Yiran Du

Yiran Du, Jinlong Li, Huimin He et al.

This study investigates Chinese primary school students' acceptance of a social robot for English-as-a-foreign-language (EFL) speaking practice through a sequential explanatory mixed-methods design. Integrating the Technology Acceptance Model (TAM) and the Computers Are Social Actors (CASA) paradigm, the research explores both functional and social factors influencing learners' behavioural intention to use the robot. Quantitative data from 436 students were analysed using structural equation modelling, followed by qualitative interviews with twelve students to interpret the findings. Results show that perceived enjoyment and ease of use are the strongest predictors of acceptance, while social attributes such as warmth, anthropomorphism, and social presence significantly enhance enjoyment. Perceived intelligence affects usefulness but not ease of use. The findings suggest that emotional and social engagement are central to young learners' acceptance of educational robots, highlighting the importance of designing socially intelligent technologies that promote motivation and speaking confidence in EFL learning contexts.

Yiran Du, Huimin He

This study investigates students' AI use concealment intention in higher education by integrating the cognition-affect-conation (CAC) framework with a dual-method approach combining structural equation modelling (SEM) and fuzzy-set qualitative comparative analysis (fsQCA). Drawing on data from 1346 university students, the findings reveal two opposing mechanisms shaping concealment intention. The enabling pathway shows that perceived stigma, perceived risk, and perceived policy uncertainty increase fear of negative evaluation, which in turn promotes concealment. In contrast, the inhibitory pathway demonstrates that AI self-efficacy, perceived fairness, and perceived social support enhance psychological safety, thereby reducing concealment intention. SEM results confirm the hypothesised relationships and mediation effects, while fsQCA identifies multiple configurational pathways, highlighting equifinality and the central role of fear of negative evaluation across conditions. The study contributes to the literature by conceptualising concealment as a distinct behavioural outcome and by providing a nuanced explanation that integrates both net-effect and configurational perspectives. Practical implications emphasise the need for clear institutional policies, destigmatisation of appropriate AI use, and the cultivation of supportive learning environments to promote transparency.

Yiran Du, Huimin He

The growing use of generative artificial intelligence (AI) in academic writing has raised increasing concerns regarding transparency and academic integrity in higher education. This study examines the psychological factors influencing English for Academic Purposes (EAP) students' intention to disclose their use of AI tools. Drawing on the cognition-affect-conation framework, the study proposes a model integrating both enabling and inhibiting factors shaping disclosure intention. A sequential explanatory mixed-methods design was employed. Quantitative data from 324 EAP students at an English-medium instruction university in China were analysed using structural equation modelling, followed by semi-structured interviews with 15 students to further interpret the findings. The quantitative results indicate that psychological safety positively predicts AI disclosure intention, whereas fear of negative evaluation negatively predicts it. The qualitative findings further reveal that supportive teacher practices and clear guidance foster psychological safety, while policy ambiguity and reputational concerns intensify fear of negative evaluation and discourage disclosure. These findings highlight the importance of clear institutional policies and supportive pedagogical environments in promoting transparent AI use.

Bin Zou, Yijia Yuan, Chenghao Wang et al.

This study examined whether AI-mediated speaking practice can reduce acculturative stress among Chinese international students in UK universities. Using a sequential explanatory mixed-methods design, 126 participants were randomly assigned to an experimental group, which completed a four-week intervention using EAP Talk, an AI-assisted English for Academic Purposes speaking platform offering role play, scenario-based practice, free talk, and automated feedback, or a control group, which continued usual academic and English-learning activities. Pre- and post-test questionnaires measured perceived language insufficiency, social isolation, and academic pressure, while semi-structured interviews with 20 experimental-group participants contextualised the quantitative findings. Linear mixed-effects models showed that the experimental group experienced significantly greater reductions than the control group across all three outcomes, with the strongest effect on perceived language insufficiency. Interview findings suggested that EAP Talk supported low-stakes rehearsal, communicative confidence, academic speaking preparation, and greater willingness to initiate social interaction. However, participants also noted that AI-mediated practice could not fully reproduce authentic human interaction, disciplinary feedback, or broader institutional support. The findings suggest that AI-mediated speaking practice can function as a supplementary scaffold for reducing communication-related dimensions of acculturative stress, but should be integrated with peer interaction, teacher feedback, and wider support services.

