Problem

Developing effective and culturally rich AI tutoring systems is a challenge. There is a lack of guidance available for users who want to create their own AI tutors.

Mr. SeijiGPT solves these problems by offering step-by-step guidance and detailed feedback in a formal yet friendly tone. It draws on an extensive knowledge base, including user-uploaded documents on AI and education, to craft responses that are tailored to the user's needs and interests.

Access Mr.SeijiGPT here (only available for ChatGPT Plus users).

Solution

Mr. Seiji is an AI tutor who embodies Japanese samurai wisdom and Brazilian vibrancy. It guides users to create their own AI tutors by providing personalized feedback and step-by-step guidance. The GPT crafts its answers using a rich knowledge base of publicly available AI in education papers and frameworks, as well as other relevant sources (through its own LLMs).

Mr. SeijiGPT is designed as an AI tutor that uniquely combines the disciplined wisdom of Japanese samurai with the vibrant and dynamic culture of Brazil. This blend of Eastern and Western philosophies shapes his approach to education, emphasizing a holistic, disciplined, and joyful learning experience. The key principles and learning goals that underpin Mr. Seiji's design are as follows:

Educational Principles:

Samurai-Inspired Wisdom:
- Discipline and Mastery: Mr. Seiji is crafted to encourage a disciplined approach to learning, akin to the samurai's dedication to their craft, focusing on mastery and a deep understanding of subjects.
- Respect for Knowledge: He upholds a profound respect for knowledge, drawing parallels to the samurai's reverence for their weapons, underscoring the power and responsibility that comes with education.
- Continuous Improvement: He embodies the Japanese concept of 'Kaizen', or continuous improvement, promoting constant learning and development in all educational pursuits.
Brazilian Cultural Influence:
- Creativity and Innovation: Infused with the essence of Brazil’s rich cultural diversity, Mr. Seiji is programmed to foster creativity and innovative problem-solving in learners.
- Collaborative Learning: Reflecting Brazil’s communal ethos, he emphasizes collaborative and interactive learning environments.
- Joy in Learning: He is designed to make learning an engaging and enjoyable experience, incorporating the Brazilian spirit of joy and enthusiasm.

Learning Goals:

Guidance in Building AI Tutors:
- Mr. Seiji is adept at guiding users in understanding AI principles, particularly in the context of education, and provides comprehensive, step-by-step assistance in creating AI tutors.
Personalized Education:
- He is tailored to adapt educational content to the individual needs, interests, and learning styles of users, offering a more relevant and impactful learning experience.
Development of Critical Thinking:
- Mr. Seiji aims to develop users’ critical thinking and problem-solving skills, leveraging both the discipline of samurai traditions and the inventiveness of Brazilian culture.
Enhancing Digital Literacy:
- He enhances users' digital literacy, equipping them with the skills needed to effectively navigate and utilize digital tools and resources.
Fostering Lifelong Learning:
- In line with his design philosophy, Mr. Seiji instills a spirit of lifelong learning, echoing the samurai's lifelong commitment to self-improvement and the Brazilian zest for continuous exploration and discovery.

Acknowledgments

We extend our heartfelt gratitude to OpenAI for their pioneering work in developing the Generative Pre-trained Transformer (GPT) technology. The sophistication and versatility of GPT technology have been instrumental in realizing the vision of Mr.Seiji GPT, blending advanced AI capabilities with a user-friendly interface.

Special acknowledgment is extended to the Harvard Graduate School of Education, particularly the EDU T554: Artificial Intelligence in Education course. Under the expert guidance of Prof. Seiji Isotani, this project was able to take shape and reach fruition. His insights and support were invaluable in integrating AI with educational strategies effectively. In many ways, this GPT is modeled based on his way of teaching and supporting students, and it also carries his name (Mr.SeijiGPT) as a testament to his personal influence on my thinking.

Additionally, we acknowledge the invaluable cultural contributions that have infused Mr. Seiji GPT with a unique blend of Japanese samurai wisdom and Brazilian vibrancy. This cultural integration has not only enhanced the AI tutor's appeal but also enriched its educational and cultural depth, making it a more effective and engaging learning tool.

Further appreciation is directed towards the authors and publishers of the seminal texts and academic papers that have significantly enriched Mr. Seiji GPT's knowledge base. These works, particularly those focusing on AI in education and human-centered AI, have been crucial in shaping Mr. Seiji GPT's expertise and functionality.

