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Proceeding of the International Conference on Global Education and Learning
Published by Asosiasi Riset Ilmu Pendidikan Indonesia
ISSN : -     EISSN : 30898072     DOI : 10.62951
Core Subject : Education,
Education
Proceeding of the International Conference on Global Education and Learning, Its a collection of papers or scientific articles that have been presented at the National Research Conference which is held regularly every two years by the Asosiasi Riset Ilmu Pendidikan Indonesia. The paper topics published in the Proceeding of the International Conference on Global Education and Learning the sub-groups of Social Science Education, Language and Literature, Sports and Health, Mathematics and MIPA, Technology and Vocational Studies, Education and Arts, and other relevant fields and published twice a year (June and December).
Arjuna Subject : Ilmu Sosial - Pendidikan
Articles 81 Documents
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Analysis of Training Design Needs for Accounting Vocational Teachers Using Project Based Learning Approach to Achieve Deep Learning: A Systematic Literature Review Indah Murtini; Eveline Siregar; Robinson Situmorang
Proceeding of the International Conference on Global Education and Learning Vol. 2 No. 2 (2025): December : Proceeding of the International Conference on Global Education and L
Publisher : Asosiasi Riset Ilmu Pendidikan Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62951/icgel.v2i2.173

Abstract

This study analyzes the need for training design for accounting vocational teachers using Project Based Learning (PjBL) approach to achieve deep learning through systematic literature review. The gap in teachers' competence in implementing PjBL and the limitations of relevant training designs are major problems in vocational education. This research aims to identify trends, gaps, and needs for developing PjBL-based accounting teacher training designs oriented towards achieving deep learning. The systematic literature review method was employed by analyzing 25 articles from Scopus, ScienceDirect, and Google Scholar databases from 2018-2025. The research identified 25 unique articles categorized into three main themes: Deep Learning perspectives (10 articles), Vocational Teacher Training and Development (6 articles), and Project-Based Learning implementation (9 articles). The results show four main trends: (1) increased global adoption of PjBL as an innovative learning method, (2) demands from accounting professional organizations for active learning methods, (3) popularity of deep learning concepts in contemporary educational literature integrated with 21st-century skills, and (4) urgent need for comprehensive teacher training designs. Significant gaps were identified in the limitations of relevant training designs for vocational contexts and lack of systematic training development research. This study concludes that accounting vocational teacher training designs with PjBL approach are urgently needed to bridge the gap between theoretical competence and practical industry applications, with emphasis on achieving holistic deep learning encompassing cognitive, interpersonal, and intrapersonal domains.
Classroom Social Dynamics in Learning Measurement: Evidence from Contextual Learning Situations Hirpan Hirpan; Hamzah Upu; Syafruddin Side; Muhammad Darwis
Proceeding of the International Conference on Global Education and Learning Vol. 2 No. 2 (2025): December : Proceeding of the International Conference on Global Education and L
Publisher : Asosiasi Riset Ilmu Pendidikan Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62951/icgel.v2i2.180

Abstract

Measurement is a fundamental domain of mathematics that connects formal mathematical concepts with everyday experiences. Despite its practical relevance, students often experience persistent difficulties in understanding measurement conceptually, tending to approach it as a procedural activity rather than as a process of reasoning about quantities, units, and comparisons. These challenges indicate that learning measurement is influenced not only by individual cognitive factors but also by the social dynamics that shape classroom learning environments. This study aims to examine classroom social dynamics in learning measurement by providing empirical evidence from contextual learning situations. This study employed a qualitative research approach to explore how social interaction, scaffolding, and participation mediate students’ understanding of measurement in contextual learning environments. Data were collected through classroom observations, video recordings of learning activities, analysis of students’ written work, and interviews with selected students and the teacher. Contextual measurement tasks were designed to encourage collaboration, dialogue, and justification, enabling the examination of student–student and teacher–student interactions as they naturally occurred in the classroom. Data analysis was conducted iteratively to identify patterns of interaction, forms of scaffolding, and students’ learning progression within the Zone of Proximal Development. The findings reveal that students initially engaged with measurement tasks in a predominantly procedural manner, with limited conceptual understanding and minimal peer interaction. After the implementation of contextual learning situations, classroom social dynamics changed substantially. Students became more actively involved in discussion, collaborative problem-solving, and collective meaning-making. Peer interaction supported the articulation and refinement of students’ reasoning, while teacher scaffolding guided learning by extending students’ thinking without providing direct solutions. These social processes facilitated students’ movement from their actual level of understanding toward higher levels of conceptual competence within the Zone of Proximal Development. The study further shows that contextual learning tasks alone are insufficient to promote meaningful understanding unless they are supported by productive social interaction and adaptive scaffolding. Conceptual understanding of measurement emerged through socially mediated processes rather than through task completion alone. This study contributes to mathematics education research by emphasizing the central role of classroom social dynamics in contextual learning and by offering insights into how interaction and scaffolding can be orchestrated to support students’ conceptual understanding of measurement.
AI-Driven Proactive Monitoring: Mitigating Agency Costs and Financial Risk Raden Agrosamdhyo
Proceeding of the International Conference on Global Education and Learning Vol. 2 No. 2 (2025): December : Proceeding of the International Conference on Global Education and L
Publisher : Asosiasi Riset Ilmu Pendidikan Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62951/icgel.v2i2.184

