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題名 應用設計思考提升服務品質：以教學助理即服務為案例
Design Thinking for Service Enhancement: a case of Teaching Assistant as a Service
作者 郭丞哲
Guo, Cheng-Zhe
貢獻者 蔡瑞煌
Tsaih, Rua-Huan
郭丞哲
Guo, Cheng-Zhe
關鍵詞 人工智慧即服務
機器學習維運
設計思考
教學助理即服務
學習演算法
AIaaS
MLOps
Design thinking
TAaaS
learning algorithm
日期 2023
上傳時間 2-Aug-2023 14:05:19 (UTC+8)
摘要 本研究探討了在教學助理即服務（TAaaS）的背景下，應用設計思考原則來提升服務品質。所提出的TAaaS具有MLOps功能，使學生能夠在修讀新型學習演算法課程時，利用自己和他人的學習模組開發和部署他們自己的新型學習演算法、程式碼和AI模型。通過整合強調同理心、實驗和原型設計的設計思考原則，本研究旨在提高使用TAaaS系統的使用者體驗和滿意度。挑戰在於允許學生通過反覆嘗試，獨立於多個管道（如模型管道、部署管道和預測服務）創建自己的「新型學習演算法」。通過設計思考的迭代和以人為本的特性，本研究展示了將設計思考原則納入服務設計過程的潛在利益，最終形成更符合使用需求和期望的一套AI解決方案。
This study explores the application of design thinking principles for service enhancement in the context of a Teaching Assistant as a Service (TAaaS). The TAaaS is equipped with MLOps capabilities, enabling students to develop and deploy their own new learning algorithms, codes, and AI models by utilizing their own and others’ learning modules while enrolled in the New Learning Algorithms course. By integrating design thinking principles, which emphasize empathy, experimentation, and prototyping, this study aims to enhance the user experience and satisfaction in using the TAaaS system. The challenge lies in allowing students to create their own “new learning algorithm” through trial and error, independently from the multiple pipelines, such as model pipeline, deployment pipeline, and prediction service. Through the iterative and user-centric nature of design thinking, this study demonstrates the potential benefits of incorporating design thinking principles into the service design process, ultimately leading to a more successful AI solution tailored to the users’ needs and expectations.
參考文獻 Barlas, P., Kyriakou, K., Guest, O., Kleanthous, S., and Otterbacher, J. (2021). To” see” is to stereotype: Image tagging algorithms, gender recognition, and the accuracy-fairness trade-off. Proceedings of the ACM on Human-Computer Interaction, 4(CSCW3):1–31.
Boag, S., Dube, P., El Maghraoui, K., Herta, B., Hummer, W., Jayaram, K., Khalaf, R., Muthusamy, V., Kalantar, M., and Verma, A. (2018). Dependability in a multi-tenant multi-framework deep learning as-a-service platform. In 2018 48th Annual IEEE/IFIP International Conference on Dependable Systems and Networks Workshops (DSN-W), pages 43–46. IEEE.
Brown, T. et al. (2008). Design thinking. Harvard business review, 86(6):84.
Brown, T. and Katz, B. (2011). Change by design. Journal of product innovation management, 28(3):381–383.
Brown, T. and Martin, R. (2015). Design for action. Harvard Business Review, 93(9):57–64.
Clark, K., Smith, R., et al. (2008). Unleashing the power of design thinking. Design Management Review, 19(3):8–15.
Dhillon, S. K., Ganggayah, M. D., Sinnadurai, S., Lio, P., and Taib, N. A. (2022). Theory and practice of integrating machine learning and conventional statistics in medical data analysis. Diagnostics, 12(10):2526.
Ebert, C., Gallardo, G., Hernantes, J., and Serrano, N. (2016). Devops. Ieee Software, 33(3):94–100.
Elshawi, R., Sakr, S., Talia, D., and Trunfio, P. (2018). Big data systems meet machine learning challenges: towards big data science as a service. Big data research, 14:1–11.
Gasson, S. (2003). Human-centered vs. user-centered approaches to information system design. Journal of Information Technology Theory and Application (JITTA), 5(2):5.
