When Can AI Models Explain Learning? Validity Criteria for AI as Cognitive Models in Education
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| Title: | When Can AI Models Explain Learning? Validity Criteria for AI as Cognitive Models in Education |
|---|---|
| Language: | English |
| Authors: | Peng Wang (ORCID |
| Source: | Educational Psychology Review. 2026 38(1). |
| Availability: | 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/ |
| Peer Reviewed: | Y |
| Page Count: | 36 |
| Publication Date: | 2026 |
| Document Type: | Journal Articles Reports - Descriptive |
| Descriptors: | Artificial Intelligence, Technology Uses in Education, Models, Cognitive Processes, Construct Validity, Accountability, Learning Trajectories, Criteria, Natural Language Processing |
| DOI: | 10.1007/s10648-026-10202-y |
| ISSN: | 1040-726X 1573-336X |
| Abstract: | Traditional verbal theories in educational psychology often remain underspecified at the mechanistic level. While they offer rich descriptive constructs and conceptual insights, they provide limited accounts of how learning processes unfold dynamically and causally. Here, "verbal" denotes theories stated in prose and qualitative relations rather than as formal, computational process models. This lack of mechanistic precision constrains rigorous theory testing, limits integration across levels of analysis, and reduces the potential to design interventions grounded in explanatory understanding of learning processes. In this Review, we synthesize an emerging paradigm that treats artificial intelligence (AI) systems not merely as predictive tools or instructional technologies, but as cognitive models of learners, explicit, runnable instantiations of theoretical assumptions about cognition and learning. Our central question is not whether AI systems can serve as cognitive models, but when they should be allowed to count as such. We therefore organize the Review around explicit validity criteria, theoretical grounding, construct validity, mechanistic transparency, alignment with human learning trajectories, error-signature matching, causal-intervention tests, ecological validity, and instructional usefulness, that an AI system must satisfy before its cognitive-model status is granted rather than assumed. We examine how major families of AI models, including neural networks, reinforcement learning agents, cognitive architectures, and large language models, have been used to operationalize core educational constructs such as memory, strategy use, motivation, self-regulation, and social learning. Across these approaches, we highlight how mechanistic transparency, interpretability, and alignment with human learning trajectories and error patterns are essential for explanatory validity. We further discuss methodological tools, such as representation analysis, ablation, and trajectory-level comparison, that enable causal inference about learning mechanisms within models. Finally, we outline key challenges and future directions, including construct validity, ecological realism, individual differences, and ethical accountability. By positioning AI as a theoretical instrument rather than solely an engineering solution, this Review argues that AI-based cognitive models can, when they satisfy these criteria, help transform abstract learning theories into precise, testable, and educationally actionable accounts of how students learn. |
| Abstractor: | As Provided |
| Entry Date: | 20260101 |
| Accession Number: | EJ1516889 |
| Database: | ERIC |
| FullText | Text: Availability: 0 |
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| Header | DbId: eric DbLabel: ERIC An: EJ1516889 AccessLevel: 3 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: When Can AI Models Explain Learning? Validity Criteria for AI as Cognitive Models in Education – Name: Language Label: Language Group: Lang Data: English – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Peng+Wang%22">Peng Wang</searchLink> (ORCID <externalLink term="http://orcid.org/0000-0002-3475-1561">0000-0002-3475-1561</externalLink>)<br /><searchLink fieldCode="AR" term="%22Olga+Viberg%22">Olga Viberg</searchLink><br /><searchLink fieldCode="AR" term="%22Effie+Lai-Chong+Law%22">Effie Lai-Chong Law</searchLink><br /><searchLink fieldCode="AR" term="%22Samuel+Greiff%22">Samuel Greiff</searchLink> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="SO" term="%22Educational+Psychology+Review%22"><i>Educational Psychology Review</i></searchLink>. 