Aim/PurposeThis research aims to test the continued use of an AI platform through an extended Expectation Confirmation Model (ECM).

BackgroundThe paper addresses the issues of user trust and satisfaction in the context of an AI platform in education by employing the ECM to analyze how system quality, information quality, and user satisfaction influence continued use intentions among respondents from various educational institutions in Indonesia.

MethodologyWe utilized partial least squares structural equation modeling (PLS-SEM) to analyze data from 390 respondents. The analysis followed a two-step approach, focusing on measurement and structural models. The scales for system quality, information quality, confirmation, satisfaction, and continued use intention were adapted from established measures to fit the context of sustainable AI use.

ContributionThe study identifies key factors such as system quality, information quality, satisfaction, and trust that influence continued use intentions. The findings show that satisfaction predicts trust more significantly than the reverse, suggesting that enhancing user satisfaction is key to fostering trust and encouraging ongoing engagement with an AI platform.

FindingsDemographic analysis revealed a diverse sample, supporting the generalizability of our results. User satisfaction is identified as a stronger predictor of trust than vice versa, indicating that satisfied users are more likely to trust the AI platform, which is essential for continued use. Both system quality and information quality positively influence user satisfaction, which in turn leads to greater intentions to continue using the platform. Trust serves as a significant mediator between confirmation and continued use intention. Although confirmation itself does not directly impact satisfaction, it influences satisfaction through trust, ultimately affecting users’ intentions to continue engaging with the platform.

Recommendations for PractitionersPractitioners and policymakers should prioritize strategies that enhance user satisfaction with the AI platform, as this has been shown to significantly influence trust and intentions for continued use. Such strategies may include improving system usability, providing responsive customer support, and ensuring the relevance of AI outputs.

Recommendation for ResearchersFuture studies should investigate additional factors influencing user behavior with the AI platform, such as perceived ease of use, user demographics, and contextual variables in specific educational settings.

Impact on SocietyThe findings of this paper have broader implications for integrating AI platforms across sectors, especially in education. By highlighting the importance of user satisfaction and trust, the research underscores the need for ethical AI development that prioritizes user experience. This can lead to more effective AI applications that enhance learning outcomes, promote equitable access to technology, and foster a more informed public discourse about AI’s role in society.

Future ResearchFuture research should investigate the role of user experience by examining how various elements, such as interface design and personalization, influence satisfaction and trust in AI platforms. Additionally, understanding cultural influences is crucial, as it can reveal how cultural differences affect user perceptions and acceptance of AI platforms, particularly in diverse educational contexts. Lastly, evaluating the long-term effects of AI integration in education is essential, as this can provide insights into its impact on student engagement, learning outcomes, and overall satisfaction with educational technologies.