between job demands, job resources and emotional exhaustion. The interview data further indicated that job demands were experienced as a clear source of fatigue but not necessarily as a reason to disengage from teaching, while resources became effective mainly when they were usable, timely, and connected to concrete problems of practice. The study extends JD‐R research by showing how demands and resources helped explain teacher well‐being in a challenging EFL context and demonstrating that teacher well‐being is closely tied to the conditions under which professional learning can be sustained.
ABSTRACT Artificial intelligence (AI) is increasingly being integrated into language education, yet evidence regarding its influence on learners' emotional experiences and oral communication remains limited. This mixed‐methods study examined the effects of AI‐mediated speaking practice on university EFL learners' oral anxiety, speaking enjoyment, self‐perceived oral communication competence and willingness to communicate (WTC). A total of 105 intermediate‐level learners participated in an eight‐week quasi‐experimental intervention in which the experimental group engaged in regular AI‐mediated speaking activities, whereas the control group completed conventional self‐directed speaking practice. Quantitative data were analysed using descriptive and structural equation modelling techniques, and qualitative insights were obtained through semi‐structured interviews with 16 participants. Compared with the control group, learners in the AI‐mediated condition reported lower speaking anxiety and higher speaking enjoyment, self‐perceived communicative competence and willingness to communicate. The structural model indicated that the relationships among AI‐mediated practice, emotional experiences, perceived competence and WTC were consistent with the proposed theoretical framework, while interview findings suggested that learners perceived the AI‐supported environment as encouraging repeated practice, greater confidence and communicative engagement. These findings support the potential of conversational AI as a complementary tool for facilitating learners' emotional experiences and perceived communicative readiness in EFL speaking instruction, while highlighting the need for future research using behavioural measures and interaction‐equivalent comparison conditions to further examine these effects.
ABSTRACT Prior research has extensively documented the negative relationship between Foreign Language Anxiety (FLA) and learning strategies, but how this relationship operates among Chinese English majors across different educational levels remains underexplored. To address this, the present study examines the mediating role of academic buoyancy and the moderating effect of educational level in this relationship. A survey of 1609 undergraduate and postgraduate English majors from Chinese universities was conducted to examine the relationships between FLA and four types of learning strategies: memorization, elaboration, cooperation and control strategies. Additionally, the study investigated the mediating role of academic buoyancy and the moderating role of educational level in these relationships. It was found that FLA was negatively associated with academic buoyancy and all four learning strategies, whereas both academic buoyancy and educational level demonstrated positive associations with these strategies. Academic buoyancy predominantly mediated the relationship between FLA and memorization, and that between FLA and cooperation, while partially mediating the relationship between FLA and elaboration and that between FLA and control strategies. Furthermore, educational level significantly moderated the effect of FLA on elaboration specifically. These findings highlight the value of attending to positive emotions in language education and reveal how educational level shapes strategy use. The study offers general implications for language teaching and learning, suggesting that educators should foster students' academic buoyancy and provide differentiated instructional support across degree levels to alleviate anxiety and promote effective strategy employment.
ABSTRACT Simulation‐based learning is an effective teaching modality. However, there is a lack of evidence regarding scenario‐based simulation training with standardized patients on stoma care among nursing students. This study aimed to examine the effect of a scenario‐based hybrid simulation training using standardized patients and a wearable bud‐shaped stoma on nursing students' knowledge, skills, satisfaction, expectations, and opinions about stoma care. This randomized controlled study included 66 students, 33 in each group. Stoma care was practiced with a standardized patient who had a wearable bud‐shaped stoma stuck to the abdomen in the hybrid simulation group and a manikin in the control group. Study data were collected using the Stoma Care Skills Evaluation Form to measure students' skills, a questionnaire to reveal their knowledge and opinions, and the Student Satisfaction and Self‐Confidence in Learning Scale to find out about their satisfaction. Students' knowledge and opinion levels in both groups increased statistically significantly following the intervention ( p < 0.05). The students in the hybrid simulation group had higher skill ( p < 0.001) and satisfaction levels ( p = 0.043) than the control group. Scenario‐based hybrid simulation training increased students' knowledge, skills, expectations, and opinions about stoma care. It should be considered implementation and dissemination in health education.
This research aims to synthesize recent literature on machine learning-enabled automated assessment and grading in education, with emphasis on techniques, accuracy, fairness, and responsible implementation. A systematic literature review was conducted on 27 English-language peer-reviewed journal articles published between 2019 and 2025. The review focused on the major applications and evaluation issues of automated grading, including essay scoring, short-answer assessment, programming assessment, feedback generation, validity, explainability, and fairness. The findings show that natural language processing, transformer-based models, supervised machine learning, hybrid feature-based approaches, code execution, and testing frameworks are commonly used. Automated grading is more suitable for structured or semi-structured tasks, while complex writing, creativity, reasoning, and programming style require human moderation. Institutions should adopt automated grading as a supervised decision-support tool supported by validation, fairness auditing, transparency, appeal procedures, and teacher oversight. The paper contributes a focused synthesis linking techniques, accuracy, fairness, and governance in automated educational assessment.
