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All Journal Jurnal Penelitian Pendidikan IPA (JPPIPA) Language, Technology, and Social Media International Journal of Educational Practice and Policy
Saripudin
Universitas Pendidikan Indonesia
Agriculture, Biological Sciences & Forestry Humanities Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Education Languange, Linguistic, Communication & Media Materials Science & Nanotechnology Physics Social Sciences

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Automated Assessment Systems in Education: A Multi-Paradigmatic Analysis of Technological Capabilities, Pedagogical Implications, and Ethical Challenges Muh Yamin; Mumu Komaro; Saripudin
International Journal of Educational Practice and Policy Vol. 4 No. 2 (2026): June-July 2026
Publisher : PT. Global Research Collaboration

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.66314/ijepp.v4i2.546

Abstract

Artificial intelligence based automated assessment systems have developed rapidly over the past decade; however, studies that integrate technological, pedagogical, socio-cultural, and ethical dimensions simultaneously remain limited. This study presents a multi-paradigmatic analysis of 24 articles published in Scopus Q1 and Q2 indexed journals between 2020 and 2025. Three research questions are posed: the types and capacities of emerging automated assessment systems, their pedagogical and ethical implications, and the evaluative framework required. The analysis identifies six categories of automated assessment systems, with a dominant shift toward large-scale language models in recent studies. The findings indicate that technical superiority does not necessarily guarantee fairness or pedagogical validity. Three fundamental ethical issues are consistently identified: linguistic discrimination, lack of system explainability, and the indispensable need for human oversight. In response, this study introduces the TAPE-H Framework (Technology, Assessment Theory, Pedagogy, Ethics, Human Oversight) as an integrative evaluative model that assesses automated assessment systems holistically, moving beyond accuracy based metrics alone.
AI Ethics as Epistemological Governance: A Systematic Literature Review on Knowledge and Authority in the Age of Generative AI Cahyo Prianto; Mumu Komaro; Saripudin
Language, Technology, and Social Media Vol. 4 No. 2 (2026): April–June 2026
Publisher : WISE Pendidikan Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70211/ltsm.3026-7196.403

Abstract

This Systematic Literature Review (SLR) explores research trends on the ethics of generative AI use and how ethical issues are discussed within it, mapping geographical distribution and examining AI epistemic governance. This systematic literature review employs the PRISMA method, with a literature search conducted through the Scopus database, filtered based on keywords related to generative AI Ethics in Quartiles Q1 and Q2 and limited to the period 2020–2026, resulting in 39 articles for further analysis in this SLR. The research trend has continuously increased from year to year, and 2025 became the year with the highest number of studies addressing the ethics of generative AI use. This indicates a strengthening academic attention to ethical and epistemic issues in AI. The literature is dominated by themes of ethical concerns in the use of generative AI, such as bias, data privacy, transparency, accountability, misinformation, academic integrity, and cognitive dependence on generative AI. This study also finds that generative AI is most frequently positioned as a knowledge generator, while the combination of training data bias and cultural bias constitutes the most dominant epistemic issue. In the dimension of epistemic dependency, human dependence on AI is the most frequently discussed theme. This demonstrates growing concerns regarding the weakening of human autonomy, control, and cognitive capacity. From the perspective of authoritative actors, the scientific community occupies the strongest position, while multi-stakeholder governance emerges as the most widely supported governance model. These findings affirm that AI governance is understood as a complex issue that cannot be resolved by a single actor, but rather requires collaboration.
Pengembangan Green Skills dalam Model Pembelajaran Teaching Factory Tiara Maulida Yanti; Tuti Suartini; Dimas Aulia Saputra; Mustika Nuramalia Handayani; Saripudin
Jurnal Penelitian Pendidikan IPA Vol 12 No 5 (2026)
Publisher : Postgraduate, University of Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29303/jppipa.v12i5.13832

Abstract

Vocational education in Indonesia faces challenges in preparing graduates who are aligned with the needs of industries transitioning toward a green economy. This study aims to examine the effectiveness of integrating green skills into the Teaching Factory (TeFa) learning model in the Agricultural Product Processing Program at SMKN PP Lembang. The study employed a quasi-experimental approach using a one-group pretest-posttest design involving 31 students. The intervention was implemented through the modification of TeFa work instructions by embedding five green skill dimensions: environmental awareness, innovation, communication, adaptability, and waste management. Data were collected through pretest and posttest assessments and student perception questionnaires, and were enriched by input from industry assessors as contextual validation of competency applicability in the workplace. The results showed that the students’ mean score increased from 63.23 to 84.62, with an N-Gain score of 0.57, indicating moderate effectiveness. The most notable improvements were found in environmental awareness and innovation. These findings indicate that integrating green skills into TeFa work instructions is effective in improving student competencies and strengthening the relevance of vocational learning to sustainability-oriented industrial demands.
Defining, Operationalizing, and Measuring Generative AI Competence among Vocational Teachers: A Scoping Review Husni Mubarok; Yeyet Rostika; Mumu Komaro; Saripudin
International Journal of Educational Practice and Policy Vol. 4 No. 3 (2026): August-September 2026
Publisher : PT. Global Research Collaboration

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.66314/ijepp.v4i3.1182

Abstract

Generative artificial intelligence (GenAI) tools such as ChatGPT have spread quickly enough that teachers are now widely expected to develop competence in applying these technologies to instruction. Technical and vocational education and training (TVET) faces this pressure with particular force, since its teachers must simultaneously satisfy pedagogical demands, keep pace with rapidly changing technology, and stay aligned with industry practice. Research on "generative AI competence," however, has not kept these demands in a coherent frame: overlapping labels among them AI literacy, AI self-efficacy, and AI TPACK are applied inconsistently across studies, leaving the construct conceptually fragmented. Using an established scoping-review framework and reporting the process per PRISMA ScR guidance, this review charted how generative AI competence among vocational teachers has been defined, operationalized, and measured in the Scopus Q1 indexed literature published between 2021 and 2026. A structured search of five databases plus citation tracking sources identified 222 records, 28 of which all Scopus Q1 indexed satisfied the eligibility criteria after screening. Definitions in this set span a wide range, from narrowly tool specific notions such as prompt engineering competence to broad, multidimensional constructs embedded in knowledge based frameworks such as Intelligent TPACK. Operationalization efforts cluster around adaptations of three frameworks the UNESCO AI Competency Framework for Teachers, DigCompEdu, and various TPACK derived models while measurement has relied almost exclusively on self report Likert instruments; only a single instrument has been validated specifically on a vocational-teacher sample. Taken together, these patterns indicate that generative AI competence for vocational teachers remains comparatively under-theorized and under measured next to its counterpart in general education. To help close that gap, the review proposes a three layer integrative model encompassing foundational AI literacy, pedagogical technical integration, and an industry aligned contextual layer intended to guide future instrument development and the design of professional training.
Co-Authors Cahyo Prianto Dimas Aulia Saputra Husni Mubarok Muh Yamin Mumu Komaro Mustika Nuramalia Handayani Tiara Maulida Yanti Tuti Suartini Yeyet Rostika