LatinCALL24

A formação continuada é uma etapa importante na carreira profissional dos professores de línguas, principalmente para aqueles que buscam ressignificar sua prática docente. Nesse sentido, este estudo, que se situa na área de Aprendizagem de Línguas Assistida por Computador (CALL), apresenta a experiência em um curso online sobre desenvolvimento de material didático digital gamificado voltado para professores brasileiros de espanhol em atuação, denominado E -LE em (forma)ação: Reality Game(as) Alternativa para desenvolvimento de material didático digital . Para o seu desenvolvimento, utilizamos os procedimentos metodológicos da Pesquisa Baseada em Design , aplicando o design do Jogo de Realidade Alternativa (ARG) (Palmer; Petroski, 2016) à proposta de formação continuada na perspectiva dos multiletramentos (Cope; Kalantzis, 2015) . Para isso, criamos uma narrativa transmídia de mistério, denominada Fórmula Ñ , e desenvolvemos os personagens utilizando ferramentas de Inteligência Artificial (IA) para captar a atenção dos principais públicos e tornar o design visual do curso mais próximo de um jogo digital. Após a implementação do design , aplicamos o curso online entre os meses de agosto/2023 e novembro/2023, e avaliamos a alfabetização desenvolvida pelos participantes durante a experiência. Os resultados indicam que o curso foi satisfatório e contribuiu para que os professores espanhóis aperfeiçoassem sua prática docente como designers de aprendizagem na produção de materiais digitais gamificados mais atrativos aos seus contextos de atuação.

Na era da Inteligência Artificial (IA), as múltiplas ecologias de aprendizagem nas quais nos inserimos influenciam a criação de novos territórios educacionais, impactando experiências individuais e coletivas de ensino-aprendizagem em todos os níveis do sistema educacional. A partir de alguns dos desafios colocados por este contexto, neste ensaio teórico-reflexivo, buscamos tecer algumas considerações sobre  as complexas relações entre multimodalidade, multiletramentos, tecnologias digitais de informação e comunicação (TDIC) e implicações éticas do uso da IA na contemporaneidade. Para isso, contamos com suporte teórico sobre ecologias de aprendizagem (Barron, 2006; Jackson, 2013), multimodalidade e tecnologia (Canale, 2019; van Leeuwen, 2021), multiletramentos (Grupo Nova Londres, 2021; Kalantzis; Cope, 2023), contextos digitais (Jenks, 2010), territorialização (Kambeba, 2021; Leander; Vasudevan, 2011), modos de subjetivação (Foucault, 1997), cibercultura (Lévy, 2010a, 2010b), IA (Kafai; Burke, 2020; Suleyman; Bhaskar, 2023), implicações éticas (Benjamin, 1987; Diniz, 2021; Freire, 1992; Linares; Fuentes; Galdames, 2023; Peters, 2018; Santaella, 2023; Segato, 2021) e sociais associadas com seu uso (Akotirene, 2019; Almeida, 2019; hooks, 2017; Ngomane, 2022). Como reflexões preliminares, destacamos a importância da reterritorialização e de se atentar para os diferentes modos de subjetivação no contexto educacional digital, considerando aspectos éticos e sociais relevantes para a articulação de processos dinâmicos e crítico-reflexivos sobre a utilização desses avanços tecnológicos nos mais diversos setores da vida em sociedade.

H5P (paquete HTML5) es un software que está disponible de forma gratuita en plataformas educativas como Moodle. H5P permite crear y editar más de 50 tipos de contenido interactivo y multimedia de una forma relativamente fácil. Los objetivos del taller son mostrar cómo contenido H5P en Moodle y cómo utilizar ChatGPT para acelerar la creación de algunos tipos de actividades. El taller se estructura en cuatro partes. En primer lugar, se describe qué es H5P y su potencial educativo. A continuación, se muestran ejemplos de H5P en el aula de lenguas extranjeras. En tercer lugar, se ejemplifica como crear actividades H5P en Moodle. Por último, se explica cómo usar ChatGPT para acelerar la creación de algunos tipos de contenido H5P. Contenidos: 1. Breve introducción teórica a H5P. 2. Ejemplos de contenido H5P en la enseñanza de lenguas extranjeras. 3. Demostración de cómo crear contenido H5P en Moodle. 4. Demostración de cómo usar ChatGPT para acelerar la creación de algunos tipos de H5P. Agradecimientos: Este taller forma parte del Proyecto de I+D+i Artificial Intelligence in Language Learning and Teacher Education (CIGE/2023/166). Requisitos técnicos: El formador utilizará la plataforma Moodle (versión 4.1.) de su universidad para crear el contenido H5P y su cuenta de ChatGPT. Si el público quiere hacer prácticas con H5P y ChatGPT, necesita tener acceso a Moodle o crear una cuenta gratuita en https://h5p.org/ y tener cuenta en OpenAI.

