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- PinnedNewsEdSurge15 Jul 2026
How My School Used Common Sense and Collaboration to Confront AI
This news article describes how an Indigenous school's instructional technology coordinator helped teachers confront the challenge of AI-generated student writing through collaboration and common sense. They addressed the phenomenon of 'unproductive success' where students use AI to complete assignments without learning. The piece highlights the need for pedagogical shifts and teacher training in response to AI tools.
Original abstract
<p>We’ve all had some version of the nightmare: You’ve been inexplicably thrown back into high school, and now you’re standing in front of the class. You’re about to give a speech, but you can’t remember a word. The panic is exacerbated by the faces of students staring at you, some hiding snickers and others openly laughing. Everyone knows what’s going on but you.</p><p>I was reminded of this bad dream over the past few years. As my Indigenous school’s instructional technology coordinator, I have seen my office transform from a routine integration hub into a digital confessional. Colleagues and teachers who are trying to navigate destabilizing technology changes slip inside and close the door. Some are looking for practical technology triage, or just a safe space to vent their frustrations, only to recount a similar nightmare. </p><p>For these educators, the experience triggers a sharp wave of panic, a sudden realization that the pedagogical ground has shifted beneath their feet and they can no longer trust their own instincts or traditional technology guardrails. But what follows the panic is a deep, stinging embarrassment. </p><p>With open and hopeful hearts, these teachers publicly praised student writing, extolling the hard work and growth they believed the student had demonstrated, only to find themselves surrounded by the giggles of classmates who already knew the truth: the writing had been fully generated by an AI tool.</p><p>The students had technically succeeded while learning very little, a phenomenon Micah Miner describes as <em>unproductive success</em> in “<a href="https://minerclass.github.io/dissertationquestionsbeta/dissertation-sites/"><u>Beyond Secondary Orality</u></a>.” As a result, teachers and administrators are left trying to respond to tools and behaviors they don’t yet fully understand and have never been trained to address. </p><p>These individual instances of unproductive success were not isolated classroom frustrations; they were early s
- PaperSystemForthcoming · 1 Aug 2026
Spatial machine heuristic: Effects of agent type and interaction environment on foreign language speaking anxiety and willingness to communicate
Shunan Zhang, Yincen Li, Stevany Stevany, Joo-Wha Hong
Investigates how agent type and interaction environment influence foreign language speaking anxiety and willingness to communicate through the spatial machine heuristic.
- PaperSystemForthcoming · 1 Aug 2026
The effects of collaborative planning on CALF metrics and textual coherence in L2 writing under varying task complexity
Chongrui He, Eerdemutu Liu
The study investigates how collaborative planning influences CALF (complexity, accuracy, lexical diversity, fluency) metrics and textual coherence in second language writing, and whether task complexity moderates these effects.
- PaperSystemForthcoming · 1 Aug 2026
Empathy, executive function, and dynamic L2 lexical inferencing in emotionally charged contexts: Predicting performance, inferencing potential, and retention
Bangxin Hu, Yanhui Zhang, Haomin Zhang
This study explores how empathy and executive function predict L2 lexical inferencing performance, inferencing potential, and retention in emotionally charged contexts.
- PaperSystemForthcoming · 1 Aug 2026
“I Haven't felt AI writing is taking away my agency as a person”: A longitudinal study of ESL students' use of GenAI in academic literacies development
Xiao Tan
A longitudinal study of ESL students found that they do not perceive generative AI writing tools as diminishing their personal agency in developing academic literacies.
- PaperSystemForthcoming · 1 Aug 2026
Cognitive diagnosis of EFL integrated writing under interactive noise input conditions: A G-DINA model approach
Kezhen Li
Applies the G-DINA model to conduct cognitive diagnosis of EFL integrated writing performance under interactive noise input conditions.
- PaperSystemForthcoming · 1 Aug 2026
Navigating language learning choices: How Chinese students adapt out-of-school English learning choices in a constrained EFL context
Yaqing Shi, Kevin Wai Ho Yung
Chinese students adapt their out-of-school English learning choices in a constrained EFL context, revealing how learners navigate language learning options.
- PaperSystemForthcoming · 1 Aug 2026
How vocalization affects spoken word acquisition: The moderating role of phonological working memory
Xin Yuan, Xuan Tang
Vocalization affects spoken word acquisition, with phonological working memory moderating the relationship.
