AI-Powered Innovations in Mathematics Teaching & Learning: Initial Findings
dc.contributor.author | Sierra Noakes | |
dc.contributor.author | Alison Shell | |
dc.contributor.author | Parker Van Nostrand | |
dc.contributor.author | Alexis M. Murillo | |
dc.contributor.author | Pati Ruiz | |
dc.contributor.author | Babe Liberman | |
dc.date.accessioned | 2024-09-26T19:22:38Z | |
dc.date.available | 2024-09-26T19:22:38Z | |
dc.date.issued | 2024-09 | |
dc.description.abstract | This report discusses findings based on responses to a request for information (RFI) led by the Bill & Melinda Gates Foundation and Digital Promise, which received nearly 200 responses that described a variety of innovative approaches to leveraging artificial intelligence (AI) for mathematics teaching and learning. As AI becomes increasingly prevalent in education, three key questions often drive conversations around this emerging technology: What does AI in education look like today; what are the risks to leveraging AI in education and how might those risks be mitigated; and, what should AI’s role be in education? The report shares findings to support both education leaders with decisions about AI as well as providers in learning more about market saturation and strategies to mitigate risks. | |
dc.description.sponsorship | Bill & Melinda Gates Foundation | |
dc.identifier.doi | https://doi.org/10.51388/20.500.12265/229 | |
dc.identifier.uri | https://hdl.handle.net/20.500.12265/229 | |
dc.language.iso | en | |
dc.publisher | Digital Promise | |
dc.subject | AI | |
dc.subject | algorithmic bias | |
dc.subject | ethical | |
dc.subject | equitable | |
dc.subject | data privacy Funding/sponsor: Bill & Melinda Gates Foundation | |
dc.title | AI-Powered Innovations in Mathematics Teaching & Learning: Initial Findings | |
dc.type | Technical Report |
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