Effects of artificial intelligence on English speaking anxiety and speaking performance: A case study

Shazly, Reham El

doi:10.1111/exsy.12667

Cited by 79 publications

(33 citation statements)

References 49 publications

(169 reference statements)

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“…For the first correlation, it was revealed that self-efficacy showed a strongly positive correlation to learners' speaking performance. This finding echoed a number of previous studies (Anyadubalu, 2010;El Shazly, 2021;Erkan & Saban, 2011;Li & Wang, 2010;Quinto & MacAyan, 2020;Sabti et al, 2019;Tridinanti, 2018). This implies that it is inevitable that self-efficacy be cultivated to increase learners' English performance, particularly speaking.…”

Section: Self-efficacy and Speaking Performancesupporting

confidence: 77%

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Do I Speak Anxiously? A Correlation of Self-Efficacy, Foreign Language Learning Anxiety and Speaking Performance of Indonesian EFL Learners

Hermagustiana¹,

Astuti

Sucahyo

2021

scr. j. j. linguist. engl. teach.

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Background: Self-efficacy and foreign language learning anxiety are already known as two importantly affective variables in learning a foreign language. However, little is known about the relationships between the two affective variables with learners’ speaking performance. Therefore, this present study seeks to figure out: the EFL learners’ self-efficacy, foreign language learning anxiety (FLLA), speaking performance, and the correlation between their speaking performance and self-efficacy, speaking performance and FLLA, and self-efficacy and FLLA. Methodology: A correlational design was selected to answer the research questions. Seventy students of the sixth semester got involved in this study. Three research instruments were applied, namely: a self-efficacy questionnaire, a FLLA questionnaire, and one speaking test used to measure students’ speaking performance. Findings: Based on these results, it can be concluded that the null hypothesis (Ho) was rejected, while the alternative hypothesis (Ha) was accepted. Therefore, there was a significantly positive correlation between speaking performance and self-efficacy and a negative correlation between speaking performance and FLLA, and between self-efficacy and FLLA. It means that student with high-level speaking performance will have high-level of self-efficacy. Then, the higher students’ speaking performance is, the lower their FLLA becomes. It is in line with the correlation between students’ self-efficacy and FLLA, that student with high-level of self-efficacy would has low FLLA.

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Section: Self-efficacy and Speaking Performancesupporting

confidence: 77%

“…English learners use four skills to learn English: listening, speaking, reading, and writing. In order to achieve the aim of learning English, speaking ability is the most important in communicating and provides several problems (El Shazly, 2021;Le & Nguyen, 2021;Tridinanti, 2018). They are nervous when they are learning to talk.…”

Section: Introductionmentioning

confidence: 99%

Do I Speak Anxiously? A Correlation of Self-Efficacy, Foreign Language Learning Anxiety and Speaking Performance of Indonesian EFL Learners

Hermagustiana¹,

Astuti

Sucahyo

2021

scr. j. j. linguist. engl. teach.

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“…In a recent review on conversational AI in language education, the authors found that there are five main applications of conversational AI during teaching [22], the most common one being the use of large language models as a conversational partner in a written or oral form, e.g., in the context of a task-oriented dialogue that provides language practice opportunities such as pronunciation [23]. Another application is to support students when they experience foreign language learning anxiety [24] or have a lower willingness to communicate [25].…”

Section: Review Of Research Applying Large Languagementioning

confidence: 99%

ChatGPT for Good? On Opportunities and Challenges of Large Language Models for Education

Kasneci¹,

Seßler²,

Küchemann³

et al. 2023

Preprint

133

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Large language models represent a significant advancement in the field of AI. The underlying technology is key to further innovations and, despite critical views and even bans within communities and regions, large language models are here to stay. This position paper presents the potential benefits and challenges of educational applications of large language models, from student and teacher perspectives. We briefly discuss the current state of large language models and their applications. We then highlight how these models can be used to create educational content, improve student engagement and interaction, and personalize learning experiences. With regard to challenges, we argue that large language models in education require teachers and learners to develop sets of competencies and literacies necessary to both understand the technology as well as their limitations and unexpected brittleness of such systems. In addition, a clear strategy within educational systems and a clear pedagogical approach with a strong focus on critical thinking and strategies for fact checking are required to integrate and take full advantage of large language models in learning settings and teaching curricula. Other challenges such as the potential bias in the output, the need for continuous human oversight, and the potential for misuse are not unique to the application of AI in education. But we believe that, if handled sensibly, these challenges can offer insights and opportunities in education scenarios to acquaint students early on with potential societal biases, criticalities, and risks of AI applications. We conclude with recommendations for how to address these challenges and ensure that such models are used in a responsible and ethical manner in education.

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“…At the 2010 American Society for Supervision and Curriculum Development seminar on personalized learning, participants reached a consensus on five basic elements of personalized learning, namely, flexible learning time and space, reshaping teacher roles, and task-based inquiry learning, custom learning path, and custom learning pace [15]. The Bill & Melinda Gates Foundation and other organizations believe that the implementation of personalized learning should rely on electronic school bags, customized paths, free environment, and the support of competitiveness [16].…”

Section: Introductionmentioning

confidence: 99%

Macro Education Approach to Improve Learning Interest under the Background of Artificial Intelligence

Hou

Liu

2022

Wireless Communications and Mobile Computing

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With the advent of the “Internet+” era, with the rapid development of emerging technologies such as the Internet of Things, cloud computing, big data, and artificial intelligence, the era of the technological change in education has arrived, with diversification of resources and large-scale data. And the intelligence of computing provides an opportunity for the research and practice of personalized support services. Personalized learning is the future learning method under the demands of smart education, and the learner’s interest feature model is the core of personalized learning services. Although the research on smarter classrooms has achieved certain results, there are still shortcomings that cannot be ignored, that is, how to use smarter classrooms to meet the “personalized needs” of learners and give students “personalized feedback” is still an urgent problem to be solved. Therefore, building a student interest model in a smart learning environment will help teachers better capture students’ learning interests and personalized needs, so as to provide them with personalized learning services.

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Effects of artificial intelligence on English speaking anxiety and speaking performance: A case study

Cited by 79 publications

References 49 publications

Do I Speak Anxiously? A Correlation of Self-Efficacy, Foreign Language Learning Anxiety and Speaking Performance of Indonesian EFL Learners

Do I Speak Anxiously? A Correlation of Self-Efficacy, Foreign Language Learning Anxiety and Speaking Performance of Indonesian EFL Learners

ChatGPT for Good? On Opportunities and Challenges of Large Language Models for Education

Macro Education Approach to Improve Learning Interest under the Background of Artificial Intelligence

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