- PaperSystemForthcoming · 1 Aug 2026
Fostering ethical AI literacy and academic integrity through CEFR-based mediation pedagogy in secondary EFL classrooms
Miranda Karjagdi Çolak, Özgür Çelik
A study proposes a CEFR-based mediation pedagogy to foster ethical AI literacy and academic integrity in secondary EFL classrooms. It explores integrating mediation skills with critical AI awareness.
- PaperSystemForthcoming · 1 Aug 2026
Translanguaging optimizes the developmental trajectory of beginning-level L2 writing: A sequence analysis study with an experimental design
Muzi Li, Yunlu Zhao
This experimental study using sequence analysis found that translanguaging optimizes the developmental trajectory of beginning-level second language writing.
- PaperSystemForthcoming · 1 Aug 2026
Multilingual identity and English achievement: A moderated mediation model of self-regulated learning strategy use and gender among Chinese EFL learners
Qingyao Dan, Karen Forbes, Barry Bai, Sihan Zhou
Investigates how multilingual identity relates to English achievement through self-regulated learning strategy use, moderated by gender, among Chinese EFL learners.
- PaperSystemForthcoming · 1 Aug 2026
A scoping review of Global Englishes Language Teaching research from 2020 to 2025: Mapping trends, tensions, and adaptations
Yifeng Chen, Hong Zhang, Xilu Chen
This scoping review maps trends, tensions, and adaptations in Global Englishes Language Teaching research from 2020 to 2025.
- PaperSystemForthcoming · 1 Aug 2026
A structural topic modeling analysis of positive psychology in EFL teacher education from 2010 to 2025
Pelin Derinalp
Structural topic modeling was used to analyze trends in positive psychology research within EFL teacher education from 2010 to 2025.
- PaperSystemForthcoming · 1 Aug 2026
Assessing L2 legal writing through a plain English lens: Insights on complexity, accuracy, and fluency
Yiran Xu, Xiaowan Zhang, J. Elliott Casal
This study examines the assessment of second language (L2) legal writing by applying plain English principles to evaluate complexity, accuracy, and fluency.
- PaperSystemForthcoming · 1 Aug 2026
The multimodal organisation of teacher feedback: A multimodal (Inter)action analysis
Jingjie Li
Analyzes how teachers organize feedback using multiple modes of communication, applying multimodal interaction analysis.
- PaperSystemForthcoming · 1 Aug 2026
“It's Putonghua-accented English”: Language ideologies of mainland Chinese English teachers in Hong Kong
Yi Cui, Seyyed-Abdolhamid Mirhosseini
Mainland Chinese English teachers in Hong Kong navigate language ideologies around Putonghua-accented English, revealing tensions between standard English norms and local linguistic identities.
- PaperSystemForthcoming · 1 Aug 2026
Holding presenters accountable for their posters during post-presentation discussions in a project-based EFL classroom
Marwa Amri
In a project-based EFL classroom, this study explores how holding students accountable for their posters during post-presentation discussions can enhance engagement and learning outcomes.
- PaperOpenAlex — TESOL researchForthcoming · 1 Jan 2031
Depth of Processing, learner aptitude, and the acquisition of L2 English grammatical structures
Shweta Nigam
This study investigated whether task-induced depth of processing (DoP) influences the acquisition of third conditionals and comparatives in EFL learners, and whether language aptitude predicts learning gains. Results showed a significant effect of processing condition on a grammaticality judgement test, with the High DoP Implicit group outperforming others on comparatives, but no significant group differences on an elicited imitation task after controlling for pretest scores. Overall, deeper processing benefits were structure-specific, favoring more salient structures on explicit measures, while complex low-salience forms showed increased frequency of use but limited accuracy change.
