AI-based conversational agents: A scoping review from technologies to future directions
Open accessAbstract
Artificial intelligence is changing the world, especially the interaction between machines and humans. Learning and interpreting natural languages and responding have paved the way for many technologies and applications. The amalgam of machine learning, deep learning, and natural language processing helped Conversational Artificial Intelligence (AI) to change the face of Human-Computer Interaction (HCI). A conversational agent is an excellent example of conversational AI, which imitates the natural language. This article presents a sweeping overview of conversational agents that includes different techniques such as pattern-based, machine learning, and deep learning used to implement conversational agents. It also discusses the panorama of different tasks in conversational agents. This study also focuses on how conversational agents can simulate human behavior by adding emotions, sentiments, and affect to the context. With the advancements in recent trends and the rise in deep learning models, the authors review the deep learning techniques and various publicly available datasets used in conversational agents. This article unearths the research gaps in conversational agents and gives insights into future directions.
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Cited by
Sorted by citations
McIntosh, T. R., Susnjak, T., Liu, T., Watters, P., Xu, D., Liu, D., & Halgamuge, M. N. (2025) · Technologies · doi:10.3390/technologies13020051 · Cited by 102 · see citing works
“These systems not only improve user engagement and satisfaction, but also maintain the flow of conversation over multiple turns, providing coherent, contextually relevant, and engaging experiences [208], [209].”
Background
- Authors
- Kusal, S., Patil, S., Choudrie, J., Kotecha, K., Mishra, S., & Abraham, A.
- Affiliations
- Symbiosis International University (IN)
- University of Hertfordshire (GB)
- Machine Intelligence Research Labs (US)
- Venue
- IEEE Access
- Published
- 2022
- Volume
- 10
- Pages
- 92337-92356
- ISSN
- 2169-3536 (ISSN-L)
- OpenAlex
- W4292825932
- Cited by
- 140
- References
- 99
- Citations by year
- 2026: 31 · 2025: 48 · 2024: 47 · 2023: 13 · 2022: 1
- FWCI
- 8.97
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- IEEE Access (publishedVersion, cc-by)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- Analytical Center for Government of the Russian Federation (70-2021-00143 dd. 01.11.2021, IGK000000D730321P5Q0002)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Speech and dialogue systems
- Physical Sciences › Computer Science › Artificial Intelligence › AI in Service Interactions
- Physical Sciences › Computer Science › Artificial Intelligence › Intelligent Tutoring Systems and Adaptive Learning
- Keywords
- chatbots, conversational AI, human-computer interaction, natural language processing, deep learning, machine learning, sentiment, conversational datasets, research gaps
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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