基於生成式人工智慧之機器人學生對於英文故事閱讀之影響

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林亞岑(Ya-Tsen Lin) 查詢紙本館藏

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資訊工程學系

論文名稱

基於生成式人工智慧之機器人學生對於英文故事閱讀之影響
(The Effect of Manchine Tutee Based on Generative Artificial Intelligence on Students′ English Story Reading)

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至系統瀏覽論文 (2026-8-1以後開放)

摘要(中)

在教育領域上，生成式人工智慧的技術除了能作資訊提供者外，還可作資訊學習者/可訓練機器人( Teachable Agent )。本研究探討以生成式人工智慧之技術，作為可訓練機器人在英文閱讀活動中的應用，並在本研究中稱呼此可訓練機器人為「故事機器人」。學生透過教中學的學習方式，以教導故事內容訓練機器人，在教導機器人過程中，透過機器人提供之個人化反饋，協助學生漸進式建構出完整故事結構，以此增進學生故事理解與敘述能力。此外，學生可以用問答的方式挑戰同儕訓練的機器人，透過學生間互動促進學習動機。系統同時提供閱讀輔助機器人( 稱魚姊姊 )，幫助學生解決英文閱讀上的困難，如詢問英文意涵或討論故事情節，以降低學生在英文閱讀上的認知負荷。
本研究對象為國小五年級學生共77人，實驗分成上下半場共八週。分析有無故事機器人介入閱讀活動、與有無導師介入閱讀活動這兩項變因，對學生心流、情境興趣與閱讀理解能力的影響。研究結果顯示，有故事機器人介入閱讀活動，學生在心流、情境興趣顯著高於無故事機器人介入活動的學生，這反映可訓練機器人對學習體驗的有效性。而有導師介入的閱讀活動，對學生英文掌握自信度有顯著影響，並且導師與機器人協作介入閱讀活動，對學生心流、情境興趣與閱讀理解皆有顯著提升。顯示人類教師與可訓練機器人的共同協作，能有效提升學生於英文閱讀時的學習體驗與學習成效。

摘要(英)

In the field of education, generative AI technology can serve not only as an information provider but also as an information learner or teachable agent. This study explores the application of generative AI as a teachable agent in English reading, referred to in this study as the "Story Chatbot." Through learning-by-teaching activities, students train a personalized Story Chatbot by narrating stories. During the process, personalized feedback from the Story Chatbot helps students gradually construct complete story structures, thereby enhancing their reading comprehension and narrative abilities. Additionally, students can challenge other Story Chatbots trained by their classmate through question-and-answer sessions, fostering learning motivation through peer interaction. The system also includes a reading assistance chatbot called Fish Sister to help students overcome difficulties in English reading, such as asking about English meanings or discussing story plots, thus reducing cognitive load in English reading.
The study involved 77 fifth-grade students and lasted for eight weeks, divided into two phases. The study analyzed the impact of two variables—whether or not a Story Chatbot was involved in the reading activities and whether or not a class teacher was involved—on students′ flow, situational interest, and reading comprehension. The results showed that students with the Story Chatbot involved in their reading activities had significantly higher levels of flow and situational interest compared to those without the Story Chatbot, reflecting the effectiveness of the teachable agent in enhancing the learning experience. Additionally, reading activities with class teacher involvement had a significant impact on students′ confidence in their English reading. Furthermore, the collaboration between the class teacher and the Story Chatbot in reading activities significantly improved students′ flow, situational interest, and reading comprehension. The collaborative efforts between human teachers and teachable agent effectively enhance students′ learning experience and outcomes in English reading.

關鍵字(中)

★ 生成式人工智慧
★ 可訓練機器人
★ 英文閱讀
★ 教中學
★ 敘述故事
★ 心流
★ 情境興趣

關鍵字(英)

★ Generative artificial intelligence
★ teachable agent
★ English reading
★ learning by teaching
★ story narrative
★ flow
★ situational interest

