近年來，學習管理系統(LMS)被廣泛應用，而隨著AI科技的興起，AI聊天機器人如ChatGPT應用於教育領域日益受到重視。然而，ChatGPT大多應用於傳遞知識、回答問題的功能，但學生情意技能的培養，亦為學習成功的關鍵因素。學生於線上學習環境中，經常面臨時間管理、持續學習及處理挫折等挑戰，要解決學生個體所遇到的挑戰，需要仰賴學生長期脈絡的資訊以提供個人化的支持，但現行的LMS平台與ChatGPT等工具，缺乏缺少對學生長期脈絡的追蹤和利用，使個人化支持有限。此外，現有的聊天機器人多服務於單一主體，較少同時顧慮學習過程中的利害關係人如教師、助教、學生，這使得其應用範圍受限。
為解決上述問題，本研究提出一個具學生長期脈絡，學習能力管理之學習管家系統。建立學生長時日誌，具諮商對話技巧與學習管理能力如恆毅力、成長型思維與時間管理等知能，透過檢索增強生成與劇本式提詞的提示工程技術，使學習管家了解情境，根據每位學生的特定情況提供依人、依時的專業化回覆。透過學習管家的互動、對話，培養學生持續學習、面對挫折與時間管理的能力，以提升學習事件的參與和完成率，最終達到提升學習成效的目的。同時，學習管家與各利害關係人協作，採納各方需求並給予學生學習資訊，以協助教師教學。
為了驗證系統的有效性，本研究將系統實際應用於餐旅日語課程中。研究結果顯示，在學習管理系統中，相比於僅具當前脈絡之學習管家，使用具學生長時脈絡與學習能力管理之學習管家，學生於恆毅力、時間管理及學習成效上，皆有顯著提升。這不僅證明AI對話機器人如ChatGPT應用於教育中，需要建立學生的長期脈絡模式，提供個性化的回饋，關注學生情意技能如持續學習、面對挫折和時間管理的培養，亦為學習重要的層面。除此之外，須整合所有教育過程中利害關係人的需求，使其與大型語言模型工具有效協作，使ChatGPT應用於學習管理系統的效益最大化。

In recent years, Learning Management Systems (LMS) have been extensively applied, and with the development of AI technology, the application of AI chatbots like ChatGPT in educational fields has become increasingly prominent. However, ChatGPT is primarily used for knowledge teaching and question answering, yet the cultivation of students' affective skills is equally crucial for learning success. Students often face challenges such as time management, sustained learning, and handling setbacks in online learning environments. Addressing these individual challenges requires long-term contextual information about students to provide personalized support, but current platforms like LMS and tools like ChatGPT lack tracking and utilization of long-term student contexts, limiting personalized support. Additionally, chatbots in education mostly serve single subjects and seldom consider all stakeholders in the learning process, such as teachers, teaching assistants, and students, thus limiting their application.
To address these issues, this study proposes a learning butler system that incorporates long-term student contexts and learning skills management. By establishing long-term student journals and integrating counseling dialogue skills and learning management skills such as grit, growth mindset, and time management, the learning butler system utilizes Retrieval-Augmented Generation and script-based prompting engineering methods. This enables the learning butler to understand the context and provide personalized, timely, and professional responses based on each student's specific contexts. Through interaction and dialogue with the learning butler, students' abilities in continuous learning, facing challenges, and time management are cultivated, aiming to enhance participation and completion rates of learning tasks, ultimately improving learning outcomes. The learning butler also collaborates with all stakeholders, adapting to their needs and providing educational information to assist teachers.
To validate the effectiveness of the system, it was applied in a Hospitality Japanese course. The findings showed that within the LMS, compared to a learning butler with only current context, the use of a learning butler with long-term student contexts and learning skills management significantly enhanced students' grit, time management, and overall learning outcomes. This not only proves that educational applications of AI chatbots like ChatGPT need to establish long-term student contextual models and provide personalized feedback focusing on cultivating affective skills like continuous learning, resilience, and time management but also highlights the importance of integrating the needs of all stakeholders in the educational process. This collaboration maximizes the benefits of using AI tools like ChatGPT in learning management systems.

