The Rise of Generative Artificial Intelligence and Its Impact on Education: The Promises and Perils
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While we applaud the promise and value of generative artificial intelligence and Chat Generative Pretrained Transformer–like tools, leveraging their potential and values heavily depends on what we use them for and how, while also acknowledging and addressing their limits, limitations, and concerns.
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Research on the efficacy of Large Language Models in promoting specific educational strategies such as reflection techniques remains sparse.
“Despite the considerable potential of LLMs to improve educational practices at scale, the novelty of this technology means that research on its efficacy, particularly in promoting specific educational strategies such as reflection techniques, remains sparse [35].”
From Supporting Self-Reflection at Scale with Large Language Models: Insights from Randomized Field Experiments in Classrooms (2024) · cites this paper as [35]
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- Authors
- Murugesan, S., & Cherukuri, A. K.
- Affiliations
- BRITE Professional Services, Sydney, NSW, Australia
- Vellore Institute of Technology University (IN)
- Venue
- Computer
- Published
- 2023-05
- Volume
- 56
- Issue
- 5
- Pages
- 116-121
- ISSN
- 0018-9162 (ISSN-L)
- 1558-0814
- OpenAlex
- W4367059804
- Cited by
- 92
- References
- 5
- Citations by year
- 2026: 13 · 2025: 24 · 2024: 36 · 2023: 19
- FWCI
- 21.59
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- Locations
- Computer (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Information Systems › Artificial Intelligence in Education
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Social Sciences › Safety Research › Ethics and Social Impacts of AI
- Keywords
- generative AI, ChatGPT, large language models, artificial intelligence in education, educational technology, benefits and risks of AI, AI ethics
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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.
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