Hubungan Adopsi Teknologi AI dengan Motivasi Belajar, Pemahaman Konsep, dan Hasil Belajar Mahasiswa Akuntansi
- Nadya Angeli Gifta Faizah Universitas Pembangunan Nasional "Veteran" Jawa Timur, Surabaya, Indonesia
-
Dyah Ratnawati
*
Universitas Pembangunan Nasional "Veteran" Jawa Timur, Surabaya,
Indonesia
(*) Corresponding Author
Abstract
This study aims to examine the relationship between students' perceived use of Artificial Intelligence (AI)-based learning media and their learning motivation, understanding of accounting concepts, and learning outcomes among Accounting undergraduate students. The AI medium was operationalized through the Unified Theory of Acceptance and Use of Technology (UTAUT) framework, comprising performance expectancy, effort expectancy, social influence, and facilitating conditions. This quantitative study employed a survey method on 65 accounting students from the 2021 cohort who had used or been exposed to AI-based learning media, selected through purposive sampling. Data were analyzed using Partial Least Squares–Structural Equation Modeling (PLS-SEM). The measurement model testing showed good validity and reliability across all constructs (outer loading > 0.70; AVE > 0.50; Cronbach's Alpha > 0.89). Hypothesis testing revealed a positive and significant relationship between the use of AI-based learning media and learning motivation (O=0.475; t=4.336; p<0.001), understanding of accounting concepts (O=0.722; t=8.684; p<0.001), and learning outcomes (O=0.498; t=5.603; p<0.001). However, R-Square values indicate that the contribution of AI-based media is weak for learning motivation (22.5%) and learning outcomes (24.8%), and moderate for understanding of accounting concepts (52.1%), suggesting that AI-based media functions as a supporting rather than a dominant factor in accounting learning. Given the cross-sectional and correlational nature of the research design, these findings should not be interpreted as evidence of causal relationships. This study offers an initial contribution to educational technology research in accounting, and provides a foundation for future studies employing designs that allow for stronger causal inference.
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