Evaluation indicator system for AI certificate programs.
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| Title: | Evaluation indicator system for AI certificate programs. |
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| 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] |
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| Database: | Education Research Complete |
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