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題名 探討生成式 AI 聊天機器人對睡眠拖延行為改善的影響
Exploring the Impact of Generative AI Chatbot on Improving Bedtime Procrastination Behavior
作者 陳玟諭
Chen, Wen-Yu
貢獻者 鄭霈絨<br>廖峻鋒
Cheng, Pei-Jung<br>Liao, Chun-Feng
陳玟諭
Chen, Wen-Yu
關鍵詞 生成式AI
睡眠拖延行為
BED-PRO 對話架構
聊天機器人
報復性熬夜
Generative AI
Bedtime procrastination behavior
BED-PRO
Chatbot
Revenge bedtime procrastination
日期 2025
上傳時間 1-Sep-2025 16:51:34 (UTC+8)
摘要 睡眠拖延(Bedtime Procrastination)為一種常見於年輕族群的行為問題，其核心成 因多與自我調節困難、報復性熬夜、時間管理不足及情緒壓力相關，長期可能導致睡眠 品質下降、白天清醒感降低與心理健康風險上升。隨著生成式人工智慧(Generative AI) 對話技術的發展，􏰀類的聊天機器人具備即時對話、情緒支持與個別化建議的特性受到 重視。因􏰀，本研究採用生成式 AI 聊天機器人(Linebot)結合 BED-PRO 的對話架構， 探究其改善睡眠拖延行為的成效，以及其對於睡眠拖延者之自我覺察、情緒調控與時間 管理能力的影響。本研究針對有睡眠拖延傾向的大專院校學生，進行為期三週的睡眠拖 延改善實驗，研究資料包含睡眠日記與每週的睡眠回顧對話。 研究結果顯示，參與者於實驗後睡眠拖延量表(BPS)與睡眠拖延時間(BPD)皆 顯著下降，顯示􏰀對話機制具行為改變效果。質性資料亦指出，Linebot 有助於睡眠拖 延者辨識報復性熬夜與任務逃避等拖延成因，並培養自我調節與反思能力。多數睡眠拖 延者肯定 Linebot 溫柔、不批判的對話風格與穩定互動，視其為情緒支持與睡眠儀式建 立的重要助力。本研究主要貢獻在於結合生成式 AI 技術與 BED-PRO 對話架構，以實 證結果展現出提升睡眠自我調節與行為改變的潛力，將能提供未來在行為改善或養成工 具之互動形式與個人化設計方面的具體參考。
Bedtime procrastination is a widespread issue among young adults, often linked to poor self-regulation, revenge bedtime procrastination, and time management difficulties. These factors negatively affect sleep quality and mental well-being. With advances in generative AI, behavior-change chatbots capable of providing real-time interaction and personalized support have gained significant attention. This study evaluated the effectiveness of a generative AI chatbot (Linebot), designed with the BED-PRO framework, in reducing bedtime procrastination and improving users’ self- reflection, emotional regulation, and time management. A three-week intervention was conducted among university students with a high tendency toward bedtime procrastination. The intervention included daily sleep diary entries and weekly reflective dialogue sessions. Post-intervention data showed significant reductions in Bedtime Procrastination Scale (BPS) scores and Bedtime Procrastination Delay (BPD), indicating behavioral improvement. Qualitative results further revealed that Linebot helped participants recognize procrastination triggers, such as revenge bedtime procrastination and task avoidance, while encouraging self- regulation and reflection. Participants also valued the chatbot’s non-judgmental tone and consistent support in developing bedtime routines. This study contributes by integrating generative AI with the BED-PRO framework, providing empirical support for its potential to enhance self-regulation and behavioral change related to sleep. The findings offer practical insights for designing future interactive and personalized digital tools for behavior improvement and habit formation.
