Student information chatbot project ppt


  • Student Information Chat Bot System
  • Student Information Chatbot
  • How do chatbots work? An overview of the architecture of chatbots
  • Top 15 Use Cases for Chatbots in Healthcare
  • Student Information Chatbot Project
  • Project Idea | Amanda: A Smart Enquiry Chatbot
  • Student Information Chat Bot System

    Language conditions can be created to look at the words, their order, synonyms, common ways to phrase a question and more, to ensure that questions with the same meaning receive the same answer. However, chatbots based on a purely linguistic model can be rigid and slow to develop, due to this highly labor-intensive approach. Though these types of chatbots use Natural Language Processing, interactions with them are quite specific and structured. These type of bots tend to resemble interactive FAQs, and their capabilities are basic.

    These are the most common type of bots, of which many of us have likely interacted with — either on a live chat, through an e-commerce website, or on Facebook messenger. Machine learning AI Bots AI-powered chatbots are more complex than rule-based chatbots and tend to be more conversational, data-driven and predictive.

    These types of Artificial Intelligence chatbots are generally more sophisticated, interactive and personalized than task-oriented chatbots. Conversational systems based on machine learning can be impressive if the problem at hand is well-matched to their capabilities.

    By its nature, it learns from patterns and previous experiences. But, to perform even at the most rudimentary level, such systems often require staggering amounts of training data and highly trained skilled human specialists. In addition, a machine learning chatbot functions as a black box. If something goes wrong with the model it can be hard to intervene, let alone to optimize and improve.

    The resources required, combined with the very narrow range of scenarios in which statistical algorithms are truly excellent, makes purely machine learning-based chatbots an impractical choice for many enterprises. Hybrid Model — The Ultimate AI Bot Experience While linguistic and machine learning models have a place in developing some types of conversational systems, taking a hybrid approach offers the best of both worlds, and offers the ability to deliver more complex conversational AI chatbot solutions.

    A hybrid approach has several key advantages over both the alternatives. When considered against machine learning methods, it allows for conversational systems to be built even without data, provides transparency in how the system operates, enables business users to understand the chatbot application, and ensures that a consistent personality is maintained and that its behavior is in alignment with business expectations.

    At the same time, it allows for machine learning integrations to go beyond the realm of linguistic rules, to make smart and complex inferences in areas where a linguistic only approach is difficult, or even impossible to create.

    When a hybrid approach is delivered at a native level this allows for statistical algorithms to be embedded alongside the linguistic conditioning, maintaining them in the same visual interface. Building conversational applications using only linguistic or machine learning methods is hard, resource intensive and frequently prohibitively expensive. By taking a hybrid approach, enterprises have the muscle, flexibility and speed required to develop business-relevant AI applications that can make a difference to the customer experience and the bottom line.

    Think Big, Start Small Enterprises are moving beyond short-term chatbot strategies that solve specific pain points, to using conversational interfaces as an enabler to achieve goals at a strategic level within the organization. Consider the wider strategy but start with a smaller project in order to see the results and measure the success before deciding on the next phase. Ensure the technology used for Artificial Intelligence chatbot development can scale to meet future needs.

    For businesses this poses two main concerns — a duplication of resources and potential security risks. In recognition of the need to bring together teams tasked with delivering the innovative solutions that will drive the business forward globally, enterprises are forming Centers of Excellence. Skillsets are no longer spread across the organization but focused on collaborating and developing Artificial Intelligence chatbot solutions to solve problems, improve productivity and make the business stronger.

    Choose a chatbot technology that is advanced enough for developers to rapidly build a complex proof of concept that can still be easily understood by business users, even from day one. It can always do better and increase customer satisfaction even further. Make provisions to provide continual and continuous improvement to the system.

    By enabling the AI bot to continue to learn and improve, the value of enterprise chatbot solutions will increase. Connectors harness the power of back-office technology to deliver even greater intelligence and capabilities by integrating a chatbot into business systems, communication platforms and more.

    People use a variety of channels and devices in communicating with others. Not only is it important for organizations to be available on all channels relevant to its audience, but the experience needs to be seamless across those channels too. Ease of deployment onto a variety of channels should be a key consideration when planning a conversational bot, alongside the ability for persistent chat.

