Paper Type
Short
Paper Number
1439
Description
The motivation behind this research project stems from concerns regarding the disparity of mathematical abilities between students in Taiwan, which is the largest among all participating countries, according to the 2022 PISA assessment. Therefore, leveraging technology to provide adaptive learning for students and enhance their autonomous learning abilities is an important and urgent educational issue that needs to be addressed. In order to address the problem above, this research will utilize large language models (LLMs) to generate interactive AI tutors featuring personalized interaction and personalized difficulty adjustments as their characteristics, providing students with customized adaptive learning. Additionally, operant conditioning theory will be employed to practice interactive AI tutoring and voice interactions as reinforcement, thereby increasing the frequency and willingness of students to learn mathematics. It is expected that by integrating educational theory and information technology into practical applications through this research, highly personalized adaptive learning can be achieved.
Recommended Citation
Huang, Jo-Mei; Chaing, Shang Yun; Sung, Yi-Chieh; Chen, Jian Ting; Wu, Ying Hsuan; Liu, Tung-Lin; and Chou, Chih-Yuan, "Interactive AI Application: Operant Conditioning and Adaptive Learning" (2024). PACIS 2024 Proceedings. 2.
https://aisel.aisnet.org/pacis2024/track14_educ/track14_educ/2
Interactive AI Application: Operant Conditioning and Adaptive Learning
The motivation behind this research project stems from concerns regarding the disparity of mathematical abilities between students in Taiwan, which is the largest among all participating countries, according to the 2022 PISA assessment. Therefore, leveraging technology to provide adaptive learning for students and enhance their autonomous learning abilities is an important and urgent educational issue that needs to be addressed. In order to address the problem above, this research will utilize large language models (LLMs) to generate interactive AI tutors featuring personalized interaction and personalized difficulty adjustments as their characteristics, providing students with customized adaptive learning. Additionally, operant conditioning theory will be employed to practice interactive AI tutoring and voice interactions as reinforcement, thereby increasing the frequency and willingness of students to learn mathematics. It is expected that by integrating educational theory and information technology into practical applications through this research, highly personalized adaptive learning can be achieved.
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Education