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D

Designing multimedia language learning materials, both with and without AI

This workshop will focus on the online materials authoring and sharing platform Clilstore.eu. It will provide participants with an introduction to the platform's functionality and demonstrate how this open educational resource (OER) can enable educators to create multimedia learning units combining audio, video, text, images, online dictionaries and Web 2.0 applications. Clilstore.eu is ideally suited to curating and presenting materials that have been generated by AI applications. Sample AI learning units will be presented and the process of creating AI-based learning units will be outlined. The workshop will also show how Clistore.eu can be used flexibly in both teacher-led and self-directed learning scenarios. In terms of pedagogical underpinning, Clilstore.eu has been optomised for Content and Language Integrated Learning (CLIL) and Task-based Language Learning (TBLL). Learning activities are scaffolded by the platform's inbuilt functionality, e.g. its ability to automatically link embedded transcripts word for word to online dictionaries, its vocabulary learning tools, and its integrated learner portfolio. Clilstore.eu's user interface has been fully localised for a variety of languages including: English, Spanish, Danish, Italian, Irish and Scottish Gaelic, meaning that it can help to create full-immersion learning environments.

Designing Virtual Exchange Projects that Lower Student Anxiety and Boost Online Engagement

Virtual language exchanges offer a cost-effective alternative to traditional study abroad programs by eliminating the need for travel (Hilliker, 2020). Research shows that connecting students from diverse global contexts to communicate in English as a lingua franca online boosts motivation, willingness to communicate (Zhou, 2023), and Foreign Language Enjoyment (FLE) (Resnik & Schallmoser, 2019). Additionally, these exchanges enhance English oral skills (Canals, 2020) and intercultural competence (Hagley, 2020; Lin, 2021). Despite these benefits, Foreign Language Anxiety (FLA) (Horwitz, Horwitz & Cope, 1986) can hinder participation in virtual exchanges (Fondo & Jacobetty, 2022). Unpreparedness for real-time communication further exacerbates FLA, leading to avoidance behaviors (Stroud, 2017, 2019, 2023). Challenges such as language level differences, task complexity, time zone issues, and asynchronous communication difficulties can also negatively impact engagement (McNeil, 2014; Hagley & Green, 2022). This study designed a 12-week virtual exchange project to address these challenges, involving 189 students from universities in Tokyo and Munich. Students were grouped by language level and used an online video-exchange system to create and share weekly two-minute videos on assigned topics, followed by interactive comments and questions. The project concluded with real-time interactions via Zoom. A survey collected student feedback, revealing overall positive responses, enjoyment, and a willingness to participate. Suggestions for improvement included clearer guidance, more diverse group interactions, increased Zoom sessions, relevant video topics, and consistent participation from peers. This presentation will explore these findings and discuss enhancements made to future exchange projects.

Developing a generative AI tool for dialogue data collection in online environments

This study investigates the development and implementation of a generative AI tool for collecting online audio data to assess L2 English fluency, intelligibility, and comprehensibility among frequent online gamers. A pilot study revealed participants' foreign language speaking anxiety and preference for computer-mediated communication over human interaction. In response, we created a conversational agent that simulates dialogue and gathers audio data for research purposes. The program is accessible via a webpage, utilizing the browser API to collect audio data. The participant's audio is transcribed by whisper.cpp, and the resulting text is input into a libre and self-hosted Large Language Model running on llama.cpp. The LLM's output, acting as the dialogue partner, is fed into text-to-speech software and sent back to the browser, creating an interactive dialogue. The program is designed using libre software to ensure independence from commercial interests, compliance with European privacy data laws, and complete control of data throughout the process. Preliminary tests indicate such programs could enhance participants' comfort and willingness to communicate while reducing speaking anxiety. It could also replace the variable biases of human conversation partners with more stable, observable, and reproducible biases. As an application of a novel technology, the use of libre, self-hosted generative AI in collecting online dialogue data requires future research, particularly in evaluating the reliability compared to actual dialogue situations between humans. However, this study suggests that generative AI can be a valuable tool for collecting audio data from participants in online and particularly closed environments.

Digital tools and active methodologies for online EFL learning during the COVID19 pandemic: Students’ perceptions of an action research approach

The COVID-19 pandemic outbreak in 2020 presented an unforeseen learning environment for both teachers and students. This context had a detrimental impact on the learning process, encompassing various physical and attitudinal factors. Consequently, this study aimed to investigate students’ perceptions on the efficacy of specific digital tools and active methodologies over a 17-week period in enhancing the lexico-grammatical learning of first-year English pedagogy students at a Chilean university. Employing an action research approach, two 50-minute weekly sessions were conducted throughout the 17-week duration. Data were collected through close-ended questions under the action research methodology, using a mixed-method approach. Additionally, a focus group was conducted to gain a deeper understanding of the survey findings. The results demonstrated that the learning experience met or exceeded students' expectations (92.84%). The most highly regarded active methodologies included the use of Canvas (µ = 4.42, SD = .99), Immediate Response Systems (µ = 4.32, SD = .80), and Mastery Paths (µ = 4.26, SD = .64). Discussion forums were considered the least favored active methodology (µ = 3.84, SD = 1.09). Moreover, the students expressed a preference for a return to face-to-face classes (57.37%) as opposed to a hybrid (36.84%) or online (5.79%) format.

Does ChatGPT Significantly Enhance EFL Pre-service Teachers' Teaching Plans?

The advent of ChatGPT has surprised many English educators, as theoretically, it has much potential to support English language teaching. However, the empirical results of how ChatGPT influences English as a foreign language (henceforth, EFL) preservice teachers’ teaching plans remain unclear. This study therefore purposed to investigate the effect of ChatGPT on EFL preservice teachers’ teaching plans. This study employed a mixed-method approach and recruited 17 EFL preservice teachers to join the research. The data was collected using a scoring rubric and an interview protocol. Using Mann-Whitney U Test and inductive thematic analysis, this study found that the lesson plans created by EFL preservice teachers in the experimental group failed to outperform those in the control group. Moreover, the pre-lesson plans had no significant difference from the post-lesson ones. The qualitative data explains the statistical results. Then, two implications were drawn to effectively use ChatGPT to support teaching and learning.