Article Per Year (5 Year)
p-Index From 2021 - 2026
0.61
P-Index
This Author published in this journals
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

Published : 3 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 3 Documents
Search

 1 
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 aims to present an artificial intelligence-based framework that combines learning analytics with operational optimization, which can address the ever-present problems concerning technology and vocational education. In the case of vocational institutions, it has been noticed that while learning environments are increasingly embracing the incorporation of digital technologies, the connection between the use of data for educational outcomes and operational decision-making remains disconnected. In many instances, learning-related data is analyzed separately from production-oriented activities, which include scheduling, resource allocation, and process efficiency, despite the fact that these activities are part of the learning process in the factory and learning environments. This study aims to address the disconnect between the use of learning-related data and production-oriented activities through the incorporation of perspectives from industrial engineering and informatics, which are integrated into a single framework that is oriented towards artificial intelligence. Machine learning is utilized for the representation of learning processes, while optimization techniques are used for decision-making regarding task allocation, scheduling, and resource allocation. Instead of being restricted to a particular application domain, the framework is developed with the idea of adaptability so that it can be used across different contexts of vocational education. An empirical study was conducted within a particular context of a technology-oriented vocational education domain to assess the viability of the proposed framework. It was found that the integration of learning analytics with operational optimization provides a more consistent outcome compared to the individual analysis of these factors. It was also found that the proposed AI-based framework provides a better outcome for the assessment of competency as well as the prediction of performance, which leads to the efficiency of managing a production-oriented learning process. Such findings indicate the ability of the application of AI to support the field of vocational education more comprehensively. This study contributes to the field of research by proposing an interdisciplinary framework that goes beyond the idea of individual technological tools to offer a more comprehensive perspective on the adoption of AI within the context of vocational education.
Co-Authors Dong Hwa Kim Heri Nurdiyanto Jehad A.H Hammad Leonel Hernandes Leonel Hernandes Nurdiyanto, Heri