Unlocking Foreign Language Enjoyment in GenAI-Assisted English Learning: A Q-Methodology Perspective from Chinese EFL Students

Unlocking Foreign Language Enjoyment in GenAI-Assisted English Learning: A Q-Methodology Perspective from Chinese EFL Students | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Unlocking Foreign Language Enjoyment in GenAI-Assisted English Learning: A Q-Methodology Perspective from Chinese EFL Students Yang Gao, Quan Quan This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5824065/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 14 You are reading this latest preprint version Abstract This study employs Q methodology to investigate Chinese EFL students' foreign language enjoyment in GenAI-assisted English learning environments. Through Q-sorting and follow-up written responses from university students, this research identified three distinct patterns: (1) Instrumental Support Orientation , characterized by enjoyment derived from GenAI's immediate assistance and error correction features, but showing limited engagement with deeper learning processes; (2) Independent Learning Achievement , reflecting high satisfaction from autonomous goal attainment and learning efficiency while demonstrating resistance to peer interaction and collaborative learning; and (3) Learning Feature Exploration , emphasizing enjoyment through experimenting with various GenAI functionalities but expressing significant skepticism about language learning outcomes and anxiety reduction. These findings extend our understanding of FLE in technology-enhanced language learning by revealing how learners experience enjoyment through different pathways of GenAI interaction. Social science/Education Social science/Language and linguistics Social science/Psychology Foreign language enjoyment GenAI-assisted English learning Q-methodology Chinese EFL students Figures Figure 1 Figure 2 1. Introduction The integration of generative artificial intelligence (GenAI), particularly Large Language Models (LLMs) like ChatGPT, into English language learning, has fundamentally transformed traditional learning paradigms (Wang et al., 2024 ; Gao et al., 2024 ). This technological advancement represents a significant shift from conventional Computer-Assisted Language Learning (CALL) approaches, offering unprecedented levels of interactivity and personalization in language education. As these tools become increasingly prevalent in educational settings, understanding their impact on learner psychology, particularly their influence on emotional experiences during the learning process, becomes crucial for effective implementation (Pan et al., 2025 ; Wang et al., 2024 ). Within the broader framework of Positive Psychology proposed by Seligman and Csikszentmihalyi ( 2000 ) in language education, Foreign Language Enjoyment (FLE) has emerged as a critical factor in successful language acquisition (Dewaele et al., 2017 ; Shen, 2021 ; Gao et al., 2024 ). Recent research has demonstrated that positive emotional experiences, particularly enjoyment, play a vital role in sustaining learner engagement, enhancing motivation, and facilitating deeper learning (Wang & Wang, 2024 ; Wang, 2024 ). However, while extensive research has focused on GenAI's technological capabilities and learning outcomes (Li et al., 2024 ), limited attention has been paid to understanding how learners experience enjoyment in this new learning context. This gap in knowledge is particularly significant given the rapid adoption of GenAI tools in language classrooms globally. Given the increasing prominence of GenAI in language education, GenAI-assisted English Learning or GAEL, in the study, specifically refers to English learning activities and processes mediated by GenAI technologies such as LLMs and AI chatbots. This conceptualization helps distinguish GenAI tools’ unique characteristics and affordances from traditional CALL technologies, recognizing their enhanced capabilities for natural language interaction, personalized feedback, and adaptive learning support. The Chinese context provides a particularly relevant setting for investigating FLE in GAEL environments. As one of the world's largest English learning markets, China has rapidly adopted GenAI tools in language education, with both institutional integration and informal learning applications becoming increasingly common. This study employs Q methodology to investigate Chinese EFL university students' subjective experiences of FLE in GAEL environments. This methodological approach allows for a systematic examination of learners' viewpoints and experiences, helping to identify distinct patterns in how students derive enjoyment from their interactions with GenAI tools. Through this investigation, we aim to address several key questions: How do Chinese EFL university students experience FLE in GAEL environments? What factors contribute to shaping their enjoyment? What patterns emerge in their subjective experiences of GAEL? The findings of this study will contribute to both theoretical understanding and practical implementation of GAEL. Theoretically, it will extend our knowledge of how FLE manifests in technology-enhanced learning environments, particularly in the context of advanced AI tools. Practically, insights gained from this investigation will inform the development of more effective and enjoyable GAEL experiences, helping educators and developers better understand how to leverage GenAI tools to enhance learner engagement and satisfaction. Furthermore, by focusing on the Chinese context, this study will provide valuable insights into how cultural and educational factors influence the relationship between technological innovation and learning enjoyment in language education. 2. Literature Review 2.1 Conceptualization of foreign language enjoyment Foreign language enjoyment (FLE) emerged from the broader movement of Positive Psychology in the realm of SLA, making a paradigm shift from the traditional focus on negative emotions like anxiety (E.g., Horwitz, 2001 ) to a more holistic understanding of learners’ emotional experiences including both positive and negative aspects (Dewaele, 2022 ; Dewaele & MacIntyre, 2014 ). This shift has led to increasing recognition that positive emotions, particularly enjoyment, play a crucial role in successful language learning (Macintyre et al., 2019 ; P. MacIntyre & Gregersen, 2012 ). FLE is conceptualized as a complex, multifaceted emotion that arises from the interplay between challenges and perceived abilities in foreign language learning (Dewaele & MacIntyre, 2016 ). The theoretical foundation of FLE is supported by three influential frameworks. The broaden-and-build theory (Fredrickson, 2001 ) suggests that positive emotions like enjoyment expand learners' cognitive resources and build lasting psychological resilience. The control-value theory (Pekrun & Perry, 2014 ) positions FLE as an achievement emotion that emerges when learners perceive activities as both manageable and valuable. Additionally, flow theory (Csikszentmihalyi, 1990 ) explains FLE as an optimal experience occurring when learners engage in appropriately challenging tasks that match their skill levels. Recent research has revealed that FLE is influenced by a complex interplay of learner-internal and learner-external factors (Botes et al., 2022 ; Dewaele, 2022 ). Internal factors include self-reported foreign language proficiency, academic achievement, and self-perceived competence (Dewaele & MacIntyre, 2014 ; Li, 2020 ), with a comprehensive meta-analysis confirming a moderate positive correlation between achievement and FLE (Botes et al., 2022 ). External factors encompass teacher-related variables, including instructional practices and support mechanisms (Dewaele et al., 2019 ; Li et al., 2018 ), as well as peer interaction and classroom atmosphere (Jin & Zhang, 2018 ). Cultural context also shapes FLE experiences, with studies indicating that Asian learners, particularly Chinese students, experience FLE differently compared to learners in other cultural contexts (Dewaele & MacIntyre, 2014 ; MacIntyre et al., 2019 ). These factors interact dynamically, collectively influencing students' overall experience of FLE (Li, 2022 ). These conceptualizations and influencing factors reveal the central role of FLE in foreign language learning, as FLE not only mitigates negative emotions but also enhances learner engagement, promotes language development, and contributes to improving academic achievement (Dewaele & Alfawzan, 2018 ; Li, 2020 ; Wei et al., 2019 ). 2.2 Generative AI-assisted English Learning Generative AI, particularly LLMs, represents a revolutionary advancement in artificial intelligence that can create new content, engage in human-like dialogue, and provide contextual responses based on natural language processing (Paul et al., 2023 ). In language learning contexts, GenAI differs from traditional AI tools in its ability to generate novel, contextually appropriate language output and engage in dynamic, adaptive interactions with learners (Ji et al., 2023 ). This capability enables more naturalistic and personalized language learning experiences compared to conventional CALL tools. Recent studies have demonstrated that GenAI can enhance various aspects of language learning. Through personalized interactions and immediate feedback, these tools have been shown to improve learners' engagement and reduce anxiety in language learning contexts (Zhang et al., 2024 ). A latent profile analysis by Wang et al. ( 2024 ) revealed distinct patterns in how university students engage with LLMs in EFL learning, identifying three learner profiles, namely enthusiastic explorers, adaptable learners, and ambitious-anxious pioneers, highlighting the diverse ways that students approach and utilize these tools. Liu and Ma ( 2023 ) explored Chinese EFL learners' engagement with ChatGPT for informal language learning, finding that learners' positive attitudes and perceived usefulness of AI tools significantly predicted their continued use. Their study also emphasized the importance of considering how individual learners navigate AI-mediated learning realities as autonomous language learners. This aligns with findings from Li et al. ( 2023 ), who found that ChatGPT could effectively empower L2 learners by serving as an accessible personal tutor, helping them acquire a clearer understanding of language structures while developing a sense of learning empowerment. Nevertheless, challenges exist in implementing GenAI for language learning. Studies have highlighted the need for careful pedagogical design and appropriate teacher guidance to maximize the benefits of these tools (Ji et al., 2023 ). Despite these challenges, emerging research shows promising results in terms of enhanced student engagement and improved learning experiences (Zhang et al., 2024 ). Given these findings regarding GenAI's impact on both cognitive and affective aspects of language learning, understanding learners' emotional experiences, particularly their FLE, becomes crucial for optimizing the implementation of these tools in language education. 2.3 Foreign language enjoyment in GenAI-assisted English learning The investigation of FLE in GAEL contexts merits particular attention as it represents a distinct emotional experience shaped by the unique affordances of LLMs. Drawing on the broaden-and-build theory (Fredrickson, 2001 ), enjoyment in this context not only serves as a transient emotional state but can generate lasting impacts by enabling learners to broaden their momentary thought-action repertoires and build their enduring personal resources ranging from physical and intelligent resources to social and psychological resources. Research on FLE in technology-enhanced language learning has evolved from traditional CALL environments (Lee & Drajati, 2019 ) to more advanced AI-supported contexts (Zhang et al., 2024 ; Huang & Zou, 2024 ; Yuan & Liu, 2025 ). However, most existing studies have focused on conventional AI tools like speech recognition systems and automated feedback programs (Liu & Ma, 2023 ) rather than GenAI platforms, leaving a significant gap in understanding how emerging LLMs might influence FLE. The limited research available has primarily employed quantitative approaches with some recent studies beginning to incorporate mixed methods. Zhang et al. ( 2024 ), through a survey of 383 EFL learners, found that AI-assisted speaking practice enhanced students' enjoyment while reducing anxiety. Using structural equation modeling, Huang and Zou ( 2024 ) revealed that enjoyment significantly predicted 203 Chinese EFL learners' continuance intention to use AI for speaking practice. In a mixed-method study of 389 Chinese EFL learners, Xiao et al. ( 2024 ) demonstrated how AI-supported online courses enhanced students' cognitive-emotion regulation and enjoyment. However, it is important to note that the relationship between GenAI and FLE remains largely unexplored. While existing studies with traditional AI tools suggest positive outcomes, more research is needed to understand how the unique features of GenAI, such as its ability to engage in natural dialogue and provide personalized feedback, might influence FLE. This understanding is crucial for optimizing the implementation of GenAI in language education and maximizing its potential to enhance FLE. 3. Methodology 3.1 Research Design Q methodology is a research methodology designed to systematically examine people’s perspectives on complex and subjective matters (Morea & Ghanbar, 2024 ). Factor analytic data reduction and induction are used in Q methodology to shed light on opinion development and establish testable hypotheses (Thumvichit, 2024 ). Unlike traditional quantitative methods that focus on identifying generalizable patterns, Q methodology provides insight into the personal and social dynamics of learners' experiences by systematically examining their subjective viewpoints (Rimm-Kaufman et al., 2006 ; Brown, 1980 ). By utilizing Q methodology, this study aims to identify distinct patterns in how Chinese EFL university students experience FLE when engaging with GenAI tools for English language learning. This methodological approach allows for the exploration of both shared and unique perspectives among learners, providing a rich understanding of how students subjectively experience enjoyment in AI-enhanced learning environments. The methodology's emphasis on individual viewpoints while maintaining systematic analytical rigor makes it particularly valuable for understanding the complex interplay between FLE and GAEL. 3.2 Q Sample A Q sample is a collection of heterogeneous statements used to assess participants’ opinions on a certain issue (Thumvichit, 2024 ). The Q sample consists of 40 statements designed to capture Chinese university students' subjective experiences of FLE in GAEL. The development of these statements was guided by two primary theoretical frameworks: Dewaele & MacIntyre's (2014) FLE theory and Pekrun's Control-Value Theory (Pekrun, 2006 ), with additional considerations from Li et al.'s ( 2018 ) CFLES adaptations for the Chinese context. The statements were structured along two main dimensions: FLE-Social and FLE-Private, with 20 statements in each. The FLE-Social dimension includes GenAI Support Perception and Peer Interaction, while the FLE-Private dimension comprises Achievement Experience and Learning Engagement (10 statements in each). The GenAI Support component was adapted from the traditional FLE-Teacher dimension, reconceptualizing teacher support in the context of GenAI assistance. Cultural adaptations from CFLES were integrated throughout, ensuring relevance to the Chinese university learning environment. 