Speaking to machines: motivating speaking through oral interaction with intelligent assistants (original) (raw)
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The main objective of this study was to identify ways to incorporate voice-driven Artificial Intelligence (AI) effectively in classroom language learning. This nine month teacher-led design research study employed technology probes (Amazon's Alexa, Apple's Siri, Google voice search) and co-design methods with a class of primary age English as a Foreign Language (EFL) students to explore and develop ideas for classroom activities using AI language assistants. Speaking to AI assistants was considered highly engaging by all students. Students were observed to speak more English when using AI assistants in group work, and to spontaneously reformulate, self-correct, and joyfully and playfully persist with speaking English in their attempts to get AI assistants to do what they wanted them to do.
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This article covers the didactic potential of the intelligent personal assistants (voice assistants) for reaching the communicative competence during the foreign language acquisition. The authors of the article emphasize that the variety of modern voice assistants embedded to the users’ personal devices (such as Siri, Google Assistant, etc.) and available for teachers as tools is necessary to study in detail since their functions and communicative abilities (although they have some visible similarities) nevertheless differ. These differences become especially distinctly when we use these “programs” at the foreign language classes. To illustrate that, authors describe particularly one of the newest voice assistants’ educational potential, the Russian assistant of Yandex – Alice, in Russian as a foreign language class. The article outlines the differences that Alice has with the analog voice assistants and describes its didactic capacities, as well as how they can be used from the very beginning of language learning. The authors of the research also present a system of the exercises with Alice that supplements the blended language learning model for the beginners. The system of the exercises was tested during teaching Russian in several multinational students’ groups and the results of the experiment are represented in the study in detail. Keywords: teaching foreign languages, teaching Russian as a foreign language, mobile learning, virtual assistant, voice assistants.
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Abstract. Learning a second language is a challenging endeavour which requires various degrees of support. The proliferation of smart technologies includes chatbots and conversational agents which have the potential to ‘assist’ language learners (Kukulska-Hulme, 2019). However, whilst a growing number of researchers and developers are working on such intelligent assistants across different disciplines, little is known about their application to language learning. The aim of this project was to review relevant research literature over a ten-year period (2010-2020) in order to uncover the capabilities and limitations of Intelligent Assistants (IAs) in relation to language learning. Results suggest that IAs can assist learners in a variety of ways, including provision for conversation and pronunciation practice. These tools can also fail to comprehend meaning or accented pronunciation. The analysis highlighted gaps in research around skills development, task design, pedagogy, and the use of chatbots in virtual worlds. Keywords: mobile learning, chatbots, voice assistants, smart assistants, avatars.
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CALL for widening participation: short papers from EUROCALL 2020, 2020
Learning a second language is a challenging endeavour which requires various degrees of support. The proliferation of smart technologies includes chatbots and conversational agents which have the potential to ‘assist’ language learners (Kukulska-Hulme, 2019). However, whilst a growing number of researchers and developers are working on such intelligent assistants across different disciplines, little is known about their application to language learning. The aim of this project was to review relevant research literature over a ten-year period (2010-2020) in order to uncover the capabilities and limitations of Intelligent Assistants (IAs) in relation to language learning. Results suggest that IAs can assist learners in a variety of ways, including provision for conversation and pronunciation practice. These tools can also fail to comprehend meaning or accented pronunciation. The analysis highlighted gaps in research around skills development, task design, pedagogy, and the use of chat...
INTED2018 Proceedings, 2018
In this paper, we address challenges arising from the use of the conversational interface Siri during a literacy activity in a learning context. On one hand, we investigate potentialities of Siri as a virtual assistant and knowledge navigator for task accomplishment. Thus, we attempt to suggest possible lines of mobilizing Siri to afford and improve both students' task elaboration and second language performing during a literacy activity. In human-Siri interactions, a wide range of operations are carried out through closely intertwined oral and written instances of natural language (vocal and visual accounts of 'understanding'). On the other hand, we raise some crucial, critical questions with regard to concrete situations involving Siri as a 'learning' assistant. How do students deal with the unpredictability of the interactions with the virtual conversational agent? How do students handle interaction modalities such as identifying and 'well' pronouncing the right commands and the choice of words that activate the diverse features? Our paper seeks to provide Conversation Analysis (CA) [1] informed insights into human-Siri communication in a learning context.
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The results of our previous research on the pedagogical use of Speaking Robots (SRs) revealed positive effects on motivating students to practice their oral skills in a stress-free environment. However, our findings indicated that the SR was sometimes unable to understand students' foreign accented speech. In this paper, we report the results of a study that investigated the ability of an SR to recognize and process non-native English speech from different levels of accentedness. The analysis is based on how the SR handled the participants' speech in terms of accuracy, the number and types of communication breakdowns observed, and how the participants behaved to solve the interaction problems that they experienced with the SR. Based on the study's surveys, interviews, and observations of users' interactions with the device, the results emphasize SRs' potential to recognize different types of accented L2 speech and their use as pedagogical tools.
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Conversational practice, while crucial for all language learners, can be challenging to get enough of and very expensive. Chatbots are computer programs developed to engage in conversations with humans. They are designed as software avatars with limited, but growing conversational capability. The most natural and potentially powerful application of chatbots is in line with their fundamental nature-language practice. However, their role and outcomes within (in)formal language learning are currently tangential at best. Existing research in the area has generally focused on chatbots' comprehensibility and the motivation they inspire in their users. In this paper, we provide an overview of the chatbots for learning languages, critically analyze existing approaches, and discuss the major challenges for future work.
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The digital era seems to have led to the atrophy of our ability to converse with ourselves and to empathize. Thus, in the school environment it is increasingly necessary to emphasize sharing the energy of students' emotions, generating a climate that is highly dynamic, rich, fluid, and creative. This chapter describes a didactic activity that sees conversational agents as a key to generating engaging learning experiences, thus reconsidering and reinterpreting the traditional class period. Technology can facilitate the return to a form of learning centered around conversation itself, not only between man and machine but above all between humans. It can do this by stepping aside at the right time. To help achieve this goal, we hereby present a didactic tool for the study of Greek literature — the conversational agent “Sappho the Poet” (it. “La poetessa Saffo”), modeled after one of the most mysterious and iconic figures of all classicism.