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研究生: 顏于慈
Yen, Yu-Ci
論文名稱: 大型語言模型 ChatGPT 於學測英文考科中選擇題及混合題表現之探究
Exploring the Performance of ChatGPT in Taking Multiple-Choice Questions and Integrated Questions in GSAT English Language Tests
指導教授: 陳浩然
Chen, Hao-Jan
口試委員: 陳浩然
Chen, Hao-Jan
張珮青
Chang, Pei-Chin
吳宜儒
Wu, Yi-Ju
口試日期: 2024/07/02
學位類別: 碩士
Master
系所名稱: 英語學系
Department of English
論文出版年: 2024
畢業學年度: 112
語文別: 英文
論文頁數: 102
中文關鍵詞: ChatGPT語言模型學測英語測驗
英文關鍵詞: ChatGPT, Language Model, GSAT, English Language Test
DOI URL: http://doi.org/10.6345/NTNU202400924
論文種類: 學術論文
相關次數: 點閱:102下載:28
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  • 人工智慧現今已然融入人們生活的各個層面之中。劃時代的尖端科技也逐漸改變人們對世界的認知及與之的互動方式。就英語學習而言,自OpenAI於2022年推出大型語言模型ChatGPT以來,ChatGPT已為因工作需求或正在學習語言的人們提供了方便的資源。鑑於英語測驗於評估英語學習成果中的關鍵角色,本研究旨在探討:(1) ChatGPT在英語語言測驗中是否為值得信賴的英語學習工具,及 (2) 其於作答英語語言測驗中的的能力及隱憂。
    本研究評估了ChatGPT(GPT-4和GPT-4o)於2017至2024年的台灣學科能力測驗(學測)英文考科中的表現。測驗題型包括詞彙題、綜合測驗、文意選填、篇章結構、閱讀測驗以及混合題。研究顯示,GPT-4和GPT-4o於每年度的學測英文考科之答題準確率分別落在86%到100%及92.86%到100%區間。GPT-4o在詞彙和語法知識、閱讀技能和圖像處理能力方面優於GPT-4。若以題型來看,GPT-4o在整體表現上優於GPT-4,於詞彙題和篇章結構測驗中取得滿分,並在閱讀測驗、文意選填和綜合測驗中表現出色,作答準確率分別為98.28%、97.50%和96.19%。然而,對於因台灣新課綱的實施而納入的混合題題型,GPT-4和GPT-4o的作答準確率皆僅為66.67%,顯示出大型語言模型處理此類題型的可信度相對較低。而ChatGPT於混合題型中之多選題答題中所顯現出的邏輯謬誤及錯誤解讀,抑或是相對較少的混合題題型之題目數量皆可能為其於混合題題型上表現相對不出色的潛在原因。總體而言,大型語言模型ChatGPT的學測英文答題準確率顯示其在英語學習中具有幫助學生解題的潛力。

    As software eats the world, Artificial Intelligence (AI) is now eating the software. In this era, where AI in integrated into every aspect of life, the cutting-edge technology is gradually changing how people perceive and interact with this world.
    In the field of English language learning, the OpenAI’s large language model, ChatGPT, has been providing a much-needed resources for people who are striving to use or learn language ever since its launch in 2022. Given the crucial role of tests in evaluating learning outcomes, the present study aims to examine: (1) whether ChatGPT can be a reliable language partner for English language learners in the post-exam review, and (2) its potential strengths and weaknesses in handling English language tests.

