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All Journal Jurnal Pendidikan Vokasi JPTK: Jurnal Pendidikan Teknologi dan Kejuruan International Journal of Artificial Intelligence Research
Aktansi Kindiasari
Universitas Terbuka
Computer Science & IT Education Electrical & Electronics Engineering Other

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A Study on The Job Replacement Impact of ChatGPT and Education Method Dong Hwa Kim; Aktansi Kindiasari
International Journal of Artificial Intelligence Research Vol 7, No 1 (2023): June 2023
Publisher : STMIK Dharma Wacana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29099/ijair.v7i1.1007

Abstract

This paper deals with the job impact of ChatGPT and education preparation for that, which will give an influence on many areas because it can be implemented with ease as just normal editing works and speak including code development by using huge data. Currently young generations will take a big impact on their job selection because ChatGPT can do well as much as human can do it in everywhere. Therefore, education method and system should be rearranged as new curriculums. However, government and officer do not understand well how it is serious in education. This paper provides education method and curriculum for AI education including ChatGPT through analyzing many papers and report, and experience
Impacts of Artificial Intelligence Integration on Teaching Practices and Student Engagement in Digitally Transformed Educational Settings Heri Nurdiyanto; Leonel Hernandes; Aktansi Kindiasari
Jurnal Pendidikan Teknologi dan Kejuruan Vol. 31 No. 1 (2025): (May)
Publisher : Faculty of Engineering, Universitas Negeri Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21831/jptk.v31i1.96073

Abstract

This study explores how the integration of artificial intelligence (AI) into digitally transformed educational settings reshapes everyday teaching practices and influences student engagement in real learning contexts. Rather than treating AI as a standalone technological upgrade, the study situates its use within the broader transformation of digital learning environments, where platforms, automated tools, and data-driven systems increasingly mediate classroom interactions. The findings show that AI integration gradually shifts the role of teachers from primarily delivering content toward designing learning experiences, guiding students’ learning processes, and responding to more diverse patterns of participation. In practice, AI-supported tools help streamline routine instructional tasks, open space for more personalized interaction, and provide timely support to students with different learning needs. At the same time, the results indicate that student engagement does not automatically improve simply because AI is introduced. Engagement grows when AI tools are meaningfully aligned with pedagogical goals, integrated into everyday learning activities, and supported by teachers’ readiness to adapt their instructional strategies. The study also highlights emerging tensions, including uneven levels of student participation, varying degrees of teacher confidence in using AI-based systems, and concerns about over-reliance on automated support. Overall, the findings suggest that the impact of AI in digitally transformed educational settings is shaped less by the technology itself than by how it is embedded in teaching practices and learning cultures, pointing to the importance of thoughtful integration in sustaining meaningful student engagement.
An AI-driven framework for learning analytics and operational optimization in technology and vocational education: Bridging industrial engineering and informatics Heri Nurdiyanto; Leonel Hernandes; Jehad A. H. Hammad; Aktansi Kindiasari
Jurnal Pendidikan Vokasi Vol. 15 No. 3 (2025)
Publisher : ADGVI & Graduate School of Universitas Negeri Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21831/jpv.v15i3.95617

Abstract

This study proposes an artificial intelligence (AI)-driven framework that integrates learning analytics with operational optimization to address the fragmentation between educational data and operational decision-making in technology and vocational education. Although vocational institutions increasingly adopt digital technologies, learning-related data are often analyzed separately from operational processes such as scheduling, task allocation, resource utilization, and process efficiency, despite their interdependence in teaching factory and practical learning environments. Drawing on perspectives from industrial engineering and informatics, the proposed framework integrates machine learning for analyzing learner performance and competency development with optimization techniques for supporting operational decision-making. The framework was designed to be adaptable across different vocational education contexts and was empirically evaluated in a technology-oriented vocational setting to assess its feasibility. The evaluation demonstrates the feasibility of integrating learner-related and operational information within a unified decision-support environment, enabling competency and performance information to inform decisions concerning practical tasks, scheduling, and resource allocation. Rather than positioning AI solely as a predictive or instructional tool, the framework conceptualizes it as a mechanism for connecting pedagogical and operational dimensions of vocational learning. This study contributes an interdisciplinary and system-oriented perspective on AI adoption in vocational education, providing a foundation for more integrated, context-sensitive, and data-informed decision-making in teaching factory and practical learning environments.
Co-Authors Dong Hwa Kim Jehad A. H. Hammad Leonel Hernandes Leonel Hernandes Nurdiyanto, Heri