Tanty Oktavia
Bina Nusantara University Jakarta, Indonesia

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Analysis of Generative AI Chatbot in Improving Public Services: A Case Study on Program Indonesia Pintar (PIP) for Primary and Secondary Education Muhammad Nur; Tanty Oktavia
Attractive : Innovative Education Journal Vol. 8 No. 2 (2026): Attractive : Innovative Education Journal
Publisher : CV. Creative Tugu Pena

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51278/aj.v8i2.2469

Abstract

The Smart Indonesia Program (PIP), as a supporter of educational access, faces challenges in disseminating information that still relies on conventional methods such as email and telephone, thus requiring the implementation of Generative AI-based chatbot technology as a potential solution to improve the effectiveness of communication and public service delivery. This study aims to analyze the influence of the readiness, design, and implementation of Generative AI chatbots on the effectiveness of information delivery to PIP service users. This study uses a descriptive quantitative approach by distributing questionnaires to 407 respondents. The instrument was tested for validity and reliability. Regression testing and t-tests were conducted to assess the influence between variables. The results show that the design variable (X₂) has a strong effect on service effectiveness, with a t-value of 14.757 (significance level of 0.000). The regression coefficient of 0.683 indicates that each unit increase in chatbot design quality contributes significantly to an increase in perceived service effectiveness. The implementation variable (X₃) also has a significant effect with a t-value of 7.489 and a regression coefficient of 0.217. The design and implementation variables influence service effectiveness with a model contribution of 77.8%. However, the readiness variable (X₁) did not show a significant effect (p = 0.077). The R-squared value of 0.778 indicates that 77.8% of the variation in public service effectiveness can be explained by readiness, design, and implementation variables. The remaining 22.2% is influenced by other factors, such as external policies, user digital literacy, or organizational issues. These findings provide a strong foundation for further development of AI technology in the digital transformation of public services, particularly in the education sector.