INTEGRITI AKADEMIK ERA KECERDASAN BUATAN MENERUSI ANALISIS SISTEMATIK PENTAKSIRAN PENDIDIKAN
ACADEMIC INTEGRITY IN THE AGE OF ARTIFICIAL INTELLIGENCE THROUGH SYSTEMATIC REVIEW OF EDUCATIONAL ASSESSMENT
DOI:
https://doi.org/10.55197/qjssh.v7i5.1360Keywords:
integriti akademik, kecerdasan buatan generative, pentaksiran pendidikan, etika akademik, tinjauan literatur sistematikAbstract
Perkembangan pesat kecerdasan buatan (AI), khususnya AI generatif seperti ChatGPT, telah membawa perubahan signifikan terhadap amalan pembelajaran dan pentaksiran pendidikan, sekali gus mencabar konsep integriti akademik tradisional. Keupayaan AI menghasilkan kandungan akademik yang koheren dan meyakinkan telah mengaburkan sempadan antara hasil kerja asli pelajar dan bantuan teknologi, sekali gus menimbulkan isu berkaitan kejujuran, keaslian pembelajaran, kesahan pentaksiran dan tanggungjawab akademik. Sehubungan itu, kajian ini bertujuan untuk mensintesis secara sistematik cabaran dan isu integriti akademik dalam era kecerdasan buatan serta menganalisis implikasinya terhadap amalan pentaksiran pendidikan.Kajian ini menggunakan pendekatan Tinjauan Literatur Sistematik (SLR) berpandukan garis panduan PRISMA. Proses pencarian literatur dijalankan secara sistematik melalui pangkalan data ScienceDirect melibatkan artikel jurnal berwasit yang diterbitkan antara tahun 2020 hingga 2025. Artikel disaring berdasarkan kriteria inklusi dan eksklusi yang ditetapkan, seterusnya dianalisis secara tematik. Sebanyak 28 artikel dikenal pasti layak dan dimasukkan dalam sintesis kualitatif.Dapatan kajian mengenal pasti enam tema utama cabaran integriti akademik dalam era AI, iaitu etika, dasar (polisi), teknologi, pentaksiran, kompetensi guru dan pedagogi. Tema etika dikenal pasti sebagai paling dominan, diikuti isu kekaburan dasar institusi, keterbatasan teknologi pengesanan AI, serta cabaran mengekalkan kesahan dan keaslian pentaksiran. Kajian ini menegaskan keperluan peralihan daripada pentaksiran berorientasikan produk kepada pentaksiran autentik dan berasaskan proses. Secara keseluruhannya, kajian ini menyediakan asas konseptual yang penting bagi menyokong penambahbaikan amalan pentaksiran pendidikan yang beretika, sah dan relevan dalam persekitaran pembelajaran berasaskan kecerdasan buatan.Kajian lanjutan dicadangkan untuk meneliti keberkesanan pelaksanaan pentaksiran autentik serta tahap kesediaan guru dalam mengurus penggunaan AI generatif dalam konteks pentaksiran pendidikan.
The rapid development of artificial intelligence (AI), particularly generative AI such as ChatGPT, has brought significant changes to learning and assessment practices in education, consequently challenging traditional concepts of academic integrity. The ability of AI to generate coherent and persuasive academic content has blurred the boundary between students’ original work and technological assistance, raising critical concerns related to honesty, learning authenticity, assessment validity, and academic responsibility. In this context, this study aims to systematically synthesise the challenges and issues of academic integrity in the era of artificial intelligence and to analyse their implications for educational assessment practices. This study adopts a Systematic Literature Review (SLR) approach guided by the PRISMA framework. A systematic literature search was conducted using the ScienceDirect database, focusing on peer-reviewed journal articles published between 2020 and 2025. The articles were screened based on predefined inclusion and exclusion criteria and subsequently analysed thematically. A total of 28 articles were identified as eligible and included in the qualitative synthesis. The findings identify six main themes representing academic integrity challenges in the AI era, namely ethics, policy, technology, assessment, teacher competence, and pedagogy. Ethics emerged as the most dominant theme, followed by issues related to policy ambiguity, limitations of AI detection technologies, and challenges in maintaining assessment validity and learning authenticity. The study highlights the need to shift from product-oriented assessment approaches towards authentic and process-based assessment practices. Overall, this review provides an important conceptual foundation to support the enhancement of ethical, valid, and relevant educational assessment practices within AI-driven learning environments. Future research should focus on empirically examining the effectiveness of authentic assessment models and assessing teachers’ technical readiness to adapt to the rapid evolution of generative artificial intelligence (AI).
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