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ChatGPT in education: A blessing or a curse? A qualitative study exploring early adopters’ utilization and perceptions
Open accessAbstract
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 education 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 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 others. 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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“Prior work suggested that AI-driven systems can support parenting education [61] and provide evidence-based advice through applications and chatbots, delivering micro-interventions such as teaching parents how to praise their children effectively [11, 17] or offering strategies to teach complex concepts [55, 69].”
From SET-PAiREd: Designing for Parental Involvement in Learning with an AI-Assisted Educational Robot (2025) · cites this paper as [55]
In the citing paper
“GAI offers promising support for parents by enhancing their ability to educate and engage with their children.”
From SET-PAiREd: Designing for Parental Involvement in Learning with an AI-Assisted Educational Robot (2025) · cites this paper as [55]
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“It can help create personalized learning experiences by providing timely feedback and tailoring content [28, 55, 69], enhancing positive learning experiences [39].”
From SET-PAiREd: Designing for Parental Involvement in Learning with an AI-Assisted Educational Robot (2025) · cites this paper as [55]
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Metadata
- Authors
- Hadi Mogavi, R., Deng, C., Juho Kim, J., Zhou, P., D. Kwon, Y., Hosny Saleh Metwally, A., Tlili, A., Bassanelli, S., Bucchiarone, A., Gujar, S., Nacke, L. E., & Hui, P.
- Affiliations
- University of Science and Technology of China (CN)
- Beijing Normal University (CN)
- Fondazione Bruno Kessler (IT)
- International Institute of Information Technology (IN)
- University of Waterloo (CA)
- University of Helsinki (FI)
- Corresponding author
- Hui, Pan
- ORCID
- Venue
- Computers in Human Behavior: Artificial Humans
- Publisher
- Elsevier BV
- Published
- 2024-01
- Volume
- 2
- Issue
- 1
- Pages
- 100027
- ISSN
- 2949-8821 (ISSN-L)
- OpenAlex
- W4388817859
- Semantic Scholar
- cf5bfdb0bfbf2f3b42a131a40372212217eea3d3
- Cited by
- 369
- References
- 96
- Citations by year
- 2026: 87 · 2025: 150 · 2024: 127
- FWCI
- 86.92
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Computers in Human Behavior Artificial Humans (publishedVersion, cc-by-nc-nd)
- Archivio Istituzionale della Ricerca - Università degli Studi dell Aquila (submittedVersion, cc-by-nc-nd)
- Topics
- Social Sciences › Education › Artificial Intelligence in Education
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Physical Sciences › Computer Science › Artificial Intelligence › Intelligent Tutoring Systems and Adaptive Learning
- Keywords
- ChatGPT, generative AI in education, early adopters, user perception, user experience, higher education, K-12 education, practical skills training, student self-efficacy, student motivation, superficial learning, critical thinking, human-AI interaction, AI ethics, social media discourse
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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