In the first section, the concept of a marketing complex and its significance for the company's activities are revealed, the main advantages and disadvantages of using intelligent technologies in marketing are described, and the problem of finding an effective channel for attracting customers is revealed and updated. The second section describes the industry of higher professional education, provides a comparative analysis of the channels for attracting applicants, and describes the channels used. Examples of the use of chatbot technology for business process automation are presented, the activities and structure of the Department of MUS Urfu are developed and described, the AS-IS model is created, which highlights the problem of choosing the most effective channel for attracting applicants, and then the TO-BE model is created. The third section is composed of Charter and project plan for creating a chat bot to attract students to the graduate program of the Department of the ICC, Urfa, analyzed competitive advantages and possible threats of the project, written marketing and organizational plans for the project calculation of the economic efficiency of the project, step by step described the process of project implementation. The terms of reference for development, the algorithm and screenshots of the development process, according to the planning, are given in appendices 1-4. / В работе приведено описание отрасли высшего профессионального образования, произведён сравнительный анализ каналов привлечения абитуриентов, описаны применяемые каналы. Представлены примеры применения технологии чат-бот для автоматизации бизнес-процессов, разработана и описана деятельность и структура кафедры МУС УрФУ, создана модель AS-IS, в которой выделена проблема, заключающаяся в выборе наиболее эффективного канала привлечения абитуриентов, далее создана модель TO-BE. Составлены устав и план проекта по созданию чат-бота для привлечения абитуриентов на программу магистратуры кафедры МУС УрФУ, проанализированы конкурентные преимущества и возможные угрозы проекта, составлен маркетинговый и организационный планы проекта, произведён расчёт экономической эффективности проекта, поэтапно описан процесс реализации проекта.

Актуальность темы обусловлена необходимостью различных предприятий в автоматизации процесса обработки обращений клиентов в чаты, для повышения эффективности работы сотрудников и снижения ресурсных затрат, а также для повышения уровня клиентского сервиса. Цель работы: улучшение качества обслуживания клиентов посредством разработки чат-бота, автоматизирующего бизнес-процессы обработки обращений клиентов в чат на языке программирования Python в мессенджере «Telegram» с интеграцией бота в Яндекс.Алиса. Для достижения поставленной цели необходимо решение следующих задач: изучил понятия чат-бота и его функции; использовать преимущества использования мессенджеров для бизнеса; настроить языки программирования и выбрать язык для разработки чат-бота; выбрать мессенджер, в котором будет реализован чат-бот; разработать ИТ-проект по разработке чат-бота. Объектом исследования данной выпускной квалификационной работы является информационная система предприятия. Предметом исследования является обработка бизнес-процессов клиентов с часто задаваемыми вопросами в клиентских сервисах различных предприятий. Научная новизна составила то, что проанализировано большое количество программных средств для реализации проекта, показано большое количество прикладных чат-ботов в бизнесе, а также разработан уникальный чат-бот. Практическая оригинальность заключается в том, что данного чат-бота можно применять в любой компании, в которой есть бизнес-процесс обработки обращения клиентов в чат. / The relevance of the topic is due to the need of various enterprises to automate the process of processing customer requests in chats, to increase the efficiency of employees and reduce resource costs, as well as to increase the level of customer service. Goal of work: improving the quality of customer service by developing a chat bot that automates the business processes of processing customer requests via chat in the Python programming language in the Telegram messenger with the integration of the bot into Yandex.Alice. To achieve this goal, it is necessary to solve the following tasks: studied the concepts of a chatbot and its functions; take advantage of the benefits of using instant messengers for business; configure programming languages and select a language for developing a chatbot; select the messenger in which the chatbot will be implemented; develop an IT project to develop a chatbot. The object of study of this final qualifying work is the enterprise information system. The subject of the study is the processing of customer business processes with frequently asked questions in customer services of various enterprises. The scientific novelty was that a large number of software tools for the implementation of the project were analyzed, a large number of applied chatbots in business were shown, and a unique chatbot was developed. The practical originality lies in the fact that this chatbot can be used in any company that has a business process for processing customer requests via chat.