參考文獻 林怡安（2023）。睡眠拖延之心理因素探討。《諮商與輔導》，452，13–17。https://www.airitilibrary.com/Article/Detail?DocID=16846478-N202308010015-00006 黃佳豪、朱瑩瑩（2023）。睡眠拖延及其影響因素探析。《心理學進展》，13(4)，1450–1459。https://www.hanspub.org/journal/paperinformation?paperid=64355 Line App 2024使用數據。上網日期：2025年1月3日，取自：https://linecorp.com/tw/pr/news/2024/1217 Adamopoulou, E., & Moussiades, L. (2020). Chatbots: History, technology, and applications. Machine Learning with applications, 2, 100006. https://doi.org/10.1016/j.mlwa.2020.100006. Aggarwal, A., Tam, C. C., Wu, D., Li, X., & Qiao, S. (2023). Artificial intelligence–based chatbots for promoting health behavioral changes: systematic review. Journal of medical Internet research, 25, e40789. https://doi.org/10.2196/40789 Bartneck, C., Kulić, D., Croft, E., & Zoghbi, S. (2009). Measurement instruments for the anthropomorphism, animacy, likeability, perceived intelligence, and perceived safety of robots. International journal of social robotics, 1(1), 71-81. https://doi.org/10.1007/s12369-008-0001-3 Bastien, C. H., Vallières, A., & Morin, C. M. (2001). Validation of the Insomnia Severity Index as an outcome measure for insomnia research. Sleep Medicine, 2(4), 297–307. https://doi.org/10.1016/S1389-9457(00)00065-4 Bistricky, S. L., Lopez, A. K., Pollard, T. B., Egan, A., Gimenez-Zapiola, M., Pascuzzi, B., ... & Graves, M. (2023). Brief Multimodal Intervention to Address Bedtime Procrastination and Sleep through Self-Compassion and Sleep Hygiene during Stressful Times. medRxiv, 2023-04. https://doi.org/10.1101/2023.04.16.23288655 Braun, V., & Clarke, V. (2006). Using thematic analysis in psychology. Qualitative Research in Psychology, 3(2), 77–101. https://doi.org/10.1191/1478088706qp063oa Brooke, J. (1996). SUS: A “quick and dirty” usability scale. In P. W. Jordan, B. Thomas, B. A. Weerdmeester, & I. L. McClelland (Eds.), Usability evaluation in industry (pp. 189–194). London: Taylor & Francis. Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J. D., Dhariwal, P., ... & Amodei, D. (2020). Language models are few-shot learners. Advances in neural information processing systems, 33, 1877-1901. https://proceedings.neurips.cc/paper/2020/file/1457c0d6bfcb4967418bfb8ac142f64a-Paper.pdf Chung, S. J., An, H., & Suh, S. (2020). What do people do before going to bed? A study of bedtime procrastination using time use surveys. Sleep, 43(4), zsz267. https://doi.org/10.1093/sleep/zsz267 Fitzpatrick, K. K., Darcy, A., & Vierhile, M. (2017). Delivering cognitive behavior therapy to young adults with symptoms of depression and anxiety using a fully automated conversational agent (Woebot): a randomized controlled trial. JMIR mental health, 4(2), e7785. https://doi.org/10.2196/mental.7785 Goonesekera, Y., & Donkin, L. (2022). A cognitive behavioral therapy chatbot (Otis) for health anxiety management: Mixed methods pilot study. JMIR Formative Research, 6(10), e37877. https://doi.org/10.2196/37877 Hassenzahl, M., Burmester, M., & Koller, F. (2003). AttrakDiff: Ein Fragebogen zur Messung wahrgenommener hedonischer und pragmatischer Qualität. In Mensch & computer 2003: interaktion in bewegung (pp. 187-196). Wiesbaden: Vieweg+ Teubner Verlag. Hill, V. M., Ferguson, S. A., Rebar, A. L., Meaklim, H., & Vincent, G. E. (2025). A randomised pilot trial for bedtime