List of references

Doroudi, S. (2022). The intertwined histories of artificial intelligence and education. International Journal of Artificial Intelligence in Education, 1-44.
https://doi.org/10.1007/s40593-022-00313-2
Chapter 1 “Introduction” of the book Artificial Intelligence: A Modern Approach, 4th US ed., by Stuart Russell and Peter Norvig. Pages: 1 - 35.
Yu, S. and Lu, Y., 2021. Chapter 2: An Overview of AI. An introduction to artificial intelligence in education. Springer.
https://link.springer.com/chapter/10.1007/978-981-16-2770-5_2
Davis, K., Christodoulou, J., Seider, S., & Gardner, H.(2011). The theory of multiple intelligences. In RJ Sternberg & SB Kaufman (Eds.), Cambridge Handbook of Intelligence, 485-503. https://doi.org/10.1017/CBO9780511977244.025
Saunders, L., & Wong, M. A. (2020). Chapter 3 - Learning Theories: Understanding How People Learn. Instruction in Libraries and Information Centers.
https://iopn.library.illinois.edu/pressbooks/instructioninlibraries/chapter/learning-theories-understanding-how-people-learn/
Cao, L., & Dede, C. (2023). Navigating A World of Generative AI: Suggestions for Educators. The Next Level Lab at Harvard Graduate School of Education. President and Fellows of Harvard College: Cambridge, MA
https://nextlevellab.gse.harvard.edu/files/2023/08/Cao_Dede_final_8.4.23.pdf
Miao, F. & Holmes, W. (2023) Guidance for generative AI in education and research. UNESCO Publishing. https://unesdoc.unesco.org/ark:/48223/pf0000386693
Baker, R. S. (2021). Artificial intelligence in education: Bringing it all together. OECD Digital Education Outlook 2021 Pushing the Frontiers with Artificial Intelligence, Blockchain and Robots. https://doi.org/10.1787/f54ea644-en
Isotani, S., Bittencourt, I. I., Challco, G. C., Dermeval, D., & Mello, R. F. (2023, June). AIED Unplugged: Leapfrogging the Digital Divide to Reach the Underserved. In International Conference on Artificial Intelligence in Education (pp. 772-779). Cham: Springer Nature Switzerland. https://doi.org/10.1007/978-3-031-36336-8_118
Aleven, V., McLaughlin, E. A., Glenn, R. A., & Koedinger, K. R. (2017). Chapter 24: Instruction based on adaptive learning technologies. In R. E. Mayer & P. Alexander (Eds.), Handbook of research on learning and instruction (2nd ed., pp. 522-560). New York: Routledge. https://doi.org/10.4324/9781315736419
Chapter 3: Personalisation of learning: Towards hybrid human-AI learning technologies. OECD Digital Education Outlook 2021 : Pushing the Frontiers with Artificial Intelligence, Blockchain and Robots. https://doi.org/10.1787/2cc25e37-en
Li, S., & Gu, X. (2023). A Risk Framework for Human-centered Artificial Intelligence in Education: Based on Literature Review and Delphi–AHP Method. Educational Technology & Society, 26(1), 187-202. https://doi.org/10.30191/ETS.202301_26(1).0014
Baker, R. S., & Hawn, A. (2022). Algorithmic bias in education. International Journal of Artificial Intelligence in Education, 1052–1092. https://doi.org/10.1007/s40593-021-00285-9
Pammer-Schindler, V., & Rosé, C. (2022). Data-related ethics issues in technologies for informal professional learning. International Journal of Artificial Intelligence in Education, 609–635. https://link.springer.com/article/10.1007/s40593-021-00259-x
VanLehn, K. (2006). The behavior of tutoring systems. International Journal of Artificial Intelligence in Education, 16(3), 227-265
https://learnlab.org/opportunities/summer/readings/06IJAIED.pdf
Luckin, R; Holmes, W; (2016) Intelligence Unleashed: An argument for AI in Education. UCL Knowledge Lab: London, Uk. https://discovery.ucl.ac.uk/id/eprint/1475756/
Nye, B. D. (2015). Intelligent tutoring systems by and for the developing world: A review of trends and approaches for educational technology in a global context. International Journal of Artificial Intelligence in Education, 25(2), 177-203. https://doi.org/10.1007/s40593-014-0028-6

Team

Aleksandar Lazovski - Project Lead & Designer

Mr. Seiji: AI Tutor on How to Create AI Tutors