簡鈺笙 (2023)。以聊天機器技術來培養學生恆毅力和成長型思維及提昇學習成效之學習系統數位管家。國立中央大學資訊工程系學位論文。
張育甄 (2023)。利用情繫理論與共同目標設計具長期依戀關係的寵物機器人以提升學習成效。國立中央大學資訊工程系學位論文。
黃招憲、郭德信、王淑麗(2005)。 餐旅日語(上)。 致良出版社。
Aditomo, A. (2015). Students' response to academic setback: "Growth mindset" as a buffer against demotivation. International Journal of Educational Psychology, 4(2), 198-222.
Ahmad Uzir, N. A., Gašević, D., Matcha, W., Jovanović, J., & Pardo, A. (2020). Analytics of time management strategies in a flipped classroom. Journal of Computer Assisted Learning, 36(1), 70-88.
Ain, N., Kaur, K., & Waheed, M. (2016). The influence of learning value on learning management system use: An extension of UTAUT2. Information Development, 32(5), 1306–1321. https://doi.org/10.1177/0266666915597546
Al-Fraihat, D., Joy, M., & Sinclair, J. (2020). Evaluating e-learning systems success: An empirical study. Computers in Human Behavior, 102, 67-86.
Alario-Hoyos, C., Estévez-Ayres, I., Pérez-Sanagustín, M., Delgado Kloos, C., & Fernández-Panadero, C. (2017). Understanding learners’ motivation and learning strategies in MOOCs. International Review of Research in Open and Distributed Learning, 18(3), 119-137.
Alawwad, H. A., Alhothali, A., Naseem, U., Alkhathlan, A., & Jamal, A. (2024). Enhancing textbook question answering task with large language models and retrieval augmented generation. arXiv preprint arXiv:2402.05128.
Alay, S., & Kocak, S. (2002). Validity and reliability of time management questionnaire. Hacettepe Üniversitesi Eğitim Fakültesi Dergisi, 22(22).
Aldahwan, N., & Alsaeed, N. (2020). Use of artificial intelligence in Learning Management System (LMS): a systematic literature review. International Journal of Computer Applications, 175(13), 16-26.
Alhadabi, A., & Karpinski, A. C. (2020). Grit, self-efficacy, achievement orientation goals, and academic performance in university students. International Journal of Adolescence and Youth, 25(1), 519-535.
Ali, F., Choy, D., Divaharan, S., Tay, H. Y., & Chen, W. (2023). Supporting self-directed learning and self-assessment using TeacherGAIA, a generative AI chatbot application: Learning approaches and prompt engineering. Learning: Research and Practice, 9(2), 135-147.
Ali, M., Ashaari, N. S., Noor, S. M., & Zainudin, S. (2022). Identifying students' learning patterns in online learning environments: A literature review. International Journal of Emerging Technologies in Learning (iJET), 17(8), 189-205.
Alias, N. A., & Zainuddin, A. M. (2005). Innovation for better teaching and learning: Adopting the learning management system. Malaysian Online Journal of Instructional Technology, 2(2), 27-40.
Alkhasawnh, S., & Alqahtani, M. A. M. (2019). Fostering students’ self-regulated learning through using a learning management system to enhance academic outcomes at the University of Bisha. TEM Journal, 8(2), 662.
Aljawarneh, S. A. (2020). Reviewing and exploring innovative ubiquitous learning tools in higher education. Journal of Computing in Higher Education, 32(1), 57-73.
Alserhan, S., Alqahtani, T. M., Yahaya, N., Al-Rahmi, W. M., & Abuhassna, H. (2023). Personal learning environments: Modeling students’ self-regulation enhancement through a learning management system platform. IEEE Access, 11, 5464-5482.
Alturki, U., & Aldraiweesh, A. (2021). Application of learning management system during the covid-19 pandemic: A sustainable acceptance model of the expansion technology approach. Sustainability, 13(19), 10991.
Alvarez Sainz, M., Ferrero, A. M., & Ugidos, A. (2019). Time management: Skills to learn and put into practice. Education+ Training, 61(5), 635-648.
Aparicio, M., Bacao, F., & Oliveira, T. (2017). Grit in the path to e-learning success. Computers in Human Behavior, 66, 388-399.
Appana, S. (2008). A review of benefits and limitations of online learning in the context of the student, the instructor, and the tenured faculty. International Journal on E-learning, 7(1), 5-22.
Araka, E., Maina, E., Gitonga, R., Oboko, R., & Kihoro, J. (2021). University students’ perception on the usefulness of learning management system features in promoting self-regulated learning in online learning.
Atmojo, A. E. P., & Nugroho, A. (2020). EFL classes must go online! Teaching activities and challenges during COVID-19 pandemic in Indonesia. Register Journal, 13(1), 49–76. doi:10.18326/rgt.v13i1.49-76
Avcı, Ü., & Ergün, E. (2022). Online students’ LMS activities and their effect on engagement, information literacy and academic performance. Interactive Learning Environments, 30(1), 71-84. DOI: 10.1080/10494820.2019.1636088
Bii, P. (2013). Chatbot technology: A possible means of unlocking student potential to learn how to learn. Educational Research, 4(2), 218-221.
Belda-Medina, J., & Calvo-Ferrer, J. R. (2022). Using chatbots as AI conversational partners in language learning. Applied Sciences, 12(17), 8427.
Black, E. W., Beck, D., Dawson, K., Jinks, S., & DiPietro, M. (2007). The other side of the LMS: Considering implementation and use in the adoption of an LMS in online and blended learning environments. Techtrends: Linking research & practice to improve learning, 51(2).
Bradley, V. M. (2021). Learning Management System (LMS) use with online instruction. International Journal of Technology in Education, 4(1), 68-92.
Britton, B. K., & Tesser, A. (1991). Effects of time-management practices on college grades. Journal of Educational Psychology, 83(3), 405.
Broadbent, J., & Poon, W. L. (2015). Self-regulated learning strategies & academic achievement in online higher education learning environments: A systematic review. The Internet and Higher Education, 27, 1-13.
Broadbent, J., & Fuller-Tyszkiewicz, M. (2018). Profiles in self-regulated learning and their correlates for online and blended learning students. Educational Technology Research and Development, 66, 1435–1455. https://doi.org/10.1007/s11423-018-9595-9
Brown, T., et al. (2020). Language models are few-shot learners. In Proceedings of the Annual Conference on Neural Information Processing Systems (pp. 1877-1901).
Budiu, R. (2018). The user experience of chatbots. Nielsen Norman Group, 25.
Chen, W., Hu, H., Saharia, C., & Cohen, W. W. (2022). Re-imagen: Retrieval-augmented text-to-image generator. arXiv preprint arXiv:2209.14491.
Chen, Y., Jensen, S., Albert, L. J., Gupta, S., & Lee, T. (2023). Artificial intelligence (AI) student assistants in the classroom: Designing chatbots to support student success. Information Systems Frontiers, 25(1), 161-182.
Chang, C. Y., Kuo, S. Y., & Hwang, G. H. (2022). Chatbot-facilitated nursing education. Educational Technology & Society, 25(1), 15-27.
Cheng, S. L., & Xie, K. (2021). Why college students procrastinate in online courses: A self-regulated learning perspective. The Internet and Higher Education, 50, 100807.
Choi, J. H., Hickman, K. E., Monahan, A., & Schwarcz, D. (2023). ChatGPT goes to law school. SSRN. Available at SSRN: https://ssrn.com/abstract=number
Claessens, B. J., Van Eerde, W., Rutte, C. G., & Roe, R. A. (2007). A review of the time management literature. Personnel Review, 36(2), 255-276.
Clarizia, F., Colace, F., Lombardi, M., Pascale, F., & Santaniello, D. (2018). Chatbot: An education support system for student. In Cyberspace Safety and Security: 10th International Symposium, CSS 2018, Amalfi, Italy, October 29–31, 2018, Proceedings 10 (pp. 291-302). Springer International Publishing.
Claro, S., Paunesku, D., & Dweck, C. S. (2016). Growth mindset tempers the effects of poverty on academic achievement. Proceedings of the National Academy of Sciences, 113(31), 8664-8668.
Cohen, J. (1992). A power primer. Psychological Bulletin, 112(1), 155-159. https://doi.org/10.1037/0033-2909.112.1.155
Corey, G. (2013). Theory and practice of counseling and psychotherapy. Cengage Learning.
Credé, M., Tynan, M. C., & Harms, P. D. (2016). Much ado about grit: A meta-analytic synthesis of the grit literature. Journal of Personality and Social Psychology, 113, 492-511.
Dweck, C. S. (2006). Mindset: The new psychology of success. Random House.
Dweck, C. S., Chiu, C. Y., & Hong, Y. Y. (1995). Implicit theories and their role in judgments and reactions: A word from two perspectives. Psychological Inquiry, 6(4), 267-285.
Dweck, C. S., & Leggett, E. L. (1988). A social-cognitive approach to motivation and personality. Psychological Review, 95(2), 256–273.
