Ethics of Artificial Intelligence in Academia
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A comprehensive understanding of the impact of disruptive AI technologies on education and the development of frameworks is critical for society to make informed decisions about the use of Generative AI tools.
“In addition, a comprehensive understanding of the impact of disruptive AI technologies on education and the development of frameworks is critical for society to make informed decisions about the use of Generative AI tools [28].”
From AI Governance in Higher Education: Case Studies of Guidance at Big Ten Universities (2024) · cites this paper as [28]
Cites
I.—COMPUTING MACHINERY AND INTELLIGENCE
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Survey of Hallucination in Natural Language Generation
Ji, Z., Lee, N., Frieske, R., Yu, T., Su, D., Xu, Y., Ishii, E., Bang, Y. J., Madotto, A., & Fung, P. (2023) · ACM Computing Surveys · doi:10.1145/3571730 · Cited by 5,025 · see citing works
VanLEHN, K. (2011) · Educational Psychologist · doi:10.1080/00461520.2011.611369 · Cited by 2,323 · see citing works
On Distinguishing Epistemic from Pragmatic Action
Kirsh, D., & Maglio, P. (1994) · Cognitive Science · doi:10.1207/s15516709cog1804_1 · Cited by 1,641 · see citing works
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AI in CAI: An Artificial-Intelligence Approach to Computer-Assisted Instruction
Carbonell, J. (1970) · IEEE Transactions on Man Machine Systems · doi:10.1109/tmms.1970.299942 · Cited by 955 · see citing works
Perkins, M. (2023) · Journal of University Teaching and Learning Practice · doi:10.53761/1.20.02.07 · Cited by 839 · see citing works
Artificial intelligence innovation in education: A twenty-year data-driven historical analysis
Guan, C., Mou, J., & Jiang, Z. (2020) · International Journal of Innovation Studies · doi:10.1016/j.ijis.2020.09.001 · Cited by 665 · see citing works
Smith, S., Patwary, M., Norick, B., LeGresley, P., Rajbhandari, S., Casper, J., Liu, Z., Prabhumoye, S., Zerveas, G., Korthikanti, V., et al. (2022) · arXiv · doi:10.48550/arxiv.2201.11990 · Cited by 286 · see citing works
Soft ethics, the governance of the digital and the General Data Protection Regulation
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Artificial intelligence for human flourishing – Beyond principles for machine learning
Stahl, B. C., Andreou, A., Brey, P., Hatzakis, T., Kirichenko, A., Macnish, K., Laulhé Shaelou, S., Patel, A., Ryan, M., & Wright, D. (2021) · Journal of Business Research · doi:10.1016/j.jbusres.2020.11.030 · Cited by 203 · see citing works
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Consequences of cognitive offloading: Boosting performance but diminishing memory
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What do the hands externalize in simple arithmetic?
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Racial, skin tone, and sex disparities in automated proctoring software
Yoder-Himes, D. R., Asif, A., Kinney, K., Brandt, T. J., Cecil, R. E., Himes, P. R., Cashon, C., Hopp, R. M. P., & Ross, E. (2022) · Frontiers in Education · doi:10.3389/feduc.2022.881449 · Cited by 49 · see citing works
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AI Governance in Higher Education: Case Studies of Guidance at Big Ten Universities
Wu, C., Zhang, H., & Carroll, J. M. (2024) · Future Internet · doi:10.3390/fi16100354 · Cited by 99 · see citing works
- Authors
- Khan, Z. R.
- Affiliations
- University of Wollongong in Dubai (AE)
- Corresponding author
- Khan, Zeenath Reza
- ORCID
- Khan, Zeenath Reza
- Venue
- Springer International Handbooks of Education
- Publisher
- Springer Nature Switzerland
- Published
- 2024
- Pages
- 1551-1582
- ISSN
- 2197-1951 (ISSN-L)
- 2197-196X
- OpenAlex
- W4391974075
- Cited by
- 19
- References
- 15
- Citations by year
- 2026: 5 · 2025: 7 · 2024: 5 · 2023: 2
- FWCI
- 10.66
- Type
- book chapter
- Language
- en
- Open access
- closed
- Licence
- Locations
- Springer international handbooks of education (publishedVersion)
- Research Online (University of Wollongong) (submittedVersion)
- Topics
- Physical Sciences › Computer Science › Information Systems › Artificial Intelligence in Education
- Social Sciences › Safety Research › Ethics and Social Impacts of AI
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Keywords
- AI ethics, generative AI, artificial intelligence in education, academic integrity, student learning experience, AI adoption framework, future readiness
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.
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