Assessment and evaluation of an artificial intelligence-based deep learning curriculum
DOI:
https://doi.org/10.17509/curricula.v4i2.95294Keywords:
artificial intelligence, authentic assessment, deep learning curriculum, learning evaluation, personalized learningAbstract
The rapid development of Artificial Intelligence (AI) and the implementation of deep learning curricula in education have necessitated reforms to learning assessment and evaluation. This article examines the conceptual framework and assessment practices appropriate for AI-based deep learning curricula: how assessments can measure critical thinking, collaboration, knowledge transfer, and data literacy competencies; how AI-based platforms enable personalization, automated assessment, and learning analytics; and the ethical and validity challenges of using AI for evaluation. The method used is a systematic literature review and a comparative analysis of current practices in school and higher education contexts. The results indicate that AI-based assessments can improve learning effectiveness by providing real-time feedback and progress tracking; however, their success depends heavily on authentic task design, teacher readiness, infrastructure, and ethical/data security arrangements. Recommendations are provided for educational institutions to adopt a combination of authentic assessments (portfolios, projects, observations) and AI metrics, and to conduct regular algorithm audits to ensure the validity, reliability, and fairness of assessments.
Abstrak
Perkembangan pesat di bidang Artificial Intelligence (AI) dan penerapan kurikulum pembelajaran mendalam (deep learning) dalam ranah pendidikan memunculkan kebutuhan untuk mereformasi asesmen dan evaluasi pembelajaran. Artikel ini mengkaji kerangka konseptual dan praktik asesmen yang sesuai untuk kurikulum deep-learning berbasis AI: bagaimana asesmen dapat mengukur kompetensi berpikir kritis, kolaborasi, transfer pengetahuan, dan literasi data; bagaimana platform berbasis AI memungkinkan personalisasi, penilaian otomatis, serta analitik pembelajaran; serta tantangan-etika dan validitas dalam penggunaan AI untuk evaluasi. Metode yang digunakan adalah studi literatur sistematis dan analisis komparatif terhadap praktik terkini dalam konteks sekolah dan perguruan tinggi. Hasil menunjukkan bahwa asesmen berbasis AI dapat meningkatkan efektivitas pembelajaran dengan menyediakan umpan balik real-time dan pelacakan kemajuan, namun keberhasilannya sangat bergantung pada desain tugas otentik, kesiapan guru, infrastruktur, dan pengaturan etika/keamanan data. Rekomendasi diberikan agar institusi pendidikan mengadopsi kombinasi penilaian otentik (portofolio, proyek, observasi) dengan metrik AI, serta melakukan audit algoritma secara berkala untuk memelihara validitas, reliabilitas, dan keadilan penilaian.
Kata Kunci: asesmen otentik; evaluasi pembelajaran; kecerdasan buatan; kurikulum deep learning; personalisasi pembelajaran
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