Reinders, H., Chong, S-W. & Liu, Q. (2025). Conceptualisations of and research on language teacher leadership: A scoping review. TESOL Journal, 16.
Rationale of the Study
In recent years, there has been growing interest in the concept of language teacher leadership (LTL)—the idea that teachers play influential roles not only within their classrooms but also within broader educational communities. Unlike traditional views that place leadership solely in the hands of administrators, this emerging perspective sees leadership as something that can be distributed among educators. While teacher leadership is widely discussed in general education, its conceptualization and practice in language education remain underdeveloped and under-researched. This scoping review addresses a crucial gap by exploring how LTL has been defined and investigated in the field of TESOL (Teaching English to Speakers of Other Languages), with a particular focus on its pedagogical and developmental dimensions.
How the Study Was Conducted
The authors conducted a scoping review, a type of qualitative research synthesis ideal for exploring emerging and under-defined areas of study. Using a TESOL-specific framework for scoping reviews, the researchers systematically searched databases for English-language publications since 2000 that explicitly discussed LTL or related terms like "language teacher administration" or "leadership in ELT." After applying clear inclusion criteria—such as the need for explicit definitions and relevance to TESOL—eight sources were selected, including journal articles, conceptual books, and a doctoral dissertation. Data were extracted using a standardized form and analyzed using reflexive thematic analysis to identify patterns across definitions, research methods, findings, and implications.
What the Main Findings Were
The review revealed that LTL is commonly conceptualized in terms of actions, roles, and personal qualities. Teachers demonstrate leadership through both classroom practices—such as fostering rapport and effective teaching—and external activities like mentoring colleagues, facilitating professional development, and engaging in curriculum planning. LTL roles can be formal (e.g., department chairs) or informal (e.g., peer mentors), and are often characterized by attributes such as enthusiasm, empathy, commitment, and pedagogical expertise.
Most of the studies emphasized the pedagogical dimension of leadership, often from learners’ perspectives, highlighting how good teaching and strong teacher-student relationships contribute to leadership recognition. The impacts of LTL were classified as short-term (improved teaching and learning), mid-term (enhanced collaboration and community), and long-term (empowerment and sustained professional development). However, challenges included limited leadership training, shifting expectations, institutional constraints, and teacher stress.
What the Implications Are
The study underscores the need for more comprehensive support for LTL in teacher education programs, not just through formal leadership training, but by recognizing and developing teachers’ pedagogical leadership within their everyday practice. The findings suggest that fostering leadership need not mean moving teachers into administrative roles—it can and should occur through their existing pedagogical work. Importantly, the review highlights the lack of empirical evidence linking LTL to student outcomes and calls for more robust research to explore these connections. Finally, the authors advocate for a school culture that supports shared leadership, collaboration, and recognition of teacher contributions, positioning LTL as essential for sustainable educational development in language teaching contexts.
Lee, B., Reinders, H. & Bonner, E. (2024). Monitoring engagement in the foreign language classroom: Learners’ perspectives. Languages, 9(2), 53-68.
Rationale of the Study
Student engagement is essential for successful learning, especially in foreign language classrooms where motivation often fluctuates. Previous research shows that engagement is dynamic and affected by factors like task difficulty, teaching style, and classroom interaction. Despite its importance, tools to monitor engagement in real-time, without disrupting lessons, are limited. Most studies rely on retrospective surveys or lab settings, which fail to capture how students feel and behave moment-to-moment. To address this gap, the researchers developed Classmoto, a mobile web app that allows teachers to collect real-time data on cognitive, behavioral, and emotional engagement. This study examines students’ experiences using the app in an actual university classroom in Japan, focusing on its perceived usefulness, clarity, and potential impact on learning.
