Papers
Topics
Authors
Recent
Gemini 2.5 Flash
Gemini 2.5 Flash
110 tokens/sec
GPT-4o
56 tokens/sec
Gemini 2.5 Pro Pro
44 tokens/sec
o3 Pro
6 tokens/sec
GPT-4.1 Pro
47 tokens/sec
DeepSeek R1 via Azure Pro
28 tokens/sec
2000 character limit reached

Apprentice Tutor Builder: A Platform For Users to Create and Personalize Intelligent Tutors (2404.07883v1)

Published 11 Apr 2024 in cs.HC and cs.AI

Abstract: Intelligent tutoring systems (ITS) are effective for improving students' learning outcomes. However, their development is often complex, time-consuming, and requires specialized programming and tutor design knowledge, thus hindering their widespread application and personalization. We present the Apprentice Tutor Builder (ATB) , a platform that simplifies tutor creation and personalization. Instructors can utilize ATB's drag-and-drop tool to build tutor interfaces. Instructors can then interactively train the tutors' underlying AI agent to produce expert models that can solve problems. Training is achieved via using multiple interaction modalities including demonstrations, feedback, and user labels. We conducted a user study with 14 instructors to evaluate the effectiveness of ATB's design with end users. We found that users enjoyed the flexibility of the interface builder and ease and speed of agent teaching, but often desired additional time-saving features. With these insights, we identified a set of design recommendations for our platform and others that utilize interactive AI agents for tutor creation and customization.

Definition Search Book Streamline Icon: https://streamlinehq.com
References (27)
  1. A New Paradigm for Intelligent Tutoring Systems: Example-Tracing Tutors. I. J. Artificial Intelligence in Education 19 (01 2009), 105–154.
  2. Example-tracing tutors: A new paradigm for intelligent tutoring systems. (2009).
  3. A new paradigm for intelligent tutoring systems: Example-tracing tutors. International Journal of Artificial Intelligence in Education 19, 2 (2009), 105–154.
  4. Hussam Alkaissi and Samy I McFarlane. 2023. Artificial hallucinations in ChatGPT: implications in scientific writing. Cureus 15, 2 (2023).
  5. Cognitive tutors: Lessons learned. Journal of the Learning Sciences 4, 2 (1995), 167–207. https://doi.org/10.1207/s15327809jls0402_2
  6. UMCP: A Sound and Complete Procedure for Hierarchical Task-network Planning.. In Aips, Vol. 94. 249–254.
  7. School engagement: Potential of the concept, state of the evidence. Review of educational research 74, 1 (2004), 59–109.
  8. The ASSISTment Builder: Towards an Analysis of Cost Effectiveness of ITS Creation.. In Flairs Conference. 515–520.
  9. Opening the Door to Non-programmers: Authoring Intelligent Tutor Behavior by Demonstration. (2004).
  10. Kenneth R. Koedinger and John R. Anderson. 1997. Intelligent Tutoring Goes To School in the Big City. International Journal of Artificial Intelligence in Education 8 (1997), 30–43.
  11. Nir Kshetri. 2023. The economics of generative artificial intelligence in the academic industry. Computer 56, 8 (2023), 77–83.
  12. Pumice: A multi-modal agent that learns concepts and conditionals from natural language and demonstrations. In Proceedings of the 32nd annual ACM symposium on user interface software and technology. 577–589.
  13. M. C. Lovett. 1998. Cognitive task analysis in service of intelligent tutoring systems design: A case study in statistics. In Intelligent Tutoring Systems, B. P. Goettl, H. M. Halff, C. L. Redfield, and V. J. Shute (Eds.). Lecture Notes in Computer Science, Vol. 1452. Springer, New York, 234–243.
  14. Andrés Lucero. 2015. Using Affinity Diagrams to Evaluate Interactive Prototypes. In Human-Computer Interaction – INTERACT 2015, Julio Abascal, Simone Barbosa, Mirko Fetter, Tom Gross, Philippe Palanque, and Marco Winckler (Eds.). Springer International Publishing, Cham, 231–248.
  15. Intelligent tutoring systems and learning outcomes: A meta-analysis. Journal of educational psychology 106, 4 (2014), 901.
  16. C.J. MacLellan and K.R. Koedinger. 2022a. Domain-General Tutor Authoring with Apprentice Learner Models. Int J Artif Intell Educ 32, 1 (2022), 76–117. https://doi.org/10.1007/s40593-020-00214-2
  17. Christopher J. MacLellan and Kenneth R. Koedinger. 2020. Domain-General Tutor Authoring with Apprentice Learner Models. International Journal of Artificial Intelligence in Education. https://doi.org/10.1007/s40593-020-00214-2
  18. Christopher J MacLellan and Kenneth R Koedinger. 2022b. Domain-general tutor authoring with apprentice learner models. International Journal of Artificial Intelligence in Education (2022), 1–42.
  19. Authoring tutors with SimStudent: An evaluation of efficiency and model quality. In Intelligent Tutoring Systems: 12th International Conference, ITS 2014, Honolulu, HI, USA, June 5-9, 2014. Proceedings 12. Springer, 551–560.
  20. N. Matsuda et al. 2010. Learning by Teaching SimStudent: Technical Accomplishments and an Initial Use with Students. In Intelligent Tutoring Systems (Lecture Notes in Computer Science, Vol. 6094), V. Aleven, J. Kay, and J. Mostow (Eds.). Springer, Berlin, Heidelberg, 317–326. https://doi.org/10.1007/978-3-642-13388-6_36
  21. Tom Murray. 2003. An Overview of Intelligent Tutoring System Authoring Tools: Updated analysis of the state of the art. Authoring Tools for Advanced Technology Learning Environments: Toward Cost-Effective Adaptive, Interactive and Intelligent Educational Software (2003), 491–544.
  22. Gordon D Plotkin. 1970. A note on inductive generalization. Machine intelligence 5, 1 (1970), 153–163.
  23. L. Razzaq et al. 2009. The ASSISTment Builder: Supporting the Life Cycle of Tutoring System Content Creation. IEEE Transactions on Learning Technologies 2, 2 (Apr-Jun 2009), 157–166. https://doi.org/10.1109/TLT.2009.23
  24. Adriano Scoditti and Wolfgang Stuerzlinger. 2009. A new layout method for graphical user interfaces. In 2009 IEEE Toronto International Conference Science and Technology for Humanity (TIC-STH). IEEE, 642–647.
  25. Kurt Vanlehn. 2006. The Behavior of Tutoring Systems. Int. J. Artif. Intell. Ed. 16, 3 (aug 2006), 227–265.
  26. An Interaction Design for Machine Teaching to Develop AI Tutors. Conference on Human Factors in Computing Systems - Proceedings July (2020), 1–11. https://doi.org/10.1145/3313831.3376226
  27. Xiaojin Zhu. 2015. Machine teaching: An inverse problem to machine learning and an approach toward optimal education. In Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 29.
User Edit Pencil Streamline Icon: https://streamlinehq.com
Authors (3)
  1. Glen Smith (5 papers)
  2. Adit Gupta (6 papers)
  3. Christopher MacLellan (10 papers)
Citations (1)
View on Semantic Scholar

Summary

We haven't generated a summary for this paper yet.

Ai Sparkles Streamline Icon: https://streamlinehq.com Summarize Now
X Twitter Logo Streamline Icon: https://streamlinehq.com

Tweets