Robust Transfer Learning Based Modelling for Accelerating the Learning of Ai in the Field of NLP

Ravimaram, S.; S, Jerald Nirmal Kumar; Sathish, A.; Vatchala, S.; Rawat, Ruchira; F, Michael Raj T

doi:10.1109/icaccs57279.2023.10112829

Cited by 2 publications

(1 citation statement)

References 9 publications

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“…NLP has made signi cant progress through the collaborative effort of scholars in the elds of computer science, linguistics, psychology, and neuroscience [26][27][28][29][30]. Through several developmental stages, NLP has currently reached a point where transfer learning [31][32][33], the ability of AI to transfer the pre-trained model's learned features and adapt them to the new task using a smaller dataset, and deep learning, the ability of AI to automatically learn from the data using deep neural networks [34][35][36] have emerged as key technologies. This, in turn, equips GenAI models, such as ChatGPT, with increasingly human-like communication capabilities, making them potentially more viable peers for conversational interactions on TPACK.…”

Section: Natural Language Processingmentioning

confidence: 99%

GPT-PACK: Can GenAI act as TPACK Peer Tutor for Preservice Teachers? A Comparative Study on Objective TPACK of Humans and ChatGPT 3.5

Çelik,

Ersanlı,

Drummond

2023

Preprint

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This study employs a single case-control design to evaluate the objective TPACK performance of ChatGPT by comparing it to human preservice teachers. A purposive sampling method selected ChatGPT 3.5 as a potential GenAI peer tutor and 93 Australian preservice teachers as the normative sample. Objective TPACK performance was measured using an adapted TPACK scale. Bayesian analysis revealed that ChatGPT significantly outperformed the human control group with a substantial effect size. Qualitative data analysis of ChatGPT’s explanations further demonstrated its comprehensive understanding of TPACK dimensions. While certain limitations were identified, including the narrow focus on Australian preservice teachers and the availability of more advanced AI models, the study emphasizes ChatGPT 3.5’s potential as a more knowledgeable other for preservice teachers to enhance their TPACK. This research encourages further investigation into the integration of AI in education and its implications for pedagogical practices. ChatGPT 3.5’s remarkable TPACK performance suggests its role as a valuable peer tutor in TPACK-related domains, complementing human educators while recognizing the irreplaceable human aspects of the learning experience.

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Section: Natural Language Processingmentioning

confidence: 99%

GPT-PACK: Can GenAI act as TPACK Peer Tutor for Preservice Teachers? A Comparative Study on Objective TPACK of Humans and ChatGPT 3.5

Çelik,

Ersanlı,

Drummond

2023

Preprint

View full text Add to dashboard Cite

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Impact of an Artificial Intelligence in Language Learning - A survey

Arul Raj,

Rukmani,

Rukmani

et al. 2024

Int. J. Sci. Res. Comput. Sci. Eng. Inf. Technol

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Bully Scan, an artificial intelligence system for identifying offensive language on social media, is proposed in "A Natural Language Processing and Machine Learning-Based Framework to Automatically Identify Cyberbullying. This paradigm, which aims to reduce the negative impacts of cyberbullying and encourage healthy online interactions, is a critical step in using AI for social well-being. The paper, "Research and Practice of Hybrid Teaching Based on AI technology for Foreign Language Translation," offers a novel strategy for teaching foreign languages through the incorporation of AI. The project investigates a hybrid teaching approach that combines AI-powered language translation tools with conventional classroom training. This method seeks to improve accuracy and efficiency of language learning by providing real-time translation support. Through the use of AI technologies, such as machine learning and natural language processing, the system offers students helpful translation assistance, enhancing their educational experience. The study demonstrates encouraging outcomes in terms of raising students' proficiency and effectiveness in translation in a blended learning setting. The paper "Modular Design of English Pronunciation Level Evaluation System Based on Deep Learning Algorithm" offers a novel method for determining pronunciation levels in English by utilizing deep learning algorithms. The study uses techniques like support vector machines and BP neural networks to address the problem of computational intensity in language teaching technologies. Through the application of machine deep learning, the system seeks to improve the precision and efficacy of pronunciation level assessments, providing insightful information for the development of theories for foreign language instruction in the rapidly changing field of artificial intelligence. The study's modular design approach offers a viable foundation for enhancing pronunciation assessment in language instruction.

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Robust Transfer Learning Based Modelling for Accelerating the Learning of Ai in the Field of NLP

Cited by 2 publications

References 9 publications

GPT-PACK: Can GenAI act as TPACK Peer Tutor for Preservice Teachers? A Comparative Study on Objective TPACK of Humans and ChatGPT 3.5

GPT-PACK: Can GenAI act as TPACK Peer Tutor for Preservice Teachers? A Comparative Study on Objective TPACK of Humans and ChatGPT 3.5

Impact of an Artificial Intelligence in Language Learning - A survey

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