Original abstract
Depth of Processing (DoP) is central to several models of instructed second language acquisition (ISLA) (Leow, 2015). While DoP has been extensively examined in vocabulary learning (e.g., Laufer & Hulstijn, 2001), its role in the acquisition of grammatical structures, particularly through oral and audio-visual modalities remains comparatively underexplored. Drawing on the Levels of Processing framework (Craik & Lockhart, 1972), this study investigated whether task-induced processing depth influences the acquisition of third conditionals and comparatives, and whether language aptitude (LLAMA B, D, F) predicts learning gains. Using a pretest–posttest design, four intact classes involving 108 Grade 8 EFL learners in India were assigned to one of the three experimental conditions differing in level and type of processing: Low DoP, High DoP Explicit, and High DoP Implicit. The treatment task required the participants to listen to an audio-visual story containing the target structures (third conditionals and comparatives) and complete analytic tasks (varying in different groups) before listening to the story for a second time. The oral performance of the participants was analysed for accuracy, and frequency of use of the target structures. A grammaticality judgement test (GJT) and an elicited imitation task (EIT) were used to assess the participants’ gains in the knowledge of the target structures. In addition, language aptitude (LLAMA B, D, F) was measured and modelled as a predictor of gain scores. Results showed a significant effect of processing condition on GJT performance with the High DoP Implicit group outperforming the High DoP Explicit group on total scores and comparatives. In contrast, ANCOVA analyses indicated no significant group differences on EIT outcomes once pretest performance was controlled, with pretest scores strongly predicting posttest performance. In oral production, processing effects were structure-specific: the Low DoP group showed greater accuracy gains for comparatives, while both High DoP groups revealed increased frequency of use of third conditionals. However, across all four measures, multiple regression analyses indicated that LLAMA B, D, and F did not significantly predict gain scores. Overall, the findings support a processing-by-structure pattern: deeper processing benefits were most evident for the more salient structure (comparatives) on an explicit, written measure, whereas complex, low-salience forms (third conditionals) showed limited accuracy change but increased frequency of use under deeper processing.
- PaperOpenAlex — TESOL researchForthcoming · 1 Dec 2026
Future of AI in English Language Education: Trends and Predictions
Prof. Jagadeesh Nerlekar, K Munianjinappa
The paper synthesizes current AI developments in English language education and predicts hybrid systems combining large language models with pedagogical scaffolding will have the greatest near-term impact. Key trends include personalized feedback, automated assessment, multimodal practice, and AI-assisted materials, while challenges involve bias, privacy, and teacher training. Recommendations emphasize human-AI workflows, explainability, data ethics, and teacher capacity building.
Original abstract
Artificial intelligence (AI) is transforming English language education (ELE) by enabling personalized learning, automated assessment, adaptive content generation, and immersive practice environments. This paper synthesizes current developments, identifies emergent trends, and offers evidence-informed predictions about how AI will shape classroom practice, curriculum design, assessment, teacher roles, and policy over the next decade. Drawing on interdisciplinary literature from computer-assisted language learning (CALL), intelligent tutoring systems (ITS), natural language processing (NLP), and educational policy, the paper argues that the most significant near-term impact will stem from hybrid systems that combine large language models (LLMs) with pedagogically informed scaffolding and teacher mediation. Key trends discussed include (1) ubiquitous personalized feedback and adaptive pathways; (2) automated, formative assessment with rich analytics; (3) realistic speaking/listening practice via multimodal conversational agents and immersive virtual environments; (4) AI-assisted material creation and differentiation for diverse learner needs; and (5) data-driven teacher support and professional development. Predictions address likely improvements in scalability and access, as well as persistent challenges: bias and fairness in language models, privacy and data governance, over-reliance on automated feedback, and the need for robust teacher training and curricular alignment. The paper concludes with practical recommendations for educators, institutions, and policymakers to harness AI’s affordances while safeguarding equity, transparency, and pedagogical quality. These include adopting hybrid human–AI workflows, emphasizing explainability and interpretability in tools, developing clear data-ethics policies, investing in teacher capacity building, and prioritizing research-practice partnerships. The analysis aims to be actionable for practitioners and decision-makers planning for an AI-augmented future of English language learning. Keywords: artificial intelligence, English language education, adaptive learning, large language models, assessment, teacher role, ethics
- NewsLanguage Magazine25 Jul 2026
‘Speak the New Language of Adaptability’
The 2026 ETS Human Progress Report emphasizes adaptability as the most critical career skill, surpassing traditional job tenure. Multilingual individuals are found to have a distinct advantage in adaptability, based on a survey of over 32,000 workers across 18 countries.
Original abstract
According to the 2026 ETS Human Progress Report, traditional job tenure is no longer a guarantee of security; instead, adaptability has become the single most critical capability for career survival and progress. Multilinguals have an advantage when it comes to adaptability. Educational Testing Service’s survey of over 32,000 workers and individuals across 18 countries presents […] The post ‘Speak the New Language of Adaptability’ appeared first on Language Magazine .
