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Bibliometric Analysis of AI and Cultural Literacy Integration in Mathematics Education: Systematic Literature Review Dea Ananda Dewi Larasati; Fitria Sulistyowati; Betty Kusumaningrum; Devi Septiani; Zahra Nugraheni; Patrick Bergman
Jurnal Pendidikan Matematika IKIP Veteran Semarang Vol 10 No 2 (2026): Journal of Medives: Journal of Mathematics Education IKIP Veteran Semarang
Publisher : Urogram Studi Pendidikan Matematika, Universitas Ivet

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31331/medivesveteran.v10i2.4404

Abstract

Research on the integration of Artificial Intelligence (AI) in mathematics education is rapidly developing, but still leaves a significant gap related to the neglect of the cultural dimension, with current discussions tending to focus solely on cognitive and technical aspects. This research gap is crucial because AI algorithms often operate within a logic of technology neutrality that ignores students' sociocultural backgrounds, risking academic alienation and underrepresentation in mathematical problem-solving. This study aims to identify the distribution of scientific publications linking AI and cultural literacy in mathematics and to analyze their thematic development and collaboration patterns. Using a Systematic Literature Review (SLR) approach enhanced by bibliometric analysis, data from 80 Scopus articles (2020–2025) were processed using the PRISMA framework and the external tool Biblioshiny. The findings reveal a sharp surge in publications in 2025, marking a shift toward generative AI. However, cultural literacy remains a niche theme rarely integrated into mainstream frameworks. This study concludes that despite the well-established literature, the lack of integration of cultural literacy as a mechanism for Self-Regulated Learning (SRL) hinders the inclusive and ethical implementation of AI. The implication of this research is the need for practitioners and policymakers to shift from focusing on technological sophistication to designing metacognitive frameworks that respect students' cultural contexts to ensure pedagogical sustainability in AI-based mathematics learning. Future research should focus on developing self-regulated learning (SRL) models that explicitly integrate local wisdom values into AI instructional design.
Google Sites-based statistics learning: Integrating Tri-N ethnopedagogy and cognitive load theory Istiqomah Istiqomah; Betty Kusumaningrum; Tiara Pramudianti; Sri Adi Widodo; Denik Agustito; Trisniawati Trisniawati; Hakeem Nafiu
Bulletin of Educational Management and Innovation Vol. 4 No. 1 (2026): Bulletin of Educational Management and Innovation
Publisher : Rafandha Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56587/bemi.v4i1.165

Abstract

Background: Google Sites is commonly used as a static repository for learning materials, limiting its potential to foster meaningful cognitive engagement in mathematics education. Moreover, few studies have integrated Ki Hadjar Dewantara's Tri-N philosophy (Niteni, Nirokke, Nambahi) with Cognitive Load Theory (CLT) to design culturally responsive and cognitively structured e-learning, particularly for junior high school statistics. Purpose: This study aimed to develop and validate a Google Sites-based e-learning platform grounded in Tri-N philosophy and CLT for Grade VIII statistics learning. Method: This Research and Development (R&D) study employed a modified 3D development model adapted from Thiagarajan's 4D model, consisting of the Define, Design, and Develop stages. Participants included 32 Grade VIII students and one mathematics teacher. Data were gathered through observations, interviews, expert validation questionnaires, and student response questionnaires. Specific instruments were used to measure CLT aspects (e.g., intrinsic, extraneous, and germane cognitive load items) and Tri-N integration (e.g., items assessing niteni–observation, nirokke–imitation, and nambahi–creation activities). Data were analyzed using descriptive qualitative and quantitative techniques. Findings: The developed e-learning product showed high validity and practicality. Content expert validation yielded a mean score of 4.05 (Valid), and media expert validation reached 4.44 (Highly Valid). Student practicality assessments indicated Practical levels, with overall means of 3.61 (limited trial) and 3.93 (large-scale trial). These findings suggest that integrating Tri-N with CLT provides a culturally responsive and cognitively optimized framework that enhances meaningful digital learning. This approach offers practical implications for designing local wisdom-based e-learning and warrants further research on its effectiveness in broader educational contexts and longer implementation periods