3.3 Participants The participants of the study are 26 Chinese EFL learners who had experiences in GAEL. After being fully informed of the research purpose and procedures, these learners voluntarily agreed to participate in this study and completed all research tasks. Detailed demographic information is presented in Table 1 . The sample comprised 17 females (65.38%) and 9 males (34.62%), representing a predominantly female participant pool. The age distribution of participants ranged from 18 to 26 years and above, with half of the participants (50%) falling within the 18–22 age bracket, followed by 34.62% in the 23–25 age range, and 15.38% aged 26 or above. In terms of educational background, the participants represented various academic levels. The majority were postgraduate students, with 13 participants (50%) pursuing master's degrees and three participants (11.54%) enrolled in doctoral programs. The remaining 10 participants (38.46%) were undergraduate students working toward their bachelor's degrees. This diverse educational composition provided a comprehensive representation of university students at different stages of their academic journey. Table 1 Demographic Information Demographic Variable Frequency Percentage Gender Male 9 34.62% Female 17 65.38% Age 18–22 13 50% 23–25 9 34.62% 26 and above 4 15.38% Educational Background Bachelor 10 38.46% Master 13 50% Ph.D. 3 11.54% 3.4 Q sorting The Q sorting procedure was conducted online using an Excel-based questionnaire format. This format was chosen to facilitate data collection and compilation while maintaining the rigorous requirements of the Q methodology. The questionnaire consisted of three main sections. First, participants were presented with the study objectives and demographic information collection section. Second, participants were instructed to sort 40 statements related to GAEL on an 11-point scale ranging from − 5 (most disagree) to + 5 (most agree) (see Fig. 1 ). The Excel questionnaire was designed with a forced quasi-normal distribution grid, following Brown's (1980) recommendation for Q samples between 40–60 statements. This distribution required participants to allocate statements carefully, with fewer statements allowed at the extremes and more statements in the middle positions, creating a bell-shaped curve. Each statement could only be assigned once within the distribution. After completing the Q-sort, participants were required to provide written explanations for their choices of statements placed at both extremes (+ 5 and − 5 positions). These post-sort written responses served a similar function to the post-sort interviews traditionally used in Q methodology studies (Brown, Danielson, & van Exel, 2015 ), allowing participants to elaborate on their rationales for statement placements and providing valuable qualitative data for factor interpretation. 3.5 Data Analysis Principal component analysis with varimax rotation was performed using Ken-Q Analysis Desktop Edition (KADE) (Banasick, 2019 ) to identify patterns among Q-sorts and extract factors that unite participants with similar viewpoints. Two criteria were applied to determine the factor solution: eigenvalues greater than 1 (McKeown & Thomas, 2013 ) and at least two Q-sorts significantly loading on each factor (Watts & Stenner, 2012 ). After examining correlations between factors and various solutions, a three-factor solution was adopted for Varimax rotation as it provided the most coherent representation of the data, supported by the scree plot showing a notable change in slope after the third factor (see Fig. 2 ). The preliminary analysis showed that the three factors accounted for 41% of the total variance (see Table 2 ), which represents a satisfactory solution according to Watts and Stenner's (2012) criterion of being above 35%. 23 out of 26 Q sorts loading significantly on at least one factor at the p < .05 level. Factor 1 had the highest eigenvalue of 5.721, explaining 22% of the variance. Factors 2 and 3 had eigenvalues of 2.634 and 2.468, explaining an additional 10% and 9% of the variance respectively. The cumulative explained variance of 41% indicates that this three-factor solution captures a substantial portion of the participants' shared viewpoints regarding FLE in GAEL. Table 2 Eigenvalues and explained variance Factor 1 Factor 2 Factor 3 Factor 4 Factor 5 Factor 6 Factor 7 Factor 8 Eigenvalues 5.721 2.634 2.468 2.114 1.762 1.479 1.264 1.194 % explained variance 22 10 9 8 7 6 5 5 Cumulative % explained variance 22 32 41 49 56 62 67 72 4. Results The analysis of Q sorts is presented in a narrative format, following the guidelines suggested by Watts and Stenner ( 2012 ). To identify GAEL characteristics that represent each factor, the rankings were first examined to determine whether a statement was rated significantly higher or lower for a specific factor compared to others. Additionally, the relative ranking of each statement within the same factor was considered. This approach allowed for highlighting the unique perspectives associated with each factor. The three factors were labeled as follows: Factor 1: Instrumental Support Orientation (emphasizing GenAI's instrumental support roles), Factor 2: Independent Learning Achievement (focusing on autonomous learning accomplishments), and Factor 3: Learning Feature Exploration (highlighting interest in exploring GenAI features). The z-scores and factor arrays for each statement are presented in the Appendix for comprehensive reference. The description of each factor is detailed below, accompanied by tables summarizing key statement clusters to support the interpretation of identified patterns. Each table includes relevant statements, their numbers, and corresponding Q sort rankings, allowing for a systematic presentation of the key insights derived from the data. 4.1 Factor 1: Instrumental Support Orientation Eight participants (one male and seven female) significantly loaded on Factor 1, accounting for 22 percent of the total variance. The demographic composition shows a relatively young group of primarily graduate students, with 4 participants aged 18–22 and 4 aged 23–25. In terms of educational background, 5 participants were pursuing master's degrees while 3 were undergraduate students. The most distinctive characteristic of this factor is how participants derive enjoyment from GenAI's instrumental support in their English learning process. These learners found particular enjoyment in the immediate and reliable assistance that GenAI provides, as evidenced by their high ratings for: experiencing satisfaction when GenAI responds to learning needs promptly (1, + 4), enjoying error correction without pressure (5, + 4), and finding pleasure in efficient learning experiences (27, + 5). As participant NO9 shared: "I really enjoy how GenAI makes learning more manageable and less stressful. Getting immediate responses to my questions not only helps me learn but actually makes the process enjoyable. It's like having a patient tutor who's always there to help, which makes me feel more relaxed and confident in my learning journey." In terms of FLE-Social dimension, these learners particularly enjoyed the supportive learning atmosphere created by GenAI's timely assistance. They found satisfaction in having a reliable learning companion that provided consistent support and encouragement (7, + 5). Another participant (NO19) explained: "What makes learning enjoyable with GenAI is how it removes the anxiety of waiting for answers or feedback. I find myself looking forward to practicing English more because I know I can get help whenever I need it. The immediate feedback makes me feel more confident and engaged in my learning." However, these participants seemed to derive less enjoyment from learning engagement aspects of GenAI-assisted learning. This is evidenced by their significantly negative ratings of statements related to learning engagement: they did not find GenAI creating an immersive learning experience (39, -4), enhancing learning motivation (40, -4), or getting deeply absorbed in GenAI-assisted learning (36, -4). They particularly disagreed that GenAI makes them look forward to English learning (35, -5) or helps them focus more on learning (37, -5). As participant NO18 reflected: "While I appreciate GenAI's support and immediate assistance, I find it harder to get truly engaged or immersed in the learning process. The technological support is helpful, but it doesn't necessarily make me more focused or motivated in my English learning." This pattern suggests that Factor 1 participants experience FLE primarily through the instrumental and supportive functions of GenAI rather than through deep learning engagement. Their enjoyment stems from the practical benefits - having reliable support, immediate assistance, and a low-pressure environment - rather than from heightened engagement or motivation in the learning process itself. This finding indicates that for some learners, while technological support can be a significant source of foreign language enjoyment, it may not necessarily translate into deeper learning engagement or increased motivation. 4.2 Factor 2: Independent Learning Achievement Nine participants (five male and four female) significantly loaded on Factor 2, accounting for 10 percent of the total variance. Regarding age distribution, 4 participants were aged 18–22, 2 were 23–25, and 3 were 26 or above. In terms of educational background, there was an even distribution across academic levels with 3 participants each pursuing bachelor's, master's, and doctoral degrees. This factor reveals how participants derive enjoyment primarily from their independent English learning achievements facilitated by GenAI. The most distinctive characteristic of this factor is how participants experience enjoyment through personal accomplishments and learning efficiency. Their FLE is closely tied to achievement experiences, as evidenced by their high ratings for statements reflecting learning satisfaction: experiencing joy when GenAI makes learning more efficient (27, + 5), feeling accomplished when overcoming learning difficulties with GenAI (22, + 5), enjoying prompt responses to learning needs (1, + 4), and finding satisfaction in achieving learning goals (26, + 4). As participant NO8 shared: "What makes learning truly enjoyable for me is seeing how much I can achieve on my own with GenAI's help. I get a real sense of satisfaction when I can efficiently master new content or solve challenging problems independently. The feeling of accomplishment when I reach my learning goals with GenAI's assistance is what makes the whole process enjoyable." However, these participants' source of enjoyment was distinctly individual rather than social, as reflected in their low ratings of peer interaction statements. They did not find enjoyment in collaborative aspects: they expressed minimal pleasure in peer interactions (15, -5), showed little interest in seeking peer support (11, -5), and found limited enjoyment in sharing learning experiences with classmates (12, -4). This preference for individual enjoyment over social learning pleasure was further emphasized by their low ratings for peer support (16, -4) and collaborative learning experiences (17, -4). Participant NO15 explained: "I find the most enjoyment when I'm working independently with GenAI. While others might prefer group activities, I get a special kind of satisfaction from achieving my learning goals on my own. It's not that I dislike working with peers, but my greatest sense of enjoyment comes from personal accomplishments." This pattern suggests that Factor 2 participants experience FLE primarily through individual achievement and mastery experiences. Their enjoyment stems from the sense of accomplishment in independent learning, the satisfaction of efficient progress, and the pleasure of achieving personal learning goals. Rather than finding enjoyment in social learning aspects, these learners derive pleasure from the autonomous and achievement-oriented features of GenAI-assisted learning. As another participant (NO12) reflected: "The most enjoyable moments in my learning journey are when I can see my own progress clearly. Using GenAI helps me track my improvements and achieve my goals efficiently, which gives me a deep sense of satisfaction. This personal sense of achievement is what makes learning truly enjoyable for me." This finding enriches our understanding of FLE by highlighting how some learners find their greatest enjoyment not in social interaction but in personal achievement and autonomous learning experiences. It suggests that for these learners, the pleasure of learning is intrinsically linked to individual progress and accomplishment rather than social connectivity. 4.3 Factor 3: Learning Experience Engagement Six participants (two male and four female) loaded significantly on this factor, explaining nine percent of the total variance. The demographic composition shows a relatively experienced group with most participants being graduate students: 2 participants aged 18–22, 3 aged 23–25, and 1 aged 26 or above. In terms of educational background, 4 participants were pursuing master's degrees while 2 were undergraduate students. The most distinctive characteristic of this factor is its strong emphasis on the experiential and engaging aspects of GenAI learning. Participants demonstrated high engagement with both the learning process and the learning environment, as evidenced by their highest positive ratings: enjoying trying different GenAI learning features (38, + 5), feeling that GenAI enhances learning motivation (40, + 5), appreciating that GenAI's error correction doesn't create pressure (5, + 4), valuing the relaxed learning atmosphere (10, + 4), and engaging in collaborative learning through sharing new GenAI usage methods (14, + 4). As participant NO22 explained: "What makes learning with GenAI enjoyable is how it creates an engaging environment where I can try different approaches without pressure. The technology itself is interesting, but what really matters is how it helps me stay motivated and involved in the learning process. I particularly enjoy when we can share different ways of using it in class." In terms of Learning Engagement, these participants particularly valued the immersive and experiential aspects of GenAI-assisted learning. This was reflected in their appreciation of both the technological and social dimensions of the learning experience. Another participant (NO21) shared: "The most enjoyable part is how GenAI creates different ways to engage with English learning. It's not just about practicing the language – it's about being fully involved in the learning experience. The relaxed atmosphere makes me more willing to participate and try new things." However, these participants showed skepticism toward the direct language learning outcomes, as reflected in their lowest ratings: they disagreed that GenAI helps them overcome language learning anxiety (30, -5), helps them better understand their language level (28, -5), deepens their interest in English learning itself (33, -4), makes them more confident in their English ability (24, -4), or makes English learning more interesting (31, -4). As participant NO24 reflected: "While I find myself deeply engaged in the learning activities with GenAI, I'm not necessarily seeing dramatic improvements in my English skills. The enjoyment comes from the rich learning experience itself rather than from measurable language gains." This pattern of evaluations suggests that Factor 3 participants derive their enjoyment primarily from the engaging and experiential aspects of GenAI-assisted learning rather than from perceived language proficiency improvements. They particularly value how GenAI creates an engaging learning environment that encourages active participation and exploration while maintaining a low-pressure atmosphere. Their focus on the quality of the learning experience over quantifiable outcomes indicates a perspective where enjoyment stems from the process of engagement itself rather than from achievement or instrumental benefits. This finding aligns with the broader understanding of FLE as encompassing not just achievement-related emotions but also the quality of the learning experience itself. 