Under de senaste åren har lärosäten genomgått betydande förändringar, med en ökande trend mot distansutbildning, särskilt på grund av Covid-19-pandemins påverkan. I den digitala omställning som blivit, har artificiell intelligens (AI) och särskilt chattbotar börjat spela en allt större roll inom utbildning. Genom att det erbjuder omedelbar tillgång till studiematerial och stöttning. Chattbotar har blivit alltmer populära bland studenter, för deras förmåga att tillhandahålla omedelbar hjälp eller stöd. Särskilt i en tid där distansundervisning har blivit normen. Denna studie belyser den alltmer växande integreringen av chattbotar inom utbildning. Genom inspiration av modeller som Technology Acceptance Model (TAM) och Task Technology Fit (TTF), strävar undersökningen efter att förstå faktorer som påverkar studenternas acceptans och användning av chattbotar. Syftet är att bidra till en djupare förståelse för distansstudenters inställning och syn på chattbotars användning och roll inom högre utbildning. Studien präglas av en semistrukturerad enkätundersökning som skickats ut till en grupp distansstudenter inom högre utbildning i Sverige, det vill säga individer som studerar vid högskola eller universitet. Resultatet visar att respondenterna ser chattbotar som ett värdefullt komplement till traditionella undervisningsmetoder, som effektiviserar studier genom snabb information och stöd, men också på i flera fall, utmaningar. Genom att identifiera både potentialen och utmaningarna med chattbotar öppnar denna studie upp för en bredare dialog om teknologins roll i att främja lärande och pedagogisk utveckling i det digitaliserade samhället. / In recent years, educational institutions have undergone significant changes, with a growing trend towards distance studies, particularly due to the impact of the Covid- 19 pandemic. In the digital transition that has ensued, artificial intelligence (AI), especially chatbots, has begun to play an increasingly prominent role in education by offering immediate access to study materials and support. Chatbots have become increasingly popular among students for their ability to provide immediate assistance or support, especially in a time when distance learning has become the norm. This study highlights the increasingly growing integration of chatbots in education. Drawing inspiration from models such as the Technology Acceptance Model (TAM) and Task Technology Fit (TTF), the study aims to understand the factors influencing students' acceptance and usage of chatbots. The aim is to contribute to a deeper understanding of distance students' attitudes and perceptions regarding the use and role of chatbots in higher education. The study is characterized by a semi-structured survey sent to a group of distance students in higher education in Sweden, that is, individuals studying at colleges or universities. The result shows that the respondents view chatbots as a valuable complement to traditional teaching methods, enhancing studies through quick information and support, but also presenting several challenges. By identifying both the potential and challenges of chatbots, this study opens up a broader dialogue about the role of technology in promoting learning and pedagogical development in the digitized society.

Chat-bots are computer programs designed to conduct textual or audible conversations with a single user. The job of a chat-bot is to be able to find the best response for any request the user issues. The best response is considered to answer the question and contain relevant information while following grammatical and lexical rules. Modern chat-bots often have trouble accomplishing all these tasks. State-of-the-art approaches, such as deep learning, and large datasets help chat-bots tackle this problem better. While there is a number of different approaches that can be applied for different kind of bots, datasets of suitable size are not always available. In this work, we introduce and evaluate a method of expanding the size of datasets. This will allow chat-bots, in combination with a good learning algorithm, to achieve higher precision while handling their tasks. The expansion method uses the continual learning approach that allows the bot to expand its own dataset while holding conversations with its users. In this work we test continual learning with IBM Watson Assistant chat-bot as well as a custom case study chat-bot implementation. We conduct the testing using a smaller and a larger datasets to find out if continual learning stays effective as the dataset size increases. The results show that the more conversations the chat-bot holds, the better it gets at guessing the intent of the user. They also show that continual learning works well for larger and smaller datasets, but the effect depends on the specifics of the chat-bot implementation. While continual learning makes good results better, it also turns bad results into worse ones, thus the chat-bot should be manually calibrated should the precision of the original results, measured before the expansion, decrease.

This thesis explores the implementation of a conversational user interface for Uppsala Municipality, aimed at optimising the balance between cost of usage and quality when using large language models for public services. The central issue addressed is the effective integration of large language models, such as OpenAI's GPT-4, to enhance municipal services without compromising user privacy and data security. The solution developed involves a prototype that utilises a model chooser and prompt tuner, allowing the interface to adapt the complexity of responses based on user input. This adaptive approach reduces costs while maintaining high response quality. The results indicate that the prototype not only manages costs effectively, but also adheres to standards of data privacy and security. Clear information on data use and transparency improved user trust and understanding. In addition, strategies were effectively implemented to handle sensitive and unexpected input, improving overall data security. Overall, the findings suggest that this approach to implementing conversational user interfaces in public services is viable, offering valuable insights into the cost-effective and secure integration of language models in the public sector. The success of the prototype highlights its potential to improve future municipal services, underscoring the importance of transparency and user engagement in public digital interfaces. / Den här masteruppsatsen undersöker implementeringen av ett konversationsgränssnitt för Uppsala kommun, med målet att optimera balansen mellan kostnad och kvalitet vid användning av stora språkmodeller för den offentliga sektorn. Den centrala frågan som besvaras är hur stora språkmodeller, såsom OpenAI:s GPT-4, kan integreras för att förbättra kommunala tjänster utan att kompromissa med användarnas integritet och datasäkerhet. Den utvecklade lösningen innefattar en prototyp som använder en modellväljare och promptjusterare, vilket gör det möjligt för gränssnittet att anpassa svarens komplexitet baserat på användarens meddelande. Detta tillvägagångssätt reducerar kostnaderna samtidigt som en hög svarskvalitet bibehålls. Resultaten visar att prototypen inte bara hanterar kostnaderna effektivt, utan också upprätthåller standarder för datasekretess och säkerhet. Tydlig information om dataanvändning och transparens förbättrade avsevärt användarnas förtroende och förståelse. Dessutom implementerades strategier effektivt för att hantera känslig och oväntad data, vilket förbättrade den övergripande datasäkerheten. Sammanfattningsvis tyder resultaten på att detta tillvägagångssätt för implementering av konversationsgränssnitt i offentliga tjänster är möjligt och erbjuder lärdomar om kostnadseffektiv och säker integration av språkmodeller i offentlig sektor. Prototypens framgång påvisar dess potential att förbättra framtida kommunala tjänster, men lyfter också vikten av transparens och användarengagemang i offentliga digitala gränssnitt.