    The study assesses the performance of ChatGPT (GPT-4 and GPT-4o) on the General Scholastic Academic Test (GSAT) English language tests from 2017 to 2024. The given tasks include Vocabulary, Rational Cloze, Banked Cloze, Discourse, Reading Comprehension tasks, and Integrated Questions. The results show that GPT-4 and GPT-4o achieve accuracy rates ranging from 86% to 100% and 92.86% to 100% respectively in each year of the test. GPT-4o, with generally better performance than GPT-4, achieves full marks in Vocabulary and Discourse tasks, and excels at Reading Comprehension, Banked Cloze, and Rational Cloze tasks, with accuracy rates of 98.28%, 97.50%, and 96.19% respectively. However, the 66.67% accuracy of both GPT-4 and GPT-4o in Integrated Questions, incorporated due to the implementation of new curriculum in Taiwan, suggests that the large language model is unreliable for this task type. The relatively low accuracy rate for Integrated Questions may results from the fewer items of this task type or from the logical fallacy and incorrect grasp of texts observed in its response in the research.

    Overall, the study indicates that GPT-4o may possess superior lexical and grammatical knowledge, reading skills, and image-processing capabilities than GPT-4. The remarkable accuracy rates of ChatGPT showcases its potential to assist English language learners when the learners need help.

    ACKNOWLEDGEMENTS i CHINESE ABSTRACT ii ENGLISH ABSTRACT iii TABLE OF CONTENTS v LIST OF TABLES vii LIST OF FIGURES viii Chapter One: Introduction 1 1.1 Background and Motivation 1 1.2 Purpose of the Study 3 1.3 Research Questions 4 Chapter Two: Literature Review 6 2.1 ChatGPT 6 2.1.1 Generative AI 6 2.1.2 Overview of Language Models 7 2.1.3 Techniques in Language Modeling 8 2.1.4 Capabilities of Language Models 9 2.1.5 The Rise of ChatGPT 11 2.2 ChatGPT and English Writing and Reading 12 2.2.1 Applying ChatGPT in English Writing 13 2.2.2 Applying ChatGPT in English Reading 15 2.2.3 Summary of ChatGPT’s Support in English Writing and Reading 19 2.3 ChatGPT and Test Taking 20 2.3.1 ChatGPT and English Language Test 21 2.3.2 ChatGPT and Tests in Other Fields 24 2.3.3 Summary of ChatGPT’s Test-Taking Ability 28 2.4 Concerns About ChatGPT 29 2.5 Summary of Chapter Two 32 Chapter Three: Methodology 33 3.1 GPT-4 33 3.2 GPT-4o 33 3.3 The GSAT English Language Tests 34 3.4 Procedure 40 Chapter Four: Results 42 4.1 ChatGPT’s Performance on GSAT English Language Tests Each Year 42 4.2 ChatGPT’s Performance on GSAT English Language Tests by Task Types 44 4.3 Incorrectly Answered Test Items 47 Chapter Five: Discussion 70 5.1 The Performance of ChatGPT on GSAT English Language Tests 70 5.2 ChatGPT’s Strengths and Weakness in Taking English Language Tests 73 Chapter Six: Conclusion 78 6.1 Major Findings and Pedagogical Implications 78 6.2 Limitation of the Study and Suggestions for Future Studies 80 References 83 Appendices 97 Appendix A Incorrectly Answered Integrated Question in 2024 GSAT English Language Tests 97 Appendix B Incorrectly Answered Integrated Question in 2023 GSAT English Language Tests 99 Appendix C Incorrectly Answered Integrated Question in 2022 GSAT English Language Tests 101

    Ali, J. K. M., Shamsan, M. A. A., Hezam, T. A., & Mohammed, A. A. (2023). Impact of ChatGPT on learning motivation: teachers and students' voices. Journal of English Studies in Arabia Felix, 2(1), 41-49.
    Amini-Salehi, E., Bozorgi, A., Keivanlou, M. H., Joukar, F., Dave, T., Alotaibi, A., ... & Hassanipour, S. (2023). Do You Really Want to Use Chat-GPT for Paraphrasing Your Texts?. Available at SSRN 4514430.
    Anders, B. A. (2023). Is using ChatGPT cheating, plagiarism, both, neither, or forward thinking?. Patterns, 4(3).
