Under the Wave of GAI, Where Should the Core Competitiveness of Humanities and Social Science Students 
in Higher Education Go in Terms of Employment?

Yige Liang

Under the Wave of GAI, Where Should the Core Competitiveness of Humanities and Social Science Students in Higher Education Go in Terms of Employment?

Yige Liang

Abstract

Represented by ChatGPT, large-scale AI models have reshaped the interaction between humans and technology. The tools and capabilities of large-scale AI models, such as text content production, data collection, and deep learning of massive information, have to some
extent created employment barriers for students in humanities and social sciences. It is necessary to reflect on whether the core competitiveness of students in humanities and social sciences can keep pace with the development of the GAI era, how to adapt to changes in social talent
demand, and how to find a balance between the impact of the tools and capabilities of GAI applied to enterprises on the core competitiveness
of employment for students in humanities and social sciences. However, the application of large-scale AI models in knowledge production is
integrative rather than creative, leading to a lack of creativity and imagination. Students in humanities and social sciences should fully leverage their irreplaceable abilities, align with the characteristics of interconnectedness in GAI development, and achieve deeper and higher-level
interaction, collaboration, and integration. Based on this, the core competitiveness path for employment of students in humanities and social
sciences can be pointed out from the dimensions of interdisciplinary integration training, scenario-based teaching in GAI, and the cultivation
of irreplaceable abilities.

Keywords

GAI; ChatGPT; Substitution effect; Interdisciplinary training; Scenario-based teaching

Full Text:

PDF

Included Database

References

[1] Pan Jie, Liu Qin, Wu Zhongsheng, et al. Application Thinking of ChatGPT-like Technology in Enterprise Intelligent Financial

Construction[J]. Friends of Accounting, 2024, (03): 139-144.

[2] Zhang Haisheng. Empowering Discipline Construction with Artificial Intelligence: Interpretation Model and Logical Deconstruction[J].

Higher Education Management, 2023, 17(03): 42-50+75. DOI:10.13316/j.cnki. jhem.20230504.005.

[3] Liu Yongmou, Wang Chunli. Actively Responding to the Challenge of Generative Artificial Intelligence to Liberal Arts Education[J].

Nanjing Social Sciences, 2023, (06): 119-128. DOI:10.15937/j.cnki.issn1001-8263. 2023.06.013.

[4] Liu Xiang, Li Hong. Research on Evaluation Indicators of Innovative Interdisciplinary Scientific Research in Universities: Taking Artificial Intelligence in New Engineering Disciplines as an Example[J]. Journal of Zhejiang University (Humanities and Social Sciences

Edition), 2023, 53(05): 36-46.

[5] Wang Fang, Jiang Jiongping, Yang Xiaojin. Cultivation of Cross-border Integrated Talents in Higher Vocational Education: Connotation,

Characteristics, and Training PathsTaking Tourism Majors as an Example[J]. Chinese Vocational and Technical Education, 2022, (22):

65-71.

[6] Chen Yunsong. The Value and Challenge of ChatGPT to Research in Humanities and Social Sciences[J]. Exploration and Contention,

2023, (05): 13-15.

[7] Duan Jiangli, Hu Xin. Exploration of Teaching Reform of Introduction to Artificial Intelligence Public Course in Universities[J]. Computer and Information Technology, 2024, 32(01): 141-142+146. DOI:10.19414/ j.cnki.1005-1228.2024.01.007.

[8] Lu Yunpeng, Li Chunling. Research on the Dialectical Influence of ChatGPT on Student Training from the Perspective of Technological

Ethics[J]. University Teaching in China, 2023, (07): 84-91.

[9] Ye Fei. Teacher Education Competency Structure and its Improvement Path for Digital Transformation[J]. Nanjing Social Sciences,

2023, (08): 114-122. DOI:10.15937/j.cnki.issn1001-8263.2023.08.012.

Refbacks

There are currently no refbacks.