Abstract

Background: In the domain of corporate governance, the separation of ownership and control generates significant agency conflicts, primarily manifesting as Earnings Management (EM). Traditional reactive auditing methods fail to detect manipulation concealed within unstructured data, leading to high agency costs and diminished stakeholder trust. Objective: This study proposes an "AI Proactive Monitoring Model" utilizing Generative Artificial Intelligence to fundamentally enhance the monitoring mechanisms of Agency Theory. Methods: The research employs a qualitative conceptual framework analysis. It synthesizes Agency Theory with the Technology Acceptance Model (TAM) and Systemic Risk Theory to construct a novel strategic governance model. Results: The proposed model shifts governance from periodic sampling to real-time, continuous analysis of total data populations. By cross-referencing structured financial data with unstructured communications (e.g., emails, contracts), the system generates "Risk Narratives" that contextualize anomalies and flag opportunistic behavior immediately. Conclusion: The integration of AI significantly reduces information asymmetry and moral hazard by creating a "panopticon" effect. However, successful implementation requires distinct regulatory frameworks to manage the systemic risks associated with algorithmic reliance.
Effectiveness of Using AI Perplexity in Designing Science Learning Materials Abidah Khoirunnisa Nur; Mukharomah Umi; Febriana Dhista Sela
Proceeding of the International Conference on Global Education and Learning Vol. 2 No. 2 (2025): December : Proceeding of the International Conference on Global Education and L
Publisher : Asosiasi Riset Ilmu Pendidikan Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62951/icgel.v2i2.185

Abstract

The rapid development of Artificial Intelligence (AI) has opened new opportunities for teachers in designing instructional materials. This study aims to describe the effectiveness of using Perplexity AI in designing Natural and Social Science Knowledge (IPAS) materials in elementary schools. Using a quantitative descriptive approach, data was collected through questionnaires from three elementary school teachers as a preliminary study (pilot study). The indicators measured included ease of use, time efficiency, material quality, and usefulness. The research results indicate that Perplexity AI is highly effective, with an overall average score of 4.29, falling into the "Good" category. These findings demonstrate that Perplexity AI significantly supports teachers in organizing systematic lesson plans and improving teaching material preparation. This research provides a foundation for the broader implementation of AI tools in the education sector, suggesting that AI can be an effective aid in the development of educational content, ultimately enhancing the teaching and learning experience in schools.
The Effect of AI-Based Deep Learning on Narrative Writing Skills for Elementary School Students Eka Sopiya Khoirul Muna; Nur Khotimah; Thoif Pujiwati; Atrianing Yessi Wijayanti
Proceeding of the International Conference on Global Education and Learning Vol. 2 No. 2 (2025): December : Proceeding of the International Conference on Global Education and L
Publisher : Asosiasi Riset Ilmu Pendidikan Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62951/icgel.v2i2.186