Duckworth, A. L., & Eskreis-Winkler, L. (2013). True grit. The Observer, 26(4), 1–3.
Duckworth, A. L., Peterson, C., Matthews, M. D., & Kelly, D. R. (2007). Grit: perseverance and passion for long-term goals. Journal of Personality and Social Psychology, 92(6), 1087.
Duckworth, A. L., & Quinn, P. D. (2009). Development and validation of the Short Grit Scale (GRIT–S). Journal of Personality Assessment, 91(2), 166-174.
Eskreis-Winkler, L., Shulman, E. P., Beal, S. A., & Duckworth, A. L. (2014). The grit effect: predicting retention in the military, the workplace, school, and marriage. Frontiers in Psychology, 5, 36. doi: 10.3389/fpsyg.2014.00036
Falcão, T. P., Peres, F. M. D. A., de Morais, D. C. S., & da Silva Oliveira, G. (2018). Participatory methodologies to promote student engagement in the development of educational digital games. Computers & Education, 116, 161-175.
Firat, M. (2023). Integrating AI applications into learning management systems to enhance e-learning. Instructional Technology and Lifelong Learning, 4(1), 1-14.
Følstad, A., & Brandtzæg, P. B. (2017). Chatbots and the new world of HCI. Interactions, 24(4), 38–42. https://doi.org/10.1145/3085558
Gaglo, K., Degboe, B. M., Kossingou, G. M., & Ouya, S. (2021, February). Proposal of conversational chatbots for educational remediation in the context of COVID-19. In 2021 23rd International Conference on Advanced Communication Technology (ICACT) (pp. 354-358). IEEE.
Gamage, S. H., Ayres, J. R., & Behrend, M. B. (2022). A systematic review on trends in using Moodle for teaching and learning. International Journal of STEM Education, 9(1), 9.
Gao, Y., Xiong, Y., Gao, X., Jia, K., Pan, J., Bi, Y., ... & Wang, H. (2023). Retrieval-augmented generation for large language models: A survey. arXiv preprint arXiv:2312.10997.
George, D. (2011). SPSS for windows step by step: A simple study guide and reference, 17.0 update, 10/e. Pearson Education India.
Gómez, S., Zervas, P., Sampson, D. G., & Fabregat, R. (2014). Context-aware adaptive and personalized mobile learning delivery supported by UoLmP. Journal of King Saud University-Computer and Information Sciences, 26(1), 47-61.
González-González, C. S., Muñoz-Cruz, V., Toledo-Delgado, P. A., & Nacimiento-García, E. (2023). Personalized gamification for learning: a reactive chatbot architecture proposal. Sensors, 23(1), 545.
Han, I., & Shin, W. S. (2016). The use of a mobile learning management system and academic achievement of online students. Computers & Education, 102, 79-89.
Han, S., & Lee, M. K. (2022). FAQ chatbot and inclusive learning in massive open online courses. Computers & Education, 179, 104395.
Heine, S. J., Kitayama, S., Lehman, D. R., Takata, T., Ide, E., Leung, C., & Matsumoto, H. (2001). Divergent consequences of success and failure in Japan and North America: An investigation of self-improving motivations and malleable selves. Journal of Personality and Social Psychology, 81(4), 599–615.
Hejazi, S. Y., & Sadoughi, M. (2023). How does teacher support contribute to learners’ grit? The role of learning enjoyment. Innovation in Language Learning and Teaching, 17(3), 593-606.
Hoyert, M., & O’Dell, C. (2008). Goal orientation and the aftermath of an academic failure. International Journal of Learning, 15(3), 245–251.
Hua, S., Jin, S., & Jiang, S. (2024). The limitations and ethical considerations of ChatGPT. Data Intelligence, 6(1), 201-239.
Hwang, M. H., & Nam, J. K. (2021). Enhancing grit: Possibility and intervention strategies. In Multidisciplinary perspectives on grit: Contemporary theories, assessments, applications and critiques (pp. 77-93).
Ismail, M., & Ade-Ibijola, A. (2019, November). Lecturer's apprentice: A chatbot for assisting novice programmers. In 2019 International Multidisciplinary Information Technology and Engineering Conference (IMITEC) (pp. 1-8). IEEE.
Ismail, S. N., Hamid, S., Ahmad, M., Alaboudi, A., & Jhanjhi, N. (2021). Exploring students' engagement towards the learning management system (LMS) using learning analytics. Computer Systems Science & Engineering, 37(1).