procrastination: Examining the efficacy and feasibility of the Reducing Evening Screen Time online intervention (REST O). Sleep Medicine, 129, 306–315. https://doi.org/10.1016/j.sleep.2025.02.043 Hu, E. J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., ... & Chen, W. (2022). Lora: Low-rank adaptation of large language models. ICLR, 1(2), 3. https://openreview.net/forum?id=nZeVKeeFYf9 Jeoung, S., Jeon, H., Yang, H. C., An, H., & Suh, S. (2023). A randomized controlled trial of a behavioral intervention for decreasing bedtime procrastination using a wait-list control group in a non-clinical sample of young adults. Sleep Medicine, 108, 114-123. https://doi.org/10.1016/j.sleep.2023.06.001 Kroese, F. M., De Ridder, D. T., Evers, C., & Adriaanse, M. A. (2014). Bedtime procrastination: introducing a new area of procrastination. Frontiers in psychology, 5, 89333. https://doi.org/10.3389/fpsyg.2014.00611 Kroese, F. M., Evers, C., Adriaanse, M. A., & de Ridder, D. T. D. (2016). Bedtime procrastination: A self-regulation perspective on sleep insufficiency in the general population. Journal of Health Psychology, 21(5), 853–862. https://doi.org/10.1177/1359105314540014 Kuhail, M. A., Thomas, J., Alramlawi, S., Shah, S. J. H., & Thornquist, E. (2022). Interacting with a chatbot-based advising system: Understanding the effect of chatbot personality and user gender on behavior. In Informatics (Vol. 9, No. 4, p. 81). MDPI. https://doi.org/10.3390/informatics9040081 Legashev, L., Shukhman, A., Badikov, V., & Kurynov, V. (2025). Using Large Language Models for Goal-Oriented Dialogue Systems. Applied Sciences, 15(9), 4687. https://doi.org/10.3390/app15094687 Liu, I., Chen, W., Ge, Q., Song, D., & Ni, S. (2022). Enhancing Psychological Resilience with Chatbot-Based Cognitive Behavior Therapy: A Randomized Control Pilot Study. In Proceedings of the Tenth International Symposium of Chinese CHI (pp. 216-221). https://doi.org/10.1145/3565698.3565787 Nguyen-Trung, K. (2025). ChatGPT in Thematic Analysis: Can AI become a research assistant in qualitative research?. Quality & Quantity, 1-34. https://doi.org/10.1007/s11135-025-02165-z Nimavat, K., & Champaneria, T. (2017). Chatbots: An overview types, architecture, tools and future possibilities. Int. J. Sci. Res. Dev, 5(7), 1019-1024. https://ijsrd.com/Article.php?manuscript=IJSRDV5I70501 OpenAI. (n.d.). Prompt engineering. OpenAI Platform Documentation. Retrieved August 5, 2025, from https://platform.openai.com/docs/guides/prompt-engineering Pawlik, V. P. (2021). Design matters! How visual gendered anthropomorphic design cues moderate the determinants of the behavioral intention towards using chatbots. In Springer eBooks. https://link.springer.com/chapter/10.1007/978-3-030-94890-0_12 Sabour, S., Zhang, W., Xiao, X., Zhang, Y., Zheng, Y., Wen, J., ... & Huang, M. (2023). A chatbot for mental health support: exploring the impact of Emohaa on reducing mental distress in China. Frontiers in digital health, 5, 1133987. https://doi.org/10.3389/fdgth.2023.1133987 Schmidt, L. I., Baetzner, A. S., Dreisbusch, M. I., Mertens, A., & Sieverding, M. (2024). Postponing sleep after a stressful day: Patterns of stress, bedtime procrastination, and sleep outcomes in a daily diary approach. Stress and Health, 