Revolutionizing Feedback: Integrating AI-Powered Voice Assistants in Digital Learning Platforms for Instant Student Support - Fiqih Ananda; Zain Nizam; Pong Krit (Journal of Paddisengeng Technology, 2025)
Background. The increasing use of digital learning platforms has highlighted the need for effective and immediate feedback mechanisms. Traditional feedback approaches often suffer from delays and limited personalization, which can hinder students’ learning progress. Artificial Intelligence (AI)-powered voice assistants present a promising solution by offering instant, adaptive, and interactive feedback in real time. Purpose. This study aimed to explore the integration of AI-powered voice assistants into digital learning platforms, focusing on their effectiveness in delivering instant student support. Specifically, it examined how such integration impacts students’ learning engagement, perceived usefulness, and satisfaction. Method. A mixed-method approach was employed, involving a survey of 312 university students across three digital learning environments and in-depth interviews with instructors. Quantitative data were analyzed using statistical techniques, while qualitative data provided deeper insights into user experiences. Results. Findings indicate that AI-powered voice assistants significantly enhance students’ perception of feedback immediacy, personalization, and accessibility. Students reported higher motivation and engagement when receiving real-time oral feedback, while instructors emphasized the potential of voice assistants to reduce workload and improve learning efficiency. However, challenges such as speech recognition accuracy and contextual limitations were also noted. Conclusion. The study underscores the transformative role of AI-powered voice assistants in revolutionizing digital feedback systems. Integrating such technology into digital learning platforms has the potential to support more personalized, efficient, and engaging learning experiences. Future implementations should focus on improving natural language processing accuracy and aligning feedback strategies with diverse learning contexts.