This study explores the integration of Artificial Intelligence (AI) and Computer-Assisted Language Learning (CALL) in the professional development of in-service Venezuelan English teachers. Despite the growing use of AI in education, there is a need for further research on teachers’ training needs (Warschauer, M., & Xu, Y., 2024). This research examines teachers’ opinions on AI, their familiarity with AI and CALL tools, and their experiences with these technologies in the classroom. The survey results reveal teachers’ concerns about the effectiveness and challenges of AI and CALL in their teaching practices. The findings also highlight the need for support to help teachers integrate AI-based instruction. As Lee et al. (2024) emphasize, AI-based instruction is most effective when guided by teachers’ expertise and knowledge. This study contributes to the ongoing research on AI in education by providing insights into the perspectives and experiences of Venezuelan English teachers with AI and CALL technologies.

Generative AI (GenAI) systems, such as ChatGPT, provide personalized learning opportunities in language education. However, concerns arise regarding their potential negative effects on language competence (Godwin-Jones, 2024; Kostka & Toncelli, 2023). This project investigates the pedagogical potential of GenAI systems in language learning applications. GenAI refers to computer systems that autonomously produce new content (e.g., text, images, sound) using machine learning models trained on diverse datasets (Russell & Norvig, 2020). The study employs a framework by Feuerriegel et al. (2024), categorizing GenAI into three levels: models (algorithm and architecture), systems (user interface with embedded model), and applications (real-world uses). The methodology involved four preparatory steps: identifying AI language learning applications, selecting AI systems, creating prompts, and developing sample texts. Eleven applications were identified, including text generation and revision. For each application, ChatGPT and a tailored system were chosen. Prompts were crafted for AI inputs, often paired with sample texts. For instance, text revision utilized ChatGPT and Grammarly with prompts like “Revise the following text,” applied to intermediate proficiency samples. After inputting prompts into AI systems, the assessment focused on output quality, interface suitability, and the degree of learner autonomy. Results revealed performance variations across applications and systems. Notably, superior AI performance sometimes undermined pedagogical value by limiting learner autonomy. This finding underscores the necessity of balancing AI capabilities with learner agency to enhance language learning. The study highlights the importance of considering how different systems promote or hinder learner agency in developing effective AI-assisted language learning solutions.

This study extends mindset theory to language learning with AI by developing and conducting a preliminary validation of the AI Language Learning Mindset Inventory (AILLMI). Building on Dweck's concept of growth and fixed mindsets, and incorporating Lou et al.'s (2017) decremental mindset, we explore how these constructs manifest in learners' engagement with AI language learning tools. The AILLMI was developed through a rigorous two-stage process. An initial 18-item pre-pilot (n=14) informed the design of an extended 79-item instrument. The main pilot study (n=66) provided data for comprehensive Rasch analysis, which was central to our validation process. This analysis led to the refinement of the AILLMI, resulting in a revised survey instrument with strong psychometric properties. Our findings indicate that AI language learning mindsets extend beyond the traditional growth-fixed dichotomy. The inclusion of decremental mindset items provided new insights into learners' concerns about potential skill deterioration with AI use. The Rasch analysis also revealed patterns in item difficulty, with items related to personalised AI feedback and expanding AI tool use being the most challenging for learners to endorse. We will present the current version of the AILLMI, discuss its structure, and explore its potential applications in research and educational settings. While further refinement of the AILLMI is anticipated, this instrument represents a significant advance in our understanding of, and ability to measure and shape, AI language learning mindsets, with implications for the development of AI-integrated language learning environments.

With the increasing use of artificial intelligence (AI) tools in higher education, educators are contemplating whether these technologies should be integrated into teaching and learning to improve efficiency, while also grappling with the ethical concerns surrounding their use. Some lecturers recognize the potential of AI to simplify research and course preparation but remain cautious about its impact on academic integrity. Despite the growing discourse, there is limited literature on the use of AI tools by tertiary educators, specifically in English Language and Literature courses in Jamaica and the wider Caribbean Community (CARICOM). This qualitative study aimed to explore the application of AI tools by lecturers in these courses, whether for generating course content or designing course activities, and to identify the most used tools. Data were collected through survey from 25 English Language and Literature lecturers across four teachers’ colleges in Jamaica. Preliminary findings reveal that lecturers have access to AI tools, which they use for diverse purposes, such as the generation of lecture notes and quizzes. Additionally, lecturers reported that AI tools not only save preparation time but also enhance content diversity and support their subject knowledge. However, concerns about the reliability of AI-generated content and the ethical implications of its use were noted. The results suggest that while there is a growing acceptance of AI among lecturers, with its ability to make the teaching and learning process more efficient and manageable, they remain cautious about its potential limitations.

