Zahirah, Khinsa Fairuz
School Of Information System, Universitas Ciputra Surabaya

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PREPARING AI SUPER USERS THROUGH GENERATIVE AI INTEGRATION IN EDUCATION Zahirah, Khinsa Fairuz; Irawan, Benny; Yusuf, Eddy
CENDEKIA: Jurnal Ilmu Pengetahuan Vol. 5 No. 2 (2025)
Publisher : Pusat Pengembangan Pendidikan dan Penelitian Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51878/cendekia.v5i2.4729

Abstract

Peningkatan pesat Kecerdasan Buatan Generatif (GenAI) menghadirkan peluang dan tantangan bagi pendidikan tinggi, khususnya dalam mempersiapkan mahasiswa untuk menggunakan perangkat AI secara efektif dan etis. Studi ini mengeksplorasi implementasi percontohan “AI Immersion,” program empat minggu di Universitas Ciputra yang dirancang untuk mengembangkan “pengguna super AI”, mahasiswa dari disiplin ilmu non-teknis yang dapat terlibat secara kreatif dan kritis dengan perangkat GenAI tanpa memerlukan keterampilan pemrograman. Program ini diintegrasikan ke dalam kursus yang ada di enam program akademik, yang melibatkan 182 mahasiswa sarjana. Dengan menggunakan pendekatan kualitatif, studi ini mengumpulkan data melalui wawancara, observasi, dan analisis artefak yang dibuat oleh mahasiswa. Kursus ini disusun berdasarkan empat tahap utama: konsep AI dasar, petunjuk teknik dan etika, penggunaan perangkat GenAI terapan, dan pengembangan proyek akhir. Temuan menunjukkan peningkatan dalam kecakapan teknis, pemikiran kritis, dan pemecahan masalah kreatif mahasiswa. Mahasiswa juga menunjukkan peningkatan kesadaran akan masalah etika. Bimbingan fakultas dan penggunaan perangkat gratis yang mudah diakses mendukung partisipasi inklusif di seluruh disiplin ilmu. Hasilnya menunjukkan bahwa GenAI dapat diintegrasikan ke dalam pendidikan tinggi untuk meningkatkan literasi digital dan pembelajaran interdisipliner. Makalah ini menawarkan model yang dapat diskalakan untuk inovasi kurikulum yang menyeimbangkan keterampilan praktis dengan refleksi etis, membekali siswa untuk meraih kesuksesan di dunia yang digerakkan oleh AI. ABSTRACTThe rapid rise of Generative Artificial Intelligence (GenAI) presents both opportunities and challenges for higher education, particularly in preparing students to use AI tools effectively and ethically. This study explores the pilot implementation of “AI Immersion,” a four-week program at Universitas Ciputra designed to develop “AI super users”, students from non-technical disciplines who can creatively and critically engage with GenAI tools without needing programming skills. The program was integrated into existing courses across six academic programs, involving 182 undergraduate students. Using a qualitative approach, the study collected data through interviews, observations, and analysis of student-created artifacts. The course was structured around four key stages: foundational AI concepts, prompt engineering and ethics, applied use of GenAI tools, and final project development. Findings showed improvements in students’ technical proficiency, critical thinking, and creative problem-solving. Students also demonstrated growing awareness of ethical issues. Faculty mentorship and the use of accessible, free tools supported inclusive participation across disciplines. The results suggest that GenAI can be integrated into higher education to enhance digital literacy and interdisciplinary learning. This paper offers a scalable model for curriculum innovation that balances practical skills with ethical reflection, equipping students for success in an AI-driven world.
Quantitative Evaluation of Data Science Curriculum Structures in Indonesian Universities Khinsa Fairuz Zahirah
IJECA (International Journal of Education and Curriculum Application) Vol 9, No 2 (2026): August
Publisher : Universitas Muhammadiyah Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31764/ijeca.v9i2.38963

Abstract

This study presents a quantitative structural evaluation of undergraduate data science curricula across Indonesian universities using a compositional modeling approach. Existing competency frameworks identify essential domains in data science education, yet limited empirical research evaluates how these competencies are proportionally structured across institutions. Using course-level curriculum data collected from 37 undergraduate programs, courses were mapped into five competency domains: programming, statistical foundations, machine learning, applied practice, and ethics. Each curriculum was operationalized as a normalized compositional competency vector and analyzed using the Curriculum Structural Balance Index (CSBI), a distance-based metric that measures proportional structural imbalance across competency domains. Hierarchical clustering was further applied to identify recurring curriculum typologies. The findings reveal a consistent structural pattern characterized by strong concentration in programming and statistics, moderate variation in machine learning, and limited integration of applied practice and ethics. This recurring configuration, identified as a Foundational-Dominant model, indicates that current programs prioritize technical consolidation over interdisciplinary integration. Unlike conventional curriculum evaluation approaches that primarily assess competency presence or coverage, the proposed CSBI framework quantitatively evaluates proportional competency balance and enables systematic comparison of curriculum structures across institutions. The study contributes a measurable and replicable analytic approach for evaluating interdisciplinary curriculum architecture in higher education.