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Journal : Ta'dib

Issues in the Implementing of Online Learning in Islamic Higher Education During the Covid-19 Pandemic Mahfud Junaedi; Nasikhin Nasikhin; Silviatul Hasanah
Ta'dib Vol 25, No 1 (2022)
Publisher : Universitas Islam Negeri Mahmud Yunus Batusangkar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31958/jt.v25i1.5365

Abstract

Our research aims to find out the problems experienced by students and lecturers in the implementation of online learning, during the Covid-19 pandemic and efforts to overcome styles in universities. Qualitative descriptive approaches and exploratory case studies were used to collect data from students and lecturers at the State Islamic University of Walisongo Indonesia. This study shows that the implementation of online learning during the COVID-19 pandemic has led to changes in the learning system that have led to a new lecture paradigm that can be highlighted in four sub-scales including the level of saturation, seriousness and participation in learning, learning motivation, and the level of intellectual satisfaction. Meanwhile, the efforts made by lecturers in overcoming this problem are by maximizing the use of learning technology tools, modifying certain learning methods such as project based learning, integrated curriculum, and blended learning. This study contributes to research on pedagogy and online learning by clarifying the role of lecturers in finding problem solving problems faced by students.
Assessing Artificial Intelligence Plagiarism Risk: ChatGPT vs Scite Among Islamic Education Students Ismail, Ismail; Suja'i, Suja'i; Hasanah, Silviatul
Ta'dib Vol 28 No 2 (2025)
Publisher : Universitas Islam Negeri Mahmud Yunus Batusangkar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31958/jt.v28i2.16117

Abstract

This study examines and compares the effects of ChatGPT and Scite_AI, on plagiarism tendencies among students of Islamic Religious Education in Indonesia. Adopting a quantitative research design, the study employed multiple linear regression analysis to evaluate both the partial and simultaneous influences of these tools on academic plagiarism. Prior to regression analysis, classical assumption tests—including normality (Prior to the regression analysis, classical assumption tests—including normality (Kolmogorov-Smirnov p = 0.088), multicollinearity (VIF < 10), multicollinearity (VIF < 10), heteroscedasticity (Breusch-Pagan test), and linearity (scatterplot of residuals)—were rigorously conducted to ensure model validity. The results reveal that both AI tools significantly contribute to increased plagiarism tendencies; however, ChatGPT demonstrates a markedly stronger effect (β = 0.4941; p < 0.001) compared to Scite (β = 0.1042; p < 0.001). The overall regression model is statistically significant (F = 87.32, p = 0.000) and satisfies all classical assumptions, confirming its reliability. Theoretically, this research enriches academic integrity literature by positioning AI tool typology—particularly the distinction between generative and verification tools—as a critical predictor of plagiarism behavior. Practically, it calls for differentiated AI literacy strategies in Islamic higher education, advocating for the integration of adab al-‘ilmu (ethics of knowledge) into digital literacy curricula to foster moral discernment and responsible technology use among future religious educators.