Kecerdasan Buatan Dalam Jaminan Mutu Akademik: Peluang Dan Tantangan
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Transformasi jaminan mutu akademik di perguruan tinggi berlangsung cepat seiring perkembangan kecerdasan buatan (AI). Artikel ini bertujuan mengkaji secara sistematis implementasi AI dalam sistem jaminan mutu akademik. Metode yang digunakan adalah systematic literature review terhadap 41 artikel bereputasi yang dipublikasikan pada periode 2022– 2025. Analisis dilakukan melalui sintesis tematik untuk mengidentifikasi pola penerapan AI dalam tiga fokus utama, yaitu: (1) penggunaan AI dalam pemantauan dan evaluasi kualitas akademik, (2) tata kelola integritas akademik di era AI, dan (3) kesiapan institusional menghadapi transformasi teknologi. Temuan menunjukkan bahwa keberhasilan adopsi AI ditentukan oleh keseimbangan antara efektivitas teknologi, kejelasan etika, serta dukungan institusional yang adaptif. AI menawarkan efisiensi dalam evaluasi akademik dan penilaian adaptif, namun tantangan seperti bias algoritmik, kapasitas staf yang tidak merata, dan kerangka kebijakan yang masih reaktif tetap dominan. Artikel ini mengusulkan kerangka kerja jaminan mutu berbasis AI yang terdiri atas empat komponen, yaitu evaluasi otomatis dan etis, tata kelola adaptif, dukungan institusional berorientasi manusia, serta keselarasan AI dengan pedagogi. Kerangka ini mendorong ekosistem mutu akademik yang reflektif, kolaboratif, dan berkelanjutan, sekaligus menekankan integrasi nilai etis, konteks lokal, dan pembangunan kapasitas institusional.
The transformation of academic quality assurance in higher education is rapidly advancing with the rise of artificial intelligence (AI). This article aims to systematically examine the implementation of AI in academic quality assurance systems. A systematic literature review was conducted on 41 peer-reviewed articles published between 2022 and 2025. The analysis employed a thematic synthesis approach to identify key patterns across three dimensions: (1) the use of AI in monitoring and evaluation, (2) governance of academic integrity in the AI era, and (3) institutional readiness for technological change. The findings indicate that successful AI adoption depends on balancing technological effectiveness, ethical clarity, and adaptive, human-centred institutional support. While AI enhances efficiency in evaluation and adaptive assessment challenges such as algorithmic bias, uneven staff capacity, and reactive policy frameworks persist. As a conceptual contribution, this article proposes an AI-based quality assurance framework comprising four components: automated and ethical evaluation, adaptive governance, human-centred institutional support, and AI-pedagogy alignment. Together, these components foster a reflective, collaborative, and sustainability-oriented academic quality ecosystem, while emphasising ethical values, local contexts, and institutional capacity-building. This study provides an early conceptual foundation for more responsive AI-informed quality assurance systems.
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