Abstract

Narrative writing skills are crucial for the literacy development of elementary school students. However, many students face challenges in writing narrative essays due to limited vocabulary, grammar understanding, and imagination. Traditional teaching methods often fail to address these issues. This study explores the impact of AI-based Deep Learning on narrative writing skills. Using a quantitative approach with an experimental design, the research employed a pretest-posttest design with a nonrandomized control group. The study involved 65 students from SD 4 Sidomulyo, including 30 students from class IV A and 35 from class IV B. A nonprobability sampling technique with saturated sampling was used. Data was collected through tests, and content validity was applied for validation. Hypothesis testing with the Independent Samples t-test yielded a significance value of 0.001 (p < 0.05) and a calculated t-value of 3.472, which is greater than the t-table value of 2.0024. These results indicate that AI-based Deep Learning significantly affects narrative writing skills in elementary school students. Keywords: AI-Based Deep Learning, Narrative Writing Skills.
Preservice Teachers’ Perceptions of AI-Powered Adaptive Learning Models Nida Ramadhani; Widyadhana Syahada; Rizquna Fadillah; Puji Winarti
Proceeding of the International Conference on Global Education and Learning Vol. 2 No. 2 (2025): December : Proceeding of the International Conference on Global Education and L
Publisher : Asosiasi Riset Ilmu Pendidikan Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62951/icgel.v2i2.187

Abstract

The integration of Artificial Intelligence (AI) in higher education has led to the increasing use of AI-powered adaptive learning models that support personalized and data-driven learning. However, studies examining preservice teachers’ perceptions of these models remain limited, despite their important role in future classroom implementation. This study aims to explore preservice teachers’ perceptions of AI-powered adaptive learning in higher education, focusing on perceived usefulness, learning adaptivity, learning experience, and perceived concerns. A descriptive qualitative research design was employed involving 53 preservice teachers from various universities. Data were collected using a Likert-scale questionnaire and open-ended questions. Quantitative data were analyzed descriptively using percentage distributions, while qualitative data were examined through simple thematic analysis. The findings reveal that preservice teachers generally demonstrate positive perceptions of AIpowered adaptive learning, particularly in terms of learning effectiveness, adaptability, and engagement. Nevertheless, concerns related to over-reliance on AI, ethical issues, and data privacy were also identified. These results indicate that preservice teachers show readiness to engage with AI-supported learning, while highlighting the need for teacher education programs to promote responsible and pedagogically informed AI integration.
From Rule-Based Nahwu to Adaptive Learning Systems: Reconceptualizing Arabic Grammar Instruction in the AI Era Laely Syaudah; Dadan Mardani; Muhammad Faiz Alhaq
Proceeding of the International Conference on Global Education and Learning Vol. 2 No. 2 (2025): December : Proceeding of the International Conference on Global Education and L
Publisher : Asosiasi Riset Ilmu Pendidikan Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62951/icgel.v2i2.188

Abstract

Arabic grammar (nahwu) instruction has long been dominated by rule-based approaches that emphasize memorization and formal analysis, often resulting in rigid learning structures and limited responsiveness to learners’ cognitive diversity. While such approaches play an important role in preserving grammatical accuracy, they frequently overlook individual learning trajectories, cognitive readiness, and adaptive instructional needs. In the era of artificial intelligence (AI), language education is increasingly shaped by adaptive learning systems that personalize content, pacing, and instructional strategies based on learners’ profiles. This study aims to reconceptualize Arabic grammar instruction by proposing a conceptual framework that integrates traditional nahwu principles with adaptive learning systems informed by AI. Using a qualitative conceptual analysis, this paper synthesizes classical Arabic grammar pedagogy, contemporary theories of adaptive learning, and recent developments in AI-supported language instruction. The proposed framework highlights key components, including learner profiling, cognitive-level alignment, hierarchical nahwu content structuring, and AI-assisted scaffolding mechanisms. The findings suggest that adaptive learning systems offer significant pedagogical potential to transform nahwu instruction from a static, rule-centered model into a flexible, learner-centered process. This reconceptualization is expected to enhance grammatical comprehension, reduce cognitive overload, and promote learner autonomy in Arabic language education, particularly in Islamic higher education contexts. The study concludes by discussing pedagogical implications and directions for future empirical research on AI-assisted Arabic grammar learning.
Application of Deep Learning in Reading Literacy to Improve in Depth Understanding of Texts in 6th Grade Elementary School Students Ahmad Maskur; Nizar Malik; Gayuh Bayu
Proceeding of the International Conference on Global Education and Learning Vol. 2 No. 2 (2025): December : Proceeding of the International Conference on Global Education and L
Publisher : Asosiasi Riset Ilmu Pendidikan Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62951/icgel.v2i2.189