Goodfellow, I., Bengio, Y., and Courville, A. (2016). Deep learning. MIT press.
Google Cloud Architecture Center (2020). Mlops: Continuous delivery and automation pipelines in machine learning.
Hasso Plattner Institute of Design at Stanford University (2023). Tools for taking action.
KELLEY, T. A. (2001). The art of innovation: Lessons in creativity from IDEO, America’s leading design firm, volume 10. Broadway Business.
Kreuzberger, D., Kühl, N., and Hirschl, S. (2022). Machine learning operations (mlops): Overview, definition, and architecture. arXiv preprint arXiv:2205.02302.
LeCun, Y., Bengio, Y., and Hinton, G. (2015). Deep learning. nature, 521(7553):436–444.
Leite, L., Rocha, C., Kon, F., Milojicic, D., and Meirelles, P. (2019). A survey of devops concepts and challenges. ACM Computing Surveys (CSUR), 52(6):1–35.
Lindberg, T., Meinel, C., and Wagner, R. (2011). Design thinking: A fruitful concept for it development? In Design thinking, pages 3–18. Springer.
Lins, S., Pandl, K. D., Teigeler, H., Thiebes, S., Bayer, C., and Sunyaev, A. (2021). Artificial intelligence as a service. Business & Information Systems Engineering, 63(4):441–456.
Lu, Y. (2019). Artificial intelligence: a survey on evolution, models, applications and future trends. Journal of Management Analytics, 6(1):1–29.
Lucena, P., Braz, A., Chicoria, A., and Tizzei, L. (2017). Ibm design thinking software development framework. In Brazilian workshop on agile methods, pages 98–109. Springer.
Mell, P., Grance, T., et al. (2011). The nist definition of cloud computing.
Nagarhalli, T. P., Vaze, V., and Rana, N. (2021). Impact of machine learning in natural language processing: A review. In 2021 third international conference on intelligent communication technologies and virtual mobile networks (ICICV), pages 1529–1534. IEEE.
Norman, D. (2017). Design, business models, and human-technology teamwork: As automation and artificial intelligence technologies develop, we need to think less about human-machine interfaces and more about human-machine teamwork. Research Technology Management, 60(1):26–30.
Pereira, J. C. and de F.S.M. Russo, R. (2018). Design thinking integrated in agile software development: A systematic literature review. Procedia Computer Science, 138:775–782. CENTERIS 2018 - International Conference on ENTERprise Information Systems/ ProjMAN 2018 - International Conference on Project MANagement / HCist 2018 -International Conference on Health and Social Care Information Systems and Technologies, CENTERIS/ProjMAN/HCist 2018.
Rai, A., Constantinides, P., and Sarker, S. (2019). Next generation digital platforms:: Toward human-ai hybrids. Mis Quarterly, 43(1):iii–ix.
Riedl, M. O. (2019). Human-centered artificial intelligence and machine learning. Human Behavior and Emerging Technologies, 1(1):33–36.
Shrestha, A. and Mahmood, A. (2019). Review of deep learning algorithms and architectures. IEEE access, 7:53040–53065.
Stahl, B. C., Andreou, A., Brey, P., Hatzakis, T., Kirichenko, A., Macnish, K., Shaelou, S. L., Patel, A., Ryan, M., and Wright, D. (2021). Artificial intelligence for human flourishing–beyond principles for machine learning. Journal of Business Research, 124:374–388.
Stembert, N. and Harbers, M. (2019). Accounting for the human when designing with ai: challenges identified. CHI’19-Extended Abstracts, Glasgow, Scotland Uk—May 04-09, 2019.
Tsaih, R.-H. (2022). The course materials of new learning algorithm
Verganti, R., Vendraminelli, L., and Iansiti, M. (2020). Innovation and design in the age of artificial intelligence. Journal of Product Innovation Management, 37(3):212–227.
Vetterli, C., Uebernickel, F., Brenner, W., Petrie, C., and Stermann, D. (2016). How deutsche bank’s it division used design thinking to achieve customer proximity. 15:37–53.
Xu, W. (2019). Toward human-centered ai: a perspective from human-computer interaction. interactions, 26(4):42–46.
Xu, W., Dainoff, M. J., Ge, L., and Gao, Z. (2021). From human-computer interaction to human-ai interaction: new challenges and opportunities for enabling human-centered ai. arXiv preprint arXiv:2105.05424, 5.
Zhang, C. and Lu, Y. (2021). Study on artificial intelligence: The state of the art and future prospects. Journal of Industrial Information Integration, 23:100224.
描述 碩士
國立政治大學
資訊管理學系
110356021
資料來源 http://thesis.lib.nccu.edu.tw/record/#G0110356021
資料類型 thesis
dc.contributor.advisor 蔡瑞煌zh_TW
dc.contributor.advisor Tsaih, Rua-Huanen_US
dc.contributor.author (Authors) 郭丞哲zh_TW
dc.contributor.author (Authors) Guo, Cheng-Zheen_US
dc.creator (作者) 郭丞哲zh_TW
dc.creator (作者) Guo, Cheng-Zheen_US
dc.date (日期) 2023en_US
dc.date.accessioned 2-Aug-2023 14:05:19 (UTC+8)-
dc.date.available 2-Aug-2023 14:05:19 (UTC+8)-
dc.date.issued (上傳時間) 2-Aug-2023 14:05:19 (UTC+8)-
dc.identifier (Other Identifiers) G0110356021en_US
dc.identifier.uri (URI) http://nccur.lib.nccu.edu.tw/handle/140.119/146574-
dc.description (描述) 碩士zh_TW
dc.description (描述) 國立政治大學zh_TW
dc.description (描述) 資訊管理學系zh_TW
dc.description (描述) 110356021zh_TW
dc.description.abstract (摘要) 本研究探討了在教學助理即服務（TAaaS）的背景下，應用設計思考原則來提升服務品質。所提出的TAaaS具有MLOps功能，使學生能夠在修讀新型學習演算法課程時，利用自己和他人的學習模組開發和部署他們自己的新型學習演算法、程式碼和AI模型。通過整合強調同理心、實驗和原型設計的設計思考原則，本研究旨在提高使用TAaaS系統的使用者體驗和滿意度。挑戰在於允許學生通過反覆嘗試，獨立於多個管道（如模型管道、部署管道和預測服務）創建自己的「新型學習演算法」。通過設計思考的迭代和以人為本的特性，本研究展示了將設計思考原則納入服務設計過程的潛在利益，最終形成更符合使用需求和期望的一套AI解決方案。zh_TW
dc.description.abstract (摘要) This study explores the application of design thinking principles for service enhancement in the context of a Teaching Assistant as a Service (TAaaS). The TAaaS is equipped with MLOps capabilities, enabling students to develop and deploy their own new learning algorithms, codes, and AI models by utilizing their own and others’ learning modules while enrolled in the New Learning Algorithms course. By integrating design thinking principles, which emphasize empathy, experimentation, and prototyping, this study aims to enhance the user experience and satisfaction in using the TAaaS system. The challenge lies in allowing students to create their own “new learning algorithm” through trial and error, independently from the multiple pipelines, such as model pipeline, deployment pipeline, and prediction service. Through the iterative and user-centric nature of design thinking, this study demonstrates the potential benefits of incorporating design thinking principles into the service design process, ultimately leading to a more successful AI solution tailored to the users’ needs and expectations.en_US
dc.description.tableofcontents Acknowledgements i
摘要 ii
Abstract iii
Chapter 1. Introduction 1
Chapter 2. Literature review 4
2.1 Artificial intelligence 4
2.2 Artificial intelligence as a service (AIaaS) 5
2.3 Machine learning operations (MLOps) 7
2.4 Human-Centered Design and Design Thinking 10
Chapter 3. Research methodology 13
Chapter 4. The TAaaS and its implementation 16
4.1 The AI Software Services 16
4.2 MLOps for The AI Software Services 17
4.3 The AI Developer Services 20
4.4 MLOps for The AI Developer Services 21
