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ERIC Number: EJ1471842
Record Type: Journal
Publication Date: 2025
Pages: 30
Abstractor: As Provided
ISBN: N/A
ISSN: ISSN-1059-7069
EISSN: EISSN-1943-5924
Available Date: 0000-00-00
Supporting Teachers in Integrating Machine Learning into Science Instruction
Christine Wusylko; Pavlo Antonenko; Brian Abramowitz; Jeremy Waisome; Victor Perez; Stephanie Killingsworth; Bruce MacFadden
Journal of Technology and Teacher Education, v33 n1 p213-242 2025
As artificial intelligence (AI) is rapidly being adopted and used in society, it is imperative that teachers feel supported to integrate AI and computer science (CS) into their coursework. To help support teachers to integrate CS and AI into their instruction, we designed and developed an innovative AI curriculum, Shark AI, for in-service science teachers accompanied with a robust professional development and learning community. Shark AI is designed for middle school students grades 6-8, and the curriculum blends CS and paleontology as young people are guided in building and evaluating their own machine learning models they use to classify fossil shark teeth. In this paper, we describe the curriculum model, teacher (mis)conceptions about AI, teacher's perceived self-efficacy and attitudes regarding the curriculum material and on teaching STEM, and student outcomes from the project that are the ultimate reflection of the curriculum design and implementation.
Association for the Advancement of Computing in Education. P.O. Box 719, Waynesville, NC 28786. Tel: 828-246-9558; Fax: 828-246-9557; e-mail: info@aace.org; Web site: http://www.aace.org
Publication Type: Journal Articles; Reports - Research
Education Level: Junior High Schools; Middle Schools; Secondary Education
Audience: N/A
Language: English
Authoring Institution: N/A
Identifiers - Location: Florida
Grant or Contract Numbers: 2147625
Author Affiliations: N/A