- NewsLanguage Magazine24 Jul 2026
Supports for Struggling Adolescent Readers
Middle and high school teachers face diverse literacy levels, with some students lacking basic phonics and decoding skills. This challenge leads to disengagement and requires targeted support for struggling adolescent readers. The article highlights the need for interventions beyond grade-level text exposure.
Original abstract
Middle and high school teachers often face the challenge of addressing a wide range of literacy levels among their students. While some students are ready to read complex texts independently, others struggle with fundamental skills like phonics and decoding, leading to disengagement and a loss of motivation. At the same time, teachers may be limited […] The post Supports for Struggling Adolescent Readers appeared first on Language Magazine .
- PaperTESOL Quarterly24 Jul 2026
Mixed‐Methods Research on English‐Medium Instruction in Higher Education: A Systematic Review
Haemin Kim, Keith M. Graham
This systematic review of 136 studies on English-medium instruction (EMI) in higher education reveals significant methodological gaps in mixed-methods research: only seven studies explicitly stated an epistemological orientation, six presented an integrative mixed-methods research question, and no study specified sampling procedure terminology. Questionnaires and interviews were the most common instruments. The authors provide recommendations for strengthening future EMI mixed-methods research.
Original abstract
With the rapid growth of research in English‐medium instruction (EMI) in higher education, much research has been conducted through qualitative and quantitative approaches. Yet, little attention was paid to the use of mixed‐methods research (MMR) in this field. Therefore, this systematic review aims to examine previous literature that adopted MMR in EMI research, specifically evaluating the methodology across three aspects: epistemological orientation and purpose, MMR question formulation, and MMR design. Among 136 studies identified, substantial methodological gaps were found across all three aspects. First, only seven studies explicitly stated an epistemological orientation whereas 27 studies provided explicit purpose terminology. Second, only six studies explicitly presented an integrative MMR question, while 112 studies provided qualitative and/or quantitative research questions, and 18 studies lacked explicit research questions. Finally, MMR design characteristics were rarely described: (1) nearly half of the studies did not specify a design, (2) no study used specific sampling procedure terminology, and (3) questionnaires and interviews were the most frequently used instruments. Based on these methodological gaps, this study offers recommendations for strengthening future EMI studies that employ a mixed‐methods approach.
- NewsEdSurge24 Jul 2026
Early Childhood Education Teachers Grapple with Screen Time
The American Academy of Pediatrics updated its screen time guidelines for children, focusing on digital devices rather than TV, but early childhood educators found the recommendations too generic for pre-K students. Teachers report that excessive screen time can impair cognitive development and emotional regulation in young children, leading to shorter attention spans and quick-tempered behavior.
Original abstract
<p>When the American Academy of Pediatrics updated its<a href="https://publications.aap.org/pediatrics/article/157/2/e2025075320/206129/Digital-Ecosystems-Children-and-Adolescents-Policy"> <u>guidelines</u></a> around screen time for children and teenagers for the first time in 10 years this past January, many teachers and educators applauded the news. The document focused less on television and more on digital devices. It offered educators, families, pediatricians, and other stakeholders research-backed guidance on using digital devices to support children’s learning, rather than prioritize prolonged engagement. </p><p>However, many of the recommendations were not tailored to specific age groups. Recommendations like “create a family media plan” and “protect sleep” were helpful but offered few concrete steps for pre-K students. </p><p>That left early childhood educators asking what they could do for their young learners.</p><h2>The Risks in Pre-K Through K</h2><p>Unlike the physical world, digital content comes fast, in short bursts, and when kids get used to that, it has negative consequences. “Too much of this screen and things jumping in front of them, it could be damaging overall,” says Latoya Jones, a pre-K through fifth-grade media specialist in Broward County, Florida. “They are developing their cognitive processes, and if we’re teaching them to think in six-second bursts, maybe that’s not what we’re aiming for.”</p><p>When kids interact with devices rather than people, their emotional regulation can suffer, says Kristina Turner, a first-grade teacher at the Paterson, New Jersey, campus of<a href="https://collegeachieve.org/"> <u>College Achieve Public Schools</u></a>, a network of three K-12 charter schools. “A lot of the students, they are quick-tempered because they’re just used to quick things. If something takes too long, their behavior starts to heighten,” she says.</p><p>For the youngest learners in particular, “it is so important that they are learni
- PaperLanguage Teaching Research23 Jul 2026
When Teacher Enthusiasm Meets Student Engagement: The Reciprocal Role of Perceived Learning Value in the Second Language Classroom
Hoi Vo, Hang Ho, Dung Mai, Clarence Ng
A longitudinal study of 317 Vietnamese EFL learners found that teacher enthusiasm and student engagement are reciprocally related over time, mediated by students' perceived value of L2 learning tasks. This reciprocal relationship remained robust after controlling for prior levels of each construct, highlighting the co-regulated and cyclical nature of the L2 classroom affective-motivational climate.
