Volume 30 Number 2, Special Issue: Emotional CALL

Permanent URI for this collectionhttps://hdl.handle.net/10125/113395

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Now showing 1 - 11 of 11

Vocabulary grit, task emotions, and behavioral engagement during after-class app-assisted vocabulary learning among Chinese university EFL learners
(University of Hawaii National Foreign Language Resource Center, 2026-06-01) Li, Banban; Teimouri, Yasser; Mao, Shenglan; Wang, Na; Kruk, Mariusz; Pawlak, Mirosław
Drawing on the Control-Value Theory (CVT), this study investigates the impact of vocabulary grit on behavioral engagement in AVL, with task enjoyment and boredom serving as mediators. A mixed-methods approach was employed, combining survey data from 102 Chinese university EFL learners with follow-up interviews from 10 participants. In addition, a domain-specific L2 vocabulary grit scale was developed for use in the study. Structural equation modeling (SEM) results indicate that vocabulary perseverance of effort (PE) strongly predicts behavioral engagement both directly and indirectly via task enjoyment. Vocabulary consistency of interest (CI) influences behavioral engagement primarily through its effect on task enjoyment. While task boredom negatively correlates with behavioral engagement, it does not significantly mediate the vocabulary grit-behavioral engagement relationship, suggesting the presence of coping mechanisms or external motivators. Qualitative findings further illustrate that students’ behavioral engagement is driven by a combination of perseverance of effort, interest, positive emotional reinforcement, and external motivational factors such as exams. These findings highlight the complex interplay between L2 grit, task emotions, and behavioral engagement in AVL. The study offers theoretical and pedagogical insights into fostering sustained engagement in AVL by enhancing perseverance, cultivating interest, and promoting positive emotions.
Pride and shame in CALL: Links to appraisals, engagement, and performance
(University of Hawaii National Foreign Language Resource Center, 2026-06-01) Shao, Kaiqi; Amiri, Elmakki; Kutuk, Gulsah; Kruk, Mariusz; Pawlak, Mirosław
Guided by the control-value theory of achievement emotions, this study examines the relationships among two understudied foreign language emotions, namely pride and shame, control-value appraisals, engagement, and performance in a Computer-Assisted Language Learning (CALL) setting. A total of 652 Chinese university students from a massive open online course (MOOC) participated in the study. Structural equation modeling (SEM) results showed that control and value appraisals positively predicted pride but negatively predicted shame. Pride positively predicted each of the three dimensions of engagement (i.e., cognitive, emotional, and behavioral) while shame negatively predicted these dimensions, except for cognitive engagement. Emotional and behavioral engagement, but not cognitive engagement, positively predicted performance. Pride and shame mediated the relationship between control-value appraisals and emotional and behavioral engagement, which, in turn, mediated the relationship between pride or shame and performance. By contrast, pathways through cognitive engagement were not significantly linked to performance. Overall, pride and shame, along with emotional and behavioral engagement rather than cognitive engagement serially mediated the relationship between control-value appraisals and performance. We discuss the implications for language teachers and highlight the importance of addressing pride and shame, alongside their appraisal antecedents and learning outcomes in CALL.
The developmental trajectory of L2 students’ positive achievement emotions and flow experience within AI-enhanced classrooms: A latent growth curve modeling (LGCM)
(University of Hawaii National Foreign Language Resource Center, 2026-06-01) Qin, Lili; Derakhshan, Ali; Kruk, Mariusz; Pawlak, Mirosław
The integration of artificial intelligence (AI) in L2 classrooms has garnered remarkable attention due to its potential to enhance students’ language achievements. While existing research has highlighted the implications of incorporating AI into L2 classrooms, there remains a gap in understanding how this incorporation may affect students’ achievement emotions and flow experiences. To narrow this gap, this intervention study sought to assess the influence of AI-enhanced instruction on L2 students’ positive achievement emotions: pride, hope, enjoyment, and their flow experiences. Furthermore, with the aid of latent growth curve modeling (LGCM), the study tried to track the developmental trajectory of L2 students’ positive achievement emotions and flow experiences over the course of a semester. To these aims, a large sample of 217 L2 students was recruited and randomly divided into the control or experimental groups. To measure participants’ flow and positive achievement emotions, two questionnaires were administered to them at distinct intervals throughout the intervention. The results evinced a notable enhancement in both the flow experience and positive achievement emotions of participants who were exposed to AI-enhanced instruction. This research underscores the critical role of AI-enhanced instruction in fostering students’ positive achievement emotions and flow experiences within L2 classrooms.