Abstract

Reading literacy among 6th grade elementary students is often superficial, limiting their ability to analyze implicit meanings and connect texts to real-world contexts. This review examines the potential of deep learning as a pedagogical approach to enhance in-depth text comprehension. Deep learning emphasizes active engagement, reflection, and the construction of meaningful knowledge, aiming to foster critical thinking and improve comprehension. Recent studies highlight implementation strategies such as reflective journaling and interactive discussions, which have demonstrated significant improvements in students' critical thinking and comprehension scores (p < 0.05). These findings suggest that deep learning methods surpass traditional approaches by promoting higher-order cognitive skills, enabling students to analyze and interpret texts more effectively. However, challenges such as inadequate teacher training persist, which may hinder the full integration of deep learning techniques. To address these challenges, further research is needed to explore scalable digital tools that can support deep learning in diverse classroom settings. By examining the potential for digital integration, future studies could provide insights into how technology can facilitate the widespread adoption of deep learning strategies, making them more accessible and effective for a broader range of students. Ultimately, this review underscores the promise of deep learning in enhancing reading literacy and suggests that addressing the barriers to its implementation could have significant educational benefits.
The Effect of the AI-Based Intelligent Tutor Sistem (ITS) on the Understanding of Mathematical Concepts in Grade V Students of SD Negeri 2 Badran, Temanggung M. Fahreza Azzidane; Mira Adelia; Anisa Yolanda; Ridha Sarwono
Proceeding of the International Conference on Global Education and Learning Vol. 2 No. 2 (2025): December : Proceeding of the International Conference on Global Education and L
Publisher : Asosiasi Riset Ilmu Pendidikan Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62951/icgel.v2i2.190

Abstract

This study aims to analyze the effect of the implementation of the Intelligent Tutoring Sistem (ITS) based on Artificial Intelligence (AI) on improving the understanding of mathematical concepts, especially in fractional and basic geometry materials, in Class V students of SD Negeri 2 Badran, Temanggung Regency. The research method used was a quasiexperimental experiment with a Non-equivalent Control Group Design. The research sample consisted of 48 students who were divided into two groups, namely the experimental group (n=24) who received learning with the help of AI-based ITS, and the control group (n=24) who received conventional learning with lecture methods and practice questions. The research instrument is in the form of a test of understanding of mathematical concepts that has been validated by experts and tested for reliability. Data were analyzed using parametric statistical tests of the Independent Sample t-test and N-Gain Score to measure the improvement. The results showed that there was a significant difference in understanding of mathematical concepts between the experimental group and the control group. The average post-test score of the experimental group (82.45) was significantly higher than that of the control group (70.12) with a p< value of 0.05. N-Gain analysis showed that the improvement in conceptual understanding in the experimental group was in the "moderate" category (g=0.56), while the control group was in the "low" category (g=0.32). These findings indicate that AI-based ITS is effective in improving students' understanding of mathematical concepts. The advantages of the system lie in its ability to provide instant feedback, personalize materials according to learning pace, and present interactive materials, thus helping to better construct students' conceptual understanding. It is recommended that schools consider the integration of ITS technology as a supplementary tool in mathematics learning at the elementary level.
Readiness of Elementary School Teachers in Implementing AI-Based Learning in the Era of Artificial Intelligence Ghaitsa Zahira Shaffa; Miftakhus Surur; Dewi Asmaul Husna
Proceeding of the International Conference on Global Education and Learning Vol. 2 No. 2 (2025): December : Proceeding of the International Conference on Global Education and L
Publisher : Asosiasi Riset Ilmu Pendidikan Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62951/icgel.v2i2.191

Abstract

This study examines the readiness of elementary school teachers to implement AI-based learning in the era of artificial intelligence, as technological advancements increasingly influence instructional practices in basic education. Despite the growing potential of artificial intelligence to support teaching and learning processes, empirical evidence regarding teachers’ preparedness at the elementary level remains limited. This study employed a descriptive quantitative research design involving 18 elementary school teachers. Data were collected using a structured questionnaire consisting of 15 Likert-scale items measuring technological skills, knowledge of artificial intelligence, attitudes toward AI, pedagogical readiness, and infrastructure support. Descriptive statistical analysis revealed that the overall mean score of teachers’ readiness was 4.08, indicating that teachers are generally ready to adopt AI-based learning. Technological skills emerged as the strongest aspect of readiness, reflecting teachers’ familiarity with digital tools and instructional technologies, while infrastructure and institutional support obtained the lowest mean score, highlighting challenges related to facilities, access to technology, and policy support. These findings suggest that although elementary school teachers demonstrate positive readiness and attitudes toward AI-based learning, effective and sustainable implementation requires strengthened institutional support, improved infrastructure, and continuous professional development to maximize the educational benefits of artificial intelligence in elementary education.

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