Mapping the Evolution of AI Chatbots in Indonesia (2021-2025): A PRISMA-Based Systematic Literature Review on Applications, Technologies, and Impacts
DOI:
https://doi.org/10.21512/emacsjournal.v8i1.14942Keywords:
AI chatbot, PRISMA 2020, digital services, conversational technology, digital transformation, IndonesiaAbstract
The rapid development of artificial intelligence has accelerated the adoption of chatbots in organizations in Indonesia. But there is no systematic synthesis of the development of this technology in Indonesian context. This research provides a systematic review of the development and implementation of AI chatbots in Indonesia in 2021–2025, with the aim of filling the research gap related to sectoral applications, technological trajectories, and contextual challenges. A systematic literature review was conducted following the PRISMA 2020 guidelines on the Scopus, Google Scholar and arXiv databases to collect 257 initial records. After duplicate removal and a multi-step screening process, 16 high-quality studies were included in the final synthesis. Thematic analysis identified four main findings: (1) AI Chatbots are found in higher education, healthcare, banking, public services, fintech, e-commerce, and SMEs; (2) The technology has evolved from rule-based approaches (AIML, TF-IDF) to machine learning (Seq2Seq LSTM, Rasa+IndoBERT) and the latest large language model integration (GPT-3.5, Vertex AI); (3) Reported impacts include improved user satisfaction (SUS scores 80.1), operational efficiency, and 24/7 service availability; and (4) Existing challenges include accuracy in Indonesian language processing, complexities in system integration, data privacy issues, and varied levels of digital literacy. This review is the first systematic mapping of Indonesia’s AI chatbot landscape and makes evidence-based recommendations for the development of locally-adapted, culturally-sensitive models. Results show that future chatbot development should emphasize Indonesian language datasets and hybrid architectures that combine automation and human oversight.
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