5. Discussion This study investigated Chinese EFL students' perceptions of their FLE in GAEL environments using Q-Methodology. By engaging 26 Chinese EFL learners in a Q-sort exercise, the study identified three distinct factors that represent different patterns of enjoyment and experiences in GAEL: Instrumental Support Orientation; Independent Learning Achievement; and Learning Feature Exploration. Through analyzing these factors, the study uncovered how students experience and derive enjoyment when interacting with GenAI tools in their language learning process. The findings provide an in-depth understanding of the subjective experiences that shape students' FLE in GenAI-assisted learning environments, contributing valuable insights for language educators and AI tool developers to better support students' emotional experiences in contemporary language education. The following sections explore the specific characteristics of each factor. Factor 1: Instrumental Support Orientation The Instrumental Support Orientation reflects learners who primarily view GenAI as a learning support tool. This orientation is characterized by a strong recognition of GenAI's functional value, particularly in providing immediate responses, error correction, and learning support. These learners tend to view GenAI as an intelligent assistant or personalized tutor, gaining learning support and emotional security through GenAI interaction. They particularly value GenAI's ability to provide a pressure-free learning environment where they can freely try, make mistakes, and correct them without anxiety about facing teachers or peers. This finding aligns with Li et al.'s ( 2023 ) research, which demonstrated that ChatGPT could effectively empower L2 learners by serving as a personalized tutor, not only helping learners better understand language structures but also providing emotional support. However, this orientation also reveals a notable contrast in how these learners experience enjoyment. While they strongly appreciate the instrumental support aspects of GenAI, they show markedly less enjoyment in terms of learning engagement. These learners do not find themselves deeply absorbed or immersed in the learning process, nor do they experience enhanced motivation or focused attention through GenAI interaction. This phenomenon suggests that while GenAI's supportive functions can create a comfortable learning environment, they may not necessarily lead to deeper engagement with the learning process itself. As Wang et al.'s ( 2024 ) study found, "adaptable learners," while capable of using GenAI tools, often limit themselves to basic functionalities without exploring deeper learning possibilities. Moreover, Huang and Zou ( 2024 ) point out that this focus on technical support, while beneficial for reducing anxiety, may not automatically translate into enhanced learning engagement or motivation. Factor 2: Independent Learning Achievement The Independent Learning Achievement Orientation demonstrates a more autonomous learning pattern. These learners distinctively emphasize personally-driven learning processes and achievement experiences, preferring to achieve learning goals through the independent use of GenAI tools. Their learning enjoyment primarily stems from task completion and personal goal achievement. They particularly value GenAI tools' role in improving learning efficiency and streamlining learning processes, skillfully utilizing various electronic learning resources. Zhang et al.'s ( 2024 ) research confirms this, finding that GenAI-assisted practice can enhance learning enjoyment while reducing anxiety through increased learner autonomy. Yu et al.'s ( 2022 ) study similarly indicates that mobile learning technologies can significantly improve learner engagement and learning outcomes. On the other hand, this emphasis on independence also presents certain challenges. These learners show evident discomfort in classroom expression and participation, lacking confidence in self-expression and class contributions. In terms of teamwork, they display clear resistance to peer interaction and group activities. This aligns with Lee and Lee's (2021) findings that informal digital learning, while providing autonomous learning opportunities, may lead to reduced social learning opportunities. Additionally, these learners express dissatisfaction with workload, reflecting challenges in task management during autonomous learning. Wang and Reynolds ( 2024 ) also found that while learners gain more autonomy using large language models for learning, they simultaneously face challenges in managing learning tasks. Factor 3: Learning Experience Engagement The Learning Feature Exploration Orientation reveals a technology-oriented learning pattern. These learners are primarily characterized by their strong interest in GenAI technological features, deriving learning enjoyment largely from exploring and experimenting with various GenAI functionalities. This exploratory learning pattern differs significantly from traditional language learning, reflecting new characteristics of digital-age learners. As Liu et al.'s ( 2024 ) research shows, learners' interest in exploring GenAI tools can significantly promote their engagement in informal learning environments. These learners particularly enjoy trying different GenAI features, viewing this exploration process itself as motivation-enhancing. Nevertheless, this exploratory orientation has notable limitations. While these learners are enthusiastic about technological exploration, they express skepticism about actual language learning progress and effectiveness. They are uncertain about GenAI's ability to help overcome language learning anxiety or better understand their language level. This cognitive-emotional disconnect warrants attention, as Kohnke et al. ( 2023 ) pointed out that excessive focus on technological exploration may cause learners to neglect substantive language skill development. Zhang and Liu's (2023) research further indicates that the effectiveness of informal digital learning largely depends on whether learners can effectively integrate technological exploration with language learning objectives. Moreover, these learners lack confidence in basic language application skills, reflecting a disconnect between technological exploration and practical language use. This suggests the need to help learners establish clearer learning goals to better align technological exploration with language learning objectives. 6. Conclusion This Q-methodology study has revealed three distinct patterns in how Chinese university students experience FLE in GAEL environments: Instrumental Support Orientation (emphasizing GenAI's supportive features while showing limited deep engagement), Independent Learning Achievement (valuing autonomous progress but resistant to peer interaction), and Learning Feature Exploration (enjoying technological exploration despite skepticism about language gains). These patterns demonstrate that FLE in GAEL contexts is more complex than previously recognized, encompassing both traditional aspects of language learning enjoyment and technology-specific dimensions. Through analyzing these patterns, the study has uncovered how learners derive enjoyment differently when interacting with GenAI tools - whether as a support tool, as an enabler of autonomous achievement, or as an object of exploration. The findings enrich our theoretical understanding of FLE by demonstrating that in technology-enhanced environments, enjoyment emerges not only from traditional sources like achievement and peer interaction but also from novel dimensions specific to AI interaction, such as personalized support and technological exploration. This multifaceted nature of GAEL-related FLE suggests that contemporary language learning environments need to account for diverse pathways through which learners may experience enjoyment. Several limitations of this study warrant consideration. First, the findings are based on a relatively small sample size from a single cultural context, which may limit their generalizability to other educational settings or cultural backgrounds. Second, the cross-sectional nature of the study means it cannot capture how these FLE patterns might evolve over time as learners become more familiar with GenAI tools and as the technology itself advances. Third, while Q methodology effectively revealed subjective patterns of enjoyment, it cannot establish causal relationships between these patterns and learning outcomes. The study's focus on university students also means that the findings might not fully represent the experiences of learners at other educational levels. Additionally, the research was conducted during a period of rapid technological change in language education, and the patterns identified might be influenced by the novelty of GenAI tools rather than representing stable, long-term orientations. The reliance on self-reported data and written responses, while valuable for understanding subjective experiences, may not fully capture the dynamic nature of learner-AI interactions in real learning contexts. Looking ahead, several promising directions emerge for future research. Longitudinal studies could examine how FLE patterns evolve as learners gain more experience with GAEL and as the technology becomes more sophisticated. Such research could help understand whether the identified patterns represent stable orientations or transitional stages in learners' adaptation to AI-enhanced learning environments. Cross-cultural comparative studies might reveal how different educational contexts and cultural approaches to technology influence these enjoyment patterns, potentially uncovering universal aspects of GAEL-related FLE as well as culturally specific variations. Future research could also investigate the relationship between these FLE patterns and various learning outcomes, including language proficiency, learner autonomy, and motivation. This could help establish whether certain patterns of enjoyment are more conducive to effective language learning than others. Studies examining specific pedagogical strategies that can effectively address the limitations identified in each orientation would be valuable for improving GAEL implementation. Furthermore, research exploring how these patterns might be influenced by individual differences such as personality traits, learning styles, and prior technological experience could provide insights for more personalized approaches to GAEL. Investigation into how teachers can best support students with different FLE orientations would also be valuable for developing more effective pedagogical practices in AI-enhanced language education. Declarations Competing interests The authors declare no competing interests. Data availability The datasets generated during and/or analyzed during the current study are available from the corresponding author upon reasonable request. Ethical Approval Name of the approval body: Institutional Review Board (IRB) in the School of Foreign Studies, Xi'an Jiaotong University (XJTU) Confirmation: All research was performed in accordance with XJTU IRB research guidelines and the Declaration of Helsinki. Approval number or ID: XJTUSFREA[2024]-1-001 Date of approval: 2024.11.27 Scope of approval: This research project, funded by a national grant in social science foundations of China (23XYY05), has been approved by the Institutional Review Board (IRB) at the School of Foreign Studies, Xi'an Jiaotong University (XJTU). The research involves mixed methods of data collection (e.g., surveys, interviews, and focus groups) with adult human participants who are college language teachers and learners, mostly in higher education, across different provinces in China. The research procedures adhere to the ethical principles outlined in the Declaration of Helsinki, including: Informed Consent: Participants will be provided with clear and comprehensive information about the research purpose, procedures, risks, and benefits. They will have the right to withdraw from the study at any time without consequence. Confidentiality and Privacy: Participant data will be collected and stored securely and confidentially. Data will be anonymized or pseudonymized to protect participant identity. Voluntary Participation: Participation in the study is entirely voluntary. No coercion or undue influence will be used to encourage participation. Beneficence: The research will aim to maximize potential benefits to participants and society while minimizing potential risks. Justice: The research will be conducted in a fair and equitable manner, ensuring that all participants are treated with respect and dignity. Author Contribution Yang Gao supervised the research project, developed the methodology, contributed to the conceptualization of the study, and revised the manuscript. Quan Quan collected and analyzed the data, conducted the investigation, and wrote the original draft. Both authors reviewed and edited the final manuscript. References Banasick, S. (2019). KADE: A desktop application for Q methodology. Journal of Open Source Software, 4 (36), 1360. https://doi.org/10.21105/joss.01360 Botes, E., Dewaele, J.-M., & Greiff, S. (2022). Taking stock: A meta-analysis of the effects of foreign language enjoyment. Studies in Second Language Learning and Teaching, 12 (2), 205-232. https://doi.org/10.14746/ssllt.2022.12.2.3 Brown, S. R. (1980). Political subjectivity: Applications of Q methodology in political science . Yale University Press. Brown, S. R., Danielson, S., & van Exel, J. (2015). Overly ambitious critics and the Medici effect: A reply to Kampen and Tamás. Quality & Quantity, 49 (2), 523-537. Csikszentmihalyi, M. (1990). Flow: The psychology of optimal experience . Harper & Row. Dewaele, J. M. (2022). Enjoyment. In S. Li, P. Hiver, & M. Papi (Eds.), The Routledge handbook of second language acquisition and individual differences (pp. 190–206). Routledge. Dewaele, J. M., & Alfawzan, M. (2018). Does the effect of enjoyment outweigh that of anxiety in foreign language performance? Studies in Second Language Learning and Teaching, 8 (1), 21-45. https://doi.org/10.14746/ssllt.2018.8.1.2 Dewaele, J. M., & MacIntyre, P. D. (2014). The two faces of Janus? Anxiety and enjoyment in the foreign language classroom. Studies in Second Language Learning and Teaching, 4 (2), 237–274. https://doi.org/10.14746/ssllt.2014.4.2.5 Dewaele, J. M., & MacIntyre, P. D. (2016). Foreign Language Enjoyment and Foreign Language Classroom Anxiety: The Right and Left Feet of the Language Learner. In P. D. MacIntyre, T. Gregersen, & S. Mercer (Eds.), Positive Psychology in SLA (pp. 215–236). Multilingual Matters. https://doi.org/10.21832/9781783095360-010 Dewaele, J. M., Magdalena-Franco, A., & Saito, K. (2019). The effect of perception of teacher characteristics on Spanish EFL learners' anxiety and enjoyment. The Modern Language Journal, 103 (2), 412-427. https://doi.org/10.1111/modl.12555 Dewaele, J. M., Witney, J., Saito, K., & Dewaele, L. (2017). Foreign language enjoyment and anxiety: The effect of teacher and learner variables. Language Teaching Research , 22(6). https://doi.org/10.1177/1362168817692161 Fredrickson, B. L. (2001). The role of positive emotions in positive psychology: The broaden-and-build theory of positive emotions. American Psychologist, 56 (3), 218-226. https://doi.org/10.1037/0003-066X.56.3.218 Gao, Y., Wang, Q., & Wang, X. (2024). Exploring EFL university teachers' beliefs in integrating ChatGPT and other large language models in language education: A study in China. Asia Pacific Journal of Education , 44(2). https://doi.org/10.1080/02188791.2024.2305173 Gao, Y., Liu, Y., Zeng, Y., & Wang, X. (2024). Studies on language teachers' beliefs and emotions: Current status and future directions. Heliyon , 10(4), e38695. https://doi.org/10.1016/j.heliyon.2024.e38695 Horwitz, E. (2001). Language anxiety and achievement. Annual Review of Applied Linguistics, 21 , 112–126. https://doi.org/10.1017/S0267190501000071 Huang, F., & Zou, B. (2024). English speaking with artificial intelligence (AI): The roles of enjoyment, willingness to communicate with AI, and innovativeness. Computers in Human Behavior , 159 , 108355. https://doi.org/10.1016/j.chb.2024.108355 Ji, H., Han, I., & Ko, Y. (2023). A systematic review of conversational AI in language education: Focusing on the collaboration with human teachers. Journal of Research on Technology in Education, 55 (1), 48–63. https://doi.org/10.1080/15391523.2022.2142873 Jin, Y., & Zhang, L. J. (2018). The dimensions of foreign language classroom enjoyment and their effect on foreign language achievement. International Journal of Bilingual Education and Bilingualism , 1-15. https://doi.org/10.1080/13670050.2018.1526253 Kohnke, L., Moorhouse, B. L., & Zou, D. (2023). ChatGPT for Language Teaching and Learning. RELC Journal . https://doi.org/10.1177/00336882231162868 Lee, J. S., & Drajati, N. A. (2019). Affective variables and informal digital learning of English: Keys to willingness to communicate in a second language. Australasian Journal of Educational Technology . https://doi.org/10.14742/ajet.5177 Lee, J. S., & Lee, K. (2021). The role of informal digital learning of English and L2 motivational self system in foreign language enjoyment. British Journal of Educational Technology, 52 (1), 358-373. https://doi.org/10.1111/bjet.12955 Li, C. (2020). A positive psychology perspective on Chinese EFL students' trait emotional intelligence, foreign language enjoyment and EFL learning achievement. Journal of Multilingual and Multicultural Development, 41 (3), 246-263. https://doi.org/10.1080/01434632.2019.1614187 Li, C. (2022). Foreign language learning boredom and enjoyment: The effects of learner variables and teacher variables. Language Teaching Research . Advance online publication. https://doi.org/10.1177/13621688221090324 Li, C., Jiang, G., & Dewaele, J.