TY - GEN
T1 - On the Design of AI Teaching Assistants for Algorithm Courses with Integrated Teaching, Learning, Assessment and Practice
AU - Peng, Chao
AU - Cai, Kecheng
AU - Guo, Yaying
AU - Xu, Chenyang
N1 - Publisher Copyright:
© The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2026.
PY - 2026
Y1 - 2026
N2 - Algorithm courses are fundamental in undergraduate majors related to information technology. However, with the rapid advancement of artificial intelligence, traditional instructional approaches are encountering both new opportunities and challenges. This study investigates the integration of large language model-based teaching assistants into undergraduate algorithm courses, aiming to establish a new instructional framework that combines teaching, learning, assessment, and practice. By designing intelligent teaching processes and developing an AI assistant system capable of providing personalized guidance, real-time feedback, and adaptive learning support, we seek to promote synergy between algorithm education and AI technology. The proposed model is expected to improve students’ learning efficiency and foster stronger algorithmic thinking and practical problem-solving skills in the context of AI applications. This work offers theoretical perspectives and practical experience on the transformation of algorithm teaching in higher education.
AB - Algorithm courses are fundamental in undergraduate majors related to information technology. However, with the rapid advancement of artificial intelligence, traditional instructional approaches are encountering both new opportunities and challenges. This study investigates the integration of large language model-based teaching assistants into undergraduate algorithm courses, aiming to establish a new instructional framework that combines teaching, learning, assessment, and practice. By designing intelligent teaching processes and developing an AI assistant system capable of providing personalized guidance, real-time feedback, and adaptive learning support, we seek to promote synergy between algorithm education and AI technology. The proposed model is expected to improve students’ learning efficiency and foster stronger algorithmic thinking and practical problem-solving skills in the context of AI applications. This work offers theoretical perspectives and practical experience on the transformation of algorithm teaching in higher education.
KW - AI teaching assistants
KW - Algorithm courses
KW - Intelligent education
KW - Large language models
UR - https://www.scopus.com/pages/publications/105040396843
U2 - 10.1007/978-981-95-7731-6_20
DO - 10.1007/978-981-95-7731-6_20
M3 - 会议稿件
AN - SCOPUS:105040396843
SN - 9789819577309
T3 - Communications in Computer and Information Science
SP - 243
EP - 256
BT - Computer Science and Education. AI Shaping Education - 19th International Conference, ICCSE 2025, Proceedings
A2 - Hong, Wenxing
A2 - Cui, Binyue
A2 - Weng, Yang
A2 - Li, Chao
PB - Springer Science and Business Media Deutschland GmbH
T2 - 19th International Conference on Computer Science and Education, ICCSE 2025
Y2 - 19 August 2025 through 24 August 2025
ER -