Query: Feedback chatbot ai education

title: Promoting fertility awareness and preconception health using a chatbot: a randomized controlled trial.
abstract: What are the effects of using a fertility education chatbot, i.e. automatic conversation programme, on knowledge, intentions to improve preconception behaviour and anxiety?  A three armed, randomized controlled trial was conducted using an online social research panel. Participants included 927 women aged 20 34 years who were randomly allocated to one of three groups: a fertility education chatbