0 Introduction

Artificial intelligence is triggering scientific breakthroughs that can produce chain reactions, and subversively promote world economic, political, social and military changes. However, if artificial intelligence is misused, abused, and malicious, it may trigger major security threats in multiple fields. Once it is out of human control, it may even pose a threat to human existence. On March 20, 2022, the General Office of the CPC Central Committee and the General Office of the State Council issued the "Opinions on Strengthening the Governance of Science and Technology Ethics"; on April 4, 2023, the Ministry of Science and Technology issued the "Science and Technology Ethics Review Measures (Trial)" for soliciting opinions, and the Party and the state attach great importance to the governance of science and technology ethics to an unprecedented level. Colleges and universities are important bases for young talents training and scientific research, and the impact of ethical education will accompany them throughout their lives. Building an artificial intelligence ethics case teaching platform and exploring a teaching model based on case immersion and case-driven can lead young students to establish correct awareness of artificial intelligence ethics, help build an artificial intelligence ethics education community, promote the research and practice of the artificial intelligence ethics teaching system in colleges and universities, and ultimately enable young students to practice artificial intelligence ethics in their future careers [1].

1 Problems and case teaching modes of artificial intelligence ethics courses

1.1 Current status and shortcomings of teaching of artificial intelligence ethics courses

Many American universities such as Harvard University, Stanford University, and Berkeley University have offered courses on artificial intelligence ethics, and the course topics are relatively complete covering various artificial intelligence ethics issues that humans are currently concerned about. Domestic artificial intelligence ethics education is still in its infancy, and the courses related to the application and ethical awareness of artificial intelligence ethics education developed by the organization are still in the fragmented stage, and curriculum design and teaching resources need to be standardized and improved [2]. At present, a few universities such as the University of Science and Technology of China, Nankai University, and Xi'an Jiaotong University have opened courses on artificial intelligence ethics. The teaching methods are mostly taught in traditional large courses [3]. Although theoretical knowledge can be spread in a centralized, systematic and rapid manner, there are many shortcomings in the application of artificial intelligence ethics.

(1) Lack of practical experience: Artificial intelligence ethics courses focus on cultivating students' ethical awareness and decision-making ability. Large class lectures focus on theoretical teaching, and lack practical case analysis and practice, making it difficult for students to apply the ethical theories they have learned to practical problems.

(2) Students are not interested in: In terms of cultivating professional abilities, artificial intelligence ethics courses are not as direct benefits as programming language and algorithm design courses to computer students during their studies. The lack of one-way knowledge transfer taught in large classes and the lack of interaction with students can easily lead to boring classrooms. Students’ subjective lack of attention and boring classrooms can easily lead to low interest and participation in students.

(3) There is a risk of subjectivity: There is a lot of room for interpretability in ethical issues. Artificial intelligence ethics courses often involve discussions of ethical judgments and values. One-way theoretical teaching is prone to teacher subjectivity risks. Students who are passive recipients lack the cultivation of active participation and proactive critical thinking.

(4) Lack of updated textbooks: Due to the rapid update of knowledge in the field of artificial intelligence ethics, the update of theoretical textbooks may not keep up with the latest research results, which limits the actual effect of the course.

(5) Ignore the comprehensive interdisciplinary capabilities: Artificial intelligence has changed from cutting-edge technology to basic technology and is deeply integrating with all walks of life. The teaching method of large-class teaching is often taught based on moral philosophy and ethics, and lacks the integration of knowledge in other subjects. This will make it difficult for the course to form a comprehensive perspective, which will in turn be unable to meet the needs of modern society for students' comprehensive professional abilities.

Artificial intelligence ethics is a complex ethics course with interdisciplinary subjects. The content is abstract and difficult to understand. It requires students to have strong logical thinking and problem analysis and application ability. Therefore, it is of great significance to study case teaching models that students can easily understand and accept [4].

