Australian Review of Applied Linguistics - Current Issue
Volume 47, Issue 3, 2024
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Decolonizing or recolonizing?
Author(s): Toni Dobinson, Julian Chen and Carly Steelepp.: 253–258 (6)More Less
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Co-creating stories with generative AI
Author(s): Lok Ming Eric Cheung and Huiwen Shipp.: 259–283 (25)More LessAbstractPublicly available Generative Artificial Intelligence (GenAI) tools are said to liberate students from the instrumental use of English and empower them to write creative texts to communicate with different communities. This paper reports on an undergraduate language-related service-learning subject in a Hong Kong tertiary institution. In the subject, students co-created digital stories with asylum-seeking children, in written and podcast formats, with the help of GenAI. The qualitative content analysis of semi-structured interviews with the students found that this experience expanded the students’ creative potential. Meanwhile, GenAI played a peripheral role in the story creation processes, in that the students exercised agency to use the tools and remained critical of the AI-generated content. This study argues that digital storytelling with GenAI, when used critically, promotes linguistic, digital and cultural awareness among ESL learners, offering them a third space to interact with culturally diverse communities in Hong Kong and giving them genuine ownership of English for creative and communicative purposes.
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Beyond borders or building new walls?
Author(s): Hao Tran and Annita Stellpp.: 284–308 (25)More LessAbstractGenerative Artificial Intelligence (GenAI) has been offering unprecedented opportunities for language education. However, its capacity to embrace linguistic diversity, particularly for learners of dialect-rich languages like Vietnamese and Mandarin, remains underexamined. Without careful consideration, GenAI risks reinforcing language hegemonies, thereby contributing to the recolonization of language learning landscapes by marginalizing minority dialects in favour of preferred standards. Adopting sociolinguistics interview, this study explores GenAI’s (namely ChatGPT’s) ability to recognize and generate dialect-specific content in discussing several pre-determined questions in both Vietnamese dialects (i.e., Northern, Southern, and Central) and Mandarin varieties (i.e., Mainland Standard Mandarin, Taiwanese Mandarin, and Singaporean Mandarin). A multi-stage role prompt, focusing on the topic of food, was used for both Vietnamese dialects and Mandarin varieties to generate responses. Our study reveals major inconsistencies in the representation of Vietnamese dialects and Chinese varieties within AI-generated output, raising critical questions about generative AI’s role in perpetuating linguistic hierarchies. We conclude by emphasizing the need for tailored language learning approaches that leverage generative AI’s capabilities to not only accommodate but also celebrate the rich tapestry of global dialects and languages, ensuring equitable access to language education for all learners.
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The use of Google Translate for language learning in emergency forced displacement contexts
Author(s): Tetiana Bogachenko, Rachel Burke, Yi Zhang and Qian Gongpp.: 309–339 (31)More LessAbstractThis paper explores the use of Google Translate (GT) by displaced people (DPs) from Ukraine in Australia and English language educators supporting learners from refugee backgrounds in the context of decolonizing language learning. The survey and interviews with DP participants revealed that GT provides them with a sense of confidence and freedom, inclusion and value following their emergency evacuation from the war. Significantly, the learners’ use of GT suggests high levels of metalinguistic awareness and digital literacy, and they contested the dominance of ‘privileged’ language varieties in machine translation applications. Their use of GT also promoted wider understanding of multilingual learners in educational settings. The educator interviews provided insights into their goals for supporting learners in (re)settlement and their contrasting views regarding the potential for GT to play a role in language learning. A critical discussion of different ways that learners and educators perceive tools such as GT provides insights into the complexities of the use of machine translation in language teaching and learning.
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AIsplaining
Author(s): Beatriz Carbajal-Carrerapp.: 340–365 (26)More LessAbstractThe growing implementation of Generative AI (GenAI) in education has implications on the representation of knowledge and identity across languages. In a context where content biases have been reported in AI-generated content, it becomes relevant to interrogate the ways in which AI technologies represent different linguistic identities. This article conducts a systematic analysis of AI-generated content to identify the potential discursive strategies that can contribute to the perpetuation of existing sociolinguistic hierarchies. Data for this study consist of a set of GenAI explanations of assorted linguistic identities comprising dominant and non-dominant languages. The method combines specialization codes from the sociology of knowledge with discourse analysis. Specialization codes are composed of two axes with a differing degree of emphasis (+/−) on epistemic and social relations (ER/SR). This tool is useful for understanding explanations because it focuses on what sort of information is considered legitimate knowledge and what kinds of knowers are considered valid. The analysis of epistemic and social relations reveals a sociolinguistic hierarchy articulated across three definitory aspects of identity: relationship to the world, structuring across time and space, and possibilities for the future.
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Meeting standards: (Re)colonial and subversive potential of AI modification
Author(s): Ana Tankosić, Eldin Milak, Carly Steele and Toni Dobinsonpp.: 366–382 (17)More LessAbstractAI potential to recolonise language practices by reproducing existing marginalisations in novel ways has already instilled fears of a ‘contemporary dystopia’ (Miras et al., 2022) — a space of cultural and linguistic erasure. Accents represent a distinctive aspect of language practice associated with one’s sociocultural, and ethno-racial characteristics. They account for one’s social identity, status, and proficiency (De Klerk & Bosch, 1995). This makes practices of artificially modifying accents particularly concerning, since they play into the ‘zero’ accent ideology. As a result, any deviation from the norm is marked as abnormal or deficient, and in need of, artificial correction. Using AI accent generators, therefore, has the capacity to further aggravate power inequalities between the linguistically privileged and underprivileged, and to encourage changes in self-representation towards what is perceived as the normative Standard.
Artificial modification of self to match a desired representation is not new, given the long-standing discussions on digital image alterations and their negative relationships to self-perceived attractiveness (Ozimek et al., 2023). This conceptual paper explores the (re)colonial and subversive linguistic potential of AI accent generators through the lens of the social tendency of individuals to strive to meet a given Standard. Using the notion of ‘technologies of the self’ (Foucault, 1988), we draw a parallel between self-perceived attractiveness of bodies and accents, to explain how artificial modifications do not straightforwardly support diversities, but instead encourage ‘self-corrections’ in line with those standardized sets of features which seem to promise a ‘better’ socioeconomic and educational standing within neoliberal societies.
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Generative AI’s recolonization of EFL classrooms
Author(s): Nicola Stewart and Yangsheng (Danson) Zhengpp.: 383–409 (27)More LessAbstractContinuation writing was recently introduced as a new element of China’s English as a Foreign Language (EFL) assessment. The assessment task requires students to complete the story within a set word limit. Preparing students for this task is challenging for Chinese EFL teachers, as little pedagogical guidance is supplied. Given the recent trend of incorporating Digital Multimodal Composing (DMC) in Chinese EFL classrooms, using generative AI to provide sample writings presents a potential option.. This process could be viewed as students creating mentor texts to inform their writing development. We argue that AI-generated continuation writing samples are predominantly built with complex but culturally hollow sentences. Particularly, AI-generated samples may prioritize “native-speakerism” in which local cultures are under-represented. Integrating AI-generated continuation writing samples without cultural awareness risks unintended recolonization of EFL classrooms. We illustrate our argument with data generated via AI chatbots. We mobilize Hallidayan systemic functional linguistics (SFL) to identify patterns in complex AI-generated sample texts. This study highlights potential risks when the dominant power reproduced by the language patterns in AI-generated texts is not critically considered.
Volumes & issues
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Volume 47 (2024)
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Volume 46 (2023)
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Volume 45 (2022)
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Volume 44 (2021)
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Volume 43 (2020)
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Volume 42 (2019)
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Volume 41 (2018)
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Volume 40 (2017)
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Volume 39 (2016)
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Volume 38 (2015)
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Volume 37 (2014)
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Volume 36 (2013)
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Volume 35 (2012)
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Volume 34 (2011)
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Volume 33 (2010)
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Volume 32 (2009)
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Volume 31 (2008)
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Volume 30 (2007)
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Volume 29 (2006)
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Volume 28 (2005)
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Volume 27 (2004)
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Volume 26 (2003)
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Volume 25 (2002)
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Volume 24 (2001)
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Volume 23 (2000)
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Volume 22 (1999)
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Volume 21 (1998)
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Volume 20 (1997)
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Volume 19 (1996)
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Volume 18 (1995)
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Volume 17 (1994)
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Volume 16 (1993)
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Volume 15 (1992)
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Volume 14 (1991)
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Volume 13 (1990)
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Volume 12 (1989)
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Volume 11 (1988)
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Volume 10 (1987)
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Volume 9 (1986)
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Volume 8 (1985)
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Volume 7 (1984)
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Volume 6 (1983)
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Volume 5 (1982)
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Volume 4 (1981)
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Volume 3 (1980)
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Volume 2 (1979)
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Volume 1 ([1978, 1977])
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Volume 1 ([1978, 1977])
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