-M. (2018). Understanding Chinese high school students' foreign language enjoyment: Validation of the Chinese version of the foreign language enjoyment scale. System, 76 , 183-196. https://doi.org/10.1016/j.system.2018.06.004 Li, H., Wang, Y., Luo, S., & Huang, C. (2024). The influence of GenAI on the effectiveness of argumentative writing in higher education: Evidence from a quasi-experimental study in China. Higher Education Research & Development. https://doi.org/10.1080/17516234.2024.2363128 Li, X., Li, B., & Cho, S. J. (2023). Empowering Chinese Language Learners from Low-Income Families to Improve Their Chinese Writing with ChatGPT's Assistance Afterschool. Languages, 8 (4), 238. https://doi.org/10.3390/languages8040238 Liu, G., & Ma, C. (2023). Measuring EFL learners' use of ChatGPT in informal digital learning of English based on the technology acceptance model. Innovation in Language Learning and Teaching , 1–14. https://doi.org/10.1080/17501229.2023.2240316 Liu, G. L., Darvin, R., & Ma, C. (2024). Exploring AI-mediated informal digital learning of English (AI-IDLE): A mixed-method investigation of Chinese EFL learners' AI adoption and experiences. Computer Assisted Language Learning , 1-29. https://doi.org/10.1080/09588221.2024.2310288 MacIntyre, P., & Gregersen, T. (2012). Emotions that facilitate language learning: The positive-broadening power of the imagination. Studies in Second Language Learning and Teaching, 2 (2), 193. https://doi.org/10.14746/ssllt.2012.2.2.4 MacIntyre, P. D., Gregersen, T., & Mercer, S. (2019). Setting an agenda for positive psychology in SLA: Theory, practice, and research. The Modern Language Journal, 103 (1), 262-274. https://doi.org/10.1111/modl.12544 McKeown, B., & Thomas, D. B. (2013). Q methodology (2nd ed.). SAGE Publications. Morea, N., & Ghanbar, H. (2024). Q methodology in applied linguistics: A systematic research synthesis. System, 120 , 103194. https://doi.org/10.1016/j.system.2023.103194 Pan, M., Lai, C., & Guo, K. (2025). Effects of GenAI-empowered interactive support on university EFL students' self-regulated strategy use and engagement in reading. The Internet and Higher Education , 65, 100991. https://doi.org/10.1016/j.iheduc.2024.100991 Paul, J., Ueno, A., & Dennis, C. (2023). ChatGPT and consumers: Benefits, Pitfalls and Future Research Agenda. International Journal of Consumer Studies, 47 (4), 1213–1225. https://doi.org/10.1111/ijcs.12928 Pekrun, R. (2006). The control-value theory of achievement emotions: Assumptions, corollaries, and implications for educational research and practice. Educational Psychology Review, 18 (4), 315-341. Pekrun, R., & Perry, R. P. (2014). Control-value theory of achievement emotions. In R. Pekrun & L. Linnenbrink-Garcia (Eds.), International handbook of emotions in education (pp. 120-141). Taylor & Francis. Rimm-Kaufman, S. E., Storm, M. D., Sawyer, B. E., Pianta, R. C., & LaParo, K. M. (2006). The Teacher Belief Q-Sort: A measure of teachers' priorities in relation to disciplinary practices, teaching practices, and beliefs about children. Journal of School Psychology, 44 (2), 141-165. https://doi.org/10.1016/j.jsp.2006.01.003 Seligman, M. E. P., & Csikszentmihalyi, M. (2000). Positive psychology: An introduction. American Psychologist , 55(1), 5-14.https://doi.org/10.1037/0003-066X.55.1.5 Shen, J. (2021). A review of the effectiveness of foreign language enjoyment and foreign language classroom anxiety on learners' engagement and attainment. Frontiers in Psychology , 12, 749284. https://doi.org/10.3389/fpsyg.2021.749284 Thumvichit, A. (2024). Unfolding the subjectivity of foreign language enjoyment in online classes: A Q methodology study. Journal of Multilingual and Multicultural Development, 45 (7), 2427–2444. https://doi.org/10.1080/01434632.2022.2050917 Wang, Q., Gao, Y., & Wang, X. (2024). Exploring Engagement, Self-Efficacy, and Anxiety in Large Language Model EFL Learning: A Latent Profile Analysis of Chinese University Students. International Journal of Human–Computer Interaction , 1–10. https://doi.org/10.1080/10447318.2024.2400403 Wang, X., Gao, Y., Wang, Q., & Zhang, P. (2024). Fostering engagement in AI-assisted Chinese EFL classrooms: The role of classroom climate, AI literacy, and resilience. European Journal of Education. https://doi.org/10.1111/ejed.12874 Wang, X., & Reynolds, B. L. (2024). Beyond the Books: Exploring Factors Shaping Chinese English Learners' Engagement with Large Language Models for Vocabulary Learning. Education Sciences, 14 (5), 496. https://doi.org/10.3390/educsci14050496 Watts, S., & Stenner, P. (2012). Doing Q methodological research: Theory, method and interpretation . SAGE Publications. Wang, X., & Wang, S. (2024). Exploring Chinese EFL learners' engagement with large language models: A self-determination theory perspective. Learning and Motivation, 87, 102014. https://doi.org/10.1016/j.lmot.2024.102014 Wei, H., Gao, K., & Wang, W. (2019). Understanding the relationship between grit and foreign language performance among middle school students: The roles of foreign language enjoyment and classroom environment. Frontiers in Psychology, 10, 1508. https://doi.org/10.3389/fpsyg.2019.01508 Xiao, T., Yi, S., & Akhter, S. (2024). AI-Supported Online Language Learning: Learners' Self-Esteem, Cognitive-Emotion Regulation, Academic Enjoyment, and Language Success. The International Review of Research in Open and Distributed Learning, 25 (3), 77–96. https://doi.org/10.19173/irrodl.v25i3.7666 Yu, Z., Yu, L., Xu, Q., Xu, W., & Wu, P. (2022). Effects of mobile learning technologies and social media tools on student engagement and learning outcomes of English learning. Technology, Pedagogy and Education, 31 (3), 381-398. https://doi.org/10.1080/1475939X.2022.2045215 Yuan, L., & Liu, X. (2025). The effect of artificial intelligence tools on EFL learners' engagement, enjoyment, and motivation. Computers in Human Behavior, 162 , 108474. https://doi.org/10.1016/j.chb.2024.108474 Zhang, C., Meng, Y., & Ma, X. (2024). Artificial intelligence in EFL speaking: Impact on enjoyment, anxiety, and willingness to communicate. System, 121 , 103259. https://doi.org/10.1016/j.system.2024.103259 Zhang, Y., & Liu, G. L. (2023). Examining the impacts of learner backgrounds, proficiency level, and the use of digital devices on informal digital learning of English: An explanatory mixed-method study. Computer Assisted Language Learning , 1-28. https://doi.org/10.1080/09588221.2023.2267627 Additional Declarations No competing interests reported. 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Introduction","content":"\u003cp\u003eThe integration of generative artificial intelligence (GenAI), particularly Large Language Models (LLMs) like ChatGPT, into English language learning, has fundamentally transformed traditional learning paradigms (Wang et al., \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Gao et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). This technological advancement represents a significant shift from conventional Computer-Assisted Language Learning (CALL) approaches, offering unprecedented levels of interactivity and personalization in language education. As these tools become increasingly prevalent in educational settings, understanding their impact on learner psychology, particularly their influence on emotional experiences during the learning process, becomes crucial for effective implementation (Pan et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Wang et al., \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eWithin the broader framework of Positive Psychology proposed by Seligman and Csikszentmihalyi (\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2000\u003c/span\u003e) in language education, Foreign Language Enjoyment (FLE) has emerged as a critical factor in successful language acquisition (Dewaele et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Shen, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Gao et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Recent research has demonstrated that positive emotional experiences, particularly enjoyment, play a vital role in sustaining learner engagement, enhancing motivation, and facilitating deeper learning (Wang \u0026amp; Wang, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Wang, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). However, while extensive research has focused on GenAI's technological capabilities and learning outcomes (Li et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), limited attention has been paid to understanding how learners experience enjoyment in this new learning context. This gap in knowledge is particularly significant given the rapid adoption of GenAI tools in language classrooms globally.\u003c/p\u003e \u003cp\u003eGiven the increasing prominence of GenAI in language education, GenAI-assisted English Learning or GAEL, in the study, specifically refers to English learning activities and processes mediated by GenAI technologies such as LLMs and AI chatbots. This conceptualization helps distinguish GenAI tools\u0026rsquo; unique characteristics and affordances from traditional CALL technologies, recognizing their enhanced capabilities for natural language interaction, personalized feedback, and adaptive learning support. The Chinese context provides a particularly relevant setting for investigating FLE in GAEL environments. As one of the world's largest English learning markets, China has rapidly adopted GenAI tools in language education, with both institutional integration and informal learning applications becoming increasingly common.\u003c/p\u003e \u003cp\u003eThis study employs Q methodology to investigate Chinese EFL university students' subjective experiences of FLE in GAEL environments. This methodological approach allows for a systematic examination of learners' viewpoints and experiences, helping to identify distinct patterns in how students derive enjoyment from their interactions with GenAI tools. Through this investigation, we aim to address several key questions: How do Chinese EFL university students experience FLE in GAEL environments? What factors contribute to shaping their enjoyment? What patterns emerge in their subjective experiences of GAEL? The findings of this study will contribute to both theoretical understanding and practical implementation of GAEL. Theoretically, it will extend our knowledge of how FLE manifests in technology-enhanced learning environments, particularly in the context of advanced AI tools. Practically, insights gained from this investigation will inform the development of more effective and enjoyable GAEL experiences, helping educators and developers better understand how to leverage GenAI tools to enhance learner engagement and satisfaction. Furthermore, by focusing on the Chinese context, this study will provide valuable insights into how cultural and educational factors influence the relationship between technological innovation and learning enjoyment in language education.\u003c/p\u003e"},{"header":"2. Literature Review","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Conceptualization of foreign language enjoyment\u003c/h2\u003e \u003cp\u003eForeign language enjoyment (FLE) emerged from the broader movement of Positive Psychology in the realm of SLA, making a paradigm shift from the traditional focus on negative emotions like anxiety (E.g., Horwitz, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2001\u003c/span\u003e) to a more holistic understanding of learners\u0026rsquo; emotional experiences including both positive and negative aspects (Dewaele, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Dewaele \u0026amp; MacIntyre, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). This shift has led to increasing recognition that positive emotions, particularly enjoyment, play a crucial role in successful language learning (Macintyre et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; P. MacIntyre \u0026amp; Gregersen, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). FLE is conceptualized as a complex, multifaceted emotion that arises from the interplay between challenges and perceived abilities in foreign language learning (Dewaele \u0026amp; MacIntyre, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2016\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe theoretical foundation of FLE is supported by three influential frameworks. The broaden-and-build theory (Fredrickson, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2001\u003c/span\u003e) suggests that positive emotions like enjoyment expand learners' cognitive resources and build lasting psychological resilience. The control-value theory (Pekrun \u0026amp; Perry, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2014\u003c/span\u003e) positions FLE as an achievement emotion that emerges when learners perceive activities as both manageable and valuable. Additionally, flow theory (Csikszentmihalyi, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e1990\u003c/span\u003e) explains FLE as an optimal experience occurring when learners engage in appropriately challenging tasks that match their skill levels.\u003c/p\u003e \u003cp\u003eRecent research has revealed that FLE is influenced by a complex interplay of learner-internal and learner-external factors (Botes et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Dewaele, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Internal factors include self-reported foreign language proficiency, academic achievement, and self-perceived competence (Dewaele \u0026amp; MacIntyre, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Li, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), with a comprehensive meta-analysis confirming a moderate positive correlation between achievement and FLE (Botes et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). External factors encompass teacher-related variables, including instructional practices and support mechanisms (Dewaele et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Li et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2018\u003c/span\u003e), as well as peer interaction and classroom atmosphere (Jin \u0026amp; Zhang, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Cultural context also shapes FLE experiences, with studies indicating that Asian learners, particularly Chinese students, experience FLE differently compared to learners in other cultural contexts (Dewaele \u0026amp; MacIntyre, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; MacIntyre et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). These factors interact dynamically, collectively influencing students' overall experience of FLE (Li, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). These conceptualizations and influencing factors reveal the central role of FLE in foreign language learning, as FLE not only mitigates negative emotions but also enhances learner engagement, promotes language development, and contributes to improving academic achievement (Dewaele \u0026amp; Alfawzan, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Li, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Wei et al., \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Generative AI-assisted English Learning\u003c/h2\u003e \u003cp\u003eGenerative AI, particularly LLMs, represents a revolutionary advancement in artificial intelligence that can create new content, engage in human-like dialogue, and provide contextual responses based on natural language processing (Paul et al., \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). In language learning contexts, GenAI differs from traditional AI tools in its ability to generate novel, contextually appropriate language output and engage in dynamic, adaptive interactions with learners (Ji et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). This capability enables more naturalistic and personalized language learning experiences compared to conventional CALL tools.