Limitations of using AIGC in pre-service STEM teacher education: A perspective on potential psychological stress
Abstract
With the rapid development of AIGC (Artificial Intelligence Generated Content) and its expanding role and scope in education and teaching. This study conducted a survey among 394 pre-service STEM teachers enrolled at a university located in Zhejiang Province. Data were collected and a structural model was constructed to examine interplay among psychological stress, anxiety self-efficacy, and learning burnout resulting from the utilization of AIGC. The findings indicate that pre-service STEM teachers may experience psychological stress when applying AIGC, which could exacerbate their anxiety towards artificial intelligence and potentially lead to academic burnout. In order to effectively integrate AIGC in the field of education and enhance the professional development of pre-service teachers, the key lies in the dissemination of artificial intelligence knowledge, enhancing pre-service teachers' understanding of artificial intelligence, and encouraging them to appropriately utilize AIGC as a learning auxiliary tool.
README: Limitations of Using AIGC in Pre-Service STEM Teacher Education: A Perspective on Potential Psychological Stress
https://doi.org/10.5061/dryad.ht76hdrpk
Description of the data and file structure
During the winter semester of 2023, the target audience of this study is a representative group of third year normal students in a normal university in Zhejiang Province, especially those who focus on STEM education. Given the core position of STEM (Science, Technology, Engineering, and Mathematics) disciplines in today's education system and future job markets, as well as their significant driving force for innovation and economic growth, this study specifically focuses on the situation of teacher trainees in these fields. Due to the fact that engineering majors have not yet been included in the unified training scope in China's current teacher education system, we have selected normal students from three categories: science, mathematics, and technology as samples,Among the 415 questionnaires returned, 394 (94.9%) were valid.Among the participants, 200 (50.8%) were males, 194 (49.2%) were females, 141 (35.8%) studied science education, 146 (37.1%) studied technical education, and 107 (27.1%) studied mathematics education.
Variable: value 1 is psychological stress, variable value 2 is artificial intelligence anxiety, variable value 3 is self-efficacy, and variable value 4 is learning burnout
Variable: Gender includes male and female students, grade is a third year student, majors include science teacher education, mathematics teacher education, and technical teacher education.
Variable value description: 1-Psychological pressure caused by using AIGC, 2-Artificial intelligence anxiety, 3-Self efficacy, 4-Learning fatigue; Gender: 1-male, 2-female; Major: 1-Science, 2-Technology, 3-Mathematics
Description of variable value F-AQ column：1 point for “strongly disagree” to 5 points for “strongly agree”
Code/Software
In our article, we mainly focus on the impact of generative artificial intelligence, such as CHatGPT, on the psychological pressure of pre service teachers. As a product that integrates artificial intelligence to generate content, content generation methods, and automatic content generation technology, AIGC has become one of the important ways of content creation in the Web 3.0 era, utilizing natural language processing and natural language generation technology. At present, ChatGPT is one of the representative platforms in AIGC. ChatGPT is a pre training language model developed by OpenAI. This model is trained on a large amount of Internet text data and can generate human like text. In addition to the ability to generate text, ChatGPT also has the ability to understand and interpret the meaning of text, making it suitable for various natural language processing tasks. It can be used in various applications, such as customer service chatbots, virtual assistants, or voice devices.The data analysis of this study was mainly conducted using SPSS 29.0. The main functions of SPSS include data entry and management, statistical analysis, chart statistical analysis, and output management. The statistical analysis process includes descriptive statistics, inferential statistics, and multivariate statistical analysis.
Methods
This study uses the internationally recognized five-point Likert scale as the main tool to quantitatively assess STEM teachers' psychological pressure, fear of artificial intelligence, self-efficacy and learning fatigue caused by the use of AIGC. The questionnaire design is based on proven and valid scales in published academic literature at home and abroad to ensure the reliability and validity of data collection. After collecting data using the questionnaire method, invalid data were eliminated and SPSS 29.0 was used for statistical analysis.