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<p>The current research explores how AI techniques are integrated into higher education knowledge management systems for undergraduate students in Indonesia. A comprehensive model combining Knowledge Management (KM), the Information Systems (IS) Success Model, and the Expectation-Confirmation Model (ECM) analyzes how AI tools affect student satisfaction, intention to use, and actual usage. A sample of 893 students from six universities in Sumatra was selected for this study, with a 95% confidence level and a margin of error of ± 3%. PLS-SEM was used to analyze 805 valid responses from the sampled students. Initial data management was done in Microsoft Excel, followed by cleaning and validation in SPSS 23. Instructor quality, knowledge application, sharing, and acquisition significantly improve AI tool satisfaction, confirming hypotheses H4, H6-H14. However, hypotheses H1-H3 and H5 were not supported, indicating that the quality of AI tool content and the technical system do not significantly influence satisfaction or perceived usefulness. The current study seeks to enhance educational intention and satisfaction through AI technology in support of SDG 4 (development aid and teacher training, and education quality).</p>

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Satisfaction and intention to use AI tools among Indonesian university students in Sumatra

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年2月21日

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  • Akhmad Habibi,
  • K. A. Rahman,
  • Robi Hendra,
  • Supian Supian,
  • Turki Mesfer Alharmali,
  • Mohd Sofian Omar Fauzee,
  • Hamdy Abdullah,
  • I Wayan Sumandya,
  • Edi Surya Negara

摘要

The current research explores how AI techniques are integrated into higher education knowledge management systems for undergraduate students in Indonesia. A comprehensive model combining Knowledge Management (KM), the Information Systems (IS) Success Model, and the Expectation-Confirmation Model (ECM) analyzes how AI tools affect student satisfaction, intention to use, and actual usage. A sample of 893 students from six universities in Sumatra was selected for this study, with a 95% confidence level and a margin of error of ± 3%. PLS-SEM was used to analyze 805 valid responses from the sampled students. Initial data management was done in Microsoft Excel, followed by cleaning and validation in SPSS 23. Instructor quality, knowledge application, sharing, and acquisition significantly improve AI tool satisfaction, confirming hypotheses H4, H6-H14. However, hypotheses H1-H3 and H5 were not supported, indicating that the quality of AI tool content and the technical system do not significantly influence satisfaction or perceived usefulness. The current study seeks to enhance educational intention and satisfaction through AI technology in support of SDG 4 (development aid and teacher training, and education quality).