\u003c/p\u003e \u003cp\u003eRecent studies have demonstrated that GenAI can enhance various aspects of language learning. Through personalized interactions and immediate feedback, these tools have been shown to improve learners' engagement and reduce anxiety in language learning contexts (Zhang et al., \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). A latent profile analysis by Wang et al. (\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) revealed distinct patterns in how university students engage with LLMs in EFL learning, identifying three learner profiles, namely enthusiastic explorers, adaptable learners, and ambitious-anxious pioneers, highlighting the diverse ways that students approach and utilize these tools. Liu and Ma (\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) explored Chinese EFL learners' engagement with ChatGPT for informal language learning, finding that learners' positive attitudes and perceived usefulness of AI tools significantly predicted their continued use. Their study also emphasized the importance of considering how individual learners navigate AI-mediated learning realities as autonomous language learners. This aligns with findings from Li et al. (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), who found that ChatGPT could effectively empower L2 learners by serving as an accessible personal tutor, helping them acquire a clearer understanding of language structures while developing a sense of learning empowerment.\u003c/p\u003e \u003cp\u003eNevertheless, challenges exist in implementing GenAI for language learning. Studies have highlighted the need for careful pedagogical design and appropriate teacher guidance to maximize the benefits of these tools (Ji et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Despite these challenges, emerging research shows promising results in terms of enhanced student engagement and improved learning experiences (Zhang et al., \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Given these findings regarding GenAI's impact on both cognitive and affective aspects of language learning, understanding learners' emotional experiences, particularly their FLE, becomes crucial for optimizing the implementation of these tools in language education.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Foreign language enjoyment in GenAI-assisted English learning\u003c/h2\u003e \u003cp\u003eThe investigation of FLE in GAEL contexts merits particular attention as it represents a distinct emotional experience shaped by the unique affordances of LLMs. Drawing on the broaden-and-build theory (Fredrickson, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2001\u003c/span\u003e), enjoyment in this context not only serves as a transient emotional state but can generate lasting impacts by enabling learners to broaden their momentary thought-action repertoires and build their enduring personal resources ranging from physical and intelligent resources to social and psychological resources.\u003c/p\u003e \u003cp\u003eResearch on FLE in technology-enhanced language learning has evolved from traditional CALL environments (Lee \u0026amp; Drajati, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) to more advanced AI-supported contexts (Zhang et al., \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Huang \u0026amp; Zou, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Yuan \u0026amp; Liu, \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). However, most existing studies have focused on conventional AI tools like speech recognition systems and automated feedback programs (Liu \u0026amp; Ma, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) rather than GenAI platforms, leaving a significant gap in understanding how emerging LLMs might influence FLE. The limited research available has primarily employed quantitative approaches with some recent studies beginning to incorporate mixed methods. Zhang et al. (\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), through a survey of 383 EFL learners, found that AI-assisted speaking practice enhanced students' enjoyment while reducing anxiety. Using structural equation modeling, Huang and Zou (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) revealed that enjoyment significantly predicted 203 Chinese EFL learners' continuance intention to use AI for speaking practice. In a mixed-method study of 389 Chinese EFL learners, Xiao et al. (\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) demonstrated how AI-supported online courses enhanced students' cognitive-emotion regulation and enjoyment.\u003c/p\u003e \u003cp\u003eHowever, it is important to note that the relationship between GenAI and FLE remains largely unexplored. While existing studies with traditional AI tools suggest positive outcomes, more research is needed to understand how the unique features of GenAI, such as its ability to engage in natural dialogue and provide personalized feedback, might influence FLE. This understanding is crucial for optimizing the implementation of GenAI in language education and maximizing its potential to enhance FLE.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Methodology","content":"\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Research Design\u003c/h2\u003e \u003cp\u003eQ methodology is a research methodology designed to systematically examine people\u0026rsquo;s perspectives on complex and subjective matters (Morea \u0026amp; Ghanbar, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Factor analytic data reduction and induction are used in Q methodology to shed light on opinion development and establish testable hypotheses (Thumvichit, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Unlike traditional quantitative methods that focus on identifying generalizable patterns, Q methodology provides insight into the personal and social dynamics of learners' experiences by systematically examining their subjective viewpoints (Rimm-Kaufman et al., \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2006\u003c/span\u003e; Brown, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e1980\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eBy utilizing Q methodology, this study aims to identify distinct patterns in how Chinese EFL university students experience FLE when engaging with GenAI tools for English language learning. This methodological approach allows for the exploration of both shared and unique perspectives among learners, providing a rich understanding of how students subjectively experience enjoyment in AI-enhanced learning environments. The methodology's emphasis on individual viewpoints while maintaining systematic analytical rigor makes it particularly valuable for understanding the complex interplay between FLE and GAEL.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Q Sample\u003c/h2\u003e \u003cp\u003eA Q sample is a collection of heterogeneous statements used to assess participants\u0026rsquo; opinions on a certain issue (Thumvichit, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). The Q sample consists of 40 statements designed to capture Chinese university students' subjective experiences of FLE in GAEL. The development of these statements was guided by two primary theoretical frameworks: Dewaele \u0026amp; MacIntyre's (2014) FLE theory and Pekrun's Control-Value Theory (Pekrun, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2006\u003c/span\u003e), with additional considerations from Li et al.'s (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) CFLES adaptations for the Chinese context.\u003c/p\u003e \u003cp\u003eThe statements were structured along two main dimensions: FLE-Social and FLE-Private, with 20 statements in each. The FLE-Social dimension includes GenAI Support Perception and Peer Interaction, while the FLE-Private dimension comprises Achievement Experience and Learning Engagement (10 statements in each). The GenAI Support component was adapted from the traditional FLE-Teacher dimension, reconceptualizing teacher support in the context of GenAI assistance. Cultural adaptations from CFLES were integrated throughout, ensuring relevance to the Chinese university learning environment.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Participants\u003c/h2\u003e \u003cp\u003eThe participants of the study are 26 Chinese EFL learners who had experiences in GAEL. After being fully informed of the research purpose and procedures, these learners voluntarily agreed to participate in this study and completed all research tasks. Detailed demographic information is presented in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. The sample comprised 17 females (65.38%) and 9 males (34.62%), representing a predominantly female participant pool. The age distribution of participants ranged from 18 to 26 years and above, with half of the participants (50%) falling within the 18\u0026ndash;22 age bracket, followed by 34.62% in the 23\u0026ndash;25 age range, and 15.38% aged 26 or above. In terms of educational background, the participants represented various academic levels. The majority were postgraduate students, with 13 participants (50%) pursuing master's degrees and three participants (11.54%) enrolled in doctoral programs. The remaining 10 participants (38.46%) were undergraduate students working toward their bachelor's degrees. This diverse educational composition provided a comprehensive representation of university students at different stages of their academic journey.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDemographic Information\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDemographic Variable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFrequency\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePercentage\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGender\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e34.62%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e65.38%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e18\u0026ndash;22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e50%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e23\u0026ndash;25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e34.62%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e26 and above\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15.38%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEducational Background\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBachelor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e38.46%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMaster\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e50%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePh.D.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11.54%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e3.4 Q sorting\u003c/h2\u003e \u003cp\u003eThe Q sorting procedure was conducted online using an Excel-based questionnaire format. This format was chosen to facilitate data collection and compilation while maintaining the rigorous requirements of the Q methodology. The questionnaire consisted of three main sections. First, participants were presented with the study objectives and demographic information collection section. Second, participants were instructed to sort 40 statements related to GAEL on an 11-point scale ranging from \u0026minus;\u0026thinsp;5 (most disagree) to +\u0026thinsp;5 (most agree) (see Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The Excel questionnaire was designed with a forced quasi-normal distribution grid, following Brown's (1980) recommendation for Q samples between 40\u0026ndash;60 statements. This distribution required participants to allocate statements carefully, with fewer statements allowed at the extremes and more statements in the middle positions, creating a bell-shaped curve. Each statement could only be assigned once within the distribution.\u003c/p\u003e \u003cp\u003e After completing the Q-sort, participants were required to provide written explanations for their choices of statements placed at both extremes (+\u0026thinsp;5 and \u0026minus;\u0026thinsp;5 positions). These post-sort written responses served a similar function to the post-sort interviews traditionally used in Q methodology studies (Brown, Danielson, \u0026amp; van Exel, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2015\u003c/span\u003e), allowing participants to elaborate on their rationales for statement placements and providing valuable qualitative data for factor interpretation.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e3.5 Data Analysis\u003c/h2\u003e \u003cp\u003ePrincipal component analysis with varimax rotation was performed using Ken-Q Analysis Desktop Edition (KADE) (Banasick, \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) to identify patterns among Q-sorts and extract factors that unite participants with similar viewpoints. Two criteria were applied to determine the factor solution: eigenvalues greater than 1 (McKeown \u0026amp; Thomas, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2013\u003c/span\u003e) and at least two Q-sorts significantly loading on each factor (Watts \u0026amp; Stenner, \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). After examining correlations between factors and various solutions, a three-factor solution was adopted for Varimax rotation as it provided the most coherent representation of the data, supported by the scree plot showing a notable change in slope after the third factor (see Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe preliminary analysis showed that the three factors accounted for 41% of the total variance (see Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e), which represents a satisfactory solution according to Watts and Stenner's (2012) criterion of being above 35%. 23 out of 26 Q sorts loading significantly on at least one factor at the \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.05 level. Factor 1 had the highest eigenvalue of 5.721, explaining 22% of the variance. Factors 2 and 3 had eigenvalues of 2.634 and 2.468, explaining an additional 10% and 9% of the variance respectively. The cumulative explained variance of 41% indicates that this three-factor solution captures a substantial portion of the participants' shared viewpoints regarding FLE in GAEL.