Welcome to the forefront of conversational AI as we explore the fascinating world of AI chatbots in our dedicated blog series. Discover the latest advancements, applications, and strategies that propel the evolution of chatbot technology. From enhancing customer interactions to streamlining business processes, these articles delve into the innovative ways artificial intelligence is shaping the landscape of automated conversational agents. Whether you’re a business owner, developer, or simply intrigued by the future of interactive technology, join us on this journey to unravel the transformative power and endless possibilities of AI chatbots.
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In the twenty-first century, chatbots have become one of the most widely used technologies in our lives. They have rapidly evolved from simple rule-based systems to advanced AI-powered tools, now commonly applied in customer service, healthcare, education, and online shopping. This review paper looks closely at the different types of algorithms used to build chatbots. These include rule-based, retrieval-based, generative, transformer-based, and hybrid models. This paper focuses on how these algorithms help chatbots understand user intent, manage conversations, and generate replies. The paper also explores how natural language processing (NLP)and machine learning methods from older models like Naive Bayes and SVM to more advanced ones like LSTM, GRU, and transformer models improve chatbot performance. Various evaluation methods are discussed, including automatic metrics such as BLEU, ROUGE, METEOR, and Perplexity, as well as human-based evaluations assessing fluency, relevance, and engagement. This review further highlights key challenges, such as maintaining conversational context, addressing ethical concerns, supporting multiple languages, and protecting user privacy. By providing a comprehensive summary of chatbot algorithms and previous research, this study aims to support further advancements in the field.
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As this is a review paper, no new datasets were generated or analyzed during the study. The data supporting the findings of this review come from the published articles referenced throughout the manuscript.
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Institute of Information and Communication Technology, Bangladesh University of Engineering and Technology, 1000, Dhaka, Bangladesh
Md Tousif Hasan Lavlu, Afnanul Hassan, Sanjida Akhtar, Salsa- Bil- Labiba & Hossen Asiful Mustafa
Department of Computer Science and Engineering, Shanto-Mariam University of Creative Technology, Uttara 17, 1230, Dhaka, Bangladesh
Md Tousif Hasan Lavlu
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All authors have made substantial contributions to the conception and design of the manuscript. Md Tousif Hasan Lavlu contributed to the research framework and drafting the methodology and critical revisions. Afnanul Hassan provided the overall overview of the paper. Sanjida Akhtar formatted the paper. Salsa- Bil- Labiba conducted the literature review. Dr. Hossen Asiful Mustafa supervised and provided critical revisions.
Correspondence to Md Tousif Hasan Lavlu.
As this review paper is based on secondary data from existing literature, no human or animal subjects were involved. Hence, ethical approval was not required.
The authors declare no competing interests.
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Lavlu, M.T.H., Hassan, A., Akhtar, S. et al. Reviewing chatbot algorithms: methods for intelligent dialogue systems. Hum.-Intell. Syst. Integr. 7, 17–39 (2025). https://doi.org/10.1007/s42454-025-00074-y
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DOI: https://doi.org/10.1007/s42454-025-00074-y
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