    Athaluri, S. A., Manthena, S. V., Kesapragada, V. K. M., Yarlagadda, V., Dave, T., & Duddumpudi, R. T. S. (2023). Exploring the boundaries of reality: investigating the phenomenon of artificial intelligence hallucination in scientific writing through ChatGPT references. Cureus, 15(4).
    Ausat, A. M. A., Massang, B., Efendi, M., Nofirman, N., & Riady, Y. (2023). Can chat GPT replace the role of the teacher in the classroom: A fundamental analysis. Journal on Education, 5(4), 16100-16106.
    Aydin, Ö., Karaarslan, E. (2023). Is ChatGPT Leading Generative AI? What is Beyond Expectations?
    Aydın, Ö., & Karaarslan, E. (2022). OpenAI ChatGPT generated literature review: Digital twin in healthcare. Available at SSRN 4308687.
    Ayre, J., Mac, O. A., McCaffery, K. J., McKay, B. R., Liu, M., Shi, Y., ... & Dunn, A. G. (2023). New frontiers in health literacy: Using ChatGPT to simplify health information for people in the community. medRxiv, 2023-07.
    Bai, Z., Liu, J., Wang, M., Yuan, C., & Wang, X. (2022). Exploiting diverse information in pre-trained language model for multi-choice machine reading comprehension. Applied Sciences, 12(6), 3072.
    Bandi, A., Adapa, P. V. S. R., & Kuchi, Y. E. V. P. K. (2023). The Power of Generative AI: A Review of Requirements, Models, Input–Output Formats, Evaluation Metrics, and Challenges. Future Internet, 15(8), 260.
    Bang, Y., Cahyawijaya, S., Lee, N., Dai, W., Su, D., Wilie, B., ... & Fung, P. (2023). A multitask, multilingual, multimodal evaluation of chatgpt on reasoning, hallucination, and interactivity. arXiv preprint arXiv:2302.04023.
    Bin-Hady, W. R. A., Al-Kadi, A., Hazaea, A., & Ali, J. K. M. (2023). Exploring the dimensions of ChatGPT in English language learning: A global perspective. Library Hi Tech.
    Biswas, S. (2022). Role of ChatGPT in Computer Programming.. Mesopotamian Journal of Computer Science. https://doi.org/10.58496/mjcsc/2022/004.
    Bonner, E., Lege, R., & Frazier, E. (2023). LARGE LANGUAGE MODEL-BASED ARTIFICIAL INTELLIGENCE IN THE LANGUAGE CLASSROOM: PRACTICAL IDEAS FOR TEACHING. Teaching English with Technology, 23(1).
    Chang, Y., Wang, X., Wang, J., Wu, Y., Zhu, K., Chen, H., ... & Xie, X. (2023). A survey on evaluation of large language models. arXiv preprint arXiv:2307.03109.
    Cheung, B. H. H., Lau, G. K. K., Wong, G. T. C., Lee, E. Y. P., Kulkarni, D., Seow, C. S., ... & Co, M. T. H. (2023). ChatGPT versus human in generating medical graduate exam multiple choice questions—A multinational prospective study (Hong Kong SAR, Singapore, Ireland, and the United Kingdom). PloS one, 18(8), e0290691.
    Choi, J. H., Hickman, K. E., Monahan, A., & Schwarcz, D. (2023). Chatgpt goes to law school. Available at SSRN.
    College Entrance Examination Center (n.d.). English. College Entrance Examination Center. Retrieved May 18th, 2023, from https://www.ceec.edu.tw/en/xmdoc/cont?xsmsid=0J180519944235388511
    Dao, X. Q., Le, N. B., Phan, X. D., & Ngo, B. B. (2023). An Evaluation of ChatGPT’s Proficiency in English Language Testing of The Vietnamese National High School Graduation Examination. Available at SSRN 4473369.
    de Winter, J. C. (2023). Can ChatGPT pass high school exams on English language comprehension. Researchgate. Preprint.
    Dergaa, I., Chamari, K., Zmijewski, P., & Saad, H. B. (2023). From human writing to artificial intelligence generated text: examining the prospects and potential threats of ChatGPT in academic writing. Biology of Sport, 40(2), 615-622.