1.2 Artificial Intelligence Ethics Course Case Teaching Model

In the early 20th century, Harvard University created the case teaching method [5], which is a typical process of real events and forms cases for students to think, analyze and make decisions, and encourage students to actively discover, analyze and solve problems using the knowledge they have learned [6]. This change greatly subverts the traditional educational concept and has achieved good results in practice. In terms of artificial intelligence education, some universities have currently carried out the construction of case databases related to artificial intelligence and robots, but the construction of artificial intelligence ethics teaching case database has not yet been reported in literature. In order to meet the ethical teaching needs in the context of sustainable development of artificial intelligence, it is urgent to study the construction of an artificial intelligence ethical teaching case library and teaching practice methods.

The case teaching method can well solve the problems existing in artificial intelligence ethics teaching in large-class teaching, and has a high degree of compatibility and need matching with artificial intelligence ethics teaching. The "three processes and one platform" artificial intelligence ethics case teaching model is shown in Figure 1: ① Determine the basic principles of case design and sort out the artificial intelligence ethics knowledge system; ② Select case element materials and establish teaching cases according to standards; ③ Conduct case teaching practice based on the case library, and combine the feedback of case teaching practice and cutting-edge research on artificial intelligence technology to continuously improve the case library; ④ By establishing a smart platform for the artificial intelligence ethics teaching case library, publishing the artificial intelligence ethics teaching case library with the help of cyberspace and intelligently managing students' learning behaviors can ensure that students are not restricted by time and space, and realize "everyone, every time, every place". The organic combination of teaching case database with the Internet and artificial intelligence technology will become a trend in exploring the development of teaching based on case immersion.

2 Principles of designing case library for artificial intelligence ethics teaching

(1) System comprehensiveness: sort out the system of artificial intelligence ethical knowledge (such as ensuring full autonomy of human beings, the stability and security of technology, data security and privacy, the transparency, interpretability of algorithms, fairness and non-discrimination, the sustainability of natural ecology and social welfare, the controllability and accountability of models), and construct cases based on the knowledge system to cover various related artificial intelligence ethical issues.

(2) Specific diversity: Diversity is reflected in three aspects: multiple fields, multiple scenarios and globalization background. The case library must provide cases in different situations, focusing on the fields of face recognition, voice synthesis, autonomous driving, service robots, etc., and dig up relevant teaching cases in a planned and step-by-step manner and gradually expand to other fields. For actual events and hypothetical scenarios, the role and environment description of the case should be detailed and specific so that teachers and students can have a deeper understanding of the application and ethical challenges of artificial intelligence in different fields. The case library must consider the context of globalization to ensure that cases cover ethical issues in different cultural and social environments.

(3) Interaction: Design cases with discussion links and encourage students to actively participate. Establish a mechanism for the construction of artificial intelligence ethics case in which teachers, students and enterprises participate together, openly share and continuously improve. Explore diverse case interaction presentation forms to cultivate academic critical thinking and teamwork skills.

(4) Discipline integration: Analyze cases from the perspectives of multiple disciplines such as ethics, philosophy, law, sociology, and psychology, so as to enable students to understand the influence of multi-dimensionality.

(5) Continuous improvement: Regularly update the case library to ensure that it contains the latest technological developments and ethical disputes to maintain the relevance and timeliness of the content. Establish a feedback mechanism to collect evaluations and suggestions from students and teachers on case use, and continuously improve the case library.

(6) Ease of access: Design the case library as a user-friendly smart learning platform, which is convenient for teachers and students to view and use anytime, anywhere.

3. Construction of artificial intelligence ethical knowledge system and teaching case design

3.1 Construction of the Ethical Knowledge System of Artificial Intelligence

According to the relevant documents on artificial intelligence ethics issued by China, the European Union, the United States, UNESCO and other countries and organizations, and combined with the research content of hot topics in artificial intelligence ethics that are concerned by the industry and academia, 10 chapters have been summarized and refined from five aspects: improving human welfare, respecting the right to life, adhering to fairness and justice, reasonably controlling risks, and maintaining openness and transparency, 10 chapters have been summarized and refined to form an artificial intelligence ethics knowledge system to provide guidance and direction for the establishment of subsequent case databases. The specific content is shown in Table 1.