40(3), e3330. https://doi.org/10.1002/smi.3330 Steel, P. (2007). The nature of procrastination: a meta-analytic and theoretical review of quintessential self-regulatory failure. Psychological bulletin, 133(1), 65. https://doi.org/10.1037/0033-2909.133.1.65 Suh, S., Cho, N., Jeoung, S., & An, H. (2022). Developing a psychological intervention for decreasing bedtime procrastination: the BED-PRO study. Behavioral Sleep Medicine, 20(6), 659-673. https://doi.org/10.1080/15402002.2021.1979004 Valshtein, T. J., Oettingen, G., & Gollwitzer, P. M. (2019). Using mental contrasting with implementation intentions to reduce bedtime procrastination: two randomised trials. Psychology & Health, 35(3), 275–301. https://doi.org/10.1080/08870446.2019.1652753 White, J., Fu, Q., Hays, S., Sandborn, M., Olea, C., Gilbert, H., ... & Schmidt, D. C. (2023). A prompt pattern catalog to enhance prompt engineering with chatgpt. arXiv preprint arXiv:2302.11382. https://doi.org/10.48550/arXiv.2302.11382 Yang, C. M., Hsu, S. C., Lin, S. C., Chou, Y. Y., & Chen, Y. M. (2009). Reliability and validity of the Chinese version of insomnia severity index. Arch Clin Psychol, 4(2), 95-104. Zhang, H., Wu, C., Xie, J., Lyu, Y., Cai, J., & Carroll, J. M. (2023). Redefining qualitative analysis in the AI era: Utilizing ChatGPT for efficient thematic analysis. arXiv preprint arXiv:2309.10771. https://doi.org/10.48550/arXiv.2309.10771 Zhang, Y., Sun, S., Galley, M., Chen, Y. C., Brockett, C., Gao, X., Gao, J., Liu, J., & Dolan, B. (2020). DialoGPT: Large scale generative pre training for conversational response generation. In A. Celikyilmaz & T. H. Wen (Eds.), Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics: System Demonstrations (pp. 270–278). Association for Computational Linguistics. https://doi.org/10.18653/v1/2020.acl-demos.30 Zhao, W. X., Zhou, K., Li, J., Tang, T., Wang, X., Hou, Y., ... & Wen, J. R. (2023). A survey of large language models. arXiv preprint arXiv:2303.18223, 1(2). https://doi.org/10.48550/arXiv.2303.18223
描述 碩士
國立政治大學
數位內容碩士學位學程
112462016
資料來源 http://thesis.lib.nccu.edu.tw/record/#G0112462016
資料類型 thesis
dc.contributor.advisor 鄭霈絨<br>廖峻鋒zh_TW
dc.contributor.advisor Cheng, Pei-Jung<br>Liao, Chun-Fengen_US
dc.contributor.author (Authors) 陳玟諭zh_TW
dc.contributor.author (Authors) Chen, Wen-Yuen_US
dc.creator (作者) 陳玟諭zh_TW
dc.creator (作者) Chen, Wen-Yuen_US
dc.date (日期) 2025en_US
dc.date.accessioned 1-Sep-2025 16:51:34 (UTC+8)-
dc.date.available 1-Sep-2025 16:51:34 (UTC+8)-
dc.date.issued (上傳時間) 1-Sep-2025 16:51:34 (UTC+8)-
dc.identifier (Other Identifiers) G0112462016en_US
dc.identifier.uri (URI) https://nccur.lib.nccu.edu.tw/handle/140.119/159391-
dc.description (描述) 碩士zh_TW
dc.description (描述) 國立政治大學zh_TW
dc.description (描述) 數位內容碩士學位學程zh_TW
dc.description (描述) 112462016zh_TW
dc.description.abstract (摘要) 睡眠拖延(Bedtime Procrastination)為一種常見於年輕族群的行為問題，其核心成 因多與自我調節困難、報復性熬夜、時間管理不足及情緒壓力相關，長期可能導致睡眠 品質下降、白天清醒感降低與心理健康風險上升。隨著生成式人工智慧(Generative AI) 對話技術的發展，􏰀類的聊天機器人具備即時對話、情緒支持與個別化建議的特性受到 重視。因􏰀，本研究採用生成式 AI 聊天機器人(Linebot)結合 BED-PRO 的對話架構， 探究其改善睡眠拖延行為的成效，以及其對於睡眠拖延者之自我覺察、情緒調控與時間 管理能力的影響。本研究針對有睡眠拖延傾向的大專院校學生，進行為期三週的睡眠拖 延改善實驗，研究資料包含睡眠日記與每週的睡眠回顧對話。 研究結果顯示，參與者於實驗後睡眠拖延量表(BPS)與睡眠拖延時間(BPD)皆 顯著下降，顯示􏰀對話機制具行為改變效果。質性資料亦指出，Linebot 有助於睡眠拖 延者辨識報復性熬夜與任務逃避等拖延成因，並培養自我調節與反思能力。多數睡眠拖 延者肯定 Linebot 溫柔、不批判的對話風格與穩定互動，視其為情緒支持與睡眠儀式建 立的重要助力。本研究主要貢獻在於結合生成式 AI 技術與 BED-PRO 對話架構，以實 證結果展現出提升睡眠自我調節與行為改變的潛力，將能提供未來在行為改善或養成工 具之互動形式與個人化設計方面的具體參考。zh_TW