    Donmez, I., Idil., S. & Gulen, S. (2023). Conducting academic research with the AI interface ChatGPT: Challenges and opportunities. Journal of STEAM Education, 6(2), 101-118. https://doi.org/10.55290/steam.1263404
    Du, L., Ding, X., Xiong, K., Liu, T., & Qin, B. (2022). e-CARE: a new dataset for exploring explainable causal reasoning. arXiv preprint arXiv:2205.05849.
    Duong, D., & Solomon, B. D. (2023). Analysis of large-language model versus human performance for genetics questions. European Journal of Human Genetics, 1-3.
    Fang, X., Ng, D. T. K., Leung, J. K. L., & Chu, S. K. W. (2023). A systematic review of artificial intelligence technologies used for story writing. Education and Information Technologies, 1-37.
    Fijačko, N., Gosak, L., Štiglic, G., Picard, C. T., & Douma, M. J. (2023). Can ChatGPT pass the life support exams without entering the American heart association course?. Resuscitation, 185.
    Firat, M. (2023). What ChatGPT means for universities: Perceptions of scholars and students. Journal of Applied Learning and Teaching, 6(1).
    Fitria, T. N. (2021). The use technology based on artificial intelligence in English teaching and learning. ELT Echo: The Journal of English Language Teaching in Foreign Language Context, 6(2), 213-223.
    Gao, C. A., Howard, F. M., Markov, N. S., Dyer, E. C., Ramesh, S., Luo, Y., & Pearson, A. T. (2022). Comparing scientific abstracts generated by ChatGPT to original abstracts using an artificial intelligence output detector, plagiarism detector, and blinded human reviewers. BioRxiv, 2022-12.
    Geerling, W., Mateer, G. D., Wooten, J., & Damodaran, N. (2023). ChatGPT has aced the test of understanding in college economics: Now what?. The American Economist, 05694345231169654.
    Gilson, A., Safranek, C. W., Huang, T., Socrates, V., Chi, L., Taylor, R. A., & Chartash, D. (2023). How does ChatGPT perform on the United States medical licensing examination? The implications of large language models for medical education and knowledge assessment. JMIR Medical Education, 9(1), e45312.
    Gozalo-Brizuela, R., & Garrido-Merchan, E. C. (2023). ChatGPT is not all you need. A State of the Art Review of large Generative AI models. https://doi.org/10.48550/arXiv.2301.04655
    Guo, K., Zhong, Y., Li, D., & Chu, S. K. W. (2023). Effects of chatbot-assisted in-class debates on students’ argumentation skills and task motivation. Computers & Education, 104862.
    Guo, B., Zhang, X., Wang, Z., Jiang, M., Nie, J., Ding, Y., ... & Wu, Y. (2023). How close is chatgpt to human experts? comparison corpus, evaluation, and detection. https://doi.org/10.48550/arXiv.2301.07597
    Haleem, A., Javaid, M., & Singh, R. P. (2022). An era of ChatGPT as a significant futuristic support tool: A study on features, abilities, and challenges. BenchCouncil transactions on benchmarks, standards and evaluations, 2(4), 100089.
    Han, J., Yoo, H., Myung, J., Kim, M., Lee, T. Y., Ahn, S. Y., ... & Answer, A. N. (2023). Exploring Student-ChatGPT Dialogue in EFL Writing Education. In Thirty-seventh Conference on Neural Information Processing Systems. Neural information processing systems foundation.
    Hong, W. C. H. (2023). The impact of ChatGPT on foreign language teaching and learning: opportunities in education and research. Journal of Educational Technology and Innovation, 5(1).
    Hosseini, M., Rasmussen, L. M., & Resnik, D. B. (2023). Using AI to write scholarly publications. Accountability in research, 1-9.