Jhala, A. (2008). Exploiting structure and conventions of movie scripts for information retrieval and text mining. In Interactive Storytelling: First Joint International Conference on Interactive Digital Storytelling, ICIDS 2008, Erfurt, Germany, November 26-29, 2008 Proceedings 1 (pp. 210-213). Springer Berlin Heidelberg.
Jiang, L., Zhang, S., Li, X., & Luo, F. (2021). How grit influences high school students’ academic performance and the mediation effect of academic self-efficacy and cognitive learning strategies. Current Psychology.
Kandpal, N., Deng, H., Roberts, A., Wallace, E., & Raffel, C. (2022). Large language models struggle to learn long-tail knowledge. CoRR, abs/2211.08411.
Karakose, T. (2015). The relationship between medical students’ time management skills and academic achievement. Studies on Ethno-Medicine, 9(1), 19-24.
Koraishi, O. (2023). Teaching English in the age of AI: Embracing ChatGPT to optimize EFL materials and assessment. Language Education and Technology, 3(1).
Kroeper, K. M., Fried, A. C., & Murphy, M. C. (2022). Towards fostering growth mindset classrooms: Identifying teaching behaviors that signal instructors’ fixed and growth mindsets beliefs to students. Social Psychology of Education, 25(2), 371-398.
Kuhail, M. A., Alturki, N., Alramlawi, S., et al. (2023). Interacting with educational chatbots: A systematic review. Educational Information Technology, 28, 973–1018.
Lála, J., O'Donoghue, O., Shtedritski, A., Cox, S., Rodriques, S. G., & White, A. D. (2023). Paperqa: Retrieval-augmented generative agent for scientific research. arXiv preprint arXiv:2312.07559.
Lam, K. K. L., & Zhou, M. (2019). Examining the relationship between grit and academic achievement within K‐12 and higher education: A systematic review. Psychology in the Schools, 56(10), 1654-1686.
Landrum, B. (2020). Examining students' confidence to learn online, self-regulation skills, and perceptions of satisfaction and usefulness of online classes. Online Learning, 24(3), 128-146.
Latif, E., & Zhai, X. (2024). Fine-tuning chatgpt for automatic scoring. Computers and Education: Artificial Intelligence, 100210.
Laurillard, D. (2013). Rethinking university teaching: A conversational framework for the effective use of learning technologies. Routledge.
Lee, L. K., Fung, Y. C., Pun, Y. W., Wong, K. K., Yu, M. T. Y., & Wu, N. I. (2020, August). Using a multiplatform chatbot as an online tutor in a university course. In 2020 International Symposium on Educational Technology (ISET) (pp. 53-56). IEEE.
Lee, U., Jung, H., Jeon, Y., Sohn, Y., Hwang, W., Moon, J., & Kim, H. (2023). Few-shot is enough: Exploring ChatGPT prompt engineering method for automatic question generation in English education. Education and Information Technologies.
Lewis, L. S., Williams, C. A., & Dawson, S. D. (2020). Growth mindset training and effective learning strategies in community college registered nursing students. Teach. Learn. Nurs., 15, 123–127.
Lewis, P., Perez, E., Piktus, A., Petroni, F., Karpukhin, V., Goyal, N., ... & Kiela, D. (2020). Retrieval-augmented generation for knowledge-intensive NLP tasks. Advances in Neural Information Processing Systems, 33, 9459-9474.
Levonian, Z., Li, C., Zhu, W., Gade, A., Henkel, O., Postle, M. E., & Xing, W. (2023). Retrieval-augmented generation to improve math question-answering: Trade-offs between groundedness and human preference. arXiv preprint arXiv:2310.03184.
Liang, C., Xu, C., Cheng, J., Min, W., & Lu, H. (2012). Script-to-movie: A computational framework for story movie composition. IEEE Transactions on Multimedia, 15(2), 401-414.
Lim, S., & Schmälzle, R. (2023). Artificial intelligence for health message generation: An empirical study using a large language model (LLM) and prompt engineering. Frontiers in Communication, 8, 1129082.
Lin, J., Gurung, A., Thomas, D. R., Chen, E., Borchers, C., Gupta, S., & Koedinger, K. R. (2024). Improving assessment of tutoring practices using retrieval-augmented generation. arXiv preprint arXiv:2402.14594.
Lin, W., & Byrne, B. (2022). Retrieval augmented visual question answering with outside knowledge. arXiv preprint arXiv:2210.03809.