How the Study Was Conducted
The study took place over a semester in a B2-level English listening course at a Japanese university. Fifteen students used Classmoto weekly to report their engagement during different parts of each lesson (e.g., vocabulary drills, grammar practice, listening tasks, and free conversation). The app used three simple Likert-scale prompts—each measuring one type of engagement—and responses were color-coded for the teacher to review instantly. After the data collection phase, the researcher conducted 15-minute, one-on-one interviews with each student to explore their understanding of the prompts, ease of use, and feelings about the app. Interviews were conducted in English, Japanese, or a mix of both, and the data was analyzed both quantitatively and qualitatively.
What the Main Findings Were
The majority of students clearly understood the engagement prompts and used the app as intended, with 87% correctly identifying what each question measured. Emotional engagement was the easiest to interpret, while some students confused cognitive effort with lesson difficulty. Nevertheless, all students completed their responses in under 30 seconds, and none reported any disruption to their learning. In fact, the app was seen as a valuable tool for self-reflection, helping students better understand their strengths and weaknesses during specific lesson segments. Furthermore, students appreciated that their feedback led to real-time changes in instruction—for example, more speaking activities were added when students indicated low engagement in grammar or listening tasks. Notably, not a single participant reported any negative feedback about the tool or the process.
What the Implications Are
This study provides strong support for using real-time engagement tracking tools like Classmoto in authentic classroom settings. Not only is the method minimally intrusive, but it also empowers students to reflect on their learning and gives teachers actionable insights to adapt instruction on the fly. By validating Classmoto's design and confirming its benefits from the learner’s perspective, the study contributes a practical solution to a long-standing challenge in language education. Future research could explore how different proficiency levels or educational settings affect the tool’s effectiveness and whether similar gains in student agency and learning outcomes are observed.
Klipp, J. & Reinders, H. (2025). The effects of different types of computer-assisted corrective feedback on L2 pragmatics learning. Digital Applied Linguistics, 1(3), 1-24.
https://doi.org/10.29140/dal
.v3.102515.
Rationale of the Study
Feedback is essential in second language (L2) learning, but its role in teaching pragmatics—how language is used appropriately in social contexts—remains unclear. Traditional feedback methods may not effectively help learners understand subtle pragmatic cues like politeness or tone. Furthermore, the emotional (affective) dimension of feedback is underexplored, despite emerging evidence that learners’ emotions can influence how they process and remember language input. This study investigated whether computer-assisted feedback, especially when it includes emotional responses from a virtual teacher (a “pedagogical agent”), can improve learners’ ability to use language appropriately in real-world situations.
How the Study Was Conducted
The researchers conducted a quasi-experimental study with 82 Japanese high school learners of English. Participants were randomly assigned to one of three groups, each receiving a different type of feedback via interactive PowerPoint-based simulations:
1. Affective feedback from a video-based teacher expressing emotions;
2. High-information feedback via detailed written explanations;
3. Combined feedback using both video and text.
All learners completed pre- and post-tests to assess changes in their ability to make appropriate requests, apologies, and refusals in English.
What the Main Findings Were
Only the group that received affective feedback alone showed a statistically significant improvement in pragmatic performance. Surprisingly, the group that received both affective and high-information feedback performed the worst. The authors suggest that when feedback is too complex (e.g., combining video, text, and emotion), it may overwhelm learners’ cognitive resources, making it harder to learn effectively. Learners reported that the video teacher’s tone, facial expressions, and emotional reactions helped them understand social language use better.
What the Implications Are
This study challenges the traditional view that emotion-based feedback is distracting. On the contrary, when thoughtfully designed and contextualized, emotional feedback can enhance learning, especially in digital environments. The findings support incorporating affective elements (like human-like virtual agents) into L2 teaching tools, particularly for teaching pragmatics. However, designers should avoid redundancy that could overload learners’ attention. The study opens new pathways for developing emotionally intelligent educational technology that fosters deeper engagement and learning in language education.
Liu, M. & Reinders, H. (2024). Do AI chatbots impact motivation? Insights from a longitudinal study. System, 128.