論文目次

目錄
摘要 i
Abstract ii
致謝 iii
圖目錄 viii
表目錄 xi
第一章　緒論 1
1.1　研究背景與動機 1
1.2　研究目的與問題 3
1.3　名詞解釋 4
1.3.1　心流( Flow ) 4
1.3.2　情境興趣( Situational Interest ) 4
1.4　研究範圍與限制 5
1.5　論文架構 5
第二章　文獻探討 6
2.1　生成式自然語言模型 6
2.2　可訓練的教育機器人 7
2.3　故事敘述 9
第三章　系統設計 11
3.1　系統特色 11
3.2　系統架構 12
3.2.1　前後端與資料庫 13
3.2.2　AI機器人設計 15
3.3　機器人模組 17
3.3.1　關鍵情節 17
3.3.2　問英文模組 18
3.3.3　聊故事模組 19
3.3.4　評分與回饋模組 22
3.3.5　問答模組 26
3.4　系統介面 28
3.4.1　閱讀輔助機器人 30
3.4.2　可訓練機器人 31
3.4.3　問答挑戰 35
3.4.4　機器人進展追蹤 36
第四章　研究方法 39
4.1　研究對象 39
4.2　研究流程 39
4.3　實驗設計 40
4.3.1　英文書籍 45
4.3.1　英文閱讀活動設計 45
4.3.3　機器人活動設計 46
4.4　研究工具 48
4.4.1　心流量表 49
4.4.2　情境興趣量表 50
4.4.3　閱讀理解測驗 51
4.5　資料蒐集與分析 52
4.5.1　活動心流 52
4.5.2　英文閱讀之情境興趣 52
4.5.3　英文閱讀理解 52
4.5.4　系統行為量化分析 53
4.5.5　系統行為質性分析 55
4.5.6　活動後訪談 55
第五章　研究結果與討論 58
5.1　學生在不同閱讀活動形態下的心流分析 58
5.1.1　第一階段分析 58
5.1.2　第二階段分析 64
5.2　學生在不同閱讀活動形態下的情境興趣分析 68
5.2.1　第一階段分析 68
5.2.2　第二階段分析 72
5.3　學生在不同閱讀活動形態下的閱讀理解分析 74
5.3.1　第一階段分析 74
5.3.2　第二階段分析 76
5.4　學生系統行為分析 78
5.4.1　討論英文重點 84
5.4.2　聊故事模式 85
5.4.3　訓練策略 90
5.4.4　挑戰題目類型 98
5.5　故事機器人組之學生行為與學生狀況的相關性 100
5.5.1　活動過程與學生心流之相關性 101
5.5.2　活動過程與學生情境興趣之相關性 104
5.5.3　活動過程與學生閱讀理解之相關性 106
5.6　訪談 109
5.6.1　英文閱讀興趣 109
5.6.2　英文閱讀動機 122
5.6.3　英文閱讀的困難解決 132
5.6.4　機器人互動 139
5.6.5　機器人關係 145
5.6.6　同儕關係 149
第六章　結論與建議 157
6.1　結論 157
6.1.1　基於生成式AI的可訓練機器人，對於學生閱讀的心流的影響 157
6.1.2　基於生成式AI的可訓練機器人，對於學生閱讀的情境興趣的影響 158
6.1.3　基於生成式AI的可訓練機器人，對於學生閱讀的閱讀理解的影響 159
6.1.4　學生使用基於生成式AI的可訓練機器人進行英文閱讀的行為 159
6.1.5　學生活動行為與學生心流、情境興趣、閱讀理解的相關性 160
6.2　未來展望 160
參考文獻 162
中文文獻 162
英文文獻 162
附錄 A　任務說明單 169
附錄 B　機器人挑戰申訴單 170
附錄 C　心流問卷 171
附錄 D　情境興趣問卷 172

