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Exploring Innovative Teaching Methods in University Major Courses through the Synergy of AI and HI
Soyoung Lee(Soyoung Lee),Sangyun Ahn(Sangyun Ahn) J-INSTITUTE 2024 Robotics & AI Ethics Vol.9 No.-
Purpose: The purpose of this study is to explore innovative teaching methods to enhance the effectiveness of university major courses which is “Service interview practice ll” through the synergy between Artificial Intelligence (AI) and Human Intelligence (HI). To achieve this, a quality management system for the curriculum was established based on the ADDIE model, and the direction for curriculum improvement was presented through learner needs analysis and the setting of learning objectives. The study aims to contribute to the development of learner-centered curricula by effectively utilizing AI technologies. Method: This research was conducted by analyzing a course that took place over one semester. Following the ADDIE model, the course syllabus was reviewed, and improvement points were derived based on student evaluations. The researcher utilized AI tools to generate course plans and assessment criteria, collecting various responses in the process to ultimately select the most suitable outcomes. This methodology facilitated a collaborative approach between AI and HI. Results: The analysis of the course syllabus revealed that overall course objectives, as well as the concepts of coaching by instructors, peer coaching, and self-coaching, were clearly articulated, and assessment criteria were strengthened. Notably, the introduction of results-oriented goal-setting allowed students to conduct self-assessments after midterm exams through discussions with instructors, thereby establishing a framework to monitor learner progress. The assessment methods were divided into external and internal evaluations, providing a foundation for more systematic and specific feedback. Conclusion: This study emphasizes the need for continuous enhancement of teaching effectiveness through collaboration between AI and HI. It particularly suggests that the design of major courses should enable learners to acquire competency-based skills, highlighting the necessity for the development and improvement of diverse teaching methods. Future research should seek ways to improve classes by leveraging the various functionalities of AI and expanding the possibilities of generative AI. These efforts will ultimately contribute to enhancing the quality of higher education.
Extended Siamese Convolutional Neural Networks for Discriminative Feature Learning
Sangyun Lee,홍성준 한국지능시스템학회 2022 INTERNATIONAL JOURNAL of FUZZY LOGIC and INTELLIGE Vol.22 No.4
Siamese convolutional neural networks (SCNNs) has been considered as among the bestdeep learning architectures for visual object verification. However, these models involvethe drawback that each branch extracts features independently without considering the otherbranch, which sometimes lead to unsatisfactory performance. In this study, we propose a newarchitecture called an extended SCNN (ESCNN) that addresses this limitation by learningboth independent and relative features for a pair of images. ESCNNs also have a featureaugmentation architecture that exploits the multi-level features of the underlying SCNN. Theresults of feature visualization showed that the proposed ESCNN can encode relative anddiscriminative information for the two input images at multi-level scales. Finally, we appliedan ESCNN model to a person verification problem, and the experimental results indicate thatthe ESCNN achived an accuracy of 97.7%, which outperformed an SCNN model with 91.4%accuracy. The results of ablation studies also showed that a small version of the ESCNNperformed 5.6% better than an SCNN model.