    For example, a person might use a Facebook Messenger chatbot on their smartphone to start a conversation on the commute home and want to continue it later that evening using a smart home hub, before moving to their smart speaker or watch to conclude it. So why are so many chatbots failing to deliver on their potential? The answer lies in the restrictive nature of most chatbot technology. Few chatbots offer the rich, humanlike conversation needed to engage users, nor can they guide off-topic users back to the subject at hand.

    And, they are not able to deliver over the different channels and languages by which customers want to communicate. This is not true. Just as a linguistic based conversational system requires humans to laboriously craft each rule and response, a machine learning system requires humans to collect, select, and clean every single piece of training data, because using machine learning to understand humans takes a staggering amount of information.

    What comes naturally to us as humans — the relationships between words, phrases, sentences, synonyms, lexical entities, concepts etc. In a linguistic based conversational system, humans can ensure that questions with the same meaning receive the same answer.

    A machine learning system might well fail to correctly recognize similar questions phrased in different ways, even within the same conversation. From a business point of view, this misses the opportunity to position the company and its values through a consistent brand personality. Ease of Creating Global Appeal Organizations need to support their customers in different languages — a problem that will only increase over time.

    Hence, AI-based chatbots need to be fluent in many languages, with the ability to learn more when needed. But this is only part of the problem, because they frequently need to support a variety of platforms, devices or services too.

    Most chatbot development technology requires a great deal of effort and often complete rebuilds for each new language and channel that needs to be supported, leading to multiple disparate, solutions all clumsily co-existing. These types of chatbot solution cannot reuse assets from the original build, nor can they surface the same chatbot solution through multiple devices and services.

    For organizations, the challenge is not just in storing the data, but also in retrieving the information for export or deleting in a secure and auditable way. Furthermore, many chatbot technologies restrict access to the conversational data generated, meaning businesses lose one of the key benefits to implementing a conversational bot. Without this data, businesses are effectively blind to their customers.

    Chapter 4: Chatbots vs. There are several other advantages in offering your customers an intelligent automated self-service option. Fast An Artificial Intelligence chatbot is built to recognize, understand and respond to specific queries and problems in seconds. By contrast most agents typically must refer to standardized macros for common queries — all taking extra time.

    Gartner highlights this with a report of a chatbot able to answer within 5 seconds of customer contact , while the average advisor took 51 seconds. Accurate Accuracy is key to reduce first time call resolution rates and to ensure customers return to the chatbot the next time they have a query.

    In addition, customers and companies alike can track conversations to ensure transparency and accountability. Furthermore, important updates and changes can be centrally rolled-out and a proper audit trail maintained for compliance proposes where needed. Scalable There are only so many queries a live agent can handle at once.

    Live chat allows agents to help more than one customer at a time, but call center agents must finish one call, before starting another. A conversational bot can handle millions of conversations simultaneously, all to the same high standard. But there is still a need for the human touch… Sometimes there is no substitute for the empathy live agents can deliver or the kind of intelligence that needs creativity or judgement to resolve a query.

    Chapter 5: What is a Chatbot Platform? Building engaging conversational AI chatbot solutions can be complex. Toolkits — often referred to as platforms — help to simplify the development of AI enabled chatbot systems. Enterprise chatbot platforms should contain everything a developer needs to build a conversational system , from data mining tools through scoping out the initial build to the analytics that are needed to maintain the system and deliver actionable insight back to the business.

    They allow enterprises to build advanced conversational applications using either linguistic or machine learning, or ideally a hybrid combination of both. Some can integrate into back end systems and third-party data sources to deliver answers that might need more than one information source to truly personalize the response. A graphical user interface GUI is essential to enable both developers and business users to have visibility into the system.

    A visual, drag-and-drop style user environment also makes it easier for business users and subject matter experts to correct a dialogue flow or update an answer. Data analytics from chatbot applications need to feed back into the system in real-time to increase personalization within a conversation and to automatically deliver suggestions for system improvements. The best chatbot platforms make it possible to create an application once and deploy it in multiple languages and, across multiple devices and channels, using most of the original build.

    It also enables for AI assets to be shared between applications, allowing for even faster creation and greater RoI. While there are many different enterprise chatbot platforms available in the market, they are not all built equally. Enterprises would be advised to list the criteria and functionality they need from their chatbot applications before deciding on which technology to use.

