• 한국체육학회지인문사회과학편
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• v.55 no.3
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• pp.43-57
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• 2016

This narrative study aims to describe the experience of P.E. assessment that was conducted by P.E. teachers of Korea Science Academy of KAIST, and interpret the educational significance that was found in the process. The study participants were two P.E. teachers who were selected by decisive case sampling method. Data were collected mainly through official interviews with study participants, and through researcher's field notes, informal interviews, various minutes, students' evaluation of teaching, and emails between the researcher and study participants. Data were analyzed through inductive categorization, and to gain veracity of the study, there were integration of diverse materials, advice and suggestions of fellow researchers, continuous confirmation of study texts by study participants. Study participants, while conducting P.E. assessment in Korea Science Academy of KAIST, experienced effectiveness of evaluation such as qualitative development of P,E. classes in accordance with the simplified assessment, freedom from the chores of handling assessment results, students' improved perceptions of P.E. class, realization of safe classes without excessive competition, and the possibility of giving alternative evaluations to pass/fail system but at the same time experienced limitations such as concerns over gaining validity and reliability of P.E. evaluation, the students' attitude who take lightly of P.E. class, and the reality that teachers cannot fail students. The evaluation experiences of the two P.E teachers were educationally interpreted as encounter with good P.E. classes, invitation to P.E. class criticism, and the start of school P.E. culture that is led by students.

• The Korean Society of Law and Medicine
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• v.24 no.4
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• pp.67-101
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• 2023

The utilization of generative AI in the medical field is also being rapidly researched. Access to vast data sets reduces the time and energy spent in selecting information. However, as the effort put into content creation decreases, there is a greater likelihood of associated issues arising. For example, with generative AI, users must discern the accuracy of results themselves, as these AIs learn from data within a set period and generate outcomes. While the answers may appear plausible, their sources are often unclear, making it challenging to determine their veracity. Additionally, the possibility of presenting results from a biased or distorted perspective cannot be discounted at present on ethical grounds. Despite these concerns, the field of generative AI is continually advancing, with an increasing number of users leveraging it in various sectors, including biomedical and life sciences. This raises important legal considerations regarding who bears responsibility and to what extent for any damages caused by these high-performance AI algorithms. A general overview of issues with generative AI includes those discussed above, but another perspective arises from its fundamental nature as a large-scale language model ('LLM') AI. There is a civil law concern regarding "the memorization of training data within artificial neural networks and its subsequent reproduction". Medical data, by nature, often reflects personal characteristics of patients, potentially leading to issues such as the regeneration of personal information. The extensive application of generative AI in scenarios beyond traditional AI brings forth the possibility of legal challenges that cannot be ignored. Upon examining the technical characteristics of generative AI and focusing on legal issues, especially concerning the protection of personal information, it's evident that current laws regarding personal information protection, particularly in the context of health and medical data utilization, are inadequate. These laws provide processes for anonymizing and de-identification, specific personal information but fall short when generative AI is applied as software in medical devices. To address the functionalities of generative AI in clinical software, a reevaluation and adjustment of existing laws for the protection of personal information are imperative.