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eEigenvalues and explained variance\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"9\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFactor 1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFactor 2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eFactor 3\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eFactor 4\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eFactor 5\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eFactor 6\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eFactor 7\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eFactor 8\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEigenvalues\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5.721\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.634\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.468\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.114\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.762\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.479\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.264\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.194\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e% explained variance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCumulative % explained variance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e72\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"4. Results","content":"\u003cp\u003eThe analysis of Q sorts is presented in a narrative format, following the guidelines suggested by Watts and Stenner (\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). To identify GAEL characteristics that represent each factor, the rankings were first examined to determine whether a statement was rated significantly higher or lower for a specific factor compared to others. Additionally, the relative ranking of each statement within the same factor was considered. This approach allowed for highlighting the unique perspectives associated with each factor. The three factors were labeled as follows: \u003cem\u003eFactor 1: Instrumental Support Orientation\u003c/em\u003e (emphasizing GenAI's instrumental support roles), \u003cem\u003eFactor 2: Independent Learning Achievement\u003c/em\u003e (focusing on autonomous learning accomplishments), and \u003cem\u003eFactor 3: Learning Feature Exploration\u003c/em\u003e (highlighting interest in exploring GenAI features). The z-scores and factor arrays for each statement are presented in the Appendix for comprehensive reference. The description of each factor is detailed below, accompanied by tables summarizing key statement clusters to support the interpretation of identified patterns. Each table includes relevant statements, their numbers, and corresponding Q sort rankings, allowing for a systematic presentation of the key insights derived from the data.\u003c/p\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e4.1 Factor 1: Instrumental Support Orientation\u003c/h2\u003e \u003cp\u003eEight participants (one male and seven female) significantly loaded on Factor 1, accounting for 22 percent of the total variance. The demographic composition shows a relatively young group of primarily graduate students, with 4 participants aged 18\u0026ndash;22 and 4 aged 23\u0026ndash;25. In terms of educational background, 5 participants were pursuing master's degrees while 3 were undergraduate students. The most distinctive characteristic of this factor is how participants derive enjoyment from GenAI's instrumental support in their English learning process. These learners found particular enjoyment in the immediate and reliable assistance that GenAI provides, as evidenced by their high ratings for: experiencing satisfaction when GenAI responds to learning needs promptly (1, +\u0026thinsp;4), enjoying error correction without pressure (5, +\u0026thinsp;4), and finding pleasure in efficient learning experiences (27, +\u0026thinsp;5). As participant NO9 shared:\u003c/p\u003e \u003cp\u003e \u003cem\u003e\"I really enjoy how GenAI makes learning more manageable and less stressful. Getting immediate responses to my questions not only helps me learn but actually makes the process enjoyable. It's like having a patient tutor who's always there to help, which makes me feel more relaxed and confident in my learning journey.\"\u003c/em\u003e \u003c/p\u003e \u003cp\u003eIn terms of FLE-Social dimension, these learners particularly enjoyed the supportive learning atmosphere created by GenAI's timely assistance. They found satisfaction in having a reliable learning companion that provided consistent support and encouragement (7, +\u0026thinsp;5). Another participant (NO19) explained:\u003c/p\u003e \u003cp\u003e \u003cem\u003e\"What makes learning enjoyable with GenAI is how it removes the anxiety of waiting for answers or feedback. I find myself looking forward to practicing English more because I know I can get help whenever I need it. The immediate feedback makes me feel more confident and engaged in my learning.\"\u003c/em\u003e \u003c/p\u003e \u003cp\u003eHowever, these participants seemed to derive less enjoyment from learning engagement aspects of GenAI-assisted learning. This is evidenced by their significantly negative ratings of statements related to learning engagement: they did not find GenAI creating an immersive learning experience (39, -4), enhancing learning motivation (40, -4), or getting deeply absorbed in GenAI-assisted learning (36, -4). They particularly disagreed that GenAI makes them look forward to English learning (35, -5) or helps them focus more on learning (37, -5). As participant NO18 reflected:\u003c/p\u003e \u003cp\u003e \u003cem\u003e\"While I appreciate GenAI's support and immediate assistance, I find it harder to get truly engaged or immersed in the learning process. The technological support is helpful, but it doesn't necessarily make me more focused or motivated in my English learning.\"\u003c/em\u003e \u003c/p\u003e \u003cp\u003eThis pattern suggests that Factor 1 participants experience FLE primarily through the instrumental and supportive functions of GenAI rather than through deep learning engagement. Their enjoyment stems from the practical benefits - having reliable support, immediate assistance, and a low-pressure environment - rather than from heightened engagement or motivation in the learning process itself. This finding indicates that for some learners, while technological support can be a significant source of foreign language enjoyment, it may not necessarily translate into deeper learning engagement or increased motivation.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e4.2 Factor 2: Independent Learning Achievement\u003c/h2\u003e \u003cp\u003eNine participants (five male and four female) significantly loaded on Factor 2, accounting for 10 percent of the total variance. Regarding age distribution, 4 participants were aged 18\u0026ndash;22, 2 were 23\u0026ndash;25, and 3 were 26 or above. In terms of educational background, there was an even distribution across academic levels with 3 participants each pursuing bachelor's, master's, and doctoral degrees. This factor reveals how participants derive enjoyment primarily from their independent English learning achievements facilitated by GenAI.\u003c/p\u003e \u003cp\u003eThe most distinctive characteristic of this factor is how participants experience enjoyment through personal accomplishments and learning efficiency. Their FLE is closely tied to achievement experiences, as evidenced by their high ratings for statements reflecting learning satisfaction: experiencing joy when GenAI makes learning more efficient (27, +\u0026thinsp;5), feeling accomplished when overcoming learning difficulties with GenAI (22, +\u0026thinsp;5), enjoying prompt responses to learning needs (1, +\u0026thinsp;4), and finding satisfaction in achieving learning goals (26, +\u0026thinsp;4). As participant NO8 shared:\u003c/p\u003e \u003cp\u003e \u003cem\u003e\"What makes learning truly enjoyable for me is seeing how much I can achieve on my own with GenAI's help. I get a real sense of satisfaction when I can efficiently master new content or solve challenging problems independently. The feeling of accomplishment when I reach my learning goals with GenAI's assistance is what makes the whole process enjoyable.\"\u003c/em\u003e \u003c/p\u003e \u003cp\u003e However, these participants' source of enjoyment was distinctly individual rather than social, as reflected in their low ratings of peer interaction statements. They did not find enjoyment in collaborative aspects: they expressed minimal pleasure in peer interactions (15, -5), showed little interest in seeking peer support (11, -5), and found limited enjoyment in sharing learning experiences with classmates (12, -4). This preference for individual enjoyment over social learning pleasure was further emphasized by their low ratings for peer support (16, -4) and collaborative learning experiences (17, -4). Participant NO15 explained:\u003c/p\u003e \u003cp\u003e \u003cem\u003e\"I find the most enjoyment when I'm working independently with GenAI. While others might prefer group activities, I get a special kind of satisfaction from achieving my learning goals on my own. It's not that I dislike working with peers, but my greatest sense of enjoyment comes from personal accomplishments.\"\u003c/em\u003e \u003c/p\u003e \u003cp\u003eThis pattern suggests that Factor 2 participants experience FLE primarily through individual achievement and mastery experiences. Their enjoyment stems from the sense of accomplishment in independent learning, the satisfaction of efficient progress, and the pleasure of achieving personal learning goals. Rather than finding enjoyment in social learning aspects, these learners derive pleasure from the autonomous and achievement-oriented features of GenAI-assisted learning. As another participant (NO12) reflected:\u003c/p\u003e \u003cp\u003e \u003cem\u003e\"The most enjoyable moments in my learning journey are when I can see my own progress clearly. Using GenAI helps me track my improvements and achieve my goals efficiently, which gives me a deep sense of satisfaction. This personal sense of achievement is what makes learning truly enjoyable for me.\"\u003c/em\u003e \u003c/p\u003e \u003cp\u003eThis finding enriches our understanding of FLE by highlighting how some learners find their greatest enjoyment not in social interaction but in personal achievement and autonomous learning experiences. It suggests that for these learners, the pleasure of learning is intrinsically linked to individual progress and accomplishment rather than social connectivity.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e4.3 Factor 3: Learning Experience Engagement\u003c/h2\u003e \u003cp\u003eSix participants (two male and four female) loaded significantly on this factor, explaining nine percent of the total variance. The demographic composition shows a relatively experienced group with most participants being graduate students: 2 participants aged 18\u0026ndash;22, 3 aged 23\u0026ndash;25, and 1 aged 26 or above. In terms of educational background, 4 participants were pursuing master's degrees while 2 were undergraduate students. The most distinctive characteristic of this factor is its strong emphasis on the experiential and engaging aspects of GenAI learning. Participants demonstrated high engagement with both the learning process and the learning environment, as evidenced by their highest positive ratings: enjoying trying different GenAI learning features (38, +\u0026thinsp;5), feeling that GenAI enhances learning motivation (40, +\u0026thinsp;5), appreciating that GenAI's error correction doesn't create pressure (5, +\u0026thinsp;4), valuing the relaxed learning atmosphere (10, +\u0026thinsp;4), and engaging in collaborative learning through sharing new GenAI usage methods (14, +\u0026thinsp;4). As participant NO22 explained:\u003c/p\u003e \u003cp\u003e \u003cem\u003e\"What makes learning with GenAI enjoyable is how it creates an engaging environment where I can try different approaches without pressure. The technology itself is interesting, but what really matters is how it helps me stay motivated and involved in the learning process. I particularly enjoy when we can share different ways of using it in class.\"\u003c/em\u003e \u003c/p\u003e \u003cp\u003eIn terms of Learning Engagement, these participants particularly valued the immersive and experiential aspects of GenAI-assisted learning. This was reflected in their appreciation of both the technological and social dimensions of the learning experience. Another participant (NO21) shared:\u003c/p\u003e \u003cp\u003e \u003cem\u003e\"The most enjoyable part is how GenAI creates different ways to engage with English learning. It's not just about practicing the language \u0026ndash; it's about being fully involved in the learning experience. The relaxed atmosphere makes me more willing to participate and try new things.\"\u003c/em\u003e \u003c/p\u003e \u003cp\u003eHowever, these participants showed skepticism toward the direct language learning outcomes, as reflected in their lowest ratings: they disagreed that GenAI helps them overcome language learning anxiety (30, -5), helps them better understand their language level (28, -5), deepens their interest in English learning itself (33, -4), makes them more confident in their English ability (24, -4), or makes English learning more interesting (31, -4). As participant NO24 reflected:\u003c/p\u003e \u003cp\u003e \u003cem\u003e\"While I find myself deeply engaged in the learning activities with GenAI, I'm not necessarily seeing dramatic improvements in my English skills. The enjoyment comes from the rich learning experience itself rather than from measurable language gains.\"\u003c/em\u003e \u003c/p\u003e \u003cp\u003e This pattern of evaluations suggests that Factor 3 participants derive their enjoyment primarily from the engaging and experiential aspects of GenAI-assisted learning rather than from perceived language proficiency improvements. They particularly value how GenAI creates an engaging learning environment that encourages active participation and exploration while maintaining a low-pressure atmosphere. Their focus on the quality of the learning experience over quantifiable outcomes indicates a perspective where enjoyment stems from the process of engagement itself rather than from achievement or instrumental benefits. This finding aligns with the broader understanding of FLE as encompassing not just achievement-related emotions but also the quality of the learning experience itself.\u003c/p\u003e \u003c/div\u003e"},{"header":"5. Discussion","content":"\u003cp\u003eThis study investigated Chinese EFL students' perceptions of their FLE in GAEL environments using Q-Methodology. By engaging 26 Chinese EFL learners in a Q-sort exercise, the study identified three distinct factors that represent different patterns of enjoyment and experiences in GAEL: Instrumental Support Orientation; Independent Learning Achievement; and Learning Feature Exploration. Through analyzing these factors, the study uncovered how students experience and derive enjoyment when interacting with GenAI tools in their language learning process. The findings provide an in-depth understanding of the subjective experiences that shape students' FLE in GenAI-assisted learning environments, contributing valuable insights for language educators and AI tool developers to better support students' emotional experiences in contemporary language education. The following sections explore the specific characteristics of each factor.\u003c/p\u003e \u003cp\u003e \u003cem\u003eFactor 1: Instrumental Support Orientation\u003c/em\u003e \u003c/p\u003e \u003cp\u003eThe Instrumental Support Orientation reflects learners who primarily view GenAI as a learning support tool. This orientation is characterized by a strong recognition of GenAI's functional value, particularly in providing immediate responses, error correction, and learning support. These learners tend to view GenAI as an intelligent assistant or personalized tutor, gaining learning support and emotional security through GenAI interaction. They particularly value GenAI's ability to provide a pressure-free learning environment where they can freely try, make mistakes, and correct them without anxiety about facing teachers or peers. This finding aligns with Li et al.'s (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) research, which demonstrated that ChatGPT could effectively empower L2 learners by serving as a personalized tutor, not only helping learners better understand language structures but also providing emotional support.