Expert or machine? Comparing the effect of pairing student teacher with in-service teacher and ChatGPT on their critical thinking, learning performance, and cognitive load in an integrated-STEM course

ABSTRACT

For student teachers’ professional development, the emergence of generative artificial intelligence (AI) represents both opportunity and challenge. This exploratory quasi-experimental study aims to investigate the effects of “Human-Human” and “Human-Machine” collaborative learning approaches on the SETM teaching training performance of student teachers. Twenty-three student teachers were divided into two groups within a single class, each adopting one learning method. The experiment lasted for two months with weekly three-hour sessions. Data were analysed focusing on critical thinking, learning performance, and cognitive load between the groups. The results indicated that student teachers using ChatGPT showed higher critical thinking systematicity, task completion efficiency, and experienced lower cognitive load. Student teachers paired with in-service teachers slightly outperformed those with ChatGPT on the final teaching design proposal. These findings underscore the potential and varying strengths of AI tools like ChatGPT and human teachers. For further research, refined collaborative learning scaffolding are recommended to explore the impact and potential of AI-assisted and in-service teacher-involved collaboration. The study’s implications could guide educators, policymakers, and AI developers in optimizing the AI-enhanced collaborative learning strategies and shed light on the new formation of human-machine collaborative intelligence in the scope of education.

KEYWORDS:

• STEM education
• collaborative learning
• ChatGPT
• student teacher
• AI-assisted learning

Disclosure statement

No potential conflict of interest was reported by the author(s).

Data availability statement

Data available on request from the corresponding author Zehui Zhan at [email protected].

Correction Statement

This article has been corrected with minor changes. These changes do not impact the academic content of the article.

Additional information

Funding

This research was financially supported by the National Natural Science Foundation in China (62277018; 62237001), the Ministry of Education in China Project of Humanities and Social Sciences (22YJC880106), the Major Project of Social Science in South China Normal University (ZDPY2208), the Degree and graduate education Reform research project in Guangdong (2023JGXM046).

Notes on contributors

Tingting Li

Tingting Li is a PhD candidate in the School of Information Technology in Education at the South China Normal University. Her major research interests lie in the area of STEM education, information technology education in primary and secondary schools, artificial intelligence education.

Yu Ji

Yu Ji is a PhD candidate in the School of Information Technology in Education at the South China Normal University. His current research focuses on STEM education, information technology education in primary and secondary schools, artificial intelligence education.

Zehui Zhan

Zehui Zhan is a PhD, professor, doctoral supervisor in South China Normal University, PI of the Smart Educational Equipment Industry-University-Research Cooperation Base. Her research interest includes learning science, STEAM education, smart education, entrepreneurial education.