Rationale of the Study
Artificial Intelligence (AI) is rapidly transforming education, but empirical evidence about its long-term impact, especially on learner motivation, remains scarce. Motivation is critical for successful language learning, and self-regulated learning (SRL) — where learners plan, monitor, and reflect on their own learning — plays a key role in fostering it. While chatbots have been used to support language learning, most studies have been short-term, focused on language practice, and relied on limited pre-scripted interactions. Moreover, traditional chatbots often fail to engage learners due to their technical limitations. This study addresses an important gap: Can more advanced, generative AI-powered chatbots (like ChatGPT) better support learner motivation over time compared to pre-scripted bots?
How the Study Was Conducted
The researchers conducted a 15-week longitudinal study with 24 first-year English major students at a university in China.
• Phase 1 (Weeks 1–8): Students interacted weekly with a pre-scripted chatbot designed to promote SRL by asking fixed reflection questions.
• Phase 2 (Weeks 9–15): The chatbot was replaced by a generative AI-powered chatbot that engaged students in dynamic, personalized conversations about planning, monitoring, and reflecting on their learning.
• Data collection: Motivation was self-reported weekly through a simple Likert-scale question after each chatbot interaction.
• Analysis: Piecewise mixed effects models were used to track motivational changes across the two phases.
What the Main Findings Were
• During the pre-scripted phase, students' motivation remained largely stable with no significant growth.
• After switching to the generative AI chatbot, there was a significant positive increase in motivation over time.
• Students who initially had lower motivation tended to show greater gains, suggesting that generative AI had a more transformative effect for some learners.
• Overall, generative AI offered higher interactivity, personalization, and autonomy support compared to the traditional chatbot, which likely contributed to the motivational boost.
What the Implications Are
This study provides early evidence that generative AI chatbots can enhance learner motivation more effectively than pre-scripted ones, particularly by supporting key psychological needs like autonomy and competence. However, individual differences played a major role in how learners responded to AI interaction, highlighting both the opportunities and challenges of personalization in AI-assisted learning.
The findings suggest that educators should explore the integration of generative AI to foster motivation, but also need to be mindful of tailoring AI experiences to individual learner profiles. More research with larger and more diverse samples is needed to further understand and optimize AI's role in language education.
Reinders, H., Chong, S-W. & Liu, Q. (2025). Conceptualisations of and research on language teacher leadership: A scoping review. TESOL Journal, 16.
Rationale of the Study
In recent years, there has been growing interest in the concept of language teacher leadership (LTL)—the idea that teachers play influential roles not only within their classrooms but also within broader educational communities. Unlike traditional views that place leadership solely in the hands of administrators, this emerging perspective sees leadership as something that can be distributed among educators. While teacher leadership is widely discussed in general education, its conceptualization and practice in language education remain underdeveloped and under-researched. This scoping review addresses a crucial gap by exploring how LTL has been defined and investigated in the field of TESOL (Teaching English to Speakers of Other Languages), with a particular focus on its pedagogical and developmental dimensions.
How the Study Was Conducted
The authors conducted a scoping review, a type of qualitative research synthesis ideal for exploring emerging and under-defined areas of study. Using a TESOL-specific framework for scoping reviews, the researchers systematically searched databases for English-language publications since 2000 that explicitly discussed LTL or related terms like "language teacher administration" or "leadership in ELT." After applying clear inclusion criteria—such as the need for explicit definitions and relevance to TESOL—eight sources were selected, including journal articles, conceptual books, and a doctoral dissertation. Data were extracted using a standardized form and analyzed using reflexive thematic analysis to identify patterns across definitions, research methods, findings, and implications.
What the Main Findings Were
The review revealed that LTL is commonly conceptualized in terms of actions, roles, and personal qualities. Teachers demonstrate leadership through both classroom practices—such as fostering rapport and effective teaching—and external activities like mentoring colleagues, facilitating professional development, and engaging in curriculum planning. LTL roles can be formal (e.g., department chairs) or informal (e.g., peer mentors), and are often characterized by attributes such as enthusiasm, empathy, commitment, and pedagogical expertise.