\u003c/p\u003e \u003cp\u003eHowever, this orientation also reveals a notable contrast in how these learners experience enjoyment. While they strongly appreciate the instrumental support aspects of GenAI, they show markedly less enjoyment in terms of learning engagement. These learners do not find themselves deeply absorbed or immersed in the learning process, nor do they experience enhanced motivation or focused attention through GenAI interaction. This phenomenon suggests that while GenAI's supportive functions can create a comfortable learning environment, they may not necessarily lead to deeper engagement with the learning process itself. As Wang et al.'s (\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) study found, \"adaptable learners,\" while capable of using GenAI tools, often limit themselves to basic functionalities without exploring deeper learning possibilities. Moreover, Huang and Zou (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) point out that this focus on technical support, while beneficial for reducing anxiety, may not automatically translate into enhanced learning engagement or motivation.\u003c/p\u003e \u003cp\u003e \u003cem\u003eFactor 2: Independent Learning Achievement\u003c/em\u003e \u003c/p\u003e \u003cp\u003eThe Independent Learning Achievement Orientation demonstrates a more autonomous learning pattern. These learners distinctively emphasize personally-driven learning processes and achievement experiences, preferring to achieve learning goals through the independent use of GenAI tools. Their learning enjoyment primarily stems from task completion and personal goal achievement. They particularly value GenAI tools' role in improving learning efficiency and streamlining learning processes, skillfully utilizing various electronic learning resources. Zhang et al.'s (\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) research confirms this, finding that GenAI-assisted practice can enhance learning enjoyment while reducing anxiety through increased learner autonomy. Yu et al.'s (\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) study similarly indicates that mobile learning technologies can significantly improve learner engagement and learning outcomes.\u003c/p\u003e \u003cp\u003eOn the other hand, this emphasis on independence also presents certain challenges. These learners show evident discomfort in classroom expression and participation, lacking confidence in self-expression and class contributions. In terms of teamwork, they display clear resistance to peer interaction and group activities. This aligns with Lee and Lee's (2021) findings that informal digital learning, while providing autonomous learning opportunities, may lead to reduced social learning opportunities. Additionally, these learners express dissatisfaction with workload, reflecting challenges in task management during autonomous learning. Wang and Reynolds (\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) also found that while learners gain more autonomy using large language models for learning, they simultaneously face challenges in managing learning tasks.\u003c/p\u003e \u003cp\u003e \u003cem\u003eFactor 3: Learning Experience Engagement\u003c/em\u003e \u003c/p\u003e \u003cp\u003eThe Learning Feature Exploration Orientation reveals a technology-oriented learning pattern. These learners are primarily characterized by their strong interest in GenAI technological features, deriving learning enjoyment largely from exploring and experimenting with various GenAI functionalities. This exploratory learning pattern differs significantly from traditional language learning, reflecting new characteristics of digital-age learners. As Liu et al.'s (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) research shows, learners' interest in exploring GenAI tools can significantly promote their engagement in informal learning environments. These learners particularly enjoy trying different GenAI features, viewing this exploration process itself as motivation-enhancing.\u003c/p\u003e \u003cp\u003eNevertheless, this exploratory orientation has notable limitations. While these learners are enthusiastic about technological exploration, they express skepticism about actual language learning progress and effectiveness. They are uncertain about GenAI's ability to help overcome language learning anxiety or better understand their language level. This cognitive-emotional disconnect warrants attention, as Kohnke et al. (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) pointed out that excessive focus on technological exploration may cause learners to neglect substantive language skill development. Zhang and Liu's (2023) research further indicates that the effectiveness of informal digital learning largely depends on whether learners can effectively integrate technological exploration with language learning objectives. Moreover, these learners lack confidence in basic language application skills, reflecting a disconnect between technological exploration and practical language use. This suggests the need to help learners establish clearer learning goals to better align technological exploration with language learning objectives.\u003c/p\u003e"},{"header":"6. Conclusion","content":"\u003cp\u003eThis Q-methodology study has revealed three distinct patterns in how Chinese university students experience FLE in GAEL environments: Instrumental Support Orientation (emphasizing GenAI's supportive features while showing limited deep engagement), Independent Learning Achievement (valuing autonomous progress but resistant to peer interaction), and Learning Feature Exploration (enjoying technological exploration despite skepticism about language gains). These patterns demonstrate that FLE in GAEL contexts is more complex than previously recognized, encompassing both traditional aspects of language learning enjoyment and technology-specific dimensions. Through analyzing these patterns, the study has uncovered how learners derive enjoyment differently when interacting with GenAI tools - whether as a support tool, as an enabler of autonomous achievement, or as an object of exploration. The findings enrich our theoretical understanding of FLE by demonstrating that in technology-enhanced environments, enjoyment emerges not only from traditional sources like achievement and peer interaction but also from novel dimensions specific to AI interaction, such as personalized support and technological exploration. This multifaceted nature of GAEL-related FLE suggests that contemporary language learning environments need to account for diverse pathways through which learners may experience enjoyment.\u003c/p\u003e \u003cp\u003eSeveral limitations of this study warrant consideration. First, the findings are based on a relatively small sample size from a single cultural context, which may limit their generalizability to other educational settings or cultural backgrounds. Second, the cross-sectional nature of the study means it cannot capture how these FLE patterns might evolve over time as learners become more familiar with GenAI tools and as the technology itself advances. Third, while Q methodology effectively revealed subjective patterns of enjoyment, it cannot establish causal relationships between these patterns and learning outcomes. The study's focus on university students also means that the findings might not fully represent the experiences of learners at other educational levels. Additionally, the research was conducted during a period of rapid technological change in language education, and the patterns identified might be influenced by the novelty of GenAI tools rather than representing stable, long-term orientations. The reliance on self-reported data and written responses, while valuable for understanding subjective experiences, may not fully capture the dynamic nature of learner-AI interactions in real learning contexts.\u003c/p\u003e \u003cp\u003eLooking ahead, several promising directions emerge for future research. Longitudinal studies could examine how FLE patterns evolve as learners gain more experience with GAEL and as the technology becomes more sophisticated. Such research could help understand whether the identified patterns represent stable orientations or transitional stages in learners' adaptation to AI-enhanced learning environments. Cross-cultural comparative studies might reveal how different educational contexts and cultural approaches to technology influence these enjoyment patterns, potentially uncovering universal aspects of GAEL-related FLE as well as culturally specific variations. Future research could also investigate the relationship between these FLE patterns and various learning outcomes, including language proficiency, learner autonomy, and motivation. This could help establish whether certain patterns of enjoyment are more conducive to effective language learning than others. Studies examining specific pedagogical strategies that can effectively address the limitations identified in each orientation would be valuable for improving GAEL implementation. Furthermore, research exploring how these patterns might be influenced by individual differences such as personality traits, learning styles, and prior technological experience could provide insights for more personalized approaches to GAEL. Investigation into how teachers can best support students with different FLE orientations would also be valuable for developing more effective pedagogical practices in AI-enhanced language education.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets generated during and/or analyzed during the current study are available from the corresponding author upon reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthical Approval\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eName of the approval body:\u003c/strong\u003e Institutional Review Board (IRB) in the School of Foreign Studies, Xi\u0026apos;an Jiaotong University (XJTU)\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConfirmation:\u003c/strong\u003e All research was performed in accordance with XJTU IRB research guidelines and the Declaration of Helsinki.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eApproval number or ID:\u003c/strong\u003e XJTUSFREA[2024]-1-001\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDate of approval:\u003c/strong\u003e 2024.11.27\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eScope of approval:\u003c/strong\u003e This research project, funded by a national grant in social science foundations of China (23XYY05), has been approved by the Institutional Review Board (IRB) at the School of Foreign Studies, Xi\u0026apos;an Jiaotong University (XJTU). The research involves mixed methods of data collection (e.g., surveys, interviews, and focus groups) with adult human participants who are college language teachers and learners, mostly in higher education, across different provinces in China. The research procedures adhere to the ethical principles outlined in the Declaration of Helsinki, including:\u003c/p\u003e\n\u003cul type=\"disc\"\u003e\n \u003cli\u003e\u003cstrong\u003eInformed Consent:\u003c/strong\u003e Participants will be provided with clear and comprehensive information about the research purpose, procedures, risks, and benefits. They will have the right to withdraw from the study at any time without consequence.\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eConfidentiality and Privacy:\u003c/strong\u003e Participant data will be collected and stored securely and confidentially. Data will be anonymized or pseudonymized to protect participant identity.\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eVoluntary Participation:\u003c/strong\u003e Participation in the study is entirely voluntary. No coercion or undue influence will be used to encourage participation.\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eBeneficence:\u003c/strong\u003e The research will aim to maximize potential benefits to participants and society while minimizing potential risks.\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eJustice:\u003c/strong\u003e The research will be conducted in a fair and equitable manner, ensuring that all participants are treated with respect and dignity.\u003c/li\u003e\n\u003c/ul\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eYang Gao supervised the research project, developed the methodology, contributed to the conceptualization of the study, and revised the manuscript. Quan Quan collected and analyzed the data, conducted the investigation, and wrote the original draft. Both authors reviewed and edited the final manuscript.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eBanasick, S. (2019). KADE: A desktop application for Q methodology. \u003cem\u003eJournal of Open Source Software, 4\u003c/em\u003e(36), 1360. https://doi.org/10.21105/joss.01360\u003c/li\u003e\n\u003cli\u003eBotes, E., Dewaele, J.-M., \u0026amp; Greiff, S. (2022). Taking stock: A meta-analysis of the effects of foreign language enjoyment. \u003cem\u003eStudies in Second Language Learning and Teaching, 12\u003c/em\u003e(2), 205-232. https://doi.org/10.14746/ssllt.2022.12.2.3\u003c/li\u003e\n\u003cli\u003eBrown, S. R. (1980). \u003cem\u003ePolitical subjectivity: Applications of Q methodology in political science\u003c/em\u003e. Yale University Press.\u003c/li\u003e\n\u003cli\u003eBrown, S. R., Danielson, S., \u0026amp; van Exel, J. (2015). Overly ambitious critics and the Medici effect: A reply to Kampen and Tam\u0026aacute;s. \u003cem\u003eQuality \u0026amp; Quantity, 49\u003c/em\u003e(2), 523-537.\u003c/li\u003e\n\u003cli\u003eCsikszentmihalyi, M. (1990). \u003cem\u003eFlow: The psychology of optimal experience\u003c/em\u003e. Harper \u0026amp; Row.\u003c/li\u003e\n\u003cli\u003eDewaele, J. M. (2022). Enjoyment. In S. Li, P. Hiver, \u0026amp; M. Papi (Eds.), \u003cem\u003eThe Routledge handbook of second language acquisition and individual differences\u003c/em\u003e (pp. 190\u0026ndash;206). Routledge.