Applying ChatGPT to tackle the side effects of personal learning environments from learner and learning perspective: An interview of experts in higher education

Xiao Shu Xu, Xi Bing Wang, Yun Feng Zhang, Rong Zheng

Research output: Contribution to journalArticlepeer-review

Abstract

This paper investigates the capacity of ChatGPT, an advanced language model created by OpenAI, to mitigate the side effects encountered by learners in Personal Learning Environments (PLEs) within higher education. A series of semi-structured interviews were conducted with six professors and three Information and Communication Technology (ICT) experts. Employing thematic analysis, the interview data were assessed, revealing that the side effects stemming from the learner and learning perspectives could be primarily categorized into cognitive, non-cognitive, and metacognitive challenges. The findings of the thematic analysis indicate that, from a cognitive standpoint, ChatGPT can generate relevant and trustworthy information, furnish personalized learning resources, and facilitate interdisciplinary learning to fully actualize learners’ potential. Moreover, ChatGPT can aid learners in cultivating non-cognitive skills, including motivation, perseverance, self-regulation, and self-efficacy, as well as metacognitive abilities such as self-determination, self-efficacy, and self-regulation, by providing tailored feedback, fostering creativity, and stimulating critical thinking activities. This study offers valuable insights for integrating artificial intelligence technologies to unleash the full potential of PLEs in higher education.

Original languageEnglish
Article numbere0295646
JournalPLoS ONE
Volume19
Issue number1 January
DOIs
Publication statusPublished - Jan 2024
Externally publishedYes

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