Chapter 5. Experiment 24
5.1 Research participants 24
5.2 Experiment design 24
5.2.1 DTP_1 24
5.2.2 DTP_2 27
5.3 Feedback difference between two DT cycles 29
5.3.1 DTP_1 29
5.3.2 DTP_2 31
5.3.3 Comparison between two DT cycles 33
5.4 UI/UX difference between two services 34
5.5 Insight derived from the counter-comparison of the two versions differences 44
Chapter 6. Conclusion and future work 48
6.1 Conclusion 48
6.2 Limitation and Future work 49
References 51
Appendix 54		zh_TW
dc.format.extent 2068533 bytes-
dc.format.mimetype application/pdf-
dc.source.uri (資料來源) http://thesis.lib.nccu.edu.tw/record/#G0110356021en_US
dc.subject (關鍵詞) 人工智慧即服務zh_TW
dc.subject (關鍵詞) 機器學習維運zh_TW
dc.subject (關鍵詞) 設計思考zh_TW
dc.subject (關鍵詞) 教學助理即服務zh_TW
dc.subject (關鍵詞) 學習演算法zh_TW
dc.subject (關鍵詞) AIaaSen_US
dc.subject (關鍵詞) MLOpsen_US
dc.subject (關鍵詞) Design thinkingen_US
dc.subject (關鍵詞) TAaaSen_US
dc.subject (關鍵詞) learning algorithmen_US
dc.title (題名) 應用設計思考提升服務品質：以教學助理即服務為案例zh_TW
dc.title (題名) Design Thinking for Service Enhancement: a case of Teaching Assistant as a Serviceen_US
dc.type (資料類型) thesisen_US
dc.relation.reference (參考文獻) Barlas, P., Kyriakou, K., Guest, O., Kleanthous, S., and Otterbacher, J. (2021). To” see” is to stereotype: Image tagging algorithms, gender recognition, and the accuracy-fairness trade-off. Proceedings of the ACM on Human-Computer Interaction, 4(CSCW3):1–31.
Boag, S., Dube, P., El Maghraoui, K., Herta, B., Hummer, W., Jayaram, K., Khalaf, R., Muthusamy, V., Kalantar, M., and Verma, A. (2018). Dependability in a multi-tenant multi-framework deep learning as-a-service platform. In 2018 48th Annual IEEE/IFIP International Conference on Dependable Systems and Networks Workshops (DSN-W), pages 43–46. IEEE.
Brown, T. et al. (2008). Design thinking. Harvard business review, 86(6):84.
Brown, T. and Katz, B. (2011). Change by design. Journal of product innovation management, 28(3):381–383.
Brown, T. and Martin, R. (2015). Design for action. Harvard Business Review, 93(9):57–64.
Clark, K., Smith, R., et al. (2008). Unleashing the power of design thinking. Design Management Review, 19(3):8–15.
Dhillon, S. K., Ganggayah, M. D., Sinnadurai, S., Lio, P., and Taib, N. A. (2022). Theory and practice of integrating machine learning and conventional statistics in medical data analysis. Diagnostics, 12(10):2526.
Ebert, C., Gallardo, G., Hernantes, J., and Serrano, N. (2016). Devops. Ieee Software, 33(3):94–100.
Elshawi, R., Sakr, S., Talia, D., and Trunfio, P. (2018). Big data systems meet machine learning challenges: towards big data science as a service. Big data research, 14:1–11.
Gasson, S. (2003). Human-centered vs. user-centered approaches to information system design. Journal of Information Technology Theory and Application (JITTA), 5(2):5.
Goodfellow, I., Bengio, Y., and Courville, A. (2016). Deep learning. MIT press.
Google Cloud Architecture Center (2020). Mlops: Continuous delivery and automation pipelines in machine learning.
Hasso Plattner Institute of Design at Stanford University (2023). Tools for taking action.
KELLEY, T. A. (2001). The art of innovation: Lessons in creativity from IDEO, America’s leading design firm, volume 10. Broadway Business.
Kreuzberger, D., Kühl, N., and Hirschl, S. (2022). Machine learning operations (mlops): Overview, definition, and architecture. arXiv preprint arXiv:2205.02302.
LeCun, Y., Bengio, Y., and Hinton, G. (2015). Deep learning. nature, 521(7553):436–444.
Leite, L., Rocha, C., Kon, F., Milojicic, D., and Meirelles, P. (2019). A survey of devops concepts and challenges. ACM Computing Surveys (CSUR), 52(6):1–35.
Lindberg, T., Meinel, C., and Wagner, R. (2011). Design thinking: A fruitful concept for it development? In Design thinking, pages 3–18. Springer.
Lins, S., Pandl, K. D., Teigeler, H., Thiebes, S., Bayer, C., and Sunyaev, A. (2021). Artificial intelligence as a service. Business & Information Systems Engineering, 63(4):441–456.
Lu, Y. (2019). Artificial intelligence: a survey on evolution, models, applications and future trends. Journal of Management Analytics, 6(1):1–29.
Lucena, P., Braz, A., Chicoria, A., and Tizzei, L. (2017). Ibm design thinking software development framework. In Brazilian workshop on agile methods, pages 98–109. Springer.
Mell, P., Grance, T., et al. (2011). The nist definition of cloud computing.
Nagarhalli, T. P., Vaze, V., and Rana, N. (2021). Impact of machine learning in natural language processing: A review. In 2021 third international conference on intelligent communication technologies and virtual mobile networks (ICICV), pages 1529–1534. IEEE.
Norman, D. (2017). Design, business models, and human-technology teamwork: As automation and artificial intelligence technologies develop, we need to think less about human-machine interfaces and more about human-machine teamwork. Research Technology Management, 60(1):26–30.
Pereira, J. C. and de F.S.M. Russo, R. (2018). Design thinking integrated in agile software development: A systematic literature review. Procedia Computer Science, 138:775–782. CENTERIS 2018 - International Conference on ENTERprise Information Systems/ ProjMAN 2018 - International Conference on Project MANagement / HCist 2018 -International Conference on Health and Social Care Information Systems and Technologies, CENTERIS/ProjMAN/HCist 2018.
Rai, A., Constantinides, P., and Sarker, S. (2019). Next generation digital platforms:: Toward human-ai hybrids. Mis Quarterly, 43(1):iii–ix.
Riedl, M. O. (2019). Human-centered artificial intelligence and machine learning. Human Behavior and Emerging Technologies, 1(1):33–36.
Shrestha, A. and Mahmood, A. (2019). Review of deep learning algorithms and architectures. IEEE access, 7:53040–53065.
Stahl, B. C., Andreou, A., Brey, P., Hatzakis, T., Kirichenko, A., Macnish, K., Shaelou, S. L., Patel, A., Ryan, M., and Wright, D. (2021). Artificial intelligence for human flourishing–beyond principles for machine learning. Journal of Business Research, 124:374–388.
Stembert, N. and Harbers, M. (2019). Accounting for the human when designing with ai: challenges identified. CHI’19-Extended Abstracts, Glasgow, Scotland Uk—May 04-09, 2019.
Tsaih, R.-H. (2022). The course materials of new learning algorithm
Verganti, R., Vendraminelli, L., and Iansiti, M. (2020). Innovation and design in the age of artificial intelligence. Journal of Product Innovation Management, 37(3):212–227.
Vetterli, C., Uebernickel, F., Brenner, W., Petrie, C., and Stermann, D. (2016). How deutsche bank’s it division used design thinking to achieve customer proximity. 15:37–53.
Xu, W. (2019). Toward human-centered ai: a perspective from human-computer interaction. interactions, 26(4):42–46.
Xu, W., Dainoff, M. J., Ge, L., and Gao, Z. (2021). From human-computer interaction to human-ai interaction: new challenges and opportunities for enabling human-centered ai. arXiv preprint arXiv:2105.05424, 5.
Zhang, C. and Lu, Y. (2021). Study on artificial intelligence: The state of the art and future prospects. Journal of Industrial Information Integration, 23:100224.		zh_TW