Analisis Perception & Acceptance Level Penggunaan Generative-AI dalam Proses Pembelajaran Mahasiswa (Studi Kasus: Mahasiswa Institut Teknologi Sepuluh Nopember)

Puspita, Haura Hanjrah (2025) Analisis Perception & Acceptance Level Penggunaan Generative-AI dalam Proses Pembelajaran Mahasiswa (Studi Kasus: Mahasiswa Institut Teknologi Sepuluh Nopember). Other thesis, Institut Teknologi Sepuluh Nopember.

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Abstract

Human-AI Interaction (HAII) adalah konsep interaksi antara manusia dan kecerdasan buatan yang memungkinkan AI menjalankan tugas dan belajar secara mandiri. Interaksi ini bersifat adaptif dan dapat disesuaikan dengan kebutuhan tiap individu. Generative Artificial Intelligence (Gen-AI) merupakan teknologi inovatif yang dapat mempelajari dan beradaptasi dari data yang telah dikumpulkan sebelumnya serta dapat membantu proses pengambilan keputusan secara efisien. Namun, tingkat persepsi (perception) dan penerimaan (acceptance) mahasiswa terhadap Gen-AI bervariasi sehingga perlu diteliti lebih mendalam. Oleh karena itu, penelitian ini bertujuan untuk mengukur persepsi dan penerimaan mahasiswa menggunakan scorecard berbasis konsep Human-AI Interaction, serta menganalisis kesenjangan (gap) antara persepsi dan acceptance. Metode yang digunakan adalah gap analysis, uji korelasi Spearman, uji Wilcoxon Signed-Rank, uji Kruskal-Wallis. Responden dari penelitian ini adalah mahasiswa S1 Institut Teknologi Sepuluh Nopember dengan total 104 responden. Hasil penelitian menunjukkan bahwa seluruh pasangan sub-faktor antara persepsi dan penerimaan mahasiswa terhadap Gen-AI memiliki hubungan yang positif dan signifikan. Berdasarkan uji korelasi Spearman, mayoritas hubungan berada dalam kategori rendah (koefisien 0.20–0.399) dan hanya tiga sub-faktor berada pada kategori cukup (0.40–0.599). Selain itu, analisis gap menggunakan uji Wilcoxon Signed-Rank menunjukkan adanya perbedaan signifikan antara persepsi dan penerimaan pada 10 sub-faktor. Sebagai contoh, TR1 (menggantikan peran dosen) memiliki median persepsi 4 dan penerimaan 3 dengan nilai Z = -4.842 (p < 0.001), yang menunjukkan ketidakseimbangan antara ekspektasi dan kesiapan mahasiswa. Hasil uji Kruskal-Wallis terhadap enam fakultas menunjukkan tidak terdapat perbedaan yang signifikan dalam sikap terhadap Gen-AI (seluruh sub-faktor memiliki nilai p > 0.05) sehingga latar belakang keilmuan tidak berpengaruh secara statistik. Temuan ini mendorong beberapa strategi utama, di antaranya menyediakan pelatihan praktik dan etis, scorecard sebagai alat bantu evaluatif, dan penyesuaian panduan Gen-AI sesuai pembelajaran tiap departemen.

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Human-AI Interaction (HAII) is the concept of interaction between humans and artificial intelligence that allows AI to perform tasks and learn autonomously. This interaction is adaptive and can be tailored to the needs of each individual. Generative Artificial Intelligence (Gen-AI) is an innovative technology capable of learning from previously collected data and supporting efficient decision-making processes. However, students’ levels of perception and acceptance toward Gen-AI vary, requiring deeper investigation. Therefore, this study aims to measure student perception and acceptance using a scorecard based on the Human-AI Interaction concept, as well as analyze the gap between perception and acceptance. The methods employed include gap analysis, Spearman correlation test, Wilcoxon Signed-Rank test, and Kruskal-Wallis test. Respondents were undergraduate students from the Sepuluh Nopember Institute of Technology (ITS), totaling 104 participants. The findings indicate that all pairs of sub-factors between perception and acceptance toward Gen-AI show a positive and significant correlation. According to Spearman's correlation test, most relationships fall within the low category (coefficient 0.20–0.399), with only three sub-factors in the moderate category (0.40–0.599). Furthermore, gap analysis using the Wilcoxon Signed-Rank test reveals significant differences in perception and acceptance across 10 sub-factors. For example, sub-factor TR1 (replacing lecturers) shows a median perception score of 4 and an acceptance score of 3, with Z = -4.842 (p < 0.001), highlighting an imbalance between student expectations and readiness. The Kruskal-Wallis test across six faculties indicates no significant differences in attitudes toward Gen-AI (all sub-factors have p-values > 0.05), meaning that academic background does not statistically influence perception or acceptance. These findings support several key strategies, including providing practical and ethical training, using the scorecard as an evaluative tool, and adjusting Gen-AI guidelines to fit each department’s learning context.

Item Type:	Thesis (Other)
Uncontrolled Keywords:	Acceptance, Generative AI, Human-AI Interaction, Perception, Scorecard
Subjects:	L Education > LB Theory and practice of education > LB1044.87 Internet in education (e-learning). Virtual reality in education. L Education > LB Theory and practice of education > LB2300 Higher Education T Technology > T Technology (General) > T59.7 Human-machine systems.
Divisions:	Faculty of Industrial Technology and Systems Engineering (INDSYS) > Industrial Engineering > 26201-(S1) Undergraduate Thesis
Depositing User:	Haura Hanjrah Puspita
Date Deposited:	30 Jul 2025 03:10
Last Modified:	30 Jul 2025 03:10
URI:	http://repository.its.ac.id/id/eprint/123129

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