Lo, L. S. (2023). The CLEAR path: A framework for enhancing information literacy through prompt engineering. The Journal of Academic Librarianship, 49(4), 102720.
Ma, Z., Dou, Z., Zhu, Y., Zhong, H., & Wen, J. R. (2021, July). One chatbot per person: Creating personalized chatbots based on implicit user profiles. In Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval (pp. 555-564).
Mandernach, B. J., Hudson, S., & Wise, S. (2013). Where has the time gone? Faculty activities and time commitments in the online classroom. Journal of Educators Online, 10(2), 1–15.
Mbakwe, A. B., Lourentzou, I., Celi, L. A., Mechanic, O. J., & Dagan, A. (2023). ChatGPT passing USMLE shines a spotlight on the flaws of medical education. PLOS Digital Health, 2(2).
McGill, T. J., & Klobas, J. E. (2009). A task–technology fit view of learning management system impact. Computers & Education, 52, 496–508.
Mendoza, S., Hernández-León, M., Sánchez-Adame, L. M., Rodríguez, J., Decouchant, D., & Meneses-Viveros, A. (2020). Supporting student-teacher interaction through a chatbot. In Learning and Collaboration Technologies. Human and Technology Ecosystems: 7th International Conference, LCT 2020, Held as Part of the 22nd HCI International Conference, HCII 2020, Copenhagen, Denmark, July 19–24, 2020, Proceedings, Part II (pp. 93-107). Springer International Publishing.
Mirza, T. I., Yasmeen, R., & Mahboob, U. (2021). Nurturing grit among medical students. Pakistan Journal of Medical Sciences, 37(2), 531.
Morze, N., Varchenko-Trotsenko, L., Terletska, T., & Smyrnova-Trybulska, E. (2021, March). Implementation of adaptive learning at higher education institutions by means of Moodle LMS. In Journal of Physics: Conference Series (Vol. 1840, No. 1, p. 012062). IOP Publishing.
Muenks, K., Wigfield, A., Yang, J. S., & O’Neal, C. R. (2017). How true is grit? Assessing its relations to high school and college students’ personality characteristics, self-regulation, engagement, and achievement. Journal of Educational Psychology, 109(5), 599–620.
Mukhtar, K., Javed, K., Arooj, M., & Sethi, A. (2020). Advantages, limitations, and recommendations for online learning during the COVID-19 pandemic era. Pakistan Journal of Medical Sciences, 36(COVID19-S4), S27.
Mungoli, N. (2023). Exploring the synergy of prompt engineering and reinforcement learning for enhanced control and responsiveness in Chat GPT. Journal of Electrical Electronics Engineering, 2(3), 201-205.
Mwalumbwe, I., & Mtebe, J. S. (2017). Using learning analytics to predict students’ performance in Moodle learning management system: A case of Mbeya University of Science and Technology. The Electronic Journal of Information Systems in Developing Countries, 79(1), 1-13.
Nawrot, I., & Doucet, A. (2014, April). Building engagement for MOOC students: Introducing support for time management on online learning platforms. In Proceedings of the 23rd International Conference on World Wide Web (pp. 1077-1082).
Novak, E., McDaniel, K., Daday, J., & Soyturk, I. (2022). Frustration in technology‐rich learning environments: A scale for assessing student frustration with e‐textbooks. British Journal of Educational Technology, 53(2), 408-431.
O'Rourke, E., Haimovitz, K., Ballweber, C., Dweck, C., & Popović, Z. (2014, April). Brain points: A growth mindset incentive structure boosts persistence in an educational game. In Proceedings of the SIGCHI conference on human factors in computing systems (pp. 3339-3348).
Oguguo, B. C., Nannim, F. A., Agah, J. J., Ugwuanyi, C. S., Ene, C. U., & Nzeadibe, A. C. (2021). Effect of learning management system on student’s performance in educational measurement and evaluation. Education and Information Technologies, 26, 1471-1483.
Okonkwo, C. W., & Ade-Ibijola, A. (2020). Python-Bot: A chatbot for teaching python programming. Engineering Letters, 29(1).
Oyarzun, B., Martin, F., & Moore, R. L. (2020). Time management matters: Online faculty perceptions of helpfulness of time management strategies. Distance Education, 41(1), 106-127.
Pataranutaporn, P., Danry, V., Leong, J., Punpongsanon, P., Novy, D., Maes, P., & Sra, M. (2021). AI-generated characters for supporting personalized learning and well-being. Nature Machine Intelligence, 3(12), 1013-1022.