3.2 Teaching case design based on the ethical knowledge system of artificial intelligence

3.2.1 Case source

Under the guidance of the six overall design principles, cases are systematically constructed based on the knowledge system. When selecting teaching cases, you can obtain the original information of the case from multiple channels. Common cases are obtained from the following sources.

(1) Media reports on artificial intelligence ethical events, such as the abuse of facial recognition technology and the inequalities that are caused by automated decision-making systems.

(2) Authoritative academic journals and conferences, such as CVPR, ICLR, IJCAI and other conferences, have had special ethics forums in recent years. The papers involved in the forum and the reports of expert groups usually describe in detail the ethical problems of artificial intelligence they deal with and provide corresponding suggestions and solutions.

(3) Artificial Intelligence Ethical Norms and Codes. Many countries and organizations have formulated artificial intelligence ethical norms and Codes. These norms and Codes usually contain specific cases to explain their principles and requirements.

(4) Ethical issues in artificial intelligence in cross-fields such as medical care and law, such as privacy protection, data security, and decision-making fairness. These issues can provide rich cases for artificial intelligence ethics teaching.

3.2.2 Specific case design

When building a case library, the elements contained in the case are standardized. Each case includes but is not limited to the following elements: corresponding chapters, knowledge points involved, case number, case name, case content, case interaction design, case discipline fusion, case analysis process, case teaching methods, and thinking content.

In order to stimulate students' interest, promote interaction and communication, and ensure the quality of case teaching, the case content is presented in a diverse display form: ① Describe cases in combination with charts; ② Display cases vividly in videos and animations; ③ Combined with role-playing for micro scripts, let students experience the characters and situations in the case personally; ④ Combined with virtual reality simulation environments to allow students to experience cases immersively; ⑤ Debate competition presents cases, so that students can experience the diversity of ethical views in the process of preparing for debate and debate; ⑥ Group discussion presents cases, allowing students to communicate with each other and share their views, and promote the understanding and analysis of cases; ⑦ Simulated experimental methods present cases, so that students can personally experience the process and results of case design; ⑧ Establish a smart platform for artificial intelligence ethics teaching case library to display, record, count cases and case learning situations, so that teachers and students can view and use them anytime and anywhere.

Table 2 takes the "racial discrimination" case in the Fair Chapter as an example to introduce the detailed design of the case.

4 Artificial Intelligence Ethical Case Teaching Practice

The teaching practice of artificial intelligence ethics case follows the idea of ​​"three-in-one change", that is, emphasizes the preparation and interaction between teachers and students in three positions before, during and after class, and emphasizes the continuous improvement of cases based on the opinions of teachers and students.

(1) Prepare the required materials for case analysis before class and cultivate students' habit of actively learning and independent thinking. For example, in cases of racial discrimination, you should understand the definition of racial discrimination, typical racial discrimination events, and ethnic classification before class, and complete the preparation of the facial recognition test set.

(2) Explore the teaching model of case scenario immersion and cutting-edge lecture-driven in the classroom, focus on students, guide students to substitute roles for case scenarios, and carry out speculative practices in active, practical, problem-guided and task-driven methods (such as simulation experiments, micro-films, group discussions, debate competitions, dramas, game competitions, etc.), and integrate ethical responsibilities into the innovative research process of artificial intelligence scientists. Organize multidisciplinary seminars for college and enterprise in response to ethical issues involved in emerging cutting-edge artificial intelligence technologies and applications to broaden students' thinking and horizons.

(3) After class, students are required to write case analysis reports, encourage students to expand the depth and breadth of thinking, conduct comprehensive and in-depth summary and analysis of cases, and put forward improvement opinions and measures for the case content and teaching process. On the one hand, the writing of case analysis reports can promote students' understanding and application of cases, and on the other hand, it can also continuously improve the case library to ensure the quality of case teaching practice.

