Evaluation indicator system for AI certificate programs.
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| Title: | Evaluation indicator system for AI certificate programs. |
|---|---|
| Authors: | Wu, Zijing1 (AUTHOR) wuzijing1@gzgs.edu.cn, Li, Qiang1 (AUTHOR) liqiang1@gzgs.edu.cn |
| Source: | International Journal of Educational Technology in Higher Education. 6/22/2026, Vol. 23 Issue 1, p1-26. 26p. |
| Subject Terms: | *Educational programs, *Curriculum planning, *College teachers, *Pedagogical content knowledge, Cultural adaptation, Analytic hierarchy process |
| Abstract: | Despite the rapid proliferation of Artificial Intelligence (AI)certificate programs in higher education, systematic frameworks for evaluating their pedagogical transformation potential remain absent—in contrast to the rich literature on AI tool adoption, which seldom addresses how credentials reshape teaching at the program level—and existing assessments rely on unidimensional methods that overlook both expert judgment and cultural variation. Grounded in the Technological Pedagogical Content Knowledge (TPACK) framework, we employ a hybrid Analytic Hierarchy Process (AHP) and Fuzzy AHP (FAHP) methodology, supplemented by Monte Carlo (MC) simulation (N = 10,000) for robustness verification, drawing on structured pairwise ratings and fuzzy assessments from 18 domain experts across Chinese universities. We identify and prioritize five dimensions—curriculum design, instructional implementation, faculty expertise, technological support, and cross-cultural adaptability—and quantify their relative weights and perceived performance. We find a clear hierarchy in expert priorities: student AI competency achievement (weight = 0.1438, rank stability = 99.8%) and curriculum alignment with AI frontiers (weight = 0.1056, rank stability = 98.5%) emerge as the paramount strategic levers, whereas cross-cultural adaptability receives the lowest weight (0.0735), signaling a technology-first bias in early-stage credential development. Strikingly, faculty professional competence exhibits the largest gap between its perceived importance (weight = 0.1976) and current satisfaction (score = 3.2171)—a finding that challenges the widespread assumption that technological infrastructure is the primary barrier to AI education. By revealing a developmental asymmetry in which content knowledge (CK) outweighs integrated pedagogical capacity, these findings extend TPACK theory into the credential evaluation domain and offer a robust, expert-informed strategic roadmap with explicit guidance for international adaptation. [ABSTRACT FROM AUTHOR] |
| Copyright of International Journal of Educational Technology in Higher Education is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Education Research Complete |
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| Header | DbId: ehh DbLabel: Education Research Complete An: 194723099 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Evaluation indicator system for AI certificate programs. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Wu%2C+Zijing%22">Wu, Zijing</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> wuzijing1@gzgs.edu.cn</i><br /><searchLink fieldCode="AR" term="%22Li%2C+Qiang%22">Li, Qiang</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> liqiang1@gzgs.edu.cn</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22International+Journal+of+Educational+Technology+in+Higher+Education%22">International Journal of Educational Technology in Higher Education</searchLink>. 6/22/2026, Vol. 23 Issue 1, p1-26. 26p. – Name: Subject Label: Subject Terms Group: Su Data: *<searchLink fieldCode="DE" term="%22Educational+programs%22">Educational programs</searchLink><br />*<searchLink fieldCode="DE" term="%22Curriculum+planning%22">Curriculum planning</searchLink><br />*<searchLink fieldCode="DE" term="%22College+teachers%22">College teachers</searchLink><br />*<searchLink fieldCode="DE" term="%22Pedagogical+content+knowledge%22">Pedagogical content knowledge</searchLink><br /><searchLink fieldCode="DE" term="%22Cultural+adaptation%22">Cultural adaptation</searchLink><br /><searchLink fieldCode="DE" term="%22Analytic+hierarchy+process%22">Analytic hierarchy process</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Despite the rapid proliferation of Artificial Intelligence (AI)certificate programs in higher education, systematic frameworks for evaluating their pedagogical transformation potential remain absent—in contrast to the rich literature on AI tool adoption, which seldom addresses how credentials reshape teaching at the program level—and existing assessments rely on unidimensional methods that overlook both expert judgment and cultural variation. Grounded in the Technological Pedagogical Content Knowledge (TPACK) framework, we employ a hybrid Analytic Hierarchy Process (AHP) and Fuzzy AHP (FAHP) methodology, supplemented by Monte Carlo (MC) simulation (N = 10,000) for robustness verification, drawing on structured pairwise ratings and fuzzy assessments from 18 domain experts across Chinese universities. We identify and prioritize five dimensions—curriculum design, instructional implementation, faculty expertise, technological support, and cross-cultural adaptability—and quantify their relative weights and perceived performance. We find a clear hierarchy in expert priorities: student AI competency achievement (weight = 0.1438, rank stability = 99.8%) and curriculum alignment with AI frontiers (weight = 0.1056, rank stability = 98.5%) emerge as the paramount strategic levers, whereas cross-cultural adaptability receives the lowest weight (0.0735), signaling a technology-first bias in early-stage credential development. Strikingly, faculty professional competence exhibits the largest gap between its perceived importance (weight = 0.1976) and current satisfaction (score = 3.2171)—a finding that challenges the widespread assumption that technological infrastructure is the primary barrier to AI education. By revealing a developmental asymmetry in which content knowledge (CK) outweighs integrated pedagogical capacity, these findings extend TPACK theory into the credential evaluation domain and offer a robust, expert-informed strategic roadmap with explicit guidance for international adaptation. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of International Journal of Educational Technology in Higher Education is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1186/s41239-026-00604-x Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 26 StartPage: 1 Subjects: – SubjectFull: Educational programs Type: general – SubjectFull: Curriculum planning Type: general – SubjectFull: College teachers Type: general – SubjectFull: Pedagogical content knowledge Type: general – SubjectFull: Cultural adaptation Type: general – SubjectFull: Analytic hierarchy process Type: general Titles: – TitleFull: Evaluation indicator system for AI certificate programs. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Wu, Zijing – PersonEntity: Name: NameFull: Li, Qiang IsPartOfRelationships: – BibEntity: Dates: – D: 22 M: 06 Text: 6/22/2026 Type: published Y: 2026 Identifiers: – Type: issn-print Value: 23659440 Numbering: – Type: volume Value: 23 – Type: issue Value: 1 Titles: – TitleFull: International Journal of Educational Technology in Higher Education Type: main |
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