Incorporating digital multimodal composing into game-enhanced language learning
(University of Hawaii National Foreign Language Resource Center, 2026-06-01) Li, Ke; Peterson, Mark; Fan, Qin; Kruk, Mariusz; Pawlak, Mirosław
As part of a research project on the use of digital games for language learning, this study explored the emotional trajectory and experiences of EFL learners during game-enhanced digital multimodal composing (DMC), with a focus on enjoyment and boredom. Informed by the control-value theory, this exploratory multiple case study centered on two groups of learners who volunteered to participate in this out-of-school project in which they played the digital game Genshin Impact and completed DMC over the course of six weeks. Both quantitative and qualitative data were collected and analyzed, including emotion questionnaires, participants’ gaming journals, DMC productions, semi-structured interviews, and critical incident forms. The results showed that despite fluctuations, the high achievers in DMC demonstrated high levels of enjoyment and moderate levels of boredom. Low achievers experienced relatively high levels of boredom and moderate levels of enjoyment during DMC tasks. Analysis further revealed the control and value ascribed to the tasks as potential causes for these differences and emotional fluctuations. The findings also highlight the role identity played in the control-value appraisal of the learning activity. The pedagogical implications of the study are discussed and suggestions for future research are provided.
Dynamicity of EFL learners’ emotions and WTC in Human-AI interactions
(University of Hawaii National Foreign Language Resource Center, 2026-06-01) Cheng, Qian; Chen, Xiuwen; Luo, Yiyang; Wang, Hao; Kruk, Mariusz; Pawlak, Mirosław
This study investigates dynamic fluctuations in willingness to communicate (WTC) and emotional states among six Chinese English as a Foreign Language (EFL) learners during interactions with Replika, an AI-powered chatbot. Using an idiodynamic method and stimulated recall interviews, the study provides a granular understanding of WTC and emotional dynamics. Participants exhibited a range of emotional states, including epistemic and retrospective emotions, which were intermittently elicited by moment-to-moment interactional events. Fluctuations were induced by various factors, including anthropomorphism, conversational responsiveness, perceived naturalness of interaction, and learners’ perceptions of Replika as either a conversational companion or a tool for language practice. Finally, this study proposes expectation appraisal as a key mediating mechanism between interactional factors, emotional states, and WTC. Learners’ expectations constituted a significant factor influencing emotional outcomes and ongoing WTC. These findings are discussed with reference to the L2 WTC pyramid model, emphasizing the interplay between emotions and communicative behaviors in AI-mediated language-learning environments.
Students’ emotions and cognitive loads in chatbot-versus peer-supported reading
(University of Hawaii National Foreign Language Resource Center, 2026-06-01) Guan, Xiaotian; Su, Yanfang; Jin, Tan; Lai, Chun; Li, Yuanke; Kruk, Mariusz; Pawlak, Mirosław
Using a within-subject design, this study engaged 60 Chinese EFL university students in reading English academic passages either with GenAI chatbot support or with peer support. Employing questionnaires, we compared the levels of positive and negative emotions and intrinsic and extraneous cognitive loads reported by students in these two reading conditions. Additionally, semi-structured interviews were conducted with 16 students to delve into the factors affecting their emotions and cognitive loads in the two interactive reading conditions. The results of questionnaires show that students experienced a significantly higher level of positive activating and deactivating emotions such as hope and relief and a lower level of negative activating emotion such as anxiety, as well as significantly lower levels of intrinsic and extraneous cognitive load in chatbot-supported reading. The interviews further reveal that students attributed the observed advantages of reading with chatbot to its immediate support, high efficiency, convenience of tracking the discussion record, and the low-stress learning environment. In the meantime, they reported disadvantages in emotional communication and social dynamics when communicating with AI chatbot. Overall, this study has elucidated the mechanisms underlying differential effects of reading with GenAI chatbot support versus peer support on students’ emotions and cognitive loads.
Modelling students’ emotional engagement in AI-augmented English reading: Mediation of AI learning interest and reading enjoyment
(University of Hawaii National Foreign Language Resource Center, 2026-06-01) Liu, Honggang; Fan, Jiqun; Kruk, Mariusz; Pawlak, Mirosław
As an important development in CALL, artificial intelligence (AI)-assisted foreign language teaching not only offers unique advantages in leveled reading, vocabulary, pronunciation, self-assessment, personalized testing, and information retrieval but also effectively enhances learner interaction and emotional regulation in reading. Despite these advancements, research on learner emotional engagement in AI-augmented EFL reading instruction remains limited, and there is a lack of sufficient empirical understanding of the significance of learner interaction. This study constructs a predictive model for emotional engagement among EFL learners in AI-augmented educational contexts, aiming to analyze the impact of learners’ AI literacy and peer interaction on their emotional engagement in reading. The research was conducted among 650 EFL university students in central China. Findings indicate that in AI-augmented EFL learning scenarios, learner AI literacy and peer learning interactions positively influence emotional engagement in reading. Furthermore, learner interest in AI use and reading pleasure play partial mediating and serial mediating roles in this relationship, respectively. This study not only highlights the critical role of AI literacy in AI-assisted EFL reading but also clarifies the significance of interpersonal communication for emotional engagement in AI contexts.
