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Adam Mudinillah
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alhijr@staialhikmahpariangan.ac.id
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Al-Hijr: Journal of Adulearn World
Published by Sekolah Tinggi Agama Islam Al-Hikmah Pariangan Batusangkar
ISSN : 28293894     EISSN : 28294351     DOI : https://doi.org/10.55849/alhijr
Core Subject : Science, Education,
Computer Science & IT Education Languange, Linguistic, Communication & Media Other
Al-Hijr: Journal of Adulearn World is a multi-disciplinary, peer-refereed open-access international journal which has been established for the dissemination of state-of-the-art knowledge in the field of education, teaching, development, instruction, educational projects and innovations, learning methodologies, and new technologies in education and learning. The journal publishes state-of-art papers in fundamental theory, experiments, and simulation, as well as applications, with a systematic proposed method, sufficient review on previous works, expanded discussion, and concise conclusion. As our commitment to the advancement of science and technology, the Al-Hijr follows the open access policy that allows the published articles freely available online without any subscription. Submitted papers must be written in English for initial review stage by editors and further review process by minimum two international reviewers.
Arjuna Subject : Umum - Umum
Articles 2 Documents
 1 
Search results for , issue "vol. 4 no. 4 (2025)" : 2 Documents clear
A DOUBLE-EDGED SWORD: WEIGHING THE POTENTIAL AND RISKS OF GENERATIVE AI IN MAINTAINING ACADEMIC INTEGRITY IN HIGHER EDUCATION Multajimah; Mohamed Shifaz; Aminath Nafeeza
Al-Hijr: Journal of Adulearn World Vol. 4 No. 4 (2025)
Publisher : Sekolah Tinggi Agama Islam Al-Hikmah Pariangan Batusangkar, West Sumatra, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55849/alhijr.v4i4.1147

Abstract

Generative artificial intelligence has rapidly penetrated higher education, reshaping academic writing, assessment practices, and knowledge production, while simultaneously raising serious concerns about academic integrity. This study aims to examine generative AI as a double-edged phenomenon by analyzing its potential benefits and associated risks for maintaining academic integrity in higher education institutions. The research employed a qualitative-dominant mixed analytical design, combining systematic literature review, secondary statistical analysis, policy document analysis, and a focused institutional case study to capture conceptual, empirical, and governance dimensions of AI use. The findings reveal that generative AI does not inherently erode academic integrity; instead, integrity risks emerge primarily from unclear institutional policies, assessment models reliant on final textual outputs, and limited faculty preparedness. Institutions that implemented explicit AI guidelines, faculty training, and process-oriented assessment redesign demonstrated lower perceived misconduct and higher confidence in integrity enforcement. The study concludes that generative AI should not be addressed through prohibition-driven approaches but through adaptive governance, pedagogical innovation, and ethical literacy development. Academic integrity in the AI era depends less on technological restriction and more on institutional capacity to align policy, pedagogy, and assessment with evolving human–AI academic practices. These findings offer guidance for universities navigating responsible AI integration globally.
THE ASSESSMENT REVOLUTION: THEORIES AND METHODOLOGIES OF AUTOMATED ASSESSMENT USING MACHINE LEARNING FOR EVALUATING LEARNING PROGRESS Misbahul Khairani; Cedric Butler; Markus Rolle
Al-Hijr: Journal of Adulearn World Vol. 4 No. 4 (2025)
Publisher : Sekolah Tinggi Agama Islam Al-Hikmah Pariangan Batusangkar, West Sumatra, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55849/alhijr.v4i4.1153

Abstract

Rapid advances in artificial intelligence and machine learning have fundamentally transformed educational assessment practices, shifting evaluation from episodic, human-centered measurement toward continuous, data-driven monitoring of learning progress. This study aims to examine the theoretical foundations and methodological approaches underlying automated assessment systems that employ machine learning to evaluate learning progress in diverse educational contexts. A qualitative systematic review with an integrative analytical framework was employed, drawing on peer-reviewed studies from international journals across education, learning analytics, and computer science. The selected literature was analyzed to identify dominant assessment purposes, theoretical alignments, data sources, modeling techniques, and validation strategies. The results indicate that most automated assessment systems prioritize predictive accuracy and efficiency, frequently conceptualizing learning progress through performance-oriented metrics while offering limited alignment with established assessment theories such as formative assessment and construct validity. Theory-informed and interpretable models remain underrepresented despite their pedagogical relevance. The findings reveal a persistent gap between technological innovation and educational meaning-making in automated assessment research. This study concludes that the assessment revolution driven by machine learning will remain incomplete without stronger integration of educational assessment theory, methodological transparency, and interpretability. Aligning machine learning methodologies with robust assessment principles is essential to ensure that automated systems support meaningful evaluation of learning progress, instructional decision-making, and educational equity.

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