SACM - United States of America
Permanent URI for this collectionhttps://drepo.sdl.edu.sa/handle/20.500.14154/9668
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Item Restricted The Impact of ChatGPT Use on the Motivation and Basic Psychological Needs of Saudi EFL Students(Arizona State University, 2025) Alwadai, Abdullah; Smith, BryanThe advent of generative artificial intelligence (GenAI) represents a significant technological breakthrough, enabling models like ChatGPT to facilitate human-like interactions, personalized learning, constructive feedback, and so on. The sophistication of generative AI has attracted considerable attention from researchers who explore its potential benefits for language learners. However, since it was released a few years ago, research on ChatGPT’s effectiveness in enhancing language learning motivation for English as a foreign language (EFL) learners remains limited. To this end, this research utilized the self-determination theory (SDT) to investigate the extent to which engaging in informal interactions with ChatGPT in extramural contexts influences motivation and the basic psychological needs for autonomy, competence, and relatedness. To achieve this, a quasi-experimental design was employed with fifty EFL Saudi undergraduate students majoring in English, divided equally into an experimental and a control group. The experimental group participated in nine sessions over three weeks, engaging in informal self-directed interactions with ChatGPT on common everyday topics, while the control group responded to the same topics in writing without using AI, following the same number of sessions. The post-test results analyzed using analysis of covariance (ANCOVA) indicated a substantial increase in autonomous motivation and a decrease in controlled motivation in the experimental group after the treatment. Moreover, the results revealed a significant increase in autonomy exclusively in the experimental group. However, no statistically significant difference was observed in competence and relatedness after the intervention. Informed by these findings, a number of implications and pedagogical recommendations for language instructors, policymakers, and other stakeholders were proposed.60 0Item Restricted Developing a Generative AI Model to Enhance Sentiment Analysis for the Saudi Dialect(Texas Tech University, 2024-12) Aftan, Sulaiman; Zhuang, YuSentiment Analysis (SA) is a fundamental task in Natural Language Processing (NLP) with broad applications across various real-world domains. While Arabic is a globally significant language with several well-developed NLP models for its standard form, achieving high performance in sentiment analysis for the Saudi Dialect (SD) remains challenging. A key factor contributing to this difficulty is inadequate SD datasets for training of NLP models. This study introduces a novel method for adapting a high-resource language model to a closely related but low-resource dialect by combining moderate effort in SD data collection with generative AI to address this problem of inadequacy in SD datasets. Then, AraBERT was fine-tuned using a combination of collected SD data and additional SD data generated by GPT. The results demonstrate a significant improvement in SD sentiment analysis performance compared to the AraBERT model, which is fine-tuned with only collected SD datasets. This approach highlights an efficient approach to generating high-quality datasets for fine-tuning a model trained on a high-resource language to perform well in a low-resource dialect. Leveraging generative AI enables reduced effort in data collection, making our approach a promising avenue for future research in low-resource NLP tasks.41 0