(4) Teachers establish a case analysis report evaluation system and evaluate students' case analysis reports. Optimize teaching cases and teaching processes based on evaluation results, appropriate views in student analysis reports, and ethical issues involved in cutting-edge artificial intelligence technology and applications, and drive the dynamic and sustainable development of case teaching.

5 Teaching practice effects

Sample: Two classes in the second grade of undergraduate degree in the School of Computer Science, Nanhua University, each class has about 45 people, one class serves as the experimental group and the other class serves as the control group. The experimental group used case model teaching, while the control group used traditional model teaching, and the T-test was performed through SPSS (see Table 3).

The variance test shows that the P values ​​of each item are greater than 0.15, indicating that the experimental teaching effect is obvious. By examining the two-sided approximate P values ​​(2-) of each knowledge point, we can see that except for the ethical concept, the P values ​​of the other items are less than 0.05, and the mean values ​​of the two groups are significantly different. This shows that students who use case model have made greater progress than those who use traditional teaching methods. The teaching evaluation of the same period survey reported that 76.2% of students welcomed the case teaching model, 69.5% of students believed that their learning interest and learning ability had improved, and the experimental class evaluated the excellent course and teachers of the course were 97.8%.

There are still some difficulties in teaching that need to be improved, such as the uneven level of information literacy among students in the experimental class, and some students perform poorly during group discussions and PPT explanations. How to better make case teaching applicable to students' learning needs, expression skills, understanding skills, etc. of different information literacy levels still need to be further discussed.

6 Conclusion

At present, the lack of teaching materials for domestic artificial intelligence ethics courses is quite prominent. In view of the characteristics of artificial intelligence ethics cases, we designed and implemented an artificial intelligence ethics teaching case library that integrates systematicity, practicality and cutting-edgeness, as well as a new case teaching method of "three processes and one platform". From the perspective of practical results, the continuous construction of the case library of artificial intelligence ethics teaching and the practice of case teaching methods is conducive to cultivating students' conscious ethical awareness and enabling them to have the ability to integrate multidisciplinary knowledge to independently analyze and solve practical artificial intelligence ethical problems. With the promotion and application of the artificial intelligence ethics case teaching library, it will surely have a significant impact on improving teaching quality and cultivating talents who develop correctly and use artificial intelligence technology.

References:

[1] Zhang Yuqing, Ji Tong. Practical analysis of science and technology ethics education for students of science and engineering students [J]. China Higher Education, 2023 (Supplement 2): 71-73.

[2] Tian Fengjuan, Tuo Ying, Liu Wei. An analysis of the ethical education of artificial intelligence in colleges and universities from the perspective of socialist core values ​​[J]. Ideological Education Research, 2023(5): 122-126.

[3] Ren Anbo, Ye Bin. The lack of artificial intelligence ethics education in my country and its countermeasures [J]. Science and Society, 2020, 10(3): 14-21.

[4] Jia Meng, Zhang Yingqi, Li Yunfeng, et al. Research progress in medical artificial intelligence education [J]. Medical Education Research and Practice, 2023, 31(1): 1-6.

[5] C. Task force on as a , a : for the 21st [R]. DC: Forum on and the , 1986.

[6] Xu Kai, He Zhouyang, Xu Wenxuan, et al. Construction and practice of artificial intelligence course teaching case library for rail transit[J]. Experimental Technology and Management, 2019, 5(36): 15-20.

Fund projects: Ministry of Education’s Humanities and Social Science Research Project “Research on the Issues of the Ethical Review of Biometric Technology and Organizational Governance” (); Hunan Province’s Teaching Reform Research Project “Research and Practice of the Ethical Teaching System of Artificial Intelligence in Colleges and Universities” (HNJC-2022-0748).

Author profile: Jiang Fangling, female, lecturer at Nanhua University, research directions are computer vision and artificial intelligence ethics research; Liu Bing (corresponding author), male, senior engineer at Nanhua University, research directions are network engineering technology and ethics research.

Citation format: Jiang Fangling, Liu Bing. Construction of artificial intelligence ethics teaching case library and case teaching practice [J]. Computer Education, 2025(1):131-136.

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