Original abstract
Second language (L2) classroom is a dynamic and evolving space shaped by ongoing interactions between teachers and students. Understanding how these dynamic interactions unfold from a psychological perspective is critical for advancing L2 learning and teaching. This study contributes to this endeavour by examining the reciprocal relationship between teacher enthusiasm and L2 student classroom engagement as well as the mediating role of perceived learning value in this reciprocal relationship. Data were collected from 317 English as a foreign language learners in Vietnam at three time points across an academic semester and analysed by means of random-intercept cross-lagged panel modelling. Results revealed that teacher enthusiasm and L2 student engagement were reciprocally related over time, and this reciprocity was mediated by students’ perceived value of L2 learning tasks. These mediated reciprocal relations remained robust even after controlling for prior levels of each construct. The findings highlight the co-regulated and cyclical nature of L2 classroom affective-motivational climate and offer practical implications for L2 pedagogy.
- PaperLanguage Teaching Research23 Jul 2026
The Activity Design Typology: A Practitioner-Centered Approach to Understanding Task Structure
Lara Bryfonski, Caitlyn Pineault
An analysis of 114 activities created by world language teachers in a task-based teacher education program revealed that traditional task definitions inadequately capture their design intricacies. A directed content approach identified five distinct activity categories, leading to the Activity Design Typology, which offers a practitioner-centered framework for understanding, explaining, and evaluating instructional tools.
Original abstract
As the central construct in task-based language teaching (TBLT), “task” has been defined in a myriad of ways. Though popular criterion-based definitions offer a tantalizingly straightforward description of the construct, they have, at times, proved difficult to apply in practitioner contexts. With over 17 definitions identified in the literature, prominent scholars have advocated for efforts that offer a more unified understanding of “task” that may be better able to support task-based language teaching in implementation initiatives. This exploratory, qualitative study examines how two cohorts of K–16 U.S.-based world language teachers ( n = 35) approached task design in a task-based teacher education program from a materials development perspective. An analysis of the 114 activities teachers created revealed that although submissions had task characteristics, the intricacies of their designs were not adequately captured by traditional task criterion. Subsequent analysis following a directed content approach identified five distinct categories of activity design. Advancing previous interest in expanding the binary distinction of “task” versus “non-task,” we drew on these categories to develop the Activity Design Typology. The typology serves as a resource for understanding, explaining, and evaluating instructional tools for their pedagogic and empirical value. Use cases for the task typology are discussed in relation to initiatives aimed to further instructed second language acquisition research agendas and support TBLT implementation efforts.
- PaperLanguage Teaching Research23 Jul 2026
Approaches to Boost Foreign Language Learners’ Positive Emotions: A Qualitative Study on Learners’ and Teachers’ Perspectives in the Modern Foreign Language Context in England
Pia Resnik, Christine Schallmoser, Jean-Marc Dewaele
A qualitative study of secondary-level teachers and learners in England reveals that teachers significantly impact fostering positive emotions in foreign language classrooms, while students lack awareness of language learning benefits beyond English. The findings also highlight the need for government, schools, and parents to elevate the status of modern foreign languages to promote positive social change.
Original abstract
This study builds on insights into the crucial role learner emotions play in their flourishing. It investigates learners’ and teachers’ perspectives on how to enhance positive emotions both inside and outside modern foreign language classrooms in England, amidst a language crisis and reduced modern foreign language uptake across the United Kingdom. A thematic qualitative text analysis of 18 semi-structured interviews with secondary-level teachers and learners from 7 schools across England revealed that both learners and teachers emphasise the significant impact of teachers on fostering positive emotions in the classroom. They also showed that students lack awareness of the benefits of learning languages beyond English. Furthermore, the interview data suggests that the government, schools, school leaders, and parents must also engage to elevate the status of modern foreign languages and promote positive social change.
