- A chatbot is any software that simulates conversation, from guided menus to assistants with generative AI capable of interpreting natural language and performing complex actions.
- There are chatbots based on rules, keywords, and artificial intelligence models and large language models, each with different strengths, limitations, and data requirements.
- Chatbots are already used in customer service, marketing, education, public administration, healthcare and restaurants, automating tasks 24/7 and reducing costs while still generating new risks.
- Key challenges include hallucinations, misinformation, privacy, biases, and the psychological and occupational impact, which require combining technology with supervision and strong ethical frameworks.
Chatbots have become the face of artificial intelligence for millions of people. They're on websites, apps, social media, messaging platforms, and even in toys, answering questions, closing sales, or simply chatting. But despite their widespread presence, there's still considerable confusion about exactly what they are, the different types available, how to choose AI models , and the true extent of their capabilities.
At the same time, the technology behind chatbots has changed radically : we've gone from systems based on very rigid rules to conversational agents supported by large language models capable of generating text almost like a person. This opens up a world of possibilities… and also risks, from misinformation to the impact on employment and mental health.
What is a chatbot and how does it differ from an AI chatbot and a virtual agent?
In everyday language, we often talk about chatbots, AI chatbots, virtual assistants, and virtual agents as if they were all the same thing, but not all of these terms describe exactly the same type of technology . Understanding these differences helps in choosing the right solution for each project or company.
The broadest definition of a chatbot is any software that simulates human conversation , whether through predefined menus and buttons or advanced conversational AI. This includes everything from traditional phone systems to assistants that respond on websites, mobile apps, social media, or SMS.
When we talk about AI chatbots, we're referring to those that explicitly rely on artificial intelligence techniques . They typically combine machine learning, natural language processing (NLP), natural language understanding (NLU), and, increasingly, deep learning models trained on large volumes of text. Thanks to this, they are able to interpret questions posed in natural language, detect intentions and nuances, learn from experience, and generate more flexible and natural responses.
One step further are virtual agents, which not only converse but also act . In addition to conversational AI and deep learning, they often integrate robotic process automation (RPA) or other back-office systems. This allows them to execute direct actions without human intervention: modify a reservation, process a payment, change an appointment, or trigger an internal workflow after understanding the user's intent.
A simple example illustrates the difference: if someone asks about tomorrow's weather, a traditional chatbot might only provide the forecast if the user follows a specific phrase from its menu. An AI chatbot will understand more open-ended questions like "What will the weather be like tomorrow?" and will respond that it will rain. A virtual agent, in addition to warning about rain, can suggest setting an earlier alarm to anticipate traffic jams, or even manage a rescheduled appointment in another system.
A brief history of chatbots: from ELIZA to generative models
The idea of a machine having a conversation with a person is not new. In fact, the theoretical origin of chatbots dates back to 1950 , when Alan Turing posed his famous test: if a program is able to converse in writing with a human to the point that a judge cannot distinguish who is who based solely on the responses, we could consider that the machine demonstrates intelligence.
In 1966, Professor Joseph Weizenbaum presented ELIZA, considered the first chatbot in history . It simulated a therapist and worked by searching for keywords in what the user typed to generate predefined responses, such as "Tell me more about your family" if it detected the word "mother." Eliza didn't understand the content, but it created a strong illusion of understanding, which surprised even Weizenbaum himself.
Shortly after , PARRY emerged, created in 1972 by psychiatrist Kenneth Colby , which simulated a patient with paranoia. Other pioneering projects included CYRUS, which represented then-US Secretary of State Cyrus Vance and updated its database with news, and Racter, popular in the 80s as a conversational program.
In the mid-90s , ALICE appeared, a chatbot based on the AIML (Artificial Intelligence Markup Language) markup language . Like ELIZA, it worked with predefined patterns and responses, but it incorporated tens of thousands of knowledge categories. Its development gave rise to an entire community of "Alicebots" and demonstrated that, with a good set of rules, a bot could be surprisingly convincing in focused conversations.
In the late 90s, Jabberwacky was launched, which began to learn answers from conversations with users in real time, and during the 2000s and 2010s, competitions such as the Loebner Prize or The Chatterbox Challenge proliferated, focused on rewarding the bots that best imitated a human in tests inspired by the Turing test.