Most of the studies emphasized the pedagogical dimension of leadership, often from learners’ perspectives, highlighting how good teaching and strong teacher-student relationships contribute to leadership recognition. The impacts of LTL were classified as short-term (improved teaching and learning), mid-term (enhanced collaboration and community), and long-term (empowerment and sustained professional development). However, challenges included limited leadership training, shifting expectations, institutional constraints, and teacher stress.
What the Implications Are
The study underscores the need for more comprehensive support for LTL in teacher education programs, not just through formal leadership training, but by recognizing and developing teachers’ pedagogical leadership within their everyday practice. The findings suggest that fostering leadership need not mean moving teachers into administrative roles—it can and should occur through their existing pedagogical work. Importantly, the review highlights the lack of empirical evidence linking LTL to student outcomes and calls for more robust research to explore these connections. Finally, the authors advocate for a school culture that supports shared leadership, collaboration, and recognition of teacher contributions, positioning LTL as essential for sustainable educational development in language teaching contexts.
Lee, B., Reinders, H. & Bonner, E. (2024). Monitoring engagement in the foreign language classroom: Learners’ perspectives. Languages, 9(2), 53-68.
Rationale of the Study
Student engagement is essential for successful learning, especially in foreign language classrooms where motivation often fluctuates. Previous research shows that engagement is dynamic and affected by factors like task difficulty, teaching style, and classroom interaction. Despite its importance, tools to monitor engagement in real-time, without disrupting lessons, are limited. Most studies rely on retrospective surveys or lab settings, which fail to capture how students feel and behave moment-to-moment. To address this gap, the researchers developed Classmoto, a mobile web app that allows teachers to collect real-time data on cognitive, behavioral, and emotional engagement. This study examines students’ experiences using the app in an actual university classroom in Japan, focusing on its perceived usefulness, clarity, and potential impact on learning.
How the Study Was Conducted
The study took place over a semester in a B2-level English listening course at a Japanese university. Fifteen students used Classmoto weekly to report their engagement during different parts of each lesson (e.g., vocabulary drills, grammar practice, listening tasks, and free conversation). The app used three simple Likert-scale prompts—each measuring one type of engagement—and responses were color-coded for the teacher to review instantly. After the data collection phase, the researcher conducted 15-minute, one-on-one interviews with each student to explore their understanding of the prompts, ease of use, and feelings about the app. Interviews were conducted in English, Japanese, or a mix of both, and the data was analyzed both quantitatively and qualitatively.
What the Main Findings Were
The majority of students clearly understood the engagement prompts and used the app as intended, with 87% correctly identifying what each question measured. Emotional engagement was the easiest to interpret, while some students confused cognitive effort with lesson difficulty. Nevertheless, all students completed their responses in under 30 seconds, and none reported any disruption to their learning. In fact, the app was seen as a valuable tool for self-reflection, helping students better understand their strengths and weaknesses during specific lesson segments. Furthermore, students appreciated that their feedback led to real-time changes in instruction—for example, more speaking activities were added when students indicated low engagement in grammar or listening tasks. Notably, not a single participant reported any negative feedback about the tool or the process.
What the Implications Are
This study provides strong support for using real-time engagement tracking tools like Classmoto in authentic classroom settings. Not only is the method minimally intrusive, but it also empowers students to reflect on their learning and gives teachers actionable insights to adapt instruction on the fly. By validating Classmoto's design and confirming its benefits from the learner’s perspective, the study contributes a practical solution to a long-standing challenge in language education. Future research could explore how different proficiency levels or educational settings affect the tool’s effectiveness and whether similar gains in student agency and learning outcomes are observed.
Klipp, J. & Reinders, H. (2025). The effects of different types of computer-assisted corrective feedback on L2 pragmatics learning. Digital Applied Linguistics, 1(3), 1-24. https://doi.org/10.29140/dal.v3.102515.
Rationale of the Study
Feedback is essential in second language (L2) learning, but its role in teaching pragmatics—how language is used appropriately in social contexts—remains unclear. Traditional feedback methods may not effectively help learners understand subtle pragmatic cues like politeness or tone. Furthermore, the emotional (affective) dimension of feedback is underexplored, despite emerging evidence that learners’ emotions can influence how they process and remember language input. This study investigated whether computer-assisted feedback, especially when it includes emotional responses from a virtual teacher (a “pedagogical agent”), can improve learners’ ability to use language appropriately in real-world situations.