參考文獻

中文文獻
陳宛君（2023）。訓練問答機器人對學生英文閱讀興趣之影響。桃園市國立中央大學資訊工程學系碩士論文。
林彥宇（2023）。元宇宙加入遊戲化要素之環境對國小學生英文閱讀興趣影響。桃園市國立中央大學資訊工程學系碩士論文。
邱貞瑋（2022）。聊天機器人扮演協調者角色對學生英文閱讀興趣影響。桃園市國立中央大學資訊工程學系碩士論文。
英文文獻
Anderson, L. W., & Krathwohl, D. R. (2001). A taxonomy for learning, teaching, and assessing: A revision of Bloom′s taxonomy of educational objectives: complete edition. Addison Wesley Longman, Inc.
Babayigit, S., Roulstone, S., & Wren, Y. (2021). Linguistic comprehension and narrative skills predict reading ability: A 9-year longitudinal study. British Journal Educ Psychol, 91(1), 148-168. https://doi.org/10.1111/bjep.12353
Bozkurt, A., Junhong, X., Lambert, S., Pazurek, A., Crompton, H., Koseoglu, S., Farrow, R., Bond, M., Nerantzi,C., Honeychurch, S., Bali, M., Dron, J., Mir, K., Stewart, B., Costello, E., Mason, J., Stracke, C. M., & Romero-, & Hall, E. (2023). Speculative futures on ChatGPT and generative artificial intelligence (AI): A collective reflection from the educational landscape. https://doi.org/10.5281/zenodo.7636568
Catts, H. W., Fey, M. E., Zhang, X., & Tomblin, J. B. (1999). Language basis of reading and reading disabilities: Evidence from a longitudinal investigation. Scientific studies of reading, 3(4), 331-361.
Chase, C. C., Chin, D. B., Oppezzo, M. A., & Schwartz, D. L. (2009). Teachable agents and the Protégé Effect: Increasing the Effort Towards Learning. Journal of Science Education and Technology, 18(4), 334-352. https://doi.org/10.1007/s10956-009-9180-4
Chen, T. C., Kaminski, E., Koduri, L., Singer, A., Singer, J., Couldwell, M., Delashaw, J., Dumont, A., & Wang, A. (2023). Chat GPT as a neuro-score calculator: analysis of a large language model’s performance on various neurological exam grading scales. World neurosurgery, 179, e342-e347. https://www.sciencedirect.com/science/article/abs/pii/S1878875023012019
Chen, Y., Jensen, S., Albert, L. J., Gupta, S., & Lee, T. (2022). Artificial Intelligence (AI) Student Assistants in the Classroom: Designing Chatbots to Support Student Success. Information Systems Frontiers, 25(1), 161-182. https://doi.org/10.1007/s10796-022-10291-4
Chin, C., & Osborne, J. (2008). Students′ questions: a potential resource for teaching and learning science. Studies in science education, 44(1), 1-39.
Chin, D. B., Dohmen, I. M., Cheng, B. H., Oppezzo, M. A., Chase, C. C., & Schwartz, D. L. (2010). Preparing students for future learning with teachable agents. Educational Technology Research and Development, 58, 649-669.
Cohen, C., Bauer, E., & Minniear, J. (2021). Exploring how language exposure shapes oral narrative skills in French-English emergent bilingual first graders. Linguistics and Education, 63. https://doi.org/10.1016/j.linged.2021.100905
Csikszentmihalyi, M. (1990). Flow: The psychology of optimal experience (pp. 75-77). New York: Harper & Row.
Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS quarterly, 319-340.
Doroudi, S. & Rismanchian, S. (2023). Four interactions between AI and education: Broadening our perspective on what AI can offer education. In International Conference on Artificial Intelligence in Education (pp. 1-12). Cham: Springer Nature Switzerland.
Dwivedi, Y. K., Kshetri, N., Hughes, L., Slade, E. L., Jeyaraj, A., Kar, A. K., Baabdullah, A. M., Koohang, A., Raghavan, V., Ahuja, M., Albanna, H., Albashrawi, M. A., Al-Busaidi, A. S., Balakrishnan, J., Barlette, Y., Basu, S., Bose, I., Brooks, L., Buhalis, D., . . . Wright, R. (2023). Opinion paper: “So what if ChatGPT wrote it?” Multidisciplinary perspectives on opportunities, challenges and implications of generative conversational AI for research, practice and policy. International journal of information management, 71. https://doi.org/10.1016/j.ijinfomgt.2023.102642
Fiorella, L., & Mayer, R. E. (2013). The relative benefits of learning by teaching and teaching expectancy. Contemporary educational psychology, 38(4), 281-288. https://doi.org/10.1016/j.cedpsych.2013.06.001
FLATICON. https://www.flaticon.com/
Freire, S. K., Wang, C., & Niforatos, E. (2024). Chatbots in knowledge-intensive contexts: Comparing intent and LLM-based systems. arXiv preprint arXiv:2402.04955.