In parallel, accessible platforms and frameworks emerged (such as the Watson, LUIS, and Dialogflow engines, and services like collect.chat, chatfuel, and FlowXO), which significantly reduced the technical barriers to creating decent chatbots without large development teams. This helped companies and users begin to apply bots far beyond the academic sphere.
The real revolution came with large language models (LLMs) based on transformer architectures . Systems like GPT, and commercial products like ChatGPT, Gemini, Claude, and Grok, demonstrated that it was possible to maintain fluid conversations, generate complex texts, and easily adapt to a multitude of tasks without specific training. These models have given rise to the new generation of generative chatbots, capable of responding, summarizing, translating, writing code, and even reasoning within certain limits, and they present risks as shadow models.
Main types of chatbots according to their technology
To better understand what to expect from a bot, it's helpful to distinguish between different types of chatbots based on their construction and logic . In practice, many solutions combine elements from various approaches, but this classification provides guidance.
Rule-based or menu-based chatbots
Rule-based chatbots are the simplest way to automate conversations . They function like decision trees: they offer buttons, menu options, or predefined questions, and the user navigates through them until they reach the answer or action they need. Each path is programmed in advance, and the bot doesn't "understand" free language.
In this approach, each question or combination of options is associated with a specific answer . All users who select the same option receive the same output. These systems are quick to set up for simple FAQs, but very limited if many unknown factors, unexpected questions, or changing needs come into play.
A practical alternative is the ITR (Interactive Text Response) chatbot, or one based on menus and buttons , widely used on websites and messaging platforms. These offer quick responses in the form of buttons, turning the conversation into a guided tour. They are ideal for simple tasks such as checking schedules, basic order statuses, or managing standard reservations.
Keyword-based chatbots
So-called declarative or keyword-based chatbots attempt to go a step further than the decision tree . They analyze the text entered by the user, detect specific keywords (such as "activate account", "change password", or "shipping status"), and return a response associated with those keywords.
This approach relies on keyword recognition techniques to infer intent, topic, or even sentiment . For example, if the user types "How do I activate my account?", the bot detects "activate my account" and responds with a step-by-step guide. It's still a pre-programmed system, but somewhat more flexible because it allows for some variation in the way the question is phrased.
Despite this flexibility, these bots only function correctly within the set of topics and expressions that the team has planned and configured . When they deviate from this framework, they tend to malfunction or repeat generic responses that are frustrating for the user.
Chatbots with artificial intelligence and generative models
With the maturation of machine learning and deep learning, much more powerful chatbots have emerged that combine NLP, NLU, and natural language generation (NLG) . Instead of simply matching patterns, they probabilistically estimate what the user means, what information is relevant, and how to compose a coherent, word-by-word response.
Chatbots based on generative models typically use large language models trained on massive text corpora . This allows them to hold open conversations, smoothly change topics, interpret simple sarcasm, opinions, and nuances, and even adapt their tone. For example, when faced with a message like "I know it's rush hour, but when can I get my food?", they can grasp the contextual reference (rush hour) and respond with a reasonable estimate and a clear explanation.
In practice, these systems can also connect to other services : web search engines, image generation engines, internal databases, ERPs, or CRMs. Thanks to this, a modern chatbot not only answers questions but can also search for up-to-date information, generate images on demand, or trigger actions on business systems.
However, this power comes at a price: they require vast amounts of data to train and can "hypothesize" answers . That is, they can generate explanations that sound plausible but are false or inaccurate. When this contaminated content is reused and disseminated, it becomes known as "botshit," a cheap and widespread form of AI-generated disinformation.
Uses and applications of chatbots in companies and organizations
With technological advancements, chatbots have moved from laboratories to all sectors. Today, they are used in large corporations, SMEs, public administrations, education, healthcare, and restaurants. Their main advantage is the automation of repetitive interactions with users, customers, patients, or students.
A classic area is customer service and support . Banks, insurance companies, airlines, hotel chains, online retailers, telecommunications providers, and media platforms use bots to answer frequently asked questions, guide processes, handle simple complaints, or escalate inquiries to the appropriate human agent. In many cases, the bot acts as the first level of support, filtering inquiries and reducing the workload of call centers, which is key to improving customer satisfaction.
Social media and messaging apps have become natural channels for these assistants . Facebook Messenger opened its platform to bots in 2016, and tens of thousands were created within months; WhatsApp also launched pilot programs with major brands like KLM and Aeroméxico, which were already offering flight information and boarding passes via chat. Services like WeChat had been integrating mini-apps and conversational bots for all kinds of tasks for years.
In the realm of e-commerce and marketing , chatbots are used to nurture leads, recommend products, launch personalized campaigns, gather feedback or conduct brief surveys, and guide the purchase process. They can initiate conversations using keywords, respond to social media comments, or trigger automated and trackable email opt-in campaigns; they are a natural tool for internet marketing.
There are sectors where its impact is particularly visible. In the restaurant industry, for example, platforms like OlaClick offer WhatsApp bots specialized for restaurants and bars . These can manage orders, reservations, inquiries about opening hours, abandoned cart recovery, and order status notifications. Integration with digital menus, payments, and tracking allows many establishments to increase orders by up to 40% without adding staff.
Chatbots in public administration, education and politics
Public institutions have also discovered that a well-trained virtual assistant can greatly reduce the burden of in-person and telephone customer service . An illustrative example is the assistant deployed in electoral processes such as the Andalusian Parliament elections or the Spanish General Elections, which allowed any citizen to ask in natural language about deadlines, procedures, or requirements and obtain the correct information thanks to semantic search engines.
In the educational field, chatbots are used to support teachers and students . Some institutions integrate them into their e-learning platforms or LMS to answer questions about enrollment, assessment deadlines, access to materials, or technical issues, and are an example of AI agents in education . Others are used as virtual tutors that suggest activities, ask questions, correct simple exercises, or help prepare for exams.
These bots can perform administrative functions (calendar management, reminders, email), answer FAQs, provide motivational support, or help users practice specific skills , such as conversation in a language. Tools like Duolingo or chatbots like "Soy Diego" or "Soy María" allow users to practice Spanish by correcting spelling and grammar errors in real time, which is very useful for self-directed learning.
In classrooms and schools, chatbots act as interactive mediators available 24/7 , especially valuable for resolving repetitive, low-level cognitive questions and freeing up teachers for more creative or complex tasks. For families, they can be a convenient channel for finding out about schedules, admissions procedures, or school services without the need for phone calls or in-person visits.
In the political arena, even “virtual politicians” have emerged, such as SAM in New Zealand and Leader Lars in Denmark , designed to debate public issues, gather citizen opinions, and explain policy programs. While still experiments, they open the door to new forms of participation and raise ethical questions about representation, manipulation, and algorithmic transparency.
Chatbots in health, wellness and mental health
Healthcare is another field where chatbots have begun to gain considerable traction. Many organizations use conversational assistants to help locate health centers, manage appointments, provide information about medications, or answer simple questions about symptoms , as long as they are not emergency situations.
During the COVID-19 pandemic, for example, the WHO and the Indian government collaborated with WhatsApp to launch chatbots that answered questions about the disease , symptoms, prevention measures, and travel restrictions. These tools helped alleviate the strain on phone lines and provide verified information on a large scale.
Within the field of mental health, chatbots designed to offer basic emotional support or psychological coping techniques have emerged , such as Woebot, Wysa, Vivibot, and Tess. They are usually based on guided conversation models inspired by cognitive-behavioral therapy, proposing exercises in reflection, reframing thoughts, or mood tracking.
Several studies indicate that people tend to share difficult emotions more easily with a chatbot than on open social media platforms because they feel greater anonymity and less judgment. In some cases, users even rate the responses of these bots as more empathetic than those of certain professionals, although this may be a biased perception.
However, the use of chatbots in mental health carries significant risks: algorithmic bias, data privacy, lack of human oversight, and inadequate response in crisis situations . Recent evaluations of large commercial chatbots have shown that none consistently meet the safety standards a clinician would require when handling disclosures of suicidal ideation or other psychological emergencies.
Cases such as that of the National Eating Disorders Association in the United States, which replaced its human helpline with a chatbot and had to withdraw it after it offered harmful advice , show that these systems cannot replace professionals in sensitive contexts and that they should always be implemented with clear protocols for referral to human resources in risky situations.
