Chat with Bots Intelligently: A Critical Review & Analysis
Abstract
Conversation Agents also known as Chatbots are considered the next big thing in the modern technological era. Chatbots aim to emulate humans, by mimicking human conversation in the most realistic way possible. There are several methods to generate such intelligent agents. Thus to evaluate the effectiveness of the various approaches, a systematic review of the approaches is required. This paper traces the evolution of chatbots and also elaborates on the impact chatbots have in different industries. Additionally, it also presents a survey of the different modern chatbot methodologies that different researchers have proposed. When analyzing the different approached, this paper has taken into consideration the dataset, input-output, design, development, performance metric, and limitations into consideration. In the end, we've elaborated on the key insights that were obtained pertaining to the crucial factors that influence chatbot design, generation, and development.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Most of these interventions relied on retrieval-based chatbots, which use predefined responses or static databases and often result in rigid, repetitive interactions.
“However, most of these interventions relied on retrieval-based chatbots, which use predefined responses or static databases and often result in rigid, repetitive interactions [17-19].”
From Generative AI Mental Health Chatbots as Therapeutic Tools: Systematic Review and Meta-Analysis of Their Role in Reducing Mental Health Issues (2025) · cites this paper as [19]
Most current reviews investigated the effectiveness of rule-based or retrieval-based chatbots.
“However, most of these interventions relied on retrieval-based chatbots, which use predefined responses or static databases and often result in rigid, repetitive interactions [17-19].”
From Generative AI Mental Health Chatbots as Therapeutic Tools: Systematic Review and Meta-Analysis of Their Role in Reducing Mental Health Issues (2025) · cites this paper as [19]
Cites
From Eliza to XiaoIce: Challenges and opportunities with social chatbots
Shum, H.-Y., He, X.-D., & Li, D. (2018) · Frontiers of Information Technology & Electronic Engineering · doi:10.1631/fitee.1700826 · Cited by 749 · cited 2× · see citing works
“It incorporated Rogerian Psychotherapy for conversations[19].”
BackgroundChatbot for university related FAQs
Ranoliya, B. R., Raghuwanshi, N., & Singh, S. (2017) · 2017 International Conference on Advances in Computing, Communications and Informatics (ICACCI) · doi:10.1109/icacci.2017.8126057 · Cited by 425 · see citing works
López, G., Quesada, L., & Guerrero, L. A. (2018) · Advances in Intelligent Systems and Computing · doi:10.1007/978-3-319-60366-7_23 · Cited by 277 · see citing works
Flagged first, then by citations
Cited by
Sorted by citations
Chatbots applications in education: A systematic review
Okonkwo, C. W., & Ade-Ibijola, A. (2021) · Computers and Education: Artificial Intelligence · doi:10.1016/j.caeai.2021.100033 · Cited by 879 · see citing works
Zhang, Q., Zhang, R., Xiong, Y., Sui, Y., Tong, C., & Lin, F.-H. (2025) · Journal of Medical Internet Research · doi:10.2196/78238 · Cited by 56 · see citing works
“However, most of these interventions relied on retrieval-based chatbots, which use predefined responses or static databases and often result in rigid, repetitive interactions [17-19].”
An Evaluation of Generative Pre-Training Model-based Therapy Chatbot for Caregivers
Wang, L., Mujib, M. I., Williams, J., Demiris, G., & Huh-Yoo, J. (2021) · arXiv · doi:10.48550/arxiv.2107.13115 · Cited by 21 · see citing works
- Authors
- Dsouza, R., Sahu, S., Patil, R., & Kalbande, D. R.
- Venue
- 2019 International Conference on Advances in Computing, Communication and Control (ICAC3)
- Publisher
- IEEE
- Published
- 2019-12
- Pages
- 1-6
- OpenAlex
- W3011267247
- Cited by
- 27
- References
- 3
- Citations by year
- 2026: 1 · 2025: 7 · 2024: 7 · 2023: 3 · 2022: 6 · 2021: 3
- FWCI
- 0.86
- Type
- conference paper
- Language
- en
- Open access
- closed
- Licence
- Locations
- International Conference on Advances in Computing, Communication and Control (ICAC3) (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Speech and dialogue systems
- Physical Sciences › Computer Science › Artificial Intelligence › AI in Service Interactions
- Social Sciences › Psychology › Social Psychology › Social Robot Interaction and HRI
- Keywords
- chatbots, conversation agents, chatbot design, chatbot development
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 ›
Are you an author, or do you see a problem with this page? Request a review