    Chapter 6: What is an AI Chatbot? These capabilities are the keys to successful engagements that deliver true understanding to customers requests that deliver personalized responses. The majority of chatbots available today are not AI based. They may use algorithms to determine the meaning of a question and the likelihood of the correct answer, but if you go off the chatbot script then they are left floundering.

    Interestingly, despite wanting a humanlike interaction most people are quite content knowing they are speaking to a machine. For some it means they can go over a technical problem again and again without feeling foolish. For others a machine offers a faster, more efficient experience. Memory allows a chatbot to remember pertinent details to reuse during a conversation or implicitly learn about a person to be reused later.

    For example, a mobile assistant might learn through previous requests and responses that the user clearly prefers Italian cuisine and so will use this information when asked for restaurant recommendations in future. Sentiment analysis enables a chatbot to understand the mood of the customer and the strength of that feeling. This is particularly important in customer service type applications where it can be linked to complaint escalation flows, but also can be used in other more trivial ways such as choosing which songs to play upon request.

    Personality can make a huge difference to engagement and the trust users place in the chatbot. While some companies chose to reinforce it using avatars, personality can easily be conveyed in the conversation alone. Want to meet a sarcastic chatbot? Try talking to Elbot.

    Persistence allows people to pick up a conversation where they last left off, even if they switch devices, making for a more natural and seamless user experience. Topic switching enables the user to veer off onto another subject, such as asking about payment methods while enquiring if a product is in stock.

    The conversational bot should also then be capable of bringing the user back on track if the primary intent is not reached. An engaging exchange will not only improve the customer experience but will deliver the data to help you increase your bottom line.

    To achieve this, the user interface needs to be as humanlike and conversational as possible. It needs a memory in order to reuse key pieces of information throughout the conversation for context or personalization purposes and be able to bring the conversation back on track, when the user asks off topic questions.

    Student Information Chatbot

    This System is a web application which provides answer to the query of the student. Students just have to query through the bot which is used for chatting. Students can chat using any format there is no specific format the user has to follow. The System uses built in artificial intelligence to answer the query.

    The answers are appropriate what the user queries. If the answer found to invalid, user just need to select the invalid answer button which will notify the admin about the incorrect answer. Admin can view invalid answer through portal via login. System allows admin to delete the invalid answer or to add a specific answer of that equivalent question. The User can query any college related activities through the system.

    The user does not have to personally go to the college for enquiry. The System analyzes the question and then answers to the user. The system answers to the query as if it is answered by the person. With the help of artificial intelligence, the system answers the query asked by the students. The system replies using an effective Graphical user interface which implies that as if a real person is talking to the user.

    The user can query about the college related activities through online with the help of this web application. This system helps the student to be updated about the college activities.

    Advantages User does not have to go personally to college office for the enquiry. This application enables the students to be updated with college cultural activities. This application saves time for the student as well as teaching and non-teaching staffs.

    How do chatbots work? An overview of the architecture of chatbots

    If the answer found to invalid, user just need to select the invalid answer button which will notify the admin about the incorrect answer.

    Admin can view invalid answer through portal via login. System allows admin to delete the invalid answer or to add a specific answer of that equivalent question.

    The User can query any college related activities through the system. The user does not have to personally go to the college for enquiry.

    Top 15 Use Cases for Chatbots in Healthcare

    The System analyzes the question and then answers to the user. The system answers to the query as if it is answered by the person. With the help of artificial intelligence, the system answers the query asked by the students. The system replies using an effective Graphical user interface which implies that as if a real person is talking to the user. Chat bot acts as environment that allows to user to have their queries that may be regarding anything. This proposed system not uses any language specifically in order to interact with users.

    Proposed System The chat bot system introduces the areas where the people will interact and get solutions.

    Having the environment that one paves a path to chat bot system users. That path may be based on querying and answering. Introduced chat bot system will provide an interface that depends on text.

    Student Information Chatbot Project

    This allows the users to type the commands and receive the text. It also involves receiving end may be having response as text to the speech. It uses the state ful based services which could remember the commands that previously asked that may be conversation. This system is developed in order to enhance the functionality. It allows the different set of people to be organizing in a specified space in order to make their discussion. The technology named chat bot can be highly utilized by large set of people in their working environment.

    Project Idea | Amanda: A Smart Enquiry Chatbot

    This mechanism has developed by using Artificial based algorithms that allows the people to input their queries. Queries can be taken by chat bot and it has been analysed and understudied. There is no specific format for inputting the queries.


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