The DOI matches its Crossref record; 26 of the 26 references it cites were found in a registry or an index.
A Primer on Generative Artificial Intelligence
Open accessAbstract
Many educators and professionals in different industries may need to become more familiar with the basic concepts of artificial intelligence (AI) and generative artificial intelligence (Gen-AI). Therefore, this paper aims to introduce some of the basic concepts of AI and Gen-AI. The approach of this explanatory paper is first to introduce some of the underlying concepts, such as artificial intelligence, machine learning, deep learning, artificial neural networks, and large language models (LLMs), that would allow the reader to better understand generative AI. The paper also discusses some of the applications and implications of generative AI on businesses and education, followed by the current challenges associated with generative AI.
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Cites
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I.—COMPUTING MACHINERY AND INTELLIGENCE
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Examining Science Education in ChatGPT: An Exploratory Study of Generative Artificial Intelligence
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BackgroundGenerative AI tools and assessment: Guidelines of the world's top-ranking universities
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Reconciling deep learning with symbolic artificial intelligence: representing objects and relations
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Best humans still outperform artificial intelligence in a creative divergent thinking task
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Artificial Intelligence as an Effective Classroom Assistant
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AI: New source of competitiveness in higher education
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Investigating Students’ Perceptions towards Artificial Intelligence in Medical Education
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How Generative AI Can Augment Human Creativity
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Generative Artificial Intelligence in Education, Part One: the Dynamic Frontier
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“It has also been known to support the assessment of student work and to recommend improvements to teaching strategies [50].”
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Integrating AI in medical education: embracing ethical usage and critical understanding
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AI-based Academic Advising Framework: A Knowledge Management Perspective
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Cited by
Generative AI for Consumer Behavior Prediction: Techniques and Applications
Madanchian, M. (2024) · Sustainability · doi:10.3390/su16229963 · Cited by 60
Marzano, D. (2026) · Technology, Knowledge and Learning · doi:10.1007/s10758-025-09853-7 · Cited by 51
Patel, K., Shah, M., Qureshi, K. M., & Qureshi, M. R. N. (2025) · Artificial Intelligence Review · doi:10.1007/s10462-025-11435-z · Cited by 42
Effects of GenAI Interventions on Student Academic Performance: A Meta-Analysis
Gu, J., & Yan, Z. (2025) · Journal of Educational Computing Research · doi:10.1177/07356331251349620 · Cited by 30
Zhou, C., & Wang, Y. (2026) · European Journal of Education · doi:10.1111/ejed.70478 · Cited by 29
Metadata
- Authors
- Kalota, F.
- Venue
- Education Sciences
- Publisher
- MDPI AG
- Published
- 2024-02-07
- Volume
- 14
- Issue
- 2
- Pages
- 172
- ISSN
- 2227-7102 (ISSN-L)
- OpenAlex
- W4391615642
- Cited by
- 177
- References
- 26
- FWCI
- 26.34
- Type
- journal article
- Language
- en
- Open access
- gold
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
