Talk to the News: Designing a Human-Like AI Chatbot for Verified Information - Octavianto, Adi Wibowo; Primadini, Intan; Supriadi, Dandi (Jurnal Kajian Jurnalisme, 2026)
Background:  There are challenges in journalism today, including misinformation, low-level news literacy, and declining interest in traditional news formats. To respond to the challenges, this article reports an applied study project about Arya, an AI-driven chatbot designed to convey factual information through conversational and interactive storytelling. Purpose: This research explores the feasibility, user acceptance, and indications of Arya’s communicative potential in supporting a personalized, engaging, and verifiable news consumption experience. Methods: The project employed an approach so-called Design Science Research (DRS) and Retrieval-Augmented Generation (RAG) to ensure the use of theories of anthropomorphism, parasocial interaction, and artificial communication in enhancing engagement and trust through adaptive human-like traits. Results: It is found that Arya may facilitate more personalized and engaging news experiences. Users respond positively to a conversational approach that blends storytelling and Q&A. However, some limitations were found in terms of scope, adaptability, and naturalness. Conclusion: Chatbot designs for conveying factual information need to incorporate anthropomorphism to enhance users’ trust and comfort during interactions. It is not only about technical efforts but also the need to design communication methods that feel human and consistent. Nevertheless, accuracy and transparency are priorities, especially for journalists. Implications: Combining rule-based logic, LLMs, and RAG can be a strategy for more verifiable communication. Furthermore, the application of anthropomorphism and parasocial interaction principles could strengthen engagement, but it should be balanced with flexibility, knowledge, and dialogue quality to make the system more natural and resilient.