dc.description.abstract (摘要) Bedtime procrastination is a widespread issue among young adults, often linked to poor self-regulation, revenge bedtime procrastination, and time management difficulties. These factors negatively affect sleep quality and mental well-being. With advances in generative AI, behavior-change chatbots capable of providing real-time interaction and personalized support have gained significant attention. This study evaluated the effectiveness of a generative AI chatbot (Linebot), designed with the BED-PRO framework, in reducing bedtime procrastination and improving users’ self- reflection, emotional regulation, and time management. A three-week intervention was conducted among university students with a high tendency toward bedtime procrastination. The intervention included daily sleep diary entries and weekly reflective dialogue sessions. Post-intervention data showed significant reductions in Bedtime Procrastination Scale (BPS) scores and Bedtime Procrastination Delay (BPD), indicating behavioral improvement. Qualitative results further revealed that Linebot helped participants recognize procrastination triggers, such as revenge bedtime procrastination and task avoidance, while encouraging self- regulation and reflection. Participants also valued the chatbot’s non-judgmental tone and consistent support in developing bedtime routines. This study contributes by integrating generative AI with the BED-PRO framework, providing empirical support for its potential to enhance self-regulation and behavioral change related to sleep. The findings offer practical insights for designing future interactive and personalized digital tools for behavior improvement and habit formation.en_US
dc.description.tableofcontents 摘要 I Abstract II 目次 III 表次 V 圖次 VI 第一章 緒論 1 第一節 研究背景與動機 1 第二節 研究目的與問題 2 第三節 重要名詞釋義 3 第四節 研究範圍與限制 4 第二章 文獻探討 7 第一節 睡眠拖延的成因 7 第二節 改善睡眠拖延之相關研究 10 第三節 聊天機器人之特性 15 第四節 小結 19 第三章 研究方法 21 第一節 研究設計 21 第二節 前導測試（pilot study） 34 第三節 正式實驗 38 第四節 資料研究工具與方法 40 第四章 研究結果與討論 45 第一節 正式實驗參與者資料 45 第二節 睡眠拖延改善效果 46 第三節 與Linebot互動的滿意度分析 57 第四節 綜合討論 60 第五章 研究結論與建議 65 第一節 研究結論 65 第二節 未來研究建議 66 參考文獻 69 附錄 75 附錄一 睡眠拖延量表 75 附錄二 失眠嚴重度量表 76 附錄三 實驗後測問卷 77 附錄四 實驗招募規則及注意事項 79 附錄五 倫理審查通過證明 80 附錄六 Linebot定時發送之訊息 81 附錄七 完整Default prompt & Review prompt 83 附錄八 主題一 - 睡眠習慣主觀影響及改變 91 附錄九 主題二 - 睡眠拖延主觀影響及改變 100 附錄十 主題三 - 情緒調控能力主觀影響及改變 109 附錄十一 主題四 - 時間管理與洞察力主觀影響及改變 117 附錄十二 與Linebot的互動體驗 123 附錄十三 完整實驗過程截圖 135zh_TW
dc.format.extent 10266955 bytes-
dc.format.mimetype application/pdf-
dc.source.uri (資料來源) http://thesis.lib.nccu.edu.tw/record/#G0112462016en_US
dc.subject (關鍵詞) 生成式AIzh_TW
dc.subject (關鍵詞) 睡眠拖延行為zh_TW
dc.subject (關鍵詞) BED-PRO 對話架構zh_TW
dc.subject (關鍵詞) 聊天機器人zh_TW
dc.subject (關鍵詞) 報復性熬夜zh_TW
dc.subject (關鍵詞) Generative AIen_US
dc.subject (關鍵詞) Bedtime procrastination behavioren_US
dc.subject (關鍵詞) BED-PROen_US
dc.subject (關鍵詞) Chatboten_US
dc.subject (關鍵詞) Revenge bedtime procrastinationen_US
dc.title (題名) 探討生成式 AI 聊天機器人對睡眠拖延行為改善的影響zh_TW
dc.title (題名) Exploring the Impact of Generative AI Chatbot on Improving Bedtime Procrastination Behavioren_US
dc.type (資料類型) thesisen_US
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