The inclusion in education, is defined as the most favorable mean that creates the equal opportunities in education for all the children, with or without disabled abilities. The increase of the number of children with special needs and especially with those with autism within the classes, but this is followed with new challenges for the teachers of the inclusive classes in the Albanian schools. The problems of the teaching task of the teachers in these classes are a lot, but to minimize them the teaching process worldwide is paying too much favorable space by analyzing and orientations to make these problems a bit slighter. This research aims to be an empirical survey, while from its nature and condition to be qualitative. For the collection of the datas and statistics are used observations, interviews and focus groups. And as for the target group are considered the classes and the teachers that have in their members students with autism.
ABSTRACT Despite the growing use of GenAI in English as a foreign language (EFL) writing, research on the interplay between learners' behavioural intentions and GenAI‐assisted writing performance remains scarce. To address this gap, the present study integrates the theory of planned behaviour with social cognitive theory of self‐regulation to examine how behavioural intentions relate to writing performance and the mediating role of metacognitive regulation. A total of 174 participants completed instruments assessing metacognitive regulation and behavioural intentions before engaging in GenAI‐supported writing tasks. Data were analysed via structural equation modelling (SEM) and Elastic Net regression in R. Results indicated that, while behavioural intentions were significantly predicted by learners' attitudes and perceived behavioural control, they did not directly translate into GenAI‐assisted writing performance. Importantly, evaluation mediated the relationship between behavioural intentions, grounded in attitudes and perceived behavioural control, and writing outcomes. In contrast, planning exerted a negative mediating effect in the pathway from behavioural intentions to GenAI‐assisted writing performance, whereas monitoring showed no significant mediating effect. Moreover, evaluation emerged as the strongest predictor of learners' intentions and GenAI‐assisted writing performance. These findings advance a theoretical integration, providing empirical insights into how metacognitive regulation can optimize the effective use of GenAI in EFL writing contexts.
ABSTRACT Practicing speaking has remained a permanent difficulty for English as a Foreign Language (EFL) students, mainly due to the limited chances to engage in meaningful speaking interaction in supportive settings. AI‐powered robots, in response, emerged as a successful speaking interaction in language teaching. Grounded in self‐determination theory (SDT), this study examined the effects of AI‐powered robots on EFL learners' speaking performance, with enjoyment and self‐efficacy as mediating variables. We used a pretest–posttest design and conducted the intervention over 10 weeks. The sample included 224 learners from a Chinese university, where 123 participants were assigned to the experimental group and 101 to the control group. While the former participated in robot‐based speaking tasks, the latter got involved in traditional teaching, such as textbook‐based communication activities, description, drill practice and pairwise work. The results showed that while both groups confirmed significant improvements in speaking scores over time, the experimental group had significantly higher achievement compared to the control group. These findings indicated that AI‐powered robots can create a structured, interactive and supportive learning atmosphere that more effectively improves learners' speaking ability. As for the indirect effects, running a parallel mediation analysis revealed that both self‐efficacy and enjoyment mediated the effect of the treatment, jointly accounting for 28.6% of the total intervention effect. Uniquely, enjoyment accounted for 15.5% of the total effect, and self‐efficacy for 13.3% of it. These results indicated that self‐efficacy and enjoyment serve as partial mediators in improving speaking performance through AI‐powered robots. Accordingly, these results underscore the pedagogical potential of robot‐assisted language learning in EFL instruction and highlight that integrating the robots significantly improved the learners' self‐efficacy, boosted their enjoyment and enhanced their speaking performance.
ABSTRACT Active citizenship requires individuals not only to be aware of their rights and responsibilities but also to engage in social participation, think critically and adhere to democratic values. This qualitative case study explores social studies teachers' experiences of active citizenship education in multicultural classrooms in Türkiye. Data were collected through semi‐structured interviews with 22 teachers and analysed using thematic analysis. The findings indicate that most teachers understood active citizenship in terms of knowledge, rules and responsibilities and implemented active citizenship education mainly through knowledge transmission, giving limited attention to attitudes, values and skills. Teachers' expectations of immigrant students similarly centred on compliance with rules and adaptation to the existing social order. Inclusive practices were constrained by large class sizes, limited time, language barriers and insufficient curricular and institutional support. Only a few teachers employed student‐centred practices and only one reported incorporating immigrant students' cultural experiences into the learning process. The study highlights the need for more inclusive and participatory approaches to active citizenship education in multicultural classrooms.