Perez, M. (2015). Obtaining academic success: Nurturing grit in students. Journal of Interpersonal Relation, Intergroup Relations and Identity, 8, 56-63.
Polirstok, S. (2017). Strategies to improve academic achievement in secondary school students: Perspectives on grit and mindset. Sage Open, 7(4), 2158244017745111.
Polverini, G., & Gregorcic, B. (2024). How understanding large language models can inform the use of ChatGPT in physics education. European Journal of Physics, 45(2), 025701.
Poon, L. K., Kong, S. C., Yau, T. S., Wong, M., & Ling, M. H. (2017). Learning analytics for monitoring students' participation online: Visualizing navigational patterns on learning management system. In Blended Learning. New Challenges and Innovative Practices: 10th International Conference, ICBL 2017, Hong Kong, China, June 27-29, 2017, Proceedings 10 (pp. 166-176). Springer International Publishing.
Qurotul Aini, Q. A., Mukti Budiarto, M. B., POH Putra, P. O. H., & Untung Rahardja, U. R. (2020). Exploring e-learning challenges during the global COVID-19 pandemic: A review. Jurnal Sistem Informasi (Journal of Information System), 16(2), 47-65.
Roos, S., & Lochan, R. (2018). Chatbots in Education: a passing trend or a valuable pedagogical tool? Department of Informatics and Media, Uppsala University.
Sallam, M. (2023). The utility of ChatGPT as an example of large language models in healthcare education research and practice: Systematic review on the future perspectives and potential limitations. Systematic Review on the Future Perspectives and Potential Limitations.
Sefriani, R., Nastasia, K., Sepriana, R., & Candra, Y. (2022). Time management and procrastination during the COVID-19 pandemic in higher education. Journal of Education and Learning (EduLearn), 16(4), 458-463.
Shawar, B. A., & Atwell, E. (2007). Chatbots: Are they really useful? Journal for Language Technology and Computational Linguistics, 22(1), 29-49.
Shemshack, A., & Spector, J. M. (2020). A systematic literature review of personalized learning terms. Smart Learning Environments, 7(1), 1-20.
Shieh, J. (2023, February). Best practices for prompt engineering with OpenAI API. Retrieved from https://help.openai.com/en/articles/6654000-best-practices-for-prompt-engineering-with-openai-api
Shukla, V. K., & Verma, A. (2019, April). Enhancing LMS experience through AIML base and retrieval base chatbot using R language. In 2019 International Conference on Automation, Computational and Technology Management (ICACTM) (pp. 561-567). IEEE.
Shuster, K., Poff, S., Chen, M., Kiela, D., & Weston, J. (2021). Retrieval augmentation reduces hallucination in conversation. arXiv preprint arXiv:2104.07567.
Sinha, S., Basak, S., Dey, Y., & Mondal, A. (2020). An educational chatbot for answering queries. In Emerging Technology in Modelling and Graphics: Proceedings of IEM Graph 2018 (pp. 55-60). Springer Singapore.
Smiley, P. A., Buttitta, K. V., Chung, S. Y., Dubon, V. X., & Chang, L. K. (2016). Mediation models of implicit theories and achievement goals predict planning and withdrawal after failure. Motivation and Emotion, 40(6), 878–894.
Smutny, P., & Schreiberova, P. (2020). Chatbots for learning: A review of educational chatbots for the Facebook Messenger. Computers & Education, 151, 103862.
Snekha, S., & Ayyanathan, N. (2023). An educational CRM chatbot for learning management system. Shanlax International Journal of Education, 11(4), 58-62.
Souali, K., Rahmaoui, O., Ouzzif, M., & El Haddioui, I. (2019, November). Recommending moodle resources using chatbots. In 2019 15th International Conference on Signal-Image Technology & Internet-Based Systems (SITIS) (pp. 677-680). IEEE.
Spitzer, M. W. H., Gutsfeld, R., Wirzberger, M., & Moeller, K. (2021). Evaluating students’ engagement with an online learning environment during and after COVID-19 related school closures: A survival analysis approach. Trends in Neuroscience and Education, 25, 100168.
Sreelakshmi, A. S., Abhinaya, S. B., Nair, A., & Nirmala, S. J. (2019, November). A question answering and quiz generation chatbot for education. In 2019 Grace Hopper Celebration India (GHCI) (pp. 1-6). IEEE.