- NewsLanguage Magazine22 Jul 2026
Adolescent Literacy Can’t Wait
Only 30% of eighth graders and 35% of twelfth graders read at a proficient level according to the 2024 NAEP Report Card. Laura Stewart, Terrie Noland, and Jeanne Schopf provide practical strategies for improving adolescent literacy in secondary classrooms.
Original abstract
Laura Stewart, Terrie Noland, and Jeanne Schopf offer practical strategies for the secondary classroom The 2024 NAEP Report Card delivered a stark reminder: only 30% of eighth graders and 35% of twelfth graders are reading at a proficient level. These are not abstract statistics. They represent students like the eighth grader who threw a computer […] The post Adolescent Literacy Can’t Wait appeared first on Language Magazine .
- PaperOpenAlex — TESOL research22 Jul 2026
Gamification in English Language Teaching: Effects on Vocabulary Acquisition Among First-Year Secondary School Students
Elfer Fabian Medina-Mamani, Diomar Aneska Jove-Luque, Kevin Mario Laura-De La Cruz, Pilar Valeria Zapana Aguilar et al.
A study examined the effects of gamification on vocabulary acquisition among first-year secondary school students in English language teaching, finding positive impacts.
- PaperTESOL Quarterly22 Jul 2026
Unveiling the AI Divide: A Comparative Study of Novice and Veteran EFL Teachers' Experience of Using Generative AI in Teaching
Qi Lin
A comparative study of novice and veteran EFL teachers reveals that the AI divide emerges mainly at the levels of AI capability and AI outcomes rather than access, shaped by factors such as perceived ease of use, educational background, social support, and professional development opportunities.
Original abstract
The rapid advancement of artificial intelligence (AI) has brought both opportunities and challenges to the field of education. Within this evolving landscape, the emergence of the AI divide has become a pressing concern, particularly in teacher education and EFL instruction. This study developed a three‐level three‐factor conceptual framework to explore the AI divide through the dimensions of AI access, AI capability, and AI outcomes, while also considering technological, personal, and institutional influences. Using this framework, the study employed a comparative research design to examine how novice and veteran EFL teachers integrated AI differently in their teaching practices. The findings revealed that the AI divide between novice and veteran EFL teachers mostly occurred at the second level (AI capability) and third level (AI outcomes), rather than the first level (AI access). These disparities were shaped by a combination of technological, personal, and institutional factors, including perceived ease of use of AI, educational background, social support, and access to AI‐related professional development. This study extends the concept of AI divide into the field of EFL education and offers valuable theoretical insights and practical implications for more equitable AI integration in EFL teaching.
- PaperOpenAlex — TESOL research22 Jul 2026
AI in language education
Jake Cummings
AI tools in second language acquisition promise efficiency and personalization but are often disconnected from authentic learning and equity. Teacher preparation programs lag behind, leaving educators underprepared. This chapter analyzes AI through Communities of Inquiry and Second Language Acquisition frameworks, finding that educational value depends on teacher digital competence and critical use.
Original abstract
AI tools are increasingly embedded in second language acquisition (SLA), yet their integration raises unresolved tensions between technological potential and pedagogical practice. While AI applications promise efficiency, personalization, and extended opportunities for interaction, these affordances are often overstated or disconnected from authentic learning, cultural exchange, and equity. At the same time, teacher preparation programs have been slow to adapt, leaving teachers underprepared to critically evaluate AI-mediated language learning environments. This chapter examines AI in SLA through the interpretive frameworks of the CoI and SL2, positioning teacher digital competence as a mediating construct between technological capability and educational value. Traditional SLA approaches provide historical context, while adaptive systems, chatbots, and GenAI are analyzed in terms of presence, interaction, authenticity, and cultural depth. Three themes emerge: Expanded opportunities for cognitive and social presence, persistent risks related to inequity, and the central role of teacher judgment. The chapter concludes that the educational value of AI depends less on technical innovation than on the preparedness of teachers to use it critically, ethically, and inclusively to design and mediate AI-supported language learning experiences.