Time and space constraints in foreign/second language (L2) instruction restrict learners’ exposure to varied speech (Collins & Muñoz, 2016), essential for pronunciation development (Thomson, 2018). High-Variability Phonetic Training (HVPT) offers a promising solution by exposing learners to phonetic variation; however, its implementation remains underexplored (Barriuso & Hayes-Harb, 2018). This study investigates the integration of Text-To-Speech (TTS) technology with HVPT to provide varied L2 input in a semi-autonomous (beyond-the-classroom) environment. This mixed-method study employed a pretest-posttest design to examine discrete and holistic aspects of pronunciation development. The discrete analysis assessed learners’ phonological awareness of regular past tense marking (-ed: walk/t/, play/d/, visit/ɪd/), which aligns with Celce-Murcia et al.’s (2010) initial stage of pronunciation development. In the holistic analysis, eleven English-speaking raters evaluated broader pronunciation aspects (i.e., comprehensibility, accentedness), following Munro and Derwing (1995). Thirty lower-intermediate ESL learners were divided into a Treatment Group (exposed to varied TTS voices) and a Control Group (exposed to a single TTS voice), engaging in ten self-paced activities over four weeks. Results revealed significant improvements in phonological awareness of past -ed allomorphy for both groups, with no statistical difference between them. However, the Treatment Group achieved statistically significant improvements in both comprehensibility and accentedness compared to the Control Group. These findings suggest that although TTS improves phonological awareness irrespective of HVPT implementation, TTS-based HVPT leads to superior pronunciation gains in the holistic measures adopted. This research highlights TTS’s potential to provide varied aural input, enhancing L2 pronunciation and offering insights for developing accessible language learning resources.

This exploratory study aims to evaluate the effectiveness of a generative AI assistant (i.e., Claude) as an automated corrective feedback (ACF) tool for improving academic writing performance in the International English Language Testing System (IELTS) exam. Additionally, this research seeks to determine participants' perceptions of using Generative AI in this context. Participants are third-year L2-English learners from an English language course at a Chilean university, selected through a convenience sampling method. This study adopts a mixed-method approach, combining quantitative assessment of writing progress with qualitative analysis of learners' perceptions. Thus, data collection involves pre- and post-intervention writing samples, logs of student-AI interactions, as well as a questionnaire to capture participants’ perceptions and opinions on using AI for improving academic writing. Furthermore, the study design includes AI-assisted tutoring sessions with fixed prompts (i.e., participants interact with Claude to receive ACF), and guided practice (i.e., participants analyze the ACF to improve their texts) for a month. This study contributes to the growing field of AI applications in language education and may offer preliminary insights into innovative methods for improving IELTS preparation. Finally, the exploratory findings could inform future research directions and have potential implications for curriculum design in English language teaching programs and the development of AI-assisted learning tools for academic writing. 

Artificial Intelligence (AI) technologies have gained prominence, particularly with the emergence of Generative AI tools. They are now part of daily life and are gradually making their way into classrooms. Foreign Language (FL) teaching is no exception, educators are exploring the potential/limitations of AI tools, especially in writing activities. However, AI remains an innovation to most professionals. According to Rogers’ Theory of Perceived Attributes, an innovation is an idea perceived as new, which can be adopted or rejected based on five attributes: relative advantage, compatibility, complexity, observability, and trialability. This study, still underway, aims to analyze the perception of undergraduate students in an English Language Teaching Education degree program regarding the use of AI technologies as support for teaching writing skills in FL. These students, as future language teachers, will encounter AI in their practice. This is a descriptive research study which uses a quantitative survey approach. The questionnaire, based on Roger’s theory, is the data collection instrument. It will be applied to students from an English Language Teacher Education degree program at a federal university in Brazil. The data collected will be analyzed based on the five attributes defined by Rogers (1995), Pokrivcakova (2019) and Schmidt and Strasser (2022). Ultimately, this study seeks to understand how future FL teachers are impacted by AI and how they might potentially use it in their professional practices, as they will play a crucial role in preparing students for a context in which AI is already part of everyday life.