Al-Hijr: Journal of Adulearn World

TRANSFORMING EDUCATION IN THE GENERATIVE AI ERA: AN ANALYSIS OF OPPORTUNITIES AND CHALLENGES IN INDONESIA

Nofirman Nofirman, Ton Kiot, Dikdik Firman Sidik, Ahmad Yani — Thu, 11 Dec 2025 00:00:00 +0700

The advent of Generative Artificial Intelligence (AI) represents a significant paradigm shift for global education systems. Within the context of a large, developing nation like Indonesia, its integration presents a unique and complex case. This research aims to comprehensively analyze the primary opportunities and critical challenges associated with the adoption of Generative AI within the Indonesian education sector. This study employs a qualitative methodology, utilizing a systematic literature review of contemporary academic papers, policy documents, and reputable industry reports, followed by thematic analysis to synthesize the findings. The results indicate substantial opportunities, including the potential for hyper-personalized learning pathways, democratized access to information, and the automation of administrative tasks for educators. However, significant challenges were identified, notably the exacerbation of the digital divide, profound ethical concerns regarding academic integrity, a critical deficit in teacher digital literacy, and the risk of inherent algorithmic bias. This study concludes that Generative AI acts as a double-edged sword for Indonesian education. Its successful and equitable integration hinges on a strategic, multi-faceted national approach, requiring proactive policy-making, substantial investment in educator training, and the establishment of robust ethical frameworks to harness its transformative benefits while mitigating profound risks.

THE ASSESSMENT REVOLUTION: THEORIES AND METHODOLOGIES OF AUTOMATED ASSESSMENT USING MACHINE LEARNING FOR EVALUATING LEARNING PROGRESS

Misbahul Khairani, Cedric Butler, Markus Rolle — Wed, 31 Dec 2025 00:00:00 +0700

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.

THE SHIFTING ROLES OF TEACHERS AND STUDENTS: AN EFFECTIVE INTERACTION MODEL IN A CHATGPT-ENRICHED LEARNING ENVIRONMENT

Muhammad Makhrus Ali, Syarifatul Hayati Zarfi, Hana Ben Romdhane, Liam Wilson — Sun, 28 Dec 2025 00:00:00 +0700

The integration of artificial intelligence (AI) tools like ChatGPT into education is reshaping traditional roles of teachers and students. Traditionally, teachers have been the primary knowledge providers, while students acted as passive receivers of information. However, the advent of AI-powered tools, such as ChatGPT, challenges this dynamic by allowing for more interactive, personalized, and collaborative learning experiences. This study explores the shifting roles of teachers and students in a ChatGPT-enriched learning environment, focusing on the changes in classroom interaction and the impact on student engagement and teacher facilitation. The research employs a mixed-methods approach, combining quantitative surveys and qualitative interviews, as well as classroom observations, to assess these changes. The results indicate that 75% of students reported increased engagement and autonomy in their learning, while 82% of teachers felt more effective in facilitating discussions and providing real-time feedback. The study concludes that ChatGPT enhances the teacher-student interaction by fostering a collaborative, student-centered learning environment where both parties contribute to knowledge construction. The findings suggest that the successful integration of AI into classrooms requires a shift toward more dynamic, interactive teaching methods and highlight the potential of ChatGPT in transforming educational practices.

A DOUBLE-EDGED SWORD: WEIGHING THE POTENTIAL AND RISKS OF GENERATIVE AI IN MAINTAINING ACADEMIC INTEGRITY IN HIGHER EDUCATION

Multajimah, Mohamed Shifaz, Aminath Nafeeza — Mon, 29 Dec 2025 00:00:00 +0700

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.