2026 38(1). – Name: Avail Label: Availability Group: Avail Data: 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/ – Name: PeerReviewed Label: Peer Reviewed Group: SrcInfo Data: Y – Name: Pages Label: Page Count Group: Src Data: 36 – Name: DatePubCY Label: Publication Date Group: Date Data: 2026 – Name: TypeDocument Label: Document Type Group: TypDoc Data: Journal Articles<br />Reports - Descriptive – Name: Subject Label: Descriptors Group: Su Data: <searchLink fieldCode="DE" term="%22Artificial+Intelligence%22">Artificial Intelligence</searchLink><br /><searchLink fieldCode="DE" term="%22Technology+Uses+in+Education%22">Technology Uses in Education</searchLink><br /><searchLink fieldCode="DE" term="%22Models%22">Models</searchLink><br /><searchLink fieldCode="DE" term="%22Cognitive+Processes%22">Cognitive Processes</searchLink><br /><searchLink fieldCode="DE" term="%22Construct+Validity%22">Construct Validity</searchLink><br /><searchLink fieldCode="DE" term="%22Accountability%22">Accountability</searchLink><br /><searchLink fieldCode="DE" term="%22Learning+Trajectories%22">Learning Trajectories</searchLink><br /><searchLink fieldCode="DE" term="%22Criteria%22">Criteria</searchLink><br /><searchLink fieldCode="DE" term="%22Natural+Language+Processing%22">Natural Language Processing</searchLink> – Name: DOI Label: DOI Group: ID Data: 10.1007/s10648-026-10202-y – Name: ISSN Label: ISSN Group: ISSN Data: 1040-726X<br />1573-336X – Name: Abstract Label: Abstract Group: Ab Data: Traditional verbal theories in educational psychology often remain underspecified at the mechanistic level. While they offer rich descriptive constructs and conceptual insights, they provide limited accounts of how learning processes unfold dynamically and causally. Here, "verbal" denotes theories stated in prose and qualitative relations rather than as formal, computational process models. This lack of mechanistic precision constrains rigorous theory testing, limits integration across levels of analysis, and reduces the potential to design interventions grounded in explanatory understanding of learning processes. In this Review, we synthesize an emerging paradigm that treats artificial intelligence (AI) systems not merely as predictive tools or instructional technologies, but as cognitive models of learners, explicit, runnable instantiations of theoretical assumptions about cognition and learning. Our central question is not whether AI systems can serve as cognitive models, but when they should be allowed to count as such. We therefore organize the Review around explicit validity criteria, theoretical grounding, construct validity, mechanistic transparency, alignment with human learning trajectories, error-signature matching, causal-intervention tests, ecological validity, and instructional usefulness, that an AI system must satisfy before its cognitive-model status is granted rather than assumed. We examine how major families of AI models, including neural networks, reinforcement learning agents, cognitive architectures, and large language models, have been used to operationalize core educational constructs such as memory, strategy use, motivation, self-regulation, and social learning. Across these approaches, we highlight how mechanistic transparency, interpretability, and alignment with human learning trajectories and error patterns are essential for explanatory validity. We further discuss methodological tools, such as representation analysis, ablation, and trajectory-level comparison, that enable causal inference about learning mechanisms within models. Finally, we outline key challenges and future directions, including construct validity, ecological realism, individual differences, and ethical accountability. By positioning AI as a theoretical instrument rather than solely an engineering solution, this Review argues that AI-based cognitive models can, when they satisfy these criteria, help transform abstract learning theories into precise, testable, and educationally actionable accounts of how students learn. – Name: AbstractInfo Label: Abstractor Group: Ab Data: As Provided – Name: DateEntry Label: Entry Date Group: Date Data: 20260101 – Name: AN Label: Accession Number Group: ID Data: EJ1516889 |
| PLink | https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=eric&AN=EJ1516889 |
| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1007/s10648-026-10202-y Languages: – Text: English PhysicalDescription: Pagination: PageCount: 36 Subjects: – SubjectFull: Artificial Intelligence Type: general – SubjectFull: Technology Uses in Education Type: general – SubjectFull: Models Type: general – SubjectFull: Cognitive Processes Type: general – SubjectFull: Construct Validity Type: general – SubjectFull: Accountability Type: general – SubjectFull: Learning Trajectories Type: general – SubjectFull: Criteria Type: general – SubjectFull: Natural Language Processing Type: general Titles: – TitleFull: When Can AI Models Explain Learning? Validity Criteria for AI as Cognitive Models in Education Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Peng Wang – PersonEntity: Name: NameFull: Olga Viberg – PersonEntity: Name: NameFull: Effie Lai-Chong Law – PersonEntity: Name: NameFull: Samuel Greiff IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 12 Type: published Y: 2026 Identifiers: – Type: issn-print Value: 1040-726X – Type: issn-electronic Value: 1573-336X Numbering: – Type: volume Value: 38 – Type: issue Value: 1 Titles: – TitleFull: Educational Psychology Review Type: main |
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