ABSTRACT As concerns about student mental health have intensified, responsibility for supporting student well‐being is increasingly negotiated in everyday interactions between students and staff. This study aims to foreground the relational and caring dimensions of academic practice. It does so by examining how university staff understand and navigate their professional role in relational work to support students, and how this work is shaped by structural and emotional conditions. Drawing on five focus group interviews with 27 teaching and student‐support staff across multiple campuses and analysed using reflexive thematic analysis, the study shows that care, trust, and accessibility are integral to everyday pedagogical and support practice. At the same time, relational engagement is weakly formalised and unevenly recognised, creating tensions around professional boundaries, workload, and emotional responsibility. The findings suggest that responsibility for student well‐being is channelled through everyday staff–student relationships within institutional frameworks that provide limited structural and institutional recognition.
ABSTRACT With the growing integration of artificial intelligence into higher education, digital competence has become central to learners' meaningful engagement in AI‐mediated learning environments. However, its role may be constrained by the cognitive load generated when students navigate complex digital tools, resources, and learning tasks. This study, therefore, adopted an explanatory sequential mixed‐methods design to examine whether digital competence was associated with learners' engagement and whether cognitive load moderated this relationship. Quantitative data were collected from 339 Chinese university EFL learners through a questionnaire measuring digital competence, cognitive load and engagement. Regression analysis showed that cognitive load was negatively associated with engagement and significantly moderated the relationship between digital competence and engagement. In contrast, the direct effect of digital competence became non‐significant after these factors were considered. In the subsequent qualitative phase, interviews with 10 learners were used to explain and extend these findings, showing that digital competence supported engagement through richer resources, personalised learning, autonomy, and interaction, whereas information overload, distraction, AI dependence, unequal access, and privacy concerns constrained meaningful engagement. These findings suggest that digital competence should not be understood as a stand‐alone predictor of engagement, but as a conditional educational resource whose value depends on learners' ability to regulate cognitive load in AI‐mediated learning contexts. The study contributes to current discussions on digital transformation in higher education by highlighting the need for pedagogical support, critical AI literacy and equitable learning conditions that help students engage meaningfully with emerging technologies.
ABSTRACT The aim of this study is to identify the learning strategies of students taking e‐courses in e‐learning environments using clustering algorithms, a data mining technique and to determine how these strategies are distributed according to the variables gender, class, faculty, computer and Internet usage level, and Internet usage time. The survey model was used in the study conducted in accordance with this objective. The data were collected using the Learning Strategies Scale designed for students taking distance education courses. The scale consists of 23 items and 5 sub‐dimensions: management of time and effort, use of complex cognitive strategies, use of simple cognitive strategies, contact with others, and academic thinking. The study involved 518 students taking online courses at a university. The data obtained were analysed and interpreted using descriptive statistics and cluster analysis. The study's results revealed that distance‐education students' learning strategies were generally inadequate. It was also found that those who used the Internet less, who owned a computer and who had a higher level of computer skills, as well as those who used web pages, tests, assignments, animation, and video tools, had more advanced distance‐ learning strategies.
ABSTRACT Enjoyment and engagement are critical for effective language learning, yet little is known about how these experiences evolve over time in AI‐mediated informal digital English learning (IDLE). Previous studies have mainly relied on cross‐sectional designs, limiting understanding of the dynamic development of emotional and behavioural engagement. This study aims to examine the longitudinal trajectories of students' enjoyment and engagement in AI‐mediated IDLE and to identify factors influencing these changes. A total of 494 university students participated in a semester‐long study, completing three waves of surveys to capture changes in enjoyment and engagement. Latent growth curve modelling was used to estimate initial levels, growth trajectories, and the dynamic relationship between the two constructs. In addition, 20 students participated in follow‐up semi‐structured interviews to provide qualitative insights into contextual factors affecting their experiences, analysed using a grounded approach. Both enjoyment and engagement increased steadily over the semester, developing in parallel, with qualitative findings revealing that learner characteristics, peer interaction, teacher support, and access to resources shaped these trajectories. These results extend existing research by providing a longitudinal perspective on emotional experiences and engagement in AI‐mediated IDLE. The findings highlight the importance of designing AI‐mediated learning environments that foster positive emotional experiences and sustained engagement for diverse learners.
ABSTRACT As two significant elements in education, resilience and engagement have been at the center of attention in the English as a Foreign Language (EFL) research, as they both influence learners' academic achievement and their psychological growth. Among different factors, teacher fairness has progressively been known as a vital interpersonal element forming students' motivational and emotional performance; however, its effect in foreign language teaching is overlooked. In EFL contexts, where students often experience heightened vulnerability due to linguistic and cultural barriers, the role of fairness becomes even more critical. Besides, considering the facilitative effect of positive emotions in the route of learning in line with Control‐Value Theory (CVT), the present research sought to investigate their mediating effect in the teacher fairness, engagement, and resilience relationship within EFL students in China. A total of 302 students were chosen randomly, and previously validated measures were used for data collection. Self‐Determination Theory (SDT) was utilized to explain the way teacher fairness meets students' primary psychological needs, therefore boosting their resilience and engagement. Structural equation modelling was run, and the results revealed that teacher fairness had significant correlations with both EFL students' engagement and resilience, and it indirectly influenced both concepts through its impact on learners' achievement emotions. The results indicated that teacher fairness played a critical role in developing emotionally supportive learning settings and encouraging students' resilience and engagement. By integrating CVT and SDT, the study provides a more comprehensive justification of how teacher fairness shapes learners' emotional experiences and their academic outcomes in EFL learning.
ABSTRACT Second language (L2) identity has gained increasing attention for its role in shaping study abroad (SA) experiences, yet its relationship with social networks remains underexplored. Drawing on social network theory and L2 identity framework, this qualitative study examined the interplay of L2 identity construction and social networks among three Chinese students in UK universities. Data were collected through social network questionnaires, self‐portraits and semi‐structured interviews, and analysed through a combination of ego‐network analysis, multimodal analysis, and thematic analysis. Findings reported that participants developed homogeneous‐dense, heterogeneous‐sparse, and heterogeneous‐balanced networks, respectively. These network types were associated with variations in identity‐related L2 competence, linguistic self‐concept, and L2‐mediated personal development through access to linguistic, cultural and affective resources. Resource diversity and accessibility depended on network composition and structure, with more diverse and balanced networks fostering richer identity experiences. Their L2 identities were found to be implicated in the ways they built, maintained, or withdrew ties within social networks. The study proposes a network‐based model of L2 identity construction and offers implications for creating resource‐rich SA environments that better support SA students' identity growth.
ABSTRACT Women's representation in tertiary education has reached parity in many countries, although regional disparities persist. Furthermore, there is still significant horizontal gender segregation in some fields of study. Among other explanations, it is suggested that men and women might prioritise different life and career goals. Men are often perceived as preferring fields that offer higher earning potential and opportunities for leadership, while women prefer those offering greater social utility. Since fields of study differ in the perceived potential for agentic and communal goals, this might explain horizontal segregation. However, recent empirical evidence on this association presents ambiguous findings. This study contributes to the literature by examining how agentic career goals shape first‐year students' choice of field of study at the University of Milan ( N = 11,201), moving beyond the conventional dichotomy between STEM and the humanities. Using both survey and administrative data, we tested the extent to which the endorsement of agentic career goals could explain horizontal segregation, that is, the imbalance in the representation of women and men across different fields of study in higher education. Results showed no significant gender differences in the endorsement of agentic goals once sociodemographic factors are accounted for, and agentic goals did not explain the gender gap in field choices. Instead, the gap was partially explained by educational background. However, a more detailed analysis revealed differences within STEM and humanities fields, with male students associating higher agentic goals with IT, and female students with fields such as law, healthcare and socio‐political disciplines. The observed variations seem to suggest that gender differences may lie less in the endorsement of agentic goals than in the disciplinary pathways perceived as conducive to their achievement.
ABSTRACT This paper investigates the college‐to‐work transition of vocational college graduates, who are typically viewed as a homogeneous disadvantaged group. Drawing on Bourdieu's theories, this two‐wave panel study interviewed 33 Chinese vocational college graduates about their initial labour‐market experiences during their first year after graduation. This study reveals the heterogeneity and dynamics of four transitional patterns: dwelled, diverted, shifting and stuck, based on graduates' different degrees of field‐habitus congruence and major‐occupation match. This transition typology challenges oversimplified, often deficit‐based discourses of vocational graduates by illustrating how they perceive and engage with jobs differently. We also observe the self‐reinforcing tendency of graduates' career trajectories. The study challenges the conceptualisation of ‘successful’ and ‘problematic’ transitions, as normally measured by major‐job match, and suggests providing more targeted academic and career support to vocational students at an earlier stage, well before graduates form fixed career patterns.
ABSTRACT Guided by Social Cognitive Theory, this study explored how language learning curiosity, perceived teacher support, and AI literacy predict learners' self‐regulated learning (SRL) in AI‐supported language education, with grit and flow as mediators. Data collected from 1022 Chinese undergraduate students were analysed using descriptive statistics, confirmatory factor analysis, structural equation modelling with bootstrapped mediation analysis, and psychological network analysis via SPSS, AMOS, and R. The results showed that all three antecedent variables were positively associated with grit and flow, which in turn significantly mediated their influences on self‐regulated learning. Network analysis further revealed that self‐regulated learning was the most pivotal node among the six constructs. These findings suggest that effective AI‐supported language learning requires not only technological access, but also motivational, contextual, technological, and psychological resources to foster learners into agentic and self‐regulated language learners.