Strayhorn, T. L. (2014). What role does grit play in the academic success of black male collegians at predominantly white institutions? Journal of African American Studies, 18, 1–10. doi: 10.1007/s12111-012-9243-0
Sun, X., Nancekivell, S., Gelman, S. A., & Shah, P. (2021). Growth mindset and academic outcomes: A comparison of US and Chinese students. npj Science of Learning, 6(1), 1–9.
Tan, T. K., & Samavedham, L. (2022). The learning process matter: A sequence analysis perspective of examining procrastination using learning management system. Computers and Education Open, 3, 100112.
Tao, W., Zhao, D., Yue, H., Horton, I., Tian, X., Xu, Z., & Sun, H. J. (2022). The influence of growth mindset on the mental health and life events of college students. Frontiers in Psychology, 13, 821206.
Ting, Y. S., & Yeh, Y. C. (2023). Growth-mindset intervention effects and the relationship of mindset, hope belief, and self-efficacy during creativity game-based learning. Interactive Learning Environments.
Topsakal, O., & Akinci, T. C. (2023, July). Creating large language model applications utilizing langchain: A primer on developing LLM apps fast. In International Conference on Applied Engineering and Natural Sciences (Vol. 1, No. 1, pp. 1050-1056).
USAHS. (n.d.). 9 Proven Time Management Techniques and Tools. Retrieved from https://www.usa.edu/blog/time-management-techniques/
Uzir, N. A. A., Gašević, D., Jovanović, J., Matcha, W., Lim, L. A., & Fudge, A. (2020, March). Analytics of time management and learning strategies for effective online learning in blended environments. In Proceedings of the tenth international conference on learning analytics & knowledge (pp. 392-401).
Vasarhelyi, M. A., Moffitt, K. C., Stewart, T., & Sunderland, D. (2023). Large language models: An emerging technology in accounting. Journal of emerging technologies in accounting, 20(2), 1-10.
Veluvali, P., & Surisetti, J. (2022). Learning management system for greater learner engagement in higher education—A review. Higher Education for the Future, 9(1), 107-121.
Wang, D., Yuan, F., & Wang, Y. (2020). Growth mindset and academic achievement in Chinese adolescents: A moderated mediation model of reasoning ability and self-affirmation. Current Psychology.
Wang, H., & Lehman, J. D. (2021). Using achievement goal-based personalized motivational feedback to enhance online learning. Educational Technology Research and Development, 69(2), 553-581.
Wang, J., Shi, E., Yu, S., Wu, Z., Ma, C., Dai, H., ... & Zhang, S. (2023). Prompt engineering for healthcare: Methodologies and applications. arXiv preprint arXiv:2304.14670.
Wei, J., Wang, X., Schuurmans, D., Bosma, M., Xia, F., Chi, E., ... & Zhou, D. (2022). Chain-of-thought prompting elicits reasoning in large language models. Advances in Neural Information Processing Systems, 35, 24824-24837.
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.
Wolters, C. A., & Hussain, M. (2015). Investigating grit and its relations with college students’ self-regulated learning and academic achievement. Metacognition and Learning, 10(3), 293–311.
Woods, D. M. (2020). Using goal setting assignments to promote a growth mindset in IT students. Information Systems Education Journal, 18(4), 4-11.
Wu, T., He, S., Liu, J., Sun, S., Liu, K., Han, Q. L., & Tang, Y. (2023). A brief overview of ChatGPT: The history, status quo and potential future development. IEEE/CAA Journal of Automatica Sinica, 10(5), 1122-1136.
Xie, T., & Pentina, I. (2022). Attachment theory as a framework to understand relationships with social chatbots: A case study of Replika.
Xu, J., Szlam, A., & Weston, J. (2021). Beyond goldfish memory: Long-term open-domain conversation. arXiv preprint arXiv:2107.07567.
Yeager, D. S., & Dweck, C. S. (2012). Mindsets that promote resilience: When students believe that personal characteristics can be developed. Educational Psychologist, 47, 302-314.
Yeager, D. S., Hanselman, P., Walton, G. M., Murray, J. S., Crosnoe, R., Muller, C., & Dweck, C. S. (2019). A national experiment reveals where a growth mindset improves achievement. Nature, 573(7774), 364-369.
Zhang, S., Dinan, E., Urbanek, J., Szlam, A., Kiela, D., & Weston, J. (2018). Personalizing dialogue agents: I have a dog, do you have pets too?. arXiv preprint arXiv:1801.07243.