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ERIC Number: EJ1406264
Record Type: Journal
Publication Date: 2024
Pages: 9
Abstractor: As Provided
ISBN: N/A
ISSN: ISSN-8756-3894
EISSN: EISSN-1559-7075
Generative Artificial Intelligence in Education and Its Implications for Assessment
Jin Mao; Baiyun Chen; Juhong Christie Liu
TechTrends: Linking Research and Practice to Improve Learning, v68 n1 p58-66 2024
The abrupt emergence and rapid advancement of generative artificial intelligence (AI) technologies, transitioning from research labs to potentially all aspects of social life, has brought a profound impact on education, science, arts, journalism, and every facet of human life and communication. The purpose of this paper is to recapitulate the use of AI in education and examine potential opportunities and challenges of employing generative AI for educational assessment, with systems thinking in mind. Following a review of the opportunities and challenges, we discuss key issues and dilemmas associated with using generative AI for assessment and for education in general. We hope that the opportunities, challenges, and issues discussed in this paper could serve as a foundation for educators to harness the power of AI within the digital learning ecosystem.
Springer. Available from: Springer Nature. One New York Plaza, Suite 4600, New York, NY 10004. Tel: 800-777-4643; Tel: 212-460-1500; Fax: 212-460-1700; e-mail: customerservice@springernature.com; Web site: https://link.springer.com/
Publication Type: Journal Articles; Reports - Descriptive
Education Level: N/A
Audience: N/A
Language: English
Sponsor: N/A
Authoring Institution: N/A
Grant or Contract Numbers: N/A