NEPC Review: 'Measuring Artificial Intelligence in Education' (Bellwether, October 2025)
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| Title: | NEPC Review: 'Measuring Artificial Intelligence in Education' (Bellwether, October 2025) |
|---|---|
| Language: | English |
| Authors: | Bradley Robinson, University of Colorado at Boulder, National Education Policy Center (NEPC) |
| Source: | National Education Policy Center. 2025. |
| Availability: | National Education Policy Center. School of Education 249 UCB University of Colorado, Boulder, CO 80309. Tel: 303-735-5290; e-mail: nepc@colorado.edu; Web site: http://nepc.colorado.edu |
| Peer Reviewed: | Y |
| Page Count: | 13 |
| Publication Date: | 2025 |
| Sponsoring Agency: | Great Lakes Center for Education Research and Practice |
| Intended Audience: | Policymakers |
| Document Type: | Reports - Evaluative Opinion Papers |
| Education Level: | Elementary Secondary Education |
| Descriptors: | Artificial Intelligence, Logical Thinking, Influence of Technology, Elementary Secondary Education, Measurement, Evaluation Methods, Models, Research Methodology |
| Abstract: | Because of the rapid proliferation of AI-powered technologies in education, educators and others face urgent challenges around evaluating the potential impacts of these technologies on teaching and learning. Bellwether's October 2025 report, "Measuring Artificial Intelligence in Education," aims to address such challenges by promoting logic models as a framework for moving beyond superficial metrics toward more robust evidence-based educational outcomes. Logic models involve determining four main components--inputs, activities, outputs, and outcomes--and they have a long history of use in program evaluation. The logic-model approach, however, has very real limitations that are not fully addressed in the report. Just as importantly, the report simply assumes that AI should indeed be integrated into education. That is, logic models function here as a methodological heuristic for ensuring AI fulfills its taken-for-granted potential. By positioning logic models as value-neutral, the report overlooks how such approaches ignore contextual complexity and the potential for unintended harms. Rather than offering critical guidance for assessing AI's role in education, the report provides methodological cover for predetermined conclusions about AI's inevitability and desirability. Policymakers seeking rigorous, evidence-based approaches will find little support in what is, at its core, a promotional document. |
| Abstractor: | As Provided |
| Entry Date: | 2026 |
| Accession Number: | ED681180 |
| Database: | ERIC |
| FullText | Text: Availability: 0 CustomLinks: – Url: https://eric.ed.gov/contentdelivery/servlet/ERICServlet?accno=ED681180 Name: ERIC Full Text Category: fullText Text: Full Text from ERIC |
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| Items | – Name: Title Label: Title Group: Ti Data: NEPC Review: 'Measuring Artificial Intelligence in Education' (Bellwether, October 2025) – Name: Language Label: Language Group: Lang Data: English – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Bradley+Robinson%22">Bradley Robinson</searchLink><br /><searchLink fieldCode="AR" term="%22University+of+Colorado+at+Boulder%2C+National+Education+Policy+Center+%28NEPC%29%22">University of Colorado at Boulder, National Education Policy Center (NEPC)</searchLink> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="SO" term="%22National+Education+Policy+Center%22"><i>National Education Policy Center</i></searchLink>. 2025. – Name: Avail Label: Availability Group: Avail Data: National Education Policy Center. School of Education 249 UCB University of Colorado, Boulder, CO 80309. Tel: 303-735-5290; e-mail: nepc@colorado.edu; Web site: http://nepc.colorado.edu – Name: PeerReviewed Label: Peer Reviewed Group: SrcInfo Data: Y – Name: Pages Label: Page Count Group: Src Data: 13 – Name: DatePubCY Label: Publication Date Group: Date Data: 2025 – Name: SourceSuprt Label: Sponsoring Agency Group: SrcSuprt Data: Great Lakes Center for Education Research and Practice – Name: Audience Label: Intended Audience Group: Audnce Data: Policymakers – Name: TypeDocument Label: Document Type Group: TypDoc Data: Reports - Evaluative<br />Opinion Papers – Name: Audience Label: Education Level Group: Audnce Data: <searchLink fieldCode="EL" term="%22Elementary+Secondary+Education%22">Elementary Secondary Education</searchLink> – Name: Subject Label: Descriptors Group: Su Data: <searchLink fieldCode="DE" term="%22Artificial+Intelligence%22">Artificial Intelligence</searchLink><br /><searchLink fieldCode="DE" term="%22Logical+Thinking%22">Logical Thinking</searchLink><br /><searchLink fieldCode="DE" term="%22Influence+of+Technology%22">Influence of Technology</searchLink><br /><searchLink fieldCode="DE" term="%22Elementary+Secondary+Education%22">Elementary Secondary Education</searchLink><br /><searchLink fieldCode="DE" term="%22Measurement%22">Measurement</searchLink><br /><searchLink fieldCode="DE" term="%22Evaluation+Methods%22">Evaluation Methods</searchLink><br /><searchLink fieldCode="DE" term="%22Models%22">Models</searchLink><br /><searchLink fieldCode="DE" term="%22Research+Methodology%22">Research Methodology</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Because of the rapid proliferation of AI-powered technologies in education, educators and others face urgent challenges around evaluating the potential impacts of these technologies on teaching and learning. Bellwether's October 2025 report, "Measuring Artificial Intelligence in Education," aims to address such challenges by promoting logic models as a framework for moving beyond superficial metrics toward more robust evidence-based educational outcomes. Logic models involve determining four main components--inputs, activities, outputs, and outcomes--and they have a long history of use in program evaluation. The logic-model approach, however, has very real limitations that are not fully addressed in the report. Just as importantly, the report simply assumes that AI should indeed be integrated into education. That is, logic models function here as a methodological heuristic for ensuring AI fulfills its taken-for-granted potential. By positioning logic models as value-neutral, the report overlooks how such approaches ignore contextual complexity and the potential for unintended harms. Rather than offering critical guidance for assessing AI's role in education, the report provides methodological cover for predetermined conclusions about AI's inevitability and desirability. Policymakers seeking rigorous, evidence-based approaches will find little support in what is, at its core, a promotional document. – Name: AbstractInfo Label: Abstractor Group: Ab Data: As Provided – Name: DateEntry Label: Entry Date Group: Date Data: 2026 – Name: AN Label: Accession Number Group: ID Data: ED681180 |
| PLink | https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=eric&AN=ED681180 |
| RecordInfo | BibRecord: BibEntity: Languages: – Text: English PhysicalDescription: Pagination: PageCount: 13 Subjects: – SubjectFull: Artificial Intelligence Type: general – SubjectFull: Logical Thinking Type: general – SubjectFull: Influence of Technology Type: general – SubjectFull: Elementary Secondary Education Type: general – SubjectFull: Measurement Type: general – SubjectFull: Evaluation Methods Type: general – SubjectFull: Models Type: general – SubjectFull: Research Methodology Type: general Titles: – TitleFull: NEPC Review: 'Measuring Artificial Intelligence in Education' (Bellwether, October 2025) Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: University of Colorado at Boulder, National Education Policy Center (NEPC) – PersonEntity: Name: NameFull: Bradley Robinson IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 12 Type: published Y: 2025 Titles: – TitleFull: National Education Policy Center Type: main |
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