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Exploring User Perspectives on ChatGPT: Applications, Perceptions, and Implications for AI-Integrated Education

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arxiv 2305.13114 v3 pith:IV3XOIHA submitted 2023-05-22 cs.CY cs.HC

classification cs.CYcs.HC
keywords chatgpteducationlearningsocialusersacrossadoptersai-integrated
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To foster the development of pedagogically potent and ethically sound AI-integrated learning landscapes, it is pivotal to critically explore the perceptions and experiences of the users immersed in these contexts. In this study, we perform a thorough qualitative content analysis across four key social media platforms. Our goal is to understand the user experience (UX) and views of early adopters of ChatGPT across different educational sectors. The results of our research show that ChatGPT is most commonly used in the domains of higher education, K-12 education, and practical skills training. In social media dialogues, the topics most frequently associated with ChatGPT are productivity, efficiency, and ethics. Early adopters' attitudes towards ChatGPT are multifaceted. On one hand, some users view it as a transformative tool capable of amplifying student self-efficacy and learning motivation. On the other hand, there is a degree of apprehension among concerned users. They worry about a potential overdependence on the AI system, which they fear might encourage superficial learning habits and erode students' social and critical thinking skills. This dichotomy of opinions underscores the complexity of Human-AI Interaction in educational contexts. Our investigation adds depth to this ongoing discourse, providing crowd-sourced insights for educators and learners who are considering incorporating ChatGPT or similar generative AI tools into their pedagogical strategies.

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Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. When AI Blurs the Boundaries of Contribution: An Empirical Study of Authorship Calibration

    cs.HC 2026-07 reject novelty 5.0 of 10

    Frequent AI users show more variable and slightly more overestimating authorship calibration scores than light users, though both group means are near zero.

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