The influences of ChatGPT on undergraduate students’ demonstrated and perceived interdisciplinary learning

Zhong, Tianlong; Zhu, Gaoxia; Hou, Chenyu; Wang, Yuhan; Fan, Xiuyi

doi:10.1007/s10639-024-12787-9

Cited by 3 publications

(3 citation statements)

References 61 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Creativity assumed a mediating role, while critical thinking and problem-solving adopted a moderating role in the relationship between academic achievements and machine learning attitudes (Jeon et al, 2023). The evidence of these mediating and moderating effects provides a potential explanation for inconclusive relationships observed in previous studies (Lee et al, 2024;Zhong et al, 2024), highlighting the need for researchers to examine the intertwined dynamics between higher-order thinking and cognition when investigating the impacts of these skills on learning. A crucial point emerges because not only academic achievements but also the various dimensions of higher-order thinking are necessary to foster a positive impact on students' attitudes toward machine learning.…”

Section: Discussionmentioning

confidence: 98%

“…Although previous studies (Jeon et al, 2023;Jia & Tun, 2024;Zhong et al, 2024) have pointed out the signi cant role of creativity as a mediating factor between academic achievements and machine learning attitude, there exists a signi cant research gap in understanding the moderating effect of creativity within this relationship. The absence of signi cant mediating effects is a relatively underexplored aspect of research on higher-order thinking skills, prompting questions about speci c contextual factors that may in uence students' academic achievements using generative AI and machine learning technologies.…”

Section: Introductionmentioning

confidence: 99%

“…Through interactive conversations, users who specialized in media and information literacy are encouraged to articulate complex ideas coherently, analyze complex scenarios, and synthesize information from diverse sources (Lee et al, 2019). For example, this dynamic engagement encourages users to delve into interactive discussions, evaluate multiple perspectives, and engage in thoughtful debates, thereby enhancing their capacity to think critically and make informed decisions (Zhong et al, 2024). By challenging users to communicate effectively with AI that operates at a sophisticated level, generative AI chatbots stimulate cognitive processes that are integral to higher-order thinking skills related to problem-solving, critical thinking, and creativity.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

WITHDRAWN: The role of students’ higher-order thinking skills in the relationship between academic achievements and machine learning using generative AI chatbots

Pellas

2024

Preprint

View full text Add to dashboard Cite

Students' perspectives on using generative artificial intelligence (AI) chatbots and machine learning are crucial in shaping the design, development, and implementation of their learning projects across various disciplines. Cognitive thinking, a key aspect of AI-related machine learning, aims to replicate human intelligence and behavior. However, the relation between cognitive thinking and knowledge acquisition is not always clear. Therefore, it is essential for students to engage in higher-order thinking, which allows them to critically analyze diverse viewpoints, assess their relevance, and understand the complex relationship between cognitive thinking and knowledge acquisition. This empirical study investigates the role of higher-order thinking skills, such as problem-solving, critical thinking, and creativity, in the relationship between academic achievements and attitudes toward machine learning technologies using generative AI chatbots. Four hundred sixteen undergraduate students (n = 416) from diverse academic backgrounds voluntarily took part in a project, in which they designed and developed generative AI chatbots in media and information literacy courses. The findings indicate that creativity mediated the relationship between academic achievements and attitudes toward machine learning, but its moderating impact was not significant. Problem-solving and critical thinking did not show significant mediating effects on attitudes toward machine learning, while they showed significant moderating effects in the connection between academic performance and attitudes toward machine learning. This study contributes by elucidating the interrelationships between students’ higher-order thinking skills, academic performance, and attitudes on the use of AI and machine learning technologies. By highlighting the mediating role of creativity and the moderating effects of problem-solving and critical thinking, this study offers a deeper understanding of how these skills shape students' perceptions of AI. The findings have significant implications for educational practices, suggesting that fostering higher-order thinking skills is crucial in preparing students to embrace AI and machine learning technologies.

show abstract

Section: Discussionmentioning

confidence: 98%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation