Beyond ChatGPT: Multimodal generative AI for L2 writers

Kang, Joohoon; Yi, Youngjoo

doi:10.1016/j.jslw.2023.101070

Cited by 4 publications

(2 citation statements)

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“…As generative AI technologies are advancing rapidly, an expanding array of AI-powered writing assistance, intelligent tutoring systems, and feedback tools has the promise to cater to the diverse needs and learning styles of language learners and writers. These tools are not limited to mere textual assistance; the multimodal functionalities of generative AI can also allow writers to explore creative expressions and multimodal writing, enriching students' literacy practices by integrating more visual, auditory, and interactive elements into the composition process (Kang & Yi, 2023;Li et al, 2024). As noted by Cao and Dede (2023), our educational model has long been centered around the product, directing our focus towards the outcomes and grades students achieve, often overlooking the learning process itself.…”

Section: Discussionmentioning

confidence: 99%

Exploring Students’ Generative AI-Assisted Writing Processes: Perceptions and Experiences from Native and Nonnative English Speakers

Wang

2024

Tech Know Learn

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Generative artificial intelligence (AI) can create sophisticated textual and multimodal content readily available to students. Writing intensive courses and disciplines that use writing as a major form of assessment are significantly impacted by advancements in generative AI, as the technology has the potential to revolutionize how students write and how they perceive writing as a fundamental literacy skill. However, educators are still at the beginning stage of understanding students’ integration of generative AI in their actual writing process. This study addresses the urgent need to uncover how students engage with ChatGPT throughout different components of their writing processes and their perceptions of the opportunities and challenges of generative AI. Adopting a phenomenological research design, the study explored the writing practices of six students, including both native and nonnative English speakers, in a first-year writing class at a higher education institution in the US. Thematic analysis of students’ written products, self-reflections, and interviews suggests that students utilized ChatGPT for brainstorming and organizing ideas as well as assisting with both global (e.g., argument, structure, coherence) and local issues of writing (e.g., syntax, diction, grammar), while they also had various ethical and practical concerns about the use of ChatGPT. The study brought to front two dilemmas encountered by students in their generative AI-assisted writing: (1) the challenging balance between incorporating AI to enhance writing and maintaining their authentic voice, and (2) the dilemma of weighing the potential loss of learning experiences against the emergence of new learning opportunities accompanying AI integration. These dilemmas highlight the need to rethink learning in an increasingly AI-mediated educational context, emphasizing the importance of fostering students’ critical AI literacy to promote their authorial voice and learning in AI-human collaboration.

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Section: Discussionmentioning

confidence: 99%

Exploring Students’ Generative AI-Assisted Writing Processes: Perceptions and Experiences from Native and Nonnative English Speakers

Wang

2024

Tech Know Learn

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“…NLP has practical applications in various contexts covering healthcare (analysis of clinical documentation), customer service (chatbots), etc. It has also been applied to the teaching and learning of languages in several ways that may include automated writing evaluation (AWE) tools to automatically assess L2 students' writings and give them feedback (Koltovskaia, 2020;Wang et al, 2020;Guo & Wang, 2023); generative AI as a (multimodal) writing aid (Warschauer et al, 2023;Kang & Yi, 2023); machine translation as a writing aid (Lee, 2020;Lee, 2021;Stapleton, 2021); dialogue systems to allow learners to interact with conversational agents in a meaningful way (Sydorenko, 2015;Divekar et al, 2021;Bibauw et al, 2022a;Bibauw et al, 2022b;Zhai & Wibowo, 2023); readability and text level identification systems (Filighera et al, 2019;Martinc et al, 2021), or text simplification tools (Alva-Manchego et al, 2020;Grabar & Saggion, 2022).…”

Section: The Nlp Modulementioning

confidence: 99%

Automating task design: bridging the gap between second language research and L2 instruction

Castellví,

Gilabert Guerrero,

Comelles Pujadas

2024

TEISEL

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The present study addresses the issue of the transfer from second language acquisition (SLA) research to second and foreign language (L2) instruction, particularly within the context of Task-Based Language Teaching (TBLT). The main goal of this article is to show how technology, particularly the web-based tool taskGen (https://taskgen.eu), which primarily serves to create teaching materials, also functions as a mediator for transfer and provides valuable information on teaching practices, teachers' needs, pedagogical strategies, and L2 task design. This article is organised as follows. Firstly, a web-based tool that assists and trains teachers in L2 task design is presented, along with the instantiation and integration of natural language processing (NLP) resources in the tool; later, we show how quantitative information obtained from the massive use of the tool through data analytics can be combined with qualitative methods such as interviews, observations, think aloud protocols, questionnaires, case studies, and transcripts to generate research. Automatic (quantitative) data collection gathers information of the teaching contexts, task aims, task flow structures, focus on form options, and the access and time on help files. Not only does the collected information feed back to the tool, but it also contributes to understanding teachers' design choices and patterns, and to exploring the intersection between SLA and TBLT theory and task design practises. Qualitative analysis is being conducted and oriented toward teachers’ experiences during task design. Finally, we present the future lines of research that we anticipate will be carried out with the further use and development of taskGen.

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When generative artificial intelligence meets multimodal composition: Rethinking the composition process through an AI-assisted design project

Jiang

2024

Computers and Composition

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Beyond ChatGPT: Multimodal generative AI for L2 writers

Cited by 4 publications

References 1 publication

Exploring Students’ Generative AI-Assisted Writing Processes: Perceptions and Experiences from Native and Nonnative English Speakers

Exploring Students’ Generative AI-Assisted Writing Processes: Perceptions and Experiences from Native and Nonnative English Speakers

Automating task design: bridging the gap between second language research and L2 instruction

When generative artificial intelligence meets multimodal composition: Rethinking the composition process through an AI-assisted design project

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