Machine Translation, Language Learning and the ‘Knowledge Economy’

Raído, Vanessa Enríquez; Torrón, Marina Sánchez

doi:10.4324/9781351015233-9

Cited by 5 publications

(2 citation statements)

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“…We were able to show empirically that instructors have the knowledge and skills to accurately detect MT use most of the time, at least for now. This is good news, considering that access to technology and MT tools today is widespread among secondary and postsecondary L2 learners, who engage with MT tools frequently for academic purposes (Clifford et al, 2013; Enríquez Raído & Sánchez Torrón, 2020; Farzi, 2016; Fredholm, 2015; Jolley & Maimone, 2015; Larson‐Guenette, 2013; O'Neill, 2019; Sukkhwan, 2014; Valijärvi & Tarsoly, 2019).…”

Section: Conclusion and Implications For Practicementioning

confidence: 99%

Looks like google to me: Instructor ability to detect machine translation in L2 Spanish writing

Maimone

Jolley

2023

Foreign Language Annals

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This article reports the results of an empirical study designed to determine the degree to which college instructors of Spanish can distinguish between machine translation (MT) and non‐MT writing samples produced by second language (L2) learners of Spanish in an intermediate‐level writing course. We also investigated relationships between detection accuracy rates, instructor teaching experience, and text type (narrative or argumentative), as well as signs instructors consider indicators of both kinds of writing. Results demonstrated that instructors were able to distinguish MT from non‐MT writing with a high degree of accuracy by relying on a broad array of indicators. However, neither text type difference nor instructor experience related significantly to detection ability. These findings have practical implications for the L2 classroom with regard to instructor response to MT use and the integration of MT tools to support L2 writing development.

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Section: Conclusion and Implications For Practicementioning

confidence: 99%

Looks like google to me: Instructor ability to detect machine translation in L2 Spanish writing

Maimone

Jolley

2023

Foreign Language Annals

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“…Learner perceptions, attitudes, and beliefs about MT and MT use in translation and L2 writing tasks have been investigated in several studies (Chen, 2020;Clifford et al, 2013;Enríquez Raído & Sánchez Torrón, 2020;Farzi, 2016;Fredholm, 2015a;Garcia & Pena, 2011;Giannetti, 2016;Jin & Deifell, 2013;Jolley & Maimone, 2015;Larson-Guenette, 2013Lee, 2020;Niño, 2009Niño, , 2020O'Neill, 2019a;Shei, 2002b, Sukkhwan, 2014Tsai, 2019;Valijärvi & Tarsoly, 2019;White & Heidrich, 2013;Xu & Wang, 2011). In Niño (2009), 81% of learners who completed post-editing activities using MT reported believing that MT helped them learn the language, while 75% noted that MT post-editing boosted their confidence.…”

Section: Learner Perceptions About Mtmentioning

confidence: 99%

Thirty Years of Machine Translation in Language Teaching and Learning: A Review of the Literature

Jolley¹,

Maimone²

2022

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Although use of machine translation (MT) technologies by learners may seem like a relatively new issue in foreign language (FL) education, researchers have been investigating connections between MT tools and FL teaching and learning for more than three decades, years before learners had access to free online services such as Google Translate. This literature review summarizes this rapidly expanding research domain both chronologically and thematically, identifying key concepts, insights, and findings and mapping them onto a framework inspired by questions commonly asked by both researchers and practitioners: How do learners use MT tools? What do instructors and learners think about MT? How might MT use affect language learning? How should instructors respond to MT use by learners? By summarizing and drawing connections between the assumptions, methods, and findings of key studies in these categories, this review provides a historical perspective and suggests new directions for future research.

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AI in the Foreign Language Classroom: A Pedagogical Overview of Automated Writing Assistance Tools

Alharbi

2023

Education Research International

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Recent technological advances in artificial intelligence (AI) have paved the way for improved and in many cases the creation of entirely new and innovative, electronic writing tools. These writing support systems assist during and after the writing process making them indispensable to many writers in general and to students in particular who can get human-like sentence completion suggestions and text generation. Although the wide adoption of these tools by students has been faced with a steady growth of scientific publications in the field, the results of these studies are often contradictory and their validity may be questioned. To gain a deeper understanding of the validity of AI-powered writing assistance tools, we conducted a systematic review of the recent empirical AI-powered writing assistance studies. The purpose of this review is twofold. First, we wanted to explore the recent scholarly publications that evaluated the use of AI-powered writing assistance tools in the classroom in terms of their types, uses, limits, and potential for improving students’ writing skills. Second, the review also sought to explore the perceptions of educators and researchers about learners’ use of AI-powered writing tools and review their recommendations on how to best ingrate these tools into the contemporary and future classroom. Using the Scopus research database, a total of 104 peer-reviewed papers were identified and analyzed. The findings indicate that students are increasingly using a variety of AI-powered writing assistance tools for improving their writing. The tools they are using can be categorized into four main groups: (1) automated writing evaluation tools, (2) tools that provide automated writing corrective feedback, (3) AI-powered machine translators, and (4) GPT-3 automatic text generators. The analysis also highlighted the scholars’ recommendations regarding dealing with learners’ use of AI-powered writing assistance tools and grouped the recommendations into two groups for researchers and educators.

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Machine Translation, Language Learning and the ‘Knowledge Economy’

Cited by 5 publications

References 1 publication

Looks like google to me: Instructor ability to detect machine translation in L2 Spanish writing

Looks like google to me: Instructor ability to detect machine translation in L2 Spanish writing

Thirty Years of Machine Translation in Language Teaching and Learning: A Review of the Literature

AI in the Foreign Language Classroom: A Pedagogical Overview of Automated Writing Assistance Tools

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