AI literacy for ethical use of chatbot: Will students accept AI ethics?

被引:0
作者
Kajiwara, Yusuke [1 ]
Kawabata, Kouhei [1 ]
机构
[1] Department of Production Systems Engineering and Sciences, Komatsu University, Shichomachi Nu1-3, Ishikawa, Komatsu
来源
Computers and Education: Artificial Intelligence | 2024年 / 6卷
关键词
AI literacy; Chatbot; Ethics; Large language models; Secondary education; Technology acceptance;
D O I
10.1016/j.caeai.2024.100251
中图分类号
学科分类号
摘要
In AI literacy education, there are few examples of education based on AI ethical principles, and limited knowledge exists regarding curriculum design that incorporates AI ethical principles and its effects. Therefore, in this study, we propose a curriculum that teaches the ethical use of large language models (LLM) such as ChatGPT and verify its impact on educational effectiveness and technology acceptance among students aged 12 to 24. The validation results show that the proposed curriculum particularly contributes to the understanding of LLM concepts and their ethical use in decision support. We also demonstrate that experience using ChatGPT influences the level of understanding of ethical usage. Additionally, students aged 12 to 18 may actively adopt ChatGPT responses in decision support, and careful consideration is needed when using LLMs in the 12- to 18-year-old age group. Using technology acceptance model, AI ethical principles were also examined to determine technology acceptance, and it was found that usefulness, justice and fairness, privacy, and data protection directly impact attitudes toward ChatGPT. It has also become clear that students feel uneasy about using their personal information for learning ChatGPT, even if they have consented to the use of their personal information. This result suggests that AI developers and providers need to handle personal information carefully to foster a positive AI attitude. © 2024 The Authors
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  • [21] Friedman A., Knijnenburg B.P., Vanhecke K., Martens L., Berkovsky S., Privacy aspects of recommender systems, Recommender systems handbook, pp. 649-688, (2015)
  • [22] George A.S., George A.H., A review of ChatGPT AI's impact on several business sectors, Partners Universal International Innovation Journal, 1, 1, pp. 9-23, (2023)
  • [23] Ghallab M., Responsible AI: Requirements and challenges, AI Perspectives, 1, 1, pp. 1-7, (2019)
  • [24] Goddard K., Roudsari A., Wyatt J.C., Automation bias: A systematic review of frequency, effect mediators, and mitigators, Journal of the American Medical Informatics Association, 19, 1, pp. 121-127, (2012)
  • [25] Gupta M., Akiri C., Aryal K., Parker E., Praharaj L., From chatgpt to threatgpt: Impact of generative ai in cybersecurity and privacy, IEEE Access, 11, 80218-80245, (2023)
  • [26] Habibi A., Muhaimin M., Danibao B.K., Wibowo Y.G., Wahyuni S., Octavia A., ChatGPT in higher education learning: Acceptance and use, Computers & Education: Artificial Intelligence, 5, (2023)
  • [27] Hitron T., Orlev Y., Wald I., Shamir A., Erel H., Zuckerman O., Can children understand machine learning concepts? The effect of uncovering black boxes, Proceedings of the 2019 CHI conference on human factors in computing systems, pp. 1-11, (2019)
  • [28] Huallpa J.J., Exploring the ethical considerations of using Chat GPT in university education, Periodicals of Engineering and Natural Sciences, 11, 4, pp. 105-115, (2023)
  • [29] Jagielski M., Oprea A., Biggio B., Liu C., Nita-Rotaru C., Li B., Manipulating machine learning: Poisoning attacks and countermeasures for regression learning, 2018 IEEE symposium on security and privacy (SP), pp. 19-35, (2018)
  • [30] Jeon J., Lee S., Large language models in education: A focus on the complementary relationship between human teachers and ChatGPT, Education and Information Technologies, pp. 1-20, (2023)