How the Study Was Conducted
The researchers conducted a quasi-experimental study with 82 Japanese high school learners of English. Participants were randomly assigned to one of three groups, each receiving a different type of feedback via interactive PowerPoint-based simulations:
1. Affective feedback from a video-based teacher expressing emotions;
2. High-information feedback via detailed written explanations;
3. Combined feedback using both video and text.
All learners completed pre- and post-tests to assess changes in their ability to make appropriate requests, apologies, and refusals in English.
What the Main Findings Were
Only the group that received affective feedback alone showed a statistically significant improvement in pragmatic performance. Surprisingly, the group that received both affective and high-information feedback performed the worst. The authors suggest that when feedback is too complex (e.g., combining video, text, and emotion), it may overwhelm learners’ cognitive resources, making it harder to learn effectively. Learners reported that the video teacher’s tone, facial expressions, and emotional reactions helped them understand social language use better.
What the Implications Are
This study challenges the traditional view that emotion-based feedback is distracting. On the contrary, when thoughtfully designed and contextualized, emotional feedback can enhance learning, especially in digital environments. The findings support incorporating affective elements (like human-like virtual agents) into L2 teaching tools, particularly for teaching pragmatics. However, designers should avoid redundancy that could overload learners’ attention. The study opens new pathways for developing emotionally intelligent educational technology that fosters deeper engagement and learning in language education.
Liu, M. & Reinders, H. (2024). Do AI chatbots impact motivation? Insights from a longitudinal study. System, 128.
Rationale of the Study
Artificial Intelligence (AI) is rapidly transforming education, but empirical evidence about its long-term impact, especially on learner motivation, remains scarce. Motivation is critical for successful language learning, and self-regulated learning (SRL) — where learners plan, monitor, and reflect on their own learning — plays a key role in fostering it. While chatbots have been used to support language learning, most studies have been short-term, focused on language practice, and relied on limited pre-scripted interactions. Moreover, traditional chatbots often fail to engage learners due to their technical limitations. This study addresses an important gap: Can more advanced, generative AI-powered chatbots (like ChatGPT) better support learner motivation over time compared to pre-scripted bots?
How the Study Was Conducted
The researchers conducted a 15-week longitudinal study with 24 first-year English major students at a university in China.
• Phase 1 (Weeks 1–8): Students interacted weekly with a pre-scripted chatbot designed to promote SRL by asking fixed reflection questions.
• Phase 2 (Weeks 9–15): The chatbot was replaced by a generative AI-powered chatbot that engaged students in dynamic, personalized conversations about planning, monitoring, and reflecting on their learning.
• Data collection: Motivation was self-reported weekly through a simple Likert-scale question after each chatbot interaction.
• Analysis: Piecewise mixed effects models were used to track motivational changes across the two phases.
What the Main Findings Were
• During the pre-scripted phase, students' motivation remained largely stable with no significant growth.
• After switching to the generative AI chatbot, there was a significant positive increase in motivation over time.
• Students who initially had lower motivation tended to show greater gains, suggesting that generative AI had a more transformative effect for some learners.
• Overall, generative AI offered higher interactivity, personalization, and autonomy support compared to the traditional chatbot, which likely contributed to the motivational boost.
What the Implications Are
This study provides early evidence that generative AI chatbots can enhance learner motivation more effectively than pre-scripted ones, particularly by supporting key psychological needs like autonomy and competence. However, individual differences played a major role in how learners responded to AI interaction, highlighting both the opportunities and challenges of personalization in AI-assisted learning.
The findings suggest that educators should explore the integration of generative AI to foster motivation, but also need to be mindful of tailoring AI experiences to individual learner profiles. More research with larger and more diverse samples is needed to further understand and optimize AI's role in language education.