Chatbot

About: Chatbot is a research topic. Over the lifetime, 2415 publications have been published within this topic receiving 24372 citations. The topic is also known as: IM bot & AI chatbot.

E-Commerce Assistance with a Smart Chatbot using Artificial Intelligence

Abstract: In this present research we use design science analysis to express chatbot design awareness in higher education. For previous research into the design process, we carried out a literature review. Furthermore, we studied the content of student emails and forum articles from four instances of a fundamental Java programming course. We introduce a conceptual architecture for chatbots in higher education from literature and evidence and show how this is applied. We conclude with a debate with tentative design recommendations and a plan for further research. Chatbot rules-based program performs prefixed acts based on "playbooks" that you have setup at the back of the user interface. Just like a virtual assistant, chat-based rules-based technologies can function on the basis of click acts, such as "Yes" against "No."Website and social networking outlets have become more and more popular places for people to voice their opinions on different issues, in particular their frustrations with brands and corporations.

Generative model chatbot for Human Resource using Deep Learning

Abstract: Human Resource is the working environment inside a business that is in charge of everything master related which unites selecting, checking, picking, verifying, on boarding, preparing, advancing, paying, and terminating delegates and freely utilized substances. Human Resource is besides the working environment that stays over new request controlling how experts should be treated amidst the selecting, working, and consummation process. Here we will focus on the enrolling some bit of Human Resource. A Chatbot is an automated structure expected to begin a dialog with human customers or diverse Chatbots that gives through text. The Chatbots which is being proposed for Human Resource is Artificial Intelligence based Chatbot for major measurement profiling of contenders for the explicit task. The learning strategy utilized for the Chatbot here is assorted neural structure which includes deep learning techniques like recurrent neural network.

Training a chatbot for a digital advertisement to simulate common conversations associated with similar digital advertisements

Abstract: This disclosure covers systems and methods that generate and train a chatbot to automatically communicate with users concerning subjects related to a digital advertisement received for distribution. In particular, the disclosed systems and methods train a chatbot to simulate common conversation exchanges from messaging threads associated with previous digital advertisements that are similar to a received digital advertisement. By training the chatbot to simulate such conversations, the disclosed systems and methods create a chatbot that can immediately respond to a user's inquiries concerning the received digital advertisement and tailor automated exchanges that further the objectives of an advertiser or merchant associated with the received digital advertisement.

Why Johnny Can’t Prompt: How Non-AI Experts Try (and Fail) to Design LLM Prompts

Abstract: Pre-trained large language models (“LLMs”) like GPT-3 can engage in fluent, multi-turn instruction-taking out-of-the-box, making them attractive materials for designing natural language interactions. Using natural language to steer LLM outputs (“prompting”) has emerged as an important design technique potentially accessible to non-AI-experts. Crafting effective prompts can be challenging, however, and prompt-based interactions are brittle. Here, we explore whether non-AI-experts can successfully engage in “end-user prompt engineering” using a design probe—a prototype LLM-based chatbot design tool supporting development and systematic evaluation of prompting strategies. Ultimately, our probe participants explored prompt designs opportunistically, not systematically, and struggled in ways echoing end-user programming systems and interactive machine learning systems. Expectations stemming from human-to-human instructional experiences, and a tendency to overgeneralize, were barriers to effective prompt design. These findings have implications for non-AI-expert-facing LLM-based tool design and for improving LLM-and-prompt literacy among programmers and the public, and present opportunities for further research.

Automated vs. Human Health Coaching: Exploring Participant and Practitioner Experiences

Abstract: Health coaching can be an effective intervention to support self-management of chronic conditions like diabetes, but there are not enough coaching practitioners to reach the growing population in need of support. Conversational technology, like chatbots, presents an opportunity to extend health coaching support to broader and more diverse populations. However, some have suggested that the human element is essential to health coaching and cannot be replicated with technology. In this research, we examine automated health coaching using a theory-grounded, wizard-of-oz chatbot, in comparison with text-based virtual coaching from human practitioners who start with the same protocol as the chatbot but have the freedom to embellish and adjust as needed. We found that even a scripted chatbot can create a coach-like experience for participants. While human coaches displayed advantages expressing empathy and using probing questions to tailor their support, they also encountered tremendous barriers and frustrations adapting to text-based virtual coaching. The chatbot coach had advantages in being persistent, as well as more consistently giving choices and options to foster client autonomy. We discuss implications for the design of virtual health coaching interventions.