Interaction Design (IxD) of an Intelligent Tutor for Programming Learning Based on LLM
DOI:
https://doi.org/10.47756/aihc.y9i1.137Palabras clave:
Interaction Design, Generative Artificial Intelligence, Computer Science Education, Large Language Models, Intelligent Tutoring SystemsResumen
The emergent behavior of automatic programming exhibited by Large Language Models (LLMs) has raised uncertainty about the future of programming and its teaching. To better understand this phenomenon, we conducted a field study with programming instructors and students that informed the design of an intelligent tutor to integrate Generative Artificial Intelligence (GAI) into the educational environment. The resulting tool, EVA-Tutor (Virtual Learning Environment), supports the teaching and learning process of programming by establishing bidirectional communication between the student and the LLM through a GPT-4 API and a set of prompts designed to guide and motivate the student with personalized feedback. Rather than solving the problem for the student, the tool helps direct them toward solving it independently. A preliminary evaluation with students and instructors provides evidence of EVA-Tutor's utility and ease of use for problem-solving, knowledge acquisition, and the development of programming skills.
Descargas
Citas
Achiam, J., Adler, S., Agarwal, S., Ahmad, L., Akkaya, I., Aleman, F. L., ... & McGrew, B. (2023). Gpt-4 technical report. arXiv.org, 2303.08774. https://doi.org/10.48550/arXiv.2303.08774
Brown, T. B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T., Child, R., Ramesh, A., Ziegler, D. M., Wu, J., Winter, C., . . . Amodei, D. (2020). Language Models are Few-Shot Learners. Advances in neural information processing systems, 33, 1877-1901. https://doi.org/10.48550/arXiv.2005.14165
Chen, E., Huang, R., Chen, H. S., Tseng, Y. H., & Li, L. Y. (2023). GPTutor: a ChatGPT-powered programming tool for code explanation. International Conference on Artificial Intelligence in Education (pp. 321-327). Cham: Springer Nature Switzerland. https://doi.org/10.48550/arXiv.2305.01863 DOI: https://doi.org/10.1007/978-3-031-36336-8_50
Cohen, J. (1992). A power primer. Psychological Bulletin, 112(1), 155-159. https://doi.org/10.1037/0033-2909.112.1.155 DOI: https://doi.org/10.1037//0033-2909.112.1.155
Dwivedi, Y.K., Kshetri, N., Hughes, L. et al. (2023). Opinion Paper: “So what if ChatGPT wrote it?” Multidisciplinary perspectives on opportunities, challenges and implications of generative conversational AI for research, practice and policy. International Journal Of Information Management, 71, 102642. https://doi.org/10.1016/j.ijinfomgt.2023.102642 DOI: https://doi.org/10.1016/j.ijinfomgt.2023.102642
Geng, C., Zhang, Y., Pientka, B., & Si, X. (2023). Can ChatGPT pass an introductory level functional Language programming course? arXiv.org, 2305.02230. https://doi.org/10.48550/arXiv.2305.02230
Jacques, L. (2023). Teaching CS-101 at the Dawn of ChatGPT. ACM Inroads, 14(2), 40-46. https://doi.org/10.1145/3595634 DOI: https://doi.org/10.1145/3595634
Jiao, H., Peng, B., Zong, L., Zhang, X., & Li, X. (2024). Gradable ChatGPT Translation Evaluation. arXiv.org, 2401.09984. https://doi.org/10.48550/arXiv.2401.09984
Kojima, T., Gu, S. S., Reid, M., Matsuo, Y., & Iwasawa, Y. (2022). Large Language Models are Zero-Shot Reasoners. Advances in neural information processing systems, 35, 22199-22213. https://doi.org/10.48550/arXiv.2205.11916
Qureshi, B. (2023). Exploring the Use of ChatGPT as a Tool for Learning and Assessment in Undergraduate Computer Science Curriculum: Opportunities and Challenges. arXiv.org, 2304.11214. https://doi.org/10.48550/arxiv.2304.11214
Rahman, M. M., & Watanobe, Y. (2023). ChatGPT for Education and Research: Opportunities, Threats, and Strategies. Applied Sciences, 13(9), 5783. https://doi.org/10.3390/app13095783 DOI: https://doi.org/10.3390/app13095783
Rajabi, P., Taghipour, P., Cukierman, D., & Doleck, T. (2023). Exploring ChatGPT’s impact on post-secondary education: A qualitative study. Proceedings of the 25th Western Canadian Conference on Computing Education (Article 9, 1-6). Association for Computing Machinery. https://doi.org/10.1145/3593342.3593360 DOI: https://doi.org/10.1145/3593342.3593360
Richards, M., Waugh, K., Slaymaker, M., Petre, M., Woodthorpe, J., & Gooch, D. (2023). Bob or Bot: Exploring ChatGPT’s answers to University Computer Science Assessment. ACM Transactions On Computing Education (vol. 25, pp. 1-32). https://doi.org/10.1145/3633287 DOI: https://doi.org/10.1145/3633287
Sifaleras, A., & Lin, F. (2024) Generative Intelligence and Intelligent Tutoring Systems. En: Lecture notes in computer science. Springer. https://doi.org/10.1007/978-3-031-63028-6 DOI: https://doi.org/10.1007/978-3-031-63028-6
Su, Y., Lin, Y., & Lai, C. (2023). Collaborating with ChatGPT in argumentative writing classrooms. Assessing Writing, 57, 100752. https://doi.org/10.1016/j.asw.2023.100752 DOI: https://doi.org/10.1016/j.asw.2023.100752
Yang, X., Wang, Q., & Lyu, J. (2023). Assessing ChatGPT’s Educational Capabilities and Application Potential. ECNU Review of Education, 0(0). https://doi.org/10.1177/20965311231210006 DOI: https://doi.org/10.1177/20965311231210006
Wang, T., Díaz, D. V., Brown, C., & Chen, Y. (2023). Exploring the Role of AI Assistants in Computer Science Education: Methods, Implications, and Instructor Perspectives. Symposium on Visual Languages and Human-Centric Computing (pp. 92-102). IEEE. https://doi.org/10.48550/arXiv.2306.03289 DOI: https://doi.org/10.1109/VL-HCC57772.2023.00018
Wang, X., Wei, J., Schuurmans, D., Le, Q., Chi, E., Narang, S., Chowdhery, A., & Zhou, D. (2022). Self-Consistency Improves Chain of Thought Reasoning in Language Models. arXiv.org, 2203.11171. https://doi.org/10.48550/arXiv.2203.11171
Wei, J., Wang, X., Schuurmans, D., Bosma, M., Ichter, B., Xia, F., Chi, E., Le, Q., & Zhou, D. (2022). Chain-of-Thought Prompting Elicits Reasoning in Large Language Models. Advances in neural information processing systems. https://doi.org/10.48550/arXiv.2201.11903
White, J., Fu, Q., Hays, S., Sandborn, M., Olea, C., Gilbert, H., Elnashar, A., Spencer-Smith, J., & Schmidt, D. C. (2023). A Prompt Pattern Catalog to Enhance Prompt Engineering with ChatGPT. arXiv.org, 2301.11382. https://doi.org/10.48550/arXiv.2302.11382
Descargas
Publicado
Cómo citar
Número
Sección
Licencia
Derechos de autor 2024 Oleksiy Levchuk, Carlos Sánchez, Nancy Pacheco, Jesús Favela
Esta obra está bajo una licencia internacional Creative Commons Atribución-NoComercial-SinDerivadas 4.0.
AMexIHC hace todo el esfuerzo para asegurar la precisi´n y rigurosidad de la informaci´ón (el "Contenido") contenida en nuestras publicaciones. Sin embargo, AMexIHC y nuestros representantes no representan o garantizan de ninguna manera la precisi´ón, completitud o pertinencia de el Contenido para ning´ún propósito. Cualquier opinión y punto de vista expresados en esta publicación son las opiniones y puntos de vista de los autores, y no son de ninguna manera los puntos de vista o con anuencia de AMexIHC. La precisi´ón de el Contenido no debería ser confiada en su totalidad y debería ser corroborada con las fuentes primarias de informaci´ón.