Frieder, S., Pinchetti, L., Griffiths, R.-R., Salvatori, T., Lukasiewicz, T., Petersen, P., & Berner, J. (2024). Mathematical capabilities of chatgpt. Advances in Neural Information Processing Systems, 36.
Greifenstein, L., Graßl, I., Heuer, U., & Fraser, G. (2022). Common problems and effects of feedback on fun when programming ozobots in primary school. In Proceedings of the 17th Workshop in Primary and Secondary Computing Education (pp. 1-10).
Hidi, S. (1990). Interest and its contribution as a mental resource for learning. Review of Educational research, 60(4), 549-571.
Hidi, S., & Baird, W. (1986). Interestingness—A neglected variable in discourse processing. Cognitive science, 10(2), 179-194.
Hidi, S., & Renninger, K. A. (2006). The four-phase model of interest development. Educational psychologist, 41(2), 111-127.
Huttenlocher, J., Vasilyeva, M., Cymerman, E., & Levine, S. (2002). Language input and child syntax. Cognitive psychology, 45(3), 337-374.
Igbaria, M., & Chakrabarti, A. (1990). Computer anxiety and attitudes towards microcomputer use. Behaviour & Information Technology, 9(3), 229-241.
Jeon, J., & Lee, S. (2023). Large language models in education: A focus on the complementary relationship between human teachers and ChatGPT. Education and Information Technologies, 28(12), 15873-15892. https://doi.org/10.1007/s10639-023-11834-1
Ji, H., Han, I., & Ko, Y. (2022). A systematic review of conversational AI in language education: focusing on the collaboration with human teachers. Journal of Research on Technology in Education, 55(1), 48-63. https://doi.org/10.1080/15391523.2022.2142873
Jin, H., Lee, S., Shin, H., & Kim, J. (2024). Teach AI How to Code: Using Large Language Models as Teachable Agents for Programming Education. In Proceedings of the CHI Conference on Human Factors in Computing Systems (pp. 1-28).
Karlsen, J., Hjetland, H. N., Hagtvet, B. E., Braeken, J., & Melby-Lervåg, M. (2021). The concurrent and longitudinal relationship between narrative skills and other language skills in children. First Language, 41(5), 555-572. https://doi.org/10.1177/0142723721995688
Kirginas, S. (2022). Improving Students′ Narrative Skills through Gameplay Activities: A Study of Primary School Students. Contemporary Educational Technology, 14(2).
Kline, R. B. (2023). Principles and practice of structural equation modeling. Guilford publications.
Lai, E. R. (2011). Metacognition: A literature review. Always learning: Pearson research report, 24, 1-40.
Linnenbrink-Garcia, L., Durik, A. M., Conley, A. M., Barron, K. E., Tauer, J. M., Karabenick, S. A., & Harackiewicz, J. M. (2010). Measuring situational interest in academic domains. Educational and psychological measurement, 70(4), 647-671. https://doi.org/10.1177/0013164409355699
Lippert, A., Shubeck, K., Morgan, B., Hampton, A., & Graesser, A. (2019). Multiple Agent Designs in Conversational Intelligent Tutoring Systems. Technology, Knowledge and Learning, 25(3), 443-463. https://doi.org/10.1007/s10758-019-09431-8
Liu, C.-C., Chen, H. S., Shih, J.-L., Huang, G.-T., & Liu, B.-J. (2011). An enhanced concept map approach to improving children’s storytelling ability. Computers & Education, 56(3), 873-884.
Liu, C.-C., Yang, C.-Y., & Chao, P.-Y. (2019). A longitudinal analysis of student participation in a digital collaborative storytelling activity. Educational Technology Research and Development, 67, 907-929.
Love, R., Law, E., Cohen, P. R., & Kulić, D. (2022). Natural Language Communication with a Teachable Agent. arXiv preprint arXiv:2203.09016.
Mahmood, A., Wang, J., Yao, B., Wang, D., & Huang, C.-M. (2023). LLM-Powered conversational voice assistants: Interaction patterns, opportunities, challenges, and design guidelines. arXiv preprint arXiv:2309.13879.
Matsuda, N. (2022). Teachable agent as an interactive tool for cognitive task analysis: A case study for authoring an expert model. International Journal of Artificial Intelligence in Education, 32(1), 48-75.
Murgia, E., Pera, M. S., Landoni, M., & Huibers, T. (2023). Children on ChatGPT readability in an educational context: Myth or opportunity? In Adjunct Proceedings of the 31st ACM Conference on User Modeling, Adaptation and Personalization (pp. 311-316).
Mutiarin, D., Hatmanto, E. D., Sari, M. I., Alam, M., Cahill, D., Sharifuddin, J., Senge, M., Robani, A., Saiyut, P., & Nurmandi, A. (2023). Aligning Theory and Practice: Leveraging Chat GPT for Effective English Language Teaching and Learning. E3S Web of Conferences, 440. https://doi.org/10.1051/e3sconf/202344005001
Ndlovu, T. N., & Mhlongo, S. (2020). An investigation into the effects of gamification on students’ situational interest in a learning environment. In 2020 IEEE Global Engineering Education Conference (EDUCON) (pp. 1187-1192). IEEE
Ng, D. T. K., Tan, C. W., & Leung, J. K. L. (2024). Empowering student self‐regulated learning and science education through ChatGPT: A pioneering pilot study. British Journal of Educational Technology, 55(4), 1328-1353. https://doi.org/10.1111/bjet.13454
NICHD, E. C. C. R. N. (2005). Pathways to reading: the role of oral language in the transition to reading. Dev Psychol, 41(2), 428-442. https://doi.org/10.1037/0012-1649.41.2.428
Overby, K. (2011). Student-centered learning. Essai, 9(1), 32.
Paas, F., & Sweller, J. (2014). Implications of cognitive load theory for multimedia learning. The Cambridge handbook of multimedia learning, 27, 27-42.
Pardos, Z. A., & Bhandari, S. (2023). Learning gain differences between ChatGPT and human tutorgenerated algebra hints. arXiv preprint arXiv:2302.06871
Paris, A. H., & Paris, S. G. (2003). Assessing narrative comprehension in young children. Reading Research Quarterly, 38(1), 36-76.
Pesco, D., & Gagné, A. (2015). Scaffolding Narrative Skills: A Meta-Analysis of Instruction in Early Childhood Settings. Early Education and Development, 28(7), 773-793. https://doi.org/10.1080/10409289.2015.1060800
Poskiparta, E., Niemi, P., Lepola, J., Ahtola, A., & Laine, P. (2003). Motivational‐emotional vulnerability and difficulties in learning to read and spell. British Journal of Educational Psychology, 73(2), 187-206.
Qadir, J. (2023). Engineering education in the era of ChatGPT: Promise and pitfalls of generative AI for education. 2023 IEEE Global Engineering Education Conference (EDUCON) (pp. 1-9). IEEE.
Qin, H. X., Jin, S., Gao, Z., Fan, M., & Hui, P. (2024). CharacterMeet: Supporting creative writers′ entire story character construction processes through conversation with LLM-powered chatbot avatars. In Proceedings of the CHI Conference on Human Factors in Computing Systems (pp. 1-19).
Ramsdell, C. (2011). Storytelling, Narration, and the “Who I Am” Story. Writing Spaces: Readings on Writing, 2, 270-285.
Rice, R. E., & Shook, D. E. (1988). Access to, usage of, and outcomes from an electronic messaging system. ACM Transactions on Information Systems (TOIS), 6(3), 255-276.
Silvervarg, A., Wolf, R., Blair, K. P., Haake, M., & Gulz, A. (2020). How teachable agents influence students’ responses to critical constructive feedback. Journal of Research on Technology in Education, 53(1), 67-88. https://doi.org/10.1080/15391523.2020.1784812
Trevino, L. K., Lengel, R. H., & Daft, R. L. (1987). Media symbolism, media richness, and media choice in organizations: A symbolic interactionist perspective. Communication Research, 14(5), 553-574.
Trevino, L. K., & Webster, J. (1992). Flow in Computer-Mediated Communication:Electronic Mail and Voice Mail Evaluation and Impacts. Communication Research, 19(5), 539-573. https://doi.org/10.1177/009365092019005001
Wilson, L. O. (2016). Anderson and Krathwohl–Bloom’s taxonomy revised. Understanding the new version of Bloom′s taxonomy. Understanding the new version of Bloom′s taxonomy.
Xiao, C., Xu, S. X., Zhang, K., Wang, Y., & Xia, L. (2023). 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).
Zoltan, E., & Chapanis, A. (1982). What do professional persons think about computers? Behaviour & Information Technology, 1(1), 55-68.

指導教授

劉晨鐘(Chen-Chung Liu)

審核日期

2024-7-23

推文