Yiran Du

Artificial Intelligence-Generated Content (AIGC) is increasingly used by students to support learning tasks, yet its outputs may contain inaccuracies, fabricated references, bias, and unsupported claims. This study examined students' intention to verify AIGC from the perspective of Protection Motivation Theory. A cross-sectional survey was conducted with 432 students who had experience using AIGC for learning. Structural equation modelling (SEM) was used to test the hypothesised relationships among threat appraisal, coping appraisal, protection motivation, and AIGC verification intention, while fuzzy-set qualitative comparative analysis (fsQCA) was applied to identify configurational pathways leading to high verification intention. The SEM results showed that protection motivation positively predicted AIGC verification intention. Perceived severity, perceived vulnerability, response efficacy, and self-efficacy positively influenced protection motivation, whereas maladaptive rewards and response cost had negative effects. The fsQCA results further revealed three configurations leading to high verification intention, with protection motivation appearing as a core condition across all pathways. These findings suggest that students' willingness to verify AIGC depends on both risk recognition and perceived coping capacity. The study extends Protection Motivation Theory to the context of AIGC verification and provides implications for promoting critical, responsible, and academically appropriate use of generative AI in higher education.

Yiran Du, Qian Chen, Huimin He

This study examines the psychological mechanisms underlying Chinese K-12 teachers' discontinuance intention toward generative AI. Drawing on the Cognition-Affect-Conation framework, the study investigates how cognitive evaluations of generative AI shape affective responses and subsequently influence behavioural intention. Survey data from 256 Chinese K-12 teachers were analysed using structural equation modelling and fuzzy-set qualitative comparative analysis. The results showed that privacy concern, algorithmic opacity, and information hallucination increased AI anxiety, which in turn strengthened discontinuance intention. Conversely, perceived intelligence, perceived personalisation, and perceived interactivity enhanced satisfaction, which reduced discontinuance intention. The configurational analysis further identified multiple pathways leading to high discontinuance intention, highlighting the combined roles of technological risks, AI anxiety, weak affordance perceptions, and low satisfaction. These findings extend research on post-adoption generative AI use in education and suggest that sustainable integration requires both reducing technological uncertainty and enhancing teachers' positive user experiences.

Kunjie Jia, Kai Cui, Huimin He et al.

This study investigates Chinese teachers' continuance intention to use generative artificial intelligence (AI) by integrating the Expectation-Confirmation Model with Institutional Theory. A sequential explanatory mixed-methods design was employed. Questionnaire data from 437 teachers were analysed using structural equation modelling, followed by semi-structured interviews with 15 teachers to further interpret the findings. The results indicate that confirmation, perceived usefulness, and satisfaction play important roles in shaping teachers' continuance intention, while institutional pressures, including coercive, normative, and mimetic influences, also contribute to continued use. Qualitative findings further reveal that teachers often use generative AI pragmatically to support tasks such as lesson preparation and idea generation, while simultaneously exercising caution and critically evaluating the reliability of AI-generated content. These findings highlight the combined influence of individual evaluations and institutional contexts on teachers' sustained engagement with generative AI in education.

Yiran Du, Huimin He

This study examined intermittent discontinuance in AI-mediated informal digital learning of English (AI-IDLE) through the cognition-affect-conation framework. Survey data were collected from 632 Chinese university EFL learners with prior AI-IDLE experience and analysed using structural equation modelling and fuzzy-set qualitative comparative analysis. The SEM results showed that perceived intelligence, perceived interactivity, and perceived personalisation reduced AI-IDLE intermittent discontinuance indirectly through enjoyment, whereas perceived ineffectiveness, perceived uncontrollability, and perceived complexity increased discontinuance indirectly through boredom. The fsQCA results further identified four configurational pathways leading to intermittent discontinuance, indicating that learners' temporary withdrawal from AI-IDLE can result from different combinations of cognitive barriers and affective disengagement. These findings extend AI-IDLE research from adoption and continuance to post-adoption discontinuance and highlight the need to design AI-supported English learning experiences that are enjoyable, personalised, controllable, and cognitively manageable.

