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Cognitive and Technological Factors Shaping Students’ Sustained Use of ChatGPT in Higher Education Aribowo, Arnold; Hery, Hery; Widjaja, Andree Emmanuel
Journal of Applied Data Sciences Vol 7, No 2: May 2026
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v7i2.1291

Abstract

This study examines the cognitive and technological factors shaping students' sustained use of ChatGPT in Indonesian higher education. Despite the rapid adoption of generative Artificial Intelligence (AI) in education, a clear understanding of the factors sustaining continued engagement with such systems remains limited. While continuance intention has been widely examined, the application of the Expectation–Confirmation Model (ECM) in generative AI contexts remains underexplored. This gap is especially evident when considering the role of AI-specific system attributes in shaping post-adoption evaluations. Although ECM has been extended with various constructs in prior studies, the specific integration of AI characteristics, particularly perceived intelligence and anthropomorphism, has not been explored in generative AI use in education, especially within Indonesian higher education. To address this gap, a multi-theoretic framework integrating ECM and AI characteristics was developed. Data from 322 Indonesian students were analyzed using Partial Least Squares-Structural Equation Modeling. All ten hypotheses were supported, and the model explains 43.3% of the variance in continuance intention (R² = 0.433). Perceived Intelligence strongly influences Perceived Anthropomorphism with a path coefficient of 0.591, representing the strongest relationship in the model, while other paths demonstrate moderate or modest effects. The findings confirm ECM's robustness in generative AI settings and highlight the pivotal role of AI characteristics in shaping post-adoption evaluations and sustained use. These results contribute to the growing body of research on generative AI adoption in education by demonstrating how system intelligence and human-like interaction jointly influence continuance intention. The findings also offer practical guidance for AI developers to enhance system intelligence and natural interaction. Future research could explore how students experience AI over time and what shapes their sustained use using different research methods.
Co-Authors Alencia Haryani, Calandra Alvira Putri Yudini Alya M. Amalia Amalia, Alya M. Amelia Magdalena Kaheja Amelia Magdalena Kaheja Amelinda Chendra Arnold Aribowo Arnold Aribowo Arnon M Sugiarto Azim Ashar Calandra A. Haryani Calandra Alencia Haryani Calandra Alencia Haryani Carolyn Feiby Supit Christian Marsel Wijaya2 Christopher, Raphael Debora Kathrin Yuwono Debora Kathrin Yuwono Debora Margareta Efendi Tarigan, Riswan Ellyse Obe Johanis Eric Jobiliong Feliks Victor Parningotan Samosir Ferdinand, Ferry Vincenttius Filbert Chan Fransisko, Andy Gabrielle Florencia Gennady, Erick Goestjahjanti, Francisca Sestri Habsara Hareva, David Harjono, Nathanael Joshua Haryani, Calandra A. Haryani, Calandra Alencia Hery Hery Hery Hery Hery Hery Hery Hery Hery Hery Juan Situmorang Hikam, Ihsan Nuril Husni Teja Sukmana Irene Eka Sri Saraswati Irene Eka Sri Saraswati Jamesdry Jefrin Laia Joshua Nathanael Justin A. Haratua Karnawi Kamar, Karnawi Kristina G. Simanjuntak Kusno Prasetya Kusno Prasetya Laurentia Anggun P Lisia, Vanella Maya Avinda Mayumi Utama Michelle Angelica Mitra, Aditya R. Mouw, Christ Wibowo Mulyati Mulyati Nathalie, Julia Nathanael, Joshua Prasetya, Kusno Renaldi, Ary Renaldo Luih, Joshua Ririn Ikana Desanti Riswan E Tarigan Riswan E. Tarigan Riswan E. Tarigan Riswan Efendi Tarigan Rosanna, Nadya Sugiarto, Arnon M Supriyanti, Dedeh Suryasari Suryasari Suryasari Suryasari Suryasari Suryasari Suryasari Suryasari Suryasari Tania Jovita Wibowo Tarigan, Riswan E. Toer, Guevara Ananta Vanella Lisia Veronica, Winnie Vincent Cahyadi Vivi Melinda Wijaya, Yoana Sonia Willy Darmawan Yumna, Saidah ‪Alfa Satya Putra ‪Alfa Satya Putra