    Huh, S. (2023). Are ChatGPT’s knowledge and interpretation ability comparable to those of medical students in Korea for taking a parasitology examination?: a descriptive study. J Educ Eval Health Prof, 20(1).
    Jalil, S., Rafi, S., LaToza, T. D., Moran, K., & Lam, W. (2023, April). Chatgpt and software testing education: Promises & perils. In 2023 IEEE International Conference on Software Testing, Verification and Validation Workshops (ICSTW) (pp. 4130-4137). IEEE.
    Ji, Z., Lee, N., Frieske, R., Yu, T., Su, D., Xu, Y., ... & Fung, P. (2023). Survey of hallucination in natural language generation. ACM Computing Surveys, 55(12), 1-38.
    Jiang, Z., Xu, F. F., Araki, J., & Neubig, G. (2020). How can we know what language models know?. Transactions of the Association for Computational Linguistics, 8, 423-438.
    Jiao, W., Wang, W., Huang, J. T., Wang, X., & Tu, Z. P. (2023). Is ChatGPT a good translator? Yes with GPT-4 as the engine. arXiv preprint arXiv:2301.08745.
    Kang, Y., Cai, Z., Tan, C. W., Huang, Q., & Liu, H. (2020). Natural language processing (NLP) in management research: A literature review. Journal of Management Analytics, 7(2), 139-172.
    Kasneci, E., Seßler, K., Küchemann, S., Bannert, M., Dementieva, D., Fischer, F., ... & Kasneci, G. (2023). ChatGPT for good? On opportunities and challenges of large language models for education. Learning and Individual Differences, 103, 102274.
    Kavlakoglu, E. (2020, November 12). NLP vs. NLU vs. NLG: the differences between three natural language processing concepts. IBM. https://www.ibm.com/blog/nlp-vs-nlu-vs-nlg-the-differences-between-three-natural-language-processing-concepts/
    Kholis, A. (2021). Elsa speak app: automatic speech recognition (ASR) for supplementing English pronunciation skills. Pedagogy: Journal of English Language Teaching, 9(1), 01-14.
    King, M. R., & ChatGPT. (2023). A conversation on artificial intelligence, chatbots, and plagiarism in higher education. Cellular and Molecular Bioengineering, 16(1), 1-2.
    Kirchenbauer, J., Geiping, J., Wen, Y., Katz, J., Miers, I., & Goldstein, T. (2023). A watermark for large language models. arXiv preprint arXiv:2301.10226.
    Köbis, N., & Mossink, L. D. (2021). Artificial intelligence versus Maya Angelou: Experimental evidence that people cannot differentiate AI-generated from human-written poetry. Computers in human behavior, 114, 106553.
    Kohnke, L., Moorhouse, B. L., & Zou, D. (2023). ChatGPT for language teaching and learning. RELC Journal, 00336882231162868.
    Koubaa, A., Boulila, W., Ghouti, L., Alzahem, A., & Latif, S. (2023). Exploring ChatGPT capabilities and limitations: A critical review of the nlp game changer.
    Kreps, S., McCain, R. M., & Brundage, M. (2022). All the news that’s fit to fabricate: AI-generated text as a tool of media misinformation. Journal of experimental political science, 9(1), 104-117.
    Kumar, A. H. (2023). Analysis of ChatGPT tool to assess the potential of its utility for academic writing in biomedical domain. Biology, Engineering, Medicine and Science Reports, 9(1), 24-30.
    Kung, T. H., Cheatham, M., Medenilla, A., Sillos, C., De Leon, L., Elepaño, C., ... & Tseng, V. (2023). Performance of ChatGPT on USMLE: Potential for AI-assisted medical education using large language models. PLoS digital health, 2(2), e0000198.
    Lee, M., Liang, P., & Yang, Q. (2022, April). Coauthor: Designing a human-ai collaborative writing dataset for exploring language model capabilities. In Proceedings of the 2022 CHI conference on human factors in computing systems (pp. 1-19).
    Liao, W., Liu, Z., Dai, H., Xu, S., Wu, Z., Zhang, Y., ... & Li, X. (2023). Differentiate chatgpt-generated and human-written medical texts. https://doi.org/10.48550/arXiv.2304.11567
    Liu, Y., Han, T., Ma, S., Zhang, J., Yang, Y., Tian, J., ... & Ge, B. (2023). Summary of chatgpt/gpt-4 research and perspective towards the future of large language models. arXiv preprint arXiv:2304.01852.
    Liu, H., Ning, R., Teng, Z., Liu, J., Zhou, Q., & Zhang, Y. (2023). Evaluating the logical reasoning ability of chatgpt and gpt-4. arXiv preprint arXiv:2304.03439.
    Luo, R., Sun, L., Xia, Y., Qin, T., Zhang, S., Poon, H., & Liu, T. Y. (2022). BioGPT: generative pre-trained transformer for biomedical text generation and mining. Briefings in Bioinformatics, 23(6), bbac409.
    McTear, M. (2022). Conversational ai: Dialogue systems, conversational agents, and chatbots. Springer Nature.
    Mogavi, R. H., Deng, C., Kim, J. J., Zhou, P., Kwon, Y. D., Metwally, A. H. S., ... & Hui, P. (2023). Exploring user perspectives on chatgpt: Applications, perceptions, and implications for ai-integrated education. https://doi.org/10.48550/arXiv.2305.13114
    Mohamed, A. M. (2023). Exploring the potential of an AI-based Chatbot (ChatGPT) in enhancing English as a Foreign Language (EFL) teaching: perceptions of EFL Faculty Members. Education and Information Technologies, 1-23.
    Moryossef, A., Goldberg, Y., & Dagan, I. (2019). Step-by-step: Separating planning from realization in neural data-to-text generation. arXiv preprint arXiv:1904.03396.
    Muller, M., Chilton, L. B., Kantosalo, A., Martin, C. P., & Walsh, G. (2022, April). GenAICHI: generative AI and HCI. In CHI conference on human factors in computing systems extended abstracts (pp. 1-7).
    Murgia, E., Abbasiantaeb, Z., Aliannejadi, M., Huibers, T., Landoni, M., & Pera, M. S. (2023, June). ChatGPT in the Classroom: A Preliminary Exploration on the Feasibility of Adapting ChatGPT to Support Children’s Information Discovery. In Adjunct Proceedings of the 31st ACM Conference on User Modeling, Adaptation and Personalization (pp. 22-27).
    Newton, P., & Xiromeriti, M. (2023). ChatGPT performance on multiple choice question examinations in higher education. A pragmatic scoping review. Assessment & Evaluation in Higher Education, 1-18.
    NVIDIA. (n.d.). Large Language Models. Retrieved April 24, 2023, from https://www.nvidia.com/en-us/glossary/data-science/large-language-models/
    OpenAI. (2023a). ChatGPT (July 24 version) [Large language model]. https://chat.openai.com/
    OpenAI. (n.d.a). GPT-4. Retrieved July 24, 2023, from https://openai.com/gpt-4
    OpenAI. (n.d.b). Hello GPT-4o. Retrieved May 21, 2024, from https://openai.com/index/hello-gpt-4o/
    Ouyang, F., Zheng, L., & Jiao, P. (2022). Artificial intelligence in online higher education: A systematic review of empirical research from 2011 to 2020. Education and Information Technologies, 27(6), 7893-7925.
    Pack, A., & Maloney, J. (2023a). POTENTIAL AFFORDANCES of GENERATIVE AI in LANGUAGE EDUCATION: DEMONSTRATIONS and an EVALUATIVE FRAMEWORK. Teaching English with Technology, 23(2).
    Pack, A., & Maloney, J. (2023b). Using Generative Artificial Intelligence for Language Education Research: Insights from Using OpenAI's ChatGPT. TESOL Quarterly.
    Pandey, A. K., & Roy, S. S. (2023). Natural Language Generation Using Sequential Models: A Survey. Neural Processing Letters, 1-34.
