What matters in AI-supported learning: A study of human-AI interactions in language learning using cluster analysis and epistemic network analysis

Wang, Xinghua; Liu, Qian; Pang, Hui; Tan, Seng Chee; Lei, Jun; Wallace, Matthew P.; Li, Linlin

doi:10.1016/j.compedu.2022.104703

Cited by 60 publications

(25 citation statements)

References 51 publications

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“…AI facilitates more accessible and cost-effective availability of language learning resources. AI-driven learning methodologies can enhance the advantages of personalized AI support and reinforce the sense of a collaborative learning environment between humans and AI (Gill et al, 2024;Wang, Liu et al, 2023). AIpowered language learning app, students can learn anytime and anywhere, without limitations of time or place.…”

Section: Ai In Language Educationmentioning

confidence: 99%

Assessing text comprehension proficiency: Indonesian higher education students vs ChatGPT

Juanda,

Afandi

2024

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AI has developed rapidly. However, AI research in applied linguistics in the field of language education in Indonesia still needs to be expanded to reading and writing skills. This research aims to explore students' skills in writing summaries and understanding the historical theme of the development of the Indonesian language with AI based on gender and university aspects. The quantitative method uses descriptive statistical analysis techniques, independent sample t-test, and Welch One-Way ANOVA. The research sample was 288 students from Makassar State University, Timor University, and Makassar Health Polytechnic. The results show that ChatGPT is significantly better at reprocessing text than students based on the aspects measured. ChatGPT outperforms almost every aspect of the assessment. However, in the MCT_Score aspect, the average for Universitas Negeri Makassar students is slightly higher than ChatGPT and the other two universities. Meanwhile, the Makassar Health Polytechnic almost matches the average ChatGPT score. Apart from that, the Universitas Timor average seems significantly different, with a score range of only 6.00 – 7.00. This research contributes to developing the Indonesian curriculum using Artificial Intelligence (AI) technology. The government can use these findings as a basis for making better policies to improve the quality of education. This research implies that Indonesian students have a gap in understanding texts compared to ChatGPT. The first implication is the need to revise and develop the educational curriculum. Therefore, future research can examine text comprehension abilities in more specific contexts, such as scientific texts, journalism, literature, or specific scientific disciplines. It can provide more detailed insight into students' strategies for overcoming difficulties in understanding texts. In addition, future research will be conducted on the broader impact of using Artificial Intelligence technology in language education on the development of student text comprehension and the potential social and ethical impacts.

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Section: Ai In Language Educationmentioning

confidence: 99%

Assessing text comprehension proficiency: Indonesian higher education students vs ChatGPT

Juanda,

Afandi

2024

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“…Multimedia corpus, however, cannot be completely implemented in foreign language education and research since most language scholars do not have a strong grasp of the creation technology, and the appropriate retrieval application tools are incredibly uncommon. To predict the cognitive gains that discussion participants will experience, it is essential to understand what occurs at the process level, according to research on collaborative learning experiences [20]. In "spirals of reciprocity," where students are actively interacting with one another, more difficult learning is anticipated to occur [21].…”

Section: Accurate Identification Of Cross-lingual Wordsmentioning

confidence: 99%

Design and Application of Corpus in Computational Linguistics based on Multimedia Virtual Technology

Cao,

Cui,

et al. 2023

SCPE

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In this paper a design and application of corpus in computational linguistics is proposed based on multimedia technology in order to solve the problem of the combination of corpus technology and multimedia teaching methods. The hardware interface circuit is created based on the USB interface board, and the S3C6410 CPU is chosen to build a multimedia embedded processor. The proposed study is primarily concerned with creating and using a corpus for computational linguistics that is based on multimedia virtual technology. Multimedia virtual technologies will be employed in this study to set up an environment that is appropriate for gathering and analyzing linguistic data. The goal is to offer a language analysis method that is more effective and efficient, as standard approaches have trouble dealing with complex and varied data sources. The accuracy, scope, and variety of the language data are anticipated to be improved by the suggested methodology. The research also suggests a possible way to improve the Corpus’s design and use in computational linguistics. In this study, encouraging results and an in-depth examination of important issues, such as reliability, validity, and efficiency, are examined. These issues should be measured when considering whether adopting a new technology is the right decision. According to the testing findings, the authors' shared system had the least reaction time-about 1.9 seconds. In conclusion, the author's corpus is more adaptable, usable, and practical for use in the classroom.

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“…The application of artificial intelligence (AI) in education has been a subject of growing interest over the past decade. This is especially the case in language learning, where AI agents have been deployed to provide bespoke instructions for students in large classes and offer targeted and unlimited practice opportunities that are otherwise unrealisable in traditional classroom settings [ 1 ]. Chatbots-software applications designed to simulate human-like conversations by responding to voice or text inputs-are demonstrably effective as pedagogical tools.…”

Section: Introductionmentioning

confidence: 99%

Is AI changing learning and assessment as we know it? Evidence from a ChatGPT experiment and a conceptual framework

Kolade,

Owoseni,

Egbetokun

2024

Heliyon

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What matters in AI-supported learning: A study of human-AI interactions in language learning using cluster analysis and epistemic network analysis

Cited by 60 publications

References 51 publications

Assessing text comprehension proficiency: Indonesian higher education students vs ChatGPT

Assessing text comprehension proficiency: Indonesian higher education students vs ChatGPT

Design and Application of Corpus in Computational Linguistics based on Multimedia Virtual Technology

Is AI changing learning and assessment as we know it? Evidence from a ChatGPT experiment and a conceptual framework

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