A Literature Survey of Recent Advances in Chatbots
Open accessPublished version of a preprint
Abstract
Chatbots are intelligent conversational computer systems designed to mimic human conversation to enable automated online guidance and support. The increased benefits of chatbots led to their wide adoption by many industries in order to provide virtual assistance to customers. Chatbots utilise methods and algorithms from two Artificial Intelligence domains: Natural Language Processing and Machine Learning. However, there are many challenges and limitations in their application. In this survey we review recent advances on chatbots, where Artificial Intelligence and Natural Language processing are used. We highlight the main challenges and limitations of current work and make recommendations for future research investigation.
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The recent introduction of AI chatbots has shifted how consumers access and search information online.
“The recent introduction of AI chatbots has shifted how consumers access and search information online [27].”
From Consumer Responses to Generative AI Chatbots Versus Search Engines for Product Evaluation (2025) · cites this paper as [27]
Cites
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“Turing’s description of the behaviour of an intelligent machine evokes the commonly understood concept of a chatbot [14].”
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“Nonetheless, different levels of embodiment-the way chatbots are humanlike [10]-and disclosure-how and when the nature of the chatbot is revealed to the user-seem to impact users’ engagement with and trust in chatbots [11]).”
BackgroundRecipes for Building an Open-Domain Chatbot
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“It is thus a simple yet widely general and flexible model that can be used to solve different NLP tasks [16,34].”
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A SWOT analysis of ChatGPT: Implications for educational practice and research
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Survey on Machine Learning Biases and Mitigation Techniques
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“While generative neural networks, which are built on algorithms that use deep learning to produce novel answers word by word depending on user input, are often trained on an enormous amount of natural language phrases, chatbots require a large quantity of conversational information to train [60].”
BackgroundConsumer Responses to Generative AI Chatbots Versus Search Engines for Product Evaluation
Kim, S., & Priluck, R. (2025) · Journal of Theoretical and Applied Electronic Commerce Research · doi:10.3390/jtaer20020093 · Cited by 20 · see citing works
“AI Chatbots: The recent introduction of AI chatbots has shifted how consumers access and search information online [27].”
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- Authors
- Caldarini, G., Jaf, S., & McGarry, K.
- Affiliations
- University of Sunderland (GB)
- Corresponding author
- Jaf, Sardar
- Venue
- Information
- Publisher
- MDPI AG
- Published
- 2022-01-15
- Volume
- 13
- Issue
- 1
- Pages
- 41
- ISSN
- 2078-2489 (ISSN-L)
- OpenAlex
- W4205201623
- Cited by
- 470
- References
- 10
- Citations by year
- 2026: 78 · 2025: 118 · 2024: 129 · 2023: 113 · 2022: 32
- FWCI
- 32.25
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Information (publishedVersion, cc-by)
- Preprints.org (acceptedVersion, cc-by)
- Sunderland Repository (University of Sunderland) (acceptedVersion)
- Sunderland Repository (University of Sunderland) (acceptedVersion, public-domain)
- arXiv (Cornell University) (submittedVersion)
- HAL (submittedVersion, cc-by)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- arXiv (Cornell University) (cc-by)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › AI in Service Interactions
- Physical Sciences › Computer Science › Artificial Intelligence › Speech and dialogue systems
- Physical Sciences › Computer Science › Artificial Intelligence › Natural Language Processing Techniques
- Keywords
- chatbots, natural language processing, machine learning, customer service automation, virtual assistants
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