\u003c/li\u003e\n\u003cli\u003eDewaele, J. M., \u0026amp; Alfawzan, M. (2018). Does the effect of enjoyment outweigh that of anxiety in foreign language performance? \u003cem\u003eStudies in Second Language Learning and Teaching, 8\u003c/em\u003e(1), 21-45. https://doi.org/10.14746/ssllt.2018.8.1.2\u003c/li\u003e\n\u003cli\u003eDewaele, J. M., \u0026amp; MacIntyre, P. D. (2014). The two faces of Janus? Anxiety and enjoyment in the foreign language classroom. \u003cem\u003eStudies in Second Language Learning and Teaching, 4\u003c/em\u003e(2), 237\u0026ndash;274. https://doi.org/10.14746/ssllt.2014.4.2.5\u003c/li\u003e\n\u003cli\u003eDewaele, J. M., \u0026amp; MacIntyre, P. D. (2016). Foreign Language Enjoyment and Foreign Language Classroom Anxiety: The Right and Left Feet of the Language Learner. In P. D. MacIntyre, T. Gregersen, \u0026amp; S. Mercer (Eds.), \u003cem\u003ePositive Psychology in SLA\u003c/em\u003e (pp. 215\u0026ndash;236). Multilingual Matters. https://doi.org/10.21832/9781783095360-010\u003c/li\u003e\n\u003cli\u003eDewaele, J. M., Magdalena-Franco, A., \u0026amp; Saito, K. (2019). The effect of perception of teacher characteristics on Spanish EFL learners\u0026apos; anxiety and enjoyment. \u003cem\u003eThe Modern Language Journal, 103\u003c/em\u003e(2), 412-427. https://doi.org/10.1111/modl.12555\u003c/li\u003e\n\u003cli\u003eDewaele, J. M., Witney, J., Saito, K., \u0026amp; Dewaele, L. (2017). Foreign language enjoyment and anxiety: The effect of teacher and learner variables. \u003cem\u003eLanguage Teaching Research\u003c/em\u003e, 22(6). https://doi.org/10.1177/1362168817692161\u003c/li\u003e\n\u003cli\u003eFredrickson, B. L. (2001). The role of positive emotions in positive psychology: The broaden-and-build theory of positive emotions. \u003cem\u003eAmerican Psychologist, 56\u003c/em\u003e(3), 218-226. https://doi.org/10.1037/0003-066X.56.3.218\u003c/li\u003e\n\u003cli\u003eGao, Y., Wang, Q., \u0026amp; Wang, X. (2024). Exploring EFL university teachers\u0026apos; beliefs in integrating ChatGPT and other large language models in language education: A study in China. \u003cem\u003eAsia Pacific Journal of Education\u003c/em\u003e, 44(2). https://doi.org/10.1080/02188791.2024.2305173\u003c/li\u003e\n\u003cli\u003eGao, Y., Liu, Y., Zeng, Y., \u0026amp; Wang, X. (2024). Studies on language teachers\u0026apos; beliefs and emotions: Current status and future directions. \u003cem\u003eHeliyon\u003c/em\u003e, 10(4), e38695. https://doi.org/10.1016/j.heliyon.2024.e38695 \u003c/li\u003e\n\u003cli\u003eHorwitz, E. (2001). Language anxiety and achievement. \u003cem\u003eAnnual Review of Applied Linguistics, 21\u003c/em\u003e, 112\u0026ndash;126. https://doi.org/10.1017/S0267190501000071\u003c/li\u003e\n\u003cli\u003eHuang, F., \u0026amp; Zou, B. (2024). English speaking with artificial intelligence (AI): The roles of enjoyment, willingness to communicate with AI, and innovativeness. \u003cem\u003eComputers in Human Behavior\u003c/em\u003e, \u003cem\u003e159\u003c/em\u003e, 108355. https://doi.org/10.1016/j.chb.2024.108355\u003c/li\u003e\n\u003cli\u003eJi, H., Han, I., \u0026amp; Ko, Y. (2023). A systematic review of conversational AI in language education: Focusing on the collaboration with human teachers. \u003cem\u003eJournal of Research on Technology in Education, 55\u003c/em\u003e(1), 48\u0026ndash;63. https://doi.org/10.1080/15391523.2022.2142873\u003c/li\u003e\n\u003cli\u003eJin, Y., \u0026amp; Zhang, L. J. (2018). The dimensions of foreign language classroom enjoyment and their effect on foreign language achievement. \u003cem\u003eInternational Journal of Bilingual Education and Bilingualism\u003c/em\u003e, 1-15. https://doi.org/10.1080/13670050.2018.1526253\u003c/li\u003e\n\u003cli\u003eKohnke, L., Moorhouse, B. L., \u0026amp; Zou, D. (2023). ChatGPT for Language Teaching and Learning. \u003cem\u003eRELC Journal\u003c/em\u003e. https://doi.org/10.1177/00336882231162868\u003c/li\u003e\n\u003cli\u003eLee, J. S., \u0026amp; Drajati, N. A. (2019). Affective variables and informal digital learning of English: Keys to willingness to communicate in a second language. \u003cem\u003eAustralasian Journal of Educational Technology\u003c/em\u003e. https://doi.org/10.14742/ajet.5177\u003c/li\u003e\n\u003cli\u003eLee, J. S., \u0026amp; Lee, K. (2021). The role of informal digital learning of English and L2 motivational self system in foreign language enjoyment. \u003cem\u003eBritish Journal of Educational Technology, 52\u003c/em\u003e(1), 358-373. https://doi.org/10.1111/bjet.12955\u003c/li\u003e\n\u003cli\u003eLi, C. (2020). A positive psychology perspective on Chinese EFL students\u0026apos; trait emotional intelligence, foreign language enjoyment and EFL learning achievement. \u003cem\u003eJournal of Multilingual and Multicultural Development, 41\u003c/em\u003e(3), 246-263. https://doi.org/10.1080/01434632.2019.1614187\u003c/li\u003e\n\u003cli\u003eLi, C. (2022). Foreign language learning boredom and enjoyment: The effects of learner variables and teacher variables. \u003cem\u003eLanguage Teaching Research\u003c/em\u003e. Advance online publication. https://doi.org/10.1177/13621688221090324\u003c/li\u003e\n\u003cli\u003eLi, C., Jiang, G., \u0026amp; Dewaele, J.-M. (2018). Understanding Chinese high school students\u0026apos; foreign language enjoyment: Validation of the Chinese version of the foreign language enjoyment scale. \u003cem\u003eSystem, 76\u003c/em\u003e, 183-196. https://doi.org/10.1016/j.system.2018.06.004\u003c/li\u003e\n\u003cli\u003eLi, H., Wang, Y., Luo, S., \u0026amp; Huang, C. (2024). The influence of GenAI on the effectiveness of argumentative writing in higher education: Evidence from a quasi-experimental study in China. \u003cem\u003eHigher Education Research \u0026amp; Development.\u003c/em\u003e https://doi.org/10.1080/17516234.2024.2363128 \u003c/li\u003e\n\u003cli\u003eLi, X., Li, B., \u0026amp; Cho, S. J. (2023). Empowering Chinese Language Learners from Low-Income Families to Improve Their Chinese Writing with ChatGPT\u0026apos;s Assistance Afterschool. \u003cem\u003eLanguages, 8\u003c/em\u003e(4), 238. https://doi.org/10.3390/languages8040238\u003c/li\u003e\n\u003cli\u003eLiu, G., \u0026amp; Ma, C. (2023). Measuring EFL learners\u0026apos; use of ChatGPT in informal digital learning of English based on the technology acceptance model. \u003cem\u003eInnovation in Language Learning and Teaching\u003c/em\u003e, 1\u0026ndash;14. https://doi.org/10.1080/17501229.2023.2240316\u003c/li\u003e\n\u003cli\u003eLiu, G. L., Darvin, R., \u0026amp; Ma, C. (2024). Exploring AI-mediated informal digital learning of English (AI-IDLE): A mixed-method investigation of Chinese EFL learners\u0026apos; AI adoption and experiences. \u003cem\u003eComputer Assisted Language Learning\u003c/em\u003e, 1-29. https://doi.org/10.1080/09588221.2024.2310288\u003c/li\u003e\n\u003cli\u003eMacIntyre, P., \u0026amp; Gregersen, T. (2012). Emotions that facilitate language learning: The positive-broadening power of the imagination. \u003cem\u003eStudies in Second Language Learning and Teaching, 2\u003c/em\u003e(2), 193. https://doi.org/10.14746/ssllt.2012.2.2.4\u003c/li\u003e\n\u003cli\u003eMacIntyre, P. D., Gregersen, T., \u0026amp; Mercer, S. (2019). Setting an agenda for positive psychology in SLA: Theory, practice, and research. \u003cem\u003eThe Modern Language Journal, 103\u003c/em\u003e(1), 262-274. https://doi.org/10.1111/modl.12544\u003c/li\u003e\n\u003cli\u003eMcKeown, B., \u0026amp; Thomas, D. B. (2013). \u003cem\u003eQ methodology\u003c/em\u003e (2nd ed.). SAGE Publications.\u003c/li\u003e\n\u003cli\u003eMorea, N., \u0026amp; Ghanbar, H. (2024). Q methodology in applied linguistics: A systematic research synthesis. \u003cem\u003eSystem, 120\u003c/em\u003e, 103194. https://doi.org/10.1016/j.system.2023.103194\u003c/li\u003e\n\u003cli\u003ePan, M., Lai, C., \u0026amp; Guo, K. (2025). Effects of GenAI-empowered interactive support on university EFL students\u0026apos; self-regulated strategy use and engagement in reading. \u003cem\u003eThe Internet and Higher Education\u003c/em\u003e, 65, 100991. https://doi.org/10.1016/j.iheduc.2024.100991\u003c/li\u003e\n\u003cli\u003ePaul, J., Ueno, A., \u0026amp; Dennis, C. (2023). ChatGPT and consumers: Benefits, Pitfalls and Future Research Agenda. \u003cem\u003eInternational Journal of Consumer Studies, 47\u003c/em\u003e(4), 1213\u0026ndash;1225. https://doi.org/10.1111/ijcs.12928\u003c/li\u003e\n\u003cli\u003ePekrun, R. (2006). The control-value theory of achievement emotions: Assumptions, corollaries, and implications for educational research and practice. \u003cem\u003eEducational Psychology Review, 18\u003c/em\u003e(4), 315-341.\u003c/li\u003e\n\u003cli\u003ePekrun, R., \u0026amp; Perry, R. P. (2014). Control-value theory of achievement emotions. In R. Pekrun \u0026amp; L. Linnenbrink-Garcia (Eds.), \u003cem\u003eInternational handbook of emotions in education\u003c/em\u003e (pp. 120-141). Taylor \u0026amp; Francis.\u003c/li\u003e\n\u003cli\u003eRimm-Kaufman, S. E., Storm, M. D., Sawyer, B. E., Pianta, R. C., \u0026amp; LaParo, K. M. (2006). The Teacher Belief Q-Sort: A measure of teachers\u0026apos; priorities in relation to disciplinary practices, teaching practices, and beliefs about children. \u003cem\u003eJournal of School Psychology, 44\u003c/em\u003e(2), 141-165. https://doi.org/10.1016/j.jsp.2006.01.003\u003c/li\u003e\n\u003cli\u003eSeligman, M. E. P., \u0026amp; Csikszentmihalyi, M. (2000). Positive psychology: An introduction. \u003cem\u003eAmerican Psychologist\u003c/em\u003e, 55(1), 5-14.https://doi.org/10.1037/0003-066X.55.1.5\u003c/li\u003e\n\u003cli\u003eShen, J. (2021). A review of the effectiveness of foreign language enjoyment and foreign language classroom anxiety on learners\u0026apos; engagement and attainment. \u003cem\u003eFrontiers in Psychology\u003c/em\u003e, 12, 749284. https://doi.org/10.3389/fpsyg.2021.749284\u003c/li\u003e\n\u003cli\u003eThumvichit, A. (2024). Unfolding the subjectivity of foreign language enjoyment in online classes: A Q methodology study. \u003cem\u003eJournal of Multilingual and Multicultural Development, 45\u003c/em\u003e(7), 2427\u0026ndash;2444. https://doi.org/10.1080/01434632.2022.2050917\u003c/li\u003e\n\u003cli\u003eWang, Q., Gao, Y., \u0026amp; Wang, X. (2024). Exploring Engagement, Self-Efficacy, and Anxiety in Large Language Model EFL Learning: A Latent Profile Analysis of Chinese University Students. \u003cem\u003eInternational Journal of Human\u0026ndash;Computer Interaction\u003c/em\u003e, 1\u0026ndash;10. https://doi.org/10.1080/10447318.2024.2400403\u003c/li\u003e\n\u003cli\u003eWang, X., Gao, Y., Wang, Q., \u0026amp; Zhang, P. (2024). Fostering engagement in AI-assisted Chinese EFL classrooms: The role of classroom climate, AI literacy, and resilience. \u003cem\u003eEuropean Journal of Education.\u003c/em\u003e https://doi.org/10.1111/ejed.12874 \u003c/li\u003e\n\u003cli\u003eWang, X., \u0026amp; Reynolds, B. L. (2024). Beyond the Books: Exploring Factors Shaping Chinese English Learners\u0026apos; Engagement with Large Language Models for Vocabulary Learning. \u003cem\u003eEducation Sciences, 14\u003c/em\u003e(5), 496. https://doi.org/10.3390/educsci14050496\u003c/li\u003e\n\u003cli\u003eWatts, S., \u0026amp; Stenner, P. (2012). \u003cem\u003eDoing Q methodological research: Theory, method and interpretation\u003c/em\u003e. SAGE Publications.\u003c/li\u003e\n\u003cli\u003eWang, X., \u0026amp; Wang, S. (2024). Exploring Chinese EFL learners\u0026apos; engagement with large language models: A self-determination theory perspective. Learning and Motivation, 87, 102014. https://doi.org/10.1016/j.lmot.2024.102014\u003c/li\u003e\n\u003cli\u003eWei, H., Gao, K., \u0026amp; Wang, W. (2019). Understanding the relationship between grit and foreign language performance among middle school students: The roles of foreign language enjoyment and classroom environment. \u003cem\u003eFrontiers in Psychology, \u003c/em\u003e10, 1508. https://doi.org/10.3389/fpsyg.2019.01508\u003c/li\u003e\n\u003cli\u003eXiao, T., Yi, S., \u0026amp; Akhter, S. (2024). AI-Supported Online Language Learning: Learners\u0026apos; Self-Esteem, Cognitive-Emotion Regulation, Academic Enjoyment, and Language Success. \u003cem\u003eThe International Review of Research in Open and Distributed Learning, 25\u003c/em\u003e(3), 77\u0026ndash;96. https://doi.org/10.19173/irrodl.v25i3.7666\u003c/li\u003e\n\u003cli\u003eYu, Z., Yu, L., Xu, Q., Xu, W., \u0026amp; Wu, P. (2022). Effects of mobile learning technologies and social media tools on student engagement and learning outcomes of English learning. \u003cem\u003eTechnology, Pedagogy and Education, 31\u003c/em\u003e(3), 381-398. https://doi.org/10.1080/1475939X.2022.2045215\u003c/li\u003e\n\u003cli\u003eYuan, L., \u0026amp; Liu, X. (2025). The effect of artificial intelligence tools on EFL learners\u0026apos; engagement, enjoyment, and motivation. \u003cem\u003eComputers in Human Behavior, 162\u003c/em\u003e, 108474. https://doi.org/10.1016/j.chb.2024.108474\u003c/li\u003e\n\u003cli\u003eZhang, C., Meng, Y., \u0026amp; Ma, X. (2024). Artificial intelligence in EFL speaking: Impact on enjoyment, anxiety, and willingness to communicate. \u003cem\u003eSystem, 121\u003c/em\u003e, 103259. https://doi.org/10.1016/j.system.2024.103259\u003c/li\u003e\n\u003cli\u003eZhang, Y., \u0026amp; Liu, G. L. (2023). Examining the impacts of learner backgrounds, proficiency level, and the use of digital devices on informal digital learning of English: An explanatory mixed-method study. \u003cem\u003eComputer Assisted Language Learning\u003c/em\u003e, 1-28. https://doi.org/10.1080/09588221.2023.2267627\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"humanities-and-social-sciences-communications","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"palcomms","sideBox":"Learn more about [Humanities \u0026 Social Sciences Communications](http://www.nature.com/palcomms/)","snPcode":"41599","submissionUrl":"https://submission.springernature.com/new-submission/41599/3","title":"Humanities and Social Sciences Communications","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Nature AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Foreign language enjoyment, GenAI-assisted English learning, Q-methodology, Chinese EFL students","lastPublishedDoi":"10.21203/rs.3.rs-5824065/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5824065/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis study employs Q methodology to investigate Chinese EFL students' foreign language enjoyment in GenAI-assisted English learning environments. Through Q-sorting and follow-up written responses from university students, this research identified three distinct patterns: (1) \u003cem\u003eInstrumental Support Orientation\u003c/em\u003e, characterized by enjoyment derived from GenAI's immediate assistance and error correction features, but showing limited engagement with deeper learning processes; (2) \u003cem\u003eIndependent Learning Achievement\u003c/em\u003e, reflecting high satisfaction from autonomous goal attainment and learning efficiency while demonstrating resistance to peer interaction and collaborative learning; and (3) \u003cem\u003eLearning Feature Exploration\u003c/em\u003e, emphasizing enjoyment through experimenting with various GenAI functionalities but expressing significant skepticism about language learning outcomes and anxiety reduction. These findings extend our understanding of FLE in technology-enhanced language learning by revealing how learners experience enjoyment through different pathways of GenAI interaction.\u003c/p\u003e","manuscriptTitle":"Unlocking Foreign Language Enjoyment in GenAI-Assisted English Learning: A Q-Methodology Perspective from Chinese EFL Students","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-01-30 15:17:14","doi":"10.21203/rs.3.rs-5824065/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-06-02T16:01:26+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-05-02T10:46:52+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-04-28T02:11:57+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-04-26T10:05:16+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-04-09T05:41:55+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"84656877204273123667094112773973905128","date":"2025-04-05T14:36:15+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"253244967898154337735191372230294697442","date":"2025-04-05T10:19:45+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"201671978825782520661975116782648662559","date":"2025-04-04T03:10:17+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"58121242989097326539013344517879213026","date":"2025-04-03T14:16:03+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-04-03T13:24:10+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-02-16T13:50:14+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-02-03T04:09:54+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-01-28T13:13:01+00:00","index":"","fulltext":""},{"type":"submitted","content":"Humanities and Social Sciences Communications","date":"2025-01-14T04:48:23+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"humanities-and-social-sciences-communications","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"palcomms","sideBox":"Learn more about [Humanities \u0026 Social Sciences Communications](http://www.nature.com/palcomms/)","snPcode":"41599","submissionUrl":"https://submission.springernature.com/new-submission/41599/3","title":"Humanities and Social Sciences Communications","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Nature AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"78e8987e-f9c4-4437-b37f-43f965e03523","owner":[],"postedDate":"January 30th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[{"id":43522575,"name":"Social science/Education"},{"id":43522576,"name":"Social science/Language and linguistics"},{"id":43522577,"name":"Social science/Psychology"}],"tags":[],"updatedAt":"2026-02-20T02:38:14+00:00","versionOfRecord":[],"versionCreatedAt":"2025-01-30 15:17:14","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5824065","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5824065","identity":"rs-5824065","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}