Task-specific emotions and EFL learners’ technology acceptance beliefs in AI-mediated English speaking
(University of Hawaii National Foreign Language Resource Center, 2026-06-01) Elahi Shirvan, Majid; Taherian, Tahereh; Kruk, Mariusz; Pawlak, Mirosław
This study investigates how English as a Foreign Language (EFL) learners’ beliefs about technology acceptance predict their task-specific enjoyment and boredom during an AI-mediated speaking practice course. Specifically, it examines how perceptions of ease of use and usefulness influence both general and specific aspects of enjoyment (including task characteristics, personal enjoyment, and social interactions) and boredom (including task characteristics, personal boredom, and social interactions) in language learning. Data were collected from 141 Iranian EFL learners participating in an online speaking class. To enhance the precision and accuracy of the analyses, preliminary procedures identified the optimal measurement structure for enjoyment and boredom, leading to the adoption of a bifactor exploratory structural equation modeling (bifactor-ESEM) representation. The study then used a structural model to validate the relationships among learners’ perceptions and their emotional experiences. The findings highlight the predictive role of technology acceptance beliefs in shaping both general and specific facets of enjoyment and boredom, with global task-specific enjoyment negatively associated with global task-specific boredom. These results may inform the design of emotionally supportive Intelligent Computer-Assisted Language Learning (ICALL) activities tailored to the tasks’ unique demands and provide deeper insights into learners’ experiences and emotional well-being.
Exploring the relationship between L2 belongingness, personal L2 enjoyment, and willingness to communicate across language learning contexts
(University of Hawaii National Foreign Language Resource Center, 2026-06-01) Solhi, Mehdi; Zadorozhnyy, Artem; Lee, Ju Seong; Kruk, Mariusz; Pawlak, Mirosław
While previous studies have highlighted the significant roles of both social and emotional factors in shaping learners' behavioral intention, relatively few have examined how these factors interact across diverse second or foreign language (L2) learning environments. To fill in the existing gap, this study examined the relationship between L2 belongingness, personal L2 enjoyment, and willingness to communicate (WTC) in both in-person and online classes. Data from 293 first-year Turkish English as a foreign language (EFL) university students were analyzed using hierarchical regression analysis. The results indicated that students with higher academic and social belongingness reported higher WTC in both settings. Additionally, L2 enjoyment moderated this relationship. The findings demonstrate how external factors—teacher and peer support, coupled with an internal emotional state—personal enjoyment—drives EFL learners’ communication willingness in different L2 learning contexts. The findings highlight the importance of creating a supportive and inclusive learning environment where educators foster connections among students and between students and teachers in face-to-face and online language classes. Incorporating enjoyable and engaging L2 activities is also recommended to boost students' communication willingness in different L2 learning contexts.
Profiling learners’ affective engagement: Emotion AI, intercultural pragmatics, and language learning
(University of Hawaii National Foreign Language Resource Center, 2026-06-01) Godwin-Jones, Robert; Godwin-Jones, Robert
Learning another language can be a highly emotional process, typically characterized by numerous frustrations and triumphs, big and small. For most learners, language learning does not follow a linear, predictable path, its zigzag course shaped by motivational (or demotivating) variables such as personal characteristics, teacher/peer relationships, learning materials, and dreams of a future L2 (second language) self. While some aspects of language learning (reading, grammar) are relatively mechanical, others can be stressful and unpredictable, especially conversing in the target language. That experience necessitates not only knowledge of structure and lexis, but also the ability to use the language in ways that are appropriate to the social and cultural context. A new opportunity to practice conversational abilities has arrived through the availability of AI chatbots, with both advantages (responsive, non-judgmental) and drawbacks (emotionally void, culturally biased). This column explores aspects of emotion as they arise in technology use and in particular how automatic emotion recognition and simulated human responsiveness in AI systems interface with language learning and the development of pragmatic and interactional competence. Emotion AI—the algorithmically driven interpretation of users’ affective signals—has been seen as enabling greater personalized learning, adapting to perceived learner cognitive and emotional states. Others warn of emotional manipulation and inappropriate and ineffective user profiling.
Emotional CALL: Reframing technology-mediated language learning
(University of Hawaii National Foreign Language Resource Center, 2026-06-01) Kruk, Mariusz; Pawlak, Mirosław; Kruk, Mariusz; Pawlak, Mirosław
Research on emotions has become one of the most vibrant areas of inquiry in second language acquisition, yet its integration into computer-assisted language learning (CALL) has remained uneven. While anxiety has received sustained attention in CALL research, other emotions, both positive (such as enjoyment or hope) and negative emotions (such as boredom or shame), along with affectively relevant constructs such as flow, belongingness, and emotional engagement, have only recently begun to receive the systematic attention they deserve. Emotional CALL represents an emerging area of inquiry that places emotions at the center of how technology-mediated language learning is theorized, investigated, designed, and evaluated. Emotional CALL does not refer to a single technology, method, or chronological stage. Rather, it signals a contemporary affective turn in CALL in which emotions are understood as dynamic, socially situated, task-sensitive, technologically mediated, and pedagogically consequential processes. AI-mediated language learning gives this agenda particular urgency but does not exhaust its scope. Developments in L2 emotion research, individual differences in CALL, and recent empirical work in technology-mediated language learning provide the basis for outlining the conceptual foundations of Emotional CALL and proposing a working framework for future inquiry. The central argument is that technology-mediated language learning cannot be fully understood without attention to how learners and teachers emotionally experience digital environments. Emotional CALL therefore examines how emotions interact with appraisals, engagement, willingness to communicate, cognitive load, identity, and performance, and how pedagogical design can support emotionally responsive CALL.

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