Yiran Du, Huimin He

This study examined university students' discontinuance intention towards AI-mediated informal digital learning of English (AI-IDLE). Drawing on the cognition-affect-conation framework, the study investigated how three cognitive factors, namely disconfirmation, perceived complexity, and perceived risk, influence two affective responses, namely dissatisfaction and frustration, and how these affective responses predict discontinuance intention. A cross-sectional survey was conducted with 746 Chinese university students who had experience using AI tools for informal English learning. Data were analysed using structural equation modelling (SEM) and fuzzy-set qualitative comparative analysis (fsQCA). The SEM results showed that dissatisfaction and frustration positively predicted discontinuance intention, with frustration showing the stronger effect. Disconfirmation, perceived complexity, and perceived risk also positively influenced dissatisfaction and frustration. The fsQCA results further identified multiple sufficient configurations leading to high AI-IDLE discontinuance intention, indicating that discontinuance is shaped by causal complexity and equifinality rather than by a single necessary condition. These findings extend AI-IDLE research from adoption and engagement to post-adoption disengagement and provide implications for reducing learners' dissatisfaction, frustration, perceived complexity, and risk in AI-supported informal English learning.

Yiran Du, Huimin He

The increasing integration of artificial intelligence (AI) in higher education has raised important questions regarding students' transparency in reporting AI-assisted work. This study investigates the psychological mechanisms underlying university students' willingness to disclose AI use by applying the Cognition--Affect--Conation (CAC) framework. A sequential explanatory mixed-methods design was employed. In the quantitative phase, survey data were collected from 546 university students and analysed using structural equation modelling to examine the relationships among cognitive perceptions, affective responses, and disclosure intention. In the qualitative phase, semi-structured interviews with 22 students were conducted to further interpret the quantitative findings. The results indicate that psychological safety significantly increases students' willingness to disclose AI use and is positively shaped by perceived fairness, perceived teacher support, and perceived organisational support. Conversely, evaluation apprehension reduces disclosure intention and psychological safety, and is strengthened by perceived stigma, perceived uncertainty, and privacy concern. Qualitative findings further reveal that institutional clarity and supportive instructional practices encourage openness, whereas policy ambiguity and fear of negative evaluation often lead students to adopt cautious or strategic disclosure practices. Overall, the study highlights the dual role of enabling and inhibitory psychological mechanisms in shaping AI-use disclosure and underscores the importance of supportive institutional environments and clear guidance for promoting responsible AI transparency in higher education.

Yiran Du

This study examines users' behavioural intention to use OpenClaw through the Cognition--Affect--Conation (CAC) framework. The research investigates how cognitive perceptions of the system influence affective responses and subsequently shape behavioural intention. Enabling factors include perceived personalisation, perceived intelligence, and relative advantage, while inhibiting factors include privacy concern, algorithmic opacity, and perceived risk. Survey data from 436 OpenClaw users were analysed using structural equation modelling. The results show that positive perceptions strengthen users' attitudes toward OpenClaw, which increase behavioural intention, whereas negative perceptions increase distrust and reduce intention to use the system. The study provides insights into the psychological mechanisms influencing the adoption of autonomous AI agents.

Yiran Du

This article explores the phenomenon of confirmation bias in generative AI chatbots, a relatively underexamined aspect of AI-human interaction. Drawing on cognitive psychology and computational linguistics, it examines how confirmation bias, commonly understood as the tendency to seek information that aligns with existing beliefs, can be replicated and amplified by the design and functioning of large language models. The article analyzes the mechanisms by which confirmation bias may manifest in chatbot interactions, assesses the ethical and practical risks associated with such bias, and proposes a range of mitigation strategies. These include technical interventions, interface redesign, and policy measures aimed at promoting balanced AI-generated discourse. The article concludes by outlining future research directions, emphasizing the need for interdisciplinary collaboration and empirical evaluation to better understand and address confirmation bias in generative AI systems.