    Pavlik, J. V. (2023). Collaborating with ChatGPT: Considering the implications of generative artificial intelligence for journalism and media education. Journalism & Mass Communication Educator, 78(1), 84-93.
    Peng, K., Ding, L., Zhong, Q., Shen, L., Liu, X., Zhang, M., ... & Tao, D. (2023). Towards making the most of chatgpt for machine translation. https://doi.org/10.48550/arXiv.2304.02182
    Pliakos, K., Joo, S. H., Park, J. Y., Cornillie, F., Vens, C., & Van den Noortgate, W. (2019). Integrating machine learning into item response theory for addressing the cold start problem in adaptive learning systems. Computers & Education, 137, 91-103.
    Qadir, J. (2023, May). Engineering education in the era of ChatGPT: Promise and pitfalls of generative AI for education. In 2023 IEEE Global Engineering Education Conference (EDUCON) (pp. 1-9). IEEE.
    Rudolph, J., Tan, S., & Tan, S. (2023). ChatGPT: Bullshit spewer or the end of traditional assessments in higher education?. Journal of Applied Learning and Teaching, 6(1).
    Sakai, N. (2023). Investigating the Feasibility of ChatGPT for Personalized English Language Learning: A Case Study on its Applicability to Japanese Students.
    Samant, R. M., Bachute, M. R., Gite, S., & Kotecha, K. (2022). Framework for deep learning-based language models using multi-task learning in natural language understanding: A systematic literature review and future directions. IEEE Access, 10, 17078-17097.
    Schade, M. (n.d.). How ChatGPT and Our Language Models Are Developed. OpenAI. Retrieved September 3, 2023, from https://help.openai.com/en/articles/7842364-how-chatgpt-and-our-language-models-are-developed
    Sheng, E., Chang, K. W., Natarajan, P., & Peng, N. (2019). The woman worked as a babysitter: On biases in language generation. arXiv preprint arXiv:1909.01326.
    Shoufan, A. (2023). Exploring Students’ Perceptions of ChatGPT: Thematic Analysis and Follow-Up Survey. IEEE Access.
    Smutny, P., & Schreiberova, P. (2020). Chatbots for learning: A review of educational chatbots for the Facebook Messenger. Computers & Education, 151, 103862.Strzelecki, A. (2023). To use or not to use ChatGPT in higher education? A study of students’ acceptance and use of technology. Interactive Learning Environments, 1-14.
    Su, Y., Lin, Y., & Lai, C. (2023). Collaborating with ChatGPT in argumentative writing classrooms. Assessing Writing, 57, 100752.
    Su, D., Xu, Y., Winata, G. I., Xu, P., Kim, H., Liu, Z., & Fung, P. (2019, November). Generalizing question answering system with pre-trained language model fine-tuning. In Proceedings of the 2nd Workshop on Machine Reading for Question Answering (pp. 203-211).
    Surameery, N. M. S., & Shakor, M. Y. (2023). Use chat gpt to solve programming bugs. International Journal of Information Technology & Computer Engineering (IJITC) ISSN: 2455-5290, 3(01), 17-22.
    Susnjak, T. (2022). ChatGPT: The end of online exam integrity?. arXiv preprint arXiv:2212.09292.
    Talan, T., & Kalinkara, Y. (2023). The role of artificial intelligence in higher education: ChatGPT assessment for anatomy course. Uluslararası Yönetim Bilişim Sistemleri ve Bilgisayar Bilimleri Dergisi, 7(1), 33-40.
    Tamkin, A., Brundage, M., Clark, J., & Ganguli, D. (2021). Understanding the capabilities, limitations, and societal impact of large language models. arXiv preprint arXiv:2102.02503.
    Teubner, T., Flath, C. M., Weinhardt, C., van der Aalst, W., & Hinz, O. (2023). Welcome to the era of chatgpt et al. the prospects of large language models. Business & Information Systems Engineering, 65(2), 95-101.
    Tlili, A., Shehata, B., Adarkwah, M. A., Bozkurt, A., Hickey, D. T., Huang, R., & Agyemang, B. (2023). What if the devil is my guardian angel: ChatGPT as a case study of using chatbots in education. Smart Learning Environments, 10(1), 15.
    Trivedi, A., Pant, N., Shah, P., Sonik, S., & Agrawal, S. (2018). Speech to text and text to speech recognition systems-Areview. IOSR J. Comput. Eng, 20(2), 36-43.
    Wardat, Y., Tashtoush, M. A., AlAli, R., & Jarrah, A. M. (2023). ChatGPT: A revolutionary tool for teaching and learning mathematics. Eurasia Journal of Mathematics, Science and Technology Education, 19(7), em2286.
    Whalen, J., & Mouza, C. (2023). ChatGPT: Challenges, Opportunities, and Implications for Teacher Education. Contemporary Issues in Technology and Teacher Education, 23(1), 1-23.
    What is Artificial Intelligence? (n.d.). IBM. Retrieved February 24, 2024 from https://www.ibm.com/topics/artificial-intelligence
    Wu, T., Terry, M., & Cai, C. J. (2022, April). Ai chains: Transparent and controllable human-ai interaction by chaining large language model prompts. In Proceedings of the 2022 CHI conference on human factors in computing systems (pp. 1-22).
    Xiao, C., Xu, S. X., Zhang, K., Wang, Y., & Xia, L. (2023, July). Evaluating Reading Comprehension Exercises Generated by LLMs: A Showcase of ChatGPT in Education Applications. In Proceedings of the 18th Workshop on Innovative Use of NLP for Building Educational Applications (BEA 2023) (pp. 610-625).
    Xie, H., Chu, H. C., Hwang, G. J., & Wang, C. C. (2019). Trends and development in technology-enhanced adaptive/personalized learning: A systematic review of journal publications from 2007 to 2017. Computers & Education, 140, 103599.
    Yan, D. (2023). Impact of ChatGPT on learners in a L2 writing practicum: An exploratory investigation. Education and Information Technologies, 1-25.
    Yan, R. (2018, July). " Chitty-Chitty-Chat Bot": Deep Learning for Conversational AI. In IJCAI (Vol. 18, pp. 5520-5526).
    Yoon, W., Lee, J., Kim, D., Jeong, M., & Kang, J. (2019, September). Pre-trained language model for biomedical question answering. In Joint European Conference on Machine Learning and Knowledge Discovery in Databases (pp. 727-740). Cham: Springer International Publishing.
    Young, J. C., & Shishido, M. (2023a). Evaluation of the Potential Usage of ChatGPT for Providing Easier Reading Materials for ESL Students. In EdMedia+ Innovate Learning (pp. 155-162). Association for the Advancement of Computing in Education (AACE).
    Young, J. C., & Shishido, M. (2023b). Investigating OpenAI’s ChatGPT Potentials in Generating Chatbot's Dialogue for English as a Foreign Language Learning. International Journal of Advanced Computer Science and Applications, 14(6).
    Zhang, B., Ding, D., & Jing, L. (2022). How would Stance Detection Techniques Evolve after the Launch of ChatGPT?. ArXiv, abs/2212.14548. https://doi.org/10.48550/arXiv.2212.14548.
    Zhang, H., Song, H., Li, S., Zhou, M., & Song, D. (2022). A survey of controllable text generation using transformer-based pre-trained language models. arXiv preprint arXiv:2201.05337.
    Zheng, H., & Zhan, H. (2023). ChatGPT in scientific writing: a cautionary tale. The American Journal of Medicine.
    Zhou, C., Li, Q., Li, C., Yu, J., Liu, Y., Wang, G., ... & Sun, L. (2023). A comprehensive survey on pretrained foundation models: A history from bert to chatgpt. arXiv preprint arXiv:2302.09419.

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