Adaptive Pathways within the European Platform for Personalized Language Learning PEAPL

Karoui, Aous; Alvarez, Lionel; Goffre, Thierry; Chapuis, Nathalie Dherbey; Rodi, Mireille; Ramalho, Mário

doi:10.1145/3450614.3464480

Cited by 7 publications

(2 citation statements)

References 3 publications

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“…In this context, many studies have focused on the development of learner-centric tools and content personalization to address diverse learners' preferences. These studies cover a range of applications, including personalized readability assessment for L2 reading (Ehara, 2022), difficulty detection and adaptation for practicing grammar , personalized conversational AI agents (Dizon et al, 2022), robot-assisted language learning (Randall, 2019), and simulation and games (Karoui et al, 2021;Peterson & Jabbari, 2022). Notably, there is a specific focus on personalized mobile-assisted learning (Gumbheer et al, 2022).…”

Section: Introductionmentioning

confidence: 99%

EuroCALL 2023. CALL for all Languages - Short Papers

2023

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Section: Introductionmentioning

confidence: 99%

EuroCALL 2023. CALL for all Languages - Short Papers

2023

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“…In this context, many studies have focused on the development of learner-centric tools and content personalization to address diverse learners' preferences. These studies cover a range of applications, including personalized readability assessment for L2 reading (Ehara, 2022), difficulty detection and adaptation for practicing grammar (Pandarova et al, 2019), personalized conversational AI agents (Dizon et al, 2022), robot-assisted language learning (Randall, 2019), and simulation and games (Karoui et al, 2021;Peterson & Jabbari, 2022). Notably, there is a specific focus on personalized mobile-assisted learning (Gumbheer et al, 2022).…”

Section: Introductionmentioning

confidence: 99%

The use of machine learning in developing learner-adaptive tools for second language acquisition

Mirzaei,

Meshgi

2023

EuroCALL 2023. CALL for All Languages - Short Papers

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Advancements in artificial intelligence and machine learning present opportunities to revolutionize language learning tools with learner-adaptive capabilities. These technologies facilitate the creation of trainable systems that can interact with learners, offering personalized learning experiences tailored to individual needs, interests, proficiency levels, backgrounds, and native languages. This study explores the role of machine learning in developing personalized frameworks for second language learning, introducing the Partial and Synchronized Caption (PSC) tool as an example. PSC utilizes automatic speech recognition and natural language processing to identify challenging words for language learners, which are presented in the caption while masking easy words. We used machine learning to personalize the caption for various learners. An experiment involving graduate students learning English as a second language demonstrated the adaptability of PSC's word selection to different learners. While creating entirely personalized captions may be challenging, PSC offers a promising approach to personalized and adaptable listening tools. The data collected from learner interactions also provides valuable insights into individual needs, shaping future language learning tools and pedagogical practices.

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