Advantages of chatbots and benefits for businesses and users
Despite their limitations, well-designed chatbots offer clear advantages for both businesses and end users . This explains their continued growth in adoption across all sectors.
First, they offer continuous 24/7 availability , something impossible to maintain with human staff alone without exorbitant costs. A bot can respond in the early hours of the morning, on weekends, or on holidays with the same speed as during business hours, thus improving the customer experience.
Furthermore, they can handle multiple conversations simultaneously . While a human agent is limited to managing only a few interactions at a time, a chatbot can handle hundreds or thousands, absorbing peak demand (sales, campaigns, mass incidents) and preventing waiting queues or line saturation.
They also help reduce response times and standardize the quality of service . In business contexts, this translates into a better brand image, greater customer loyalty, and savings in operating costs. In SMEs, where there are no large call centers, a good chatbot can make the difference between being able to offer reasonable support or not being able to at all.
Another important point is that they record and structure interaction data . This information is invaluable for detecting recurring problems, improving products, adjusting internal processes, or enriching predictive models. Many bots integrate with CRMs to tag leads, segment audiences, or trigger automated actions based on user behavior.
Limitations, risks, and common problems of chatbots
It's not all advantages. Many current chatbots still don't reach their full potential due to design flaws, lack of data, or poor conversational understanding . Strictly rule-based bots, for example, break down as soon as the user deviates from the intended script or doesn't know exactly which option to choose.
Even advanced generative models make significant errors. While they are very good at producing fluent text, they are not always factually accurate and may invent names, dates, or events if their training does not account for the specific situation. This tendency toward "hallucination" is especially problematic in sensitive fields such as finance, healthcare, or legal advice.
In terms of security, chatbots present significant challenges regarding data protection, encryption, and access control , making risk management crucial . If they store conversation histories containing personal information, this database becomes a highly attractive target for attackers. Furthermore, when operating on third-party platforms (messaging, social networks), they must comply with the policies of those services, which may differ from the criteria of the organization owning the bot.
There is also the risk of malicious use of chatbots for sending spam, phishing, or political propaganda . Bots designed to impersonate real people, with well-developed identities and credible behavioral patterns, can spread fake news, manipulate debates, or generate a false sense of consensus (“artificial social proof”) on a large scale and at low cost.
Finally, at a social and professional level, the automation of conversational tasks doesn't just affect repetitive jobs. Advanced chatbots are beginning to compete in creative and knowledge-based activities , such as copywriting, report writing, and programming, sparking debates about the future of employment, the quality of work, and the concentration of economic power in the hands of a few large technology platforms.
Impact on mental health, isolation and “chatbot psychosis”
Prolonged interaction with chatbots, especially those designed for companionship or general conversation, has psychological effects that research is only beginning to explore in depth . While they can be a source of support during times of loneliness, there is also a risk of becoming detached from real human contact.
It has been observed that some emotionally vulnerable people tend to attribute to bots a degree of understanding or intentionality that they do not possess , leading them to develop unrealistic beliefs about their capabilities or the information they provide. This is most concerning when the model presents conspiratorial or pseudoscientific content without the user having the tools to critically evaluate it.
Recent research even speaks of "chatbot psychosis" in users predisposed to delusional or conspiratorial thinking . By spending many hours interacting with a system that, by design, tries to maintain the conversation and doesn't always correct errors, the user can reinforce existing biases and become even more detached from shared reality.
Added to all this are structural problems of privacy, bias, and lack of regulation . Models are trained on large datasets that often contain historical or cultural biases, which can lead to discriminatory or unfair responses toward certain groups. Furthermore, the massive collection of conversational data raises questions about who controls this information, how it is used, and for what purposes.
For these reasons, numerous experts insist that mental health chatbots should be seen as complementary tools, never as substitutes for professional care , and that it is essential to establish clear limits, independent audits, and mechanisms for continuous human supervision.
Looking at this entire journey, from ELIZA to current generative models, it's clear how chatbots have gone from being mere laboratory curiosities to becoming key components of the relationship between people and digital systems; they still have many shortcomings and risks, but, well-designed and used wisely, they are one of the technologies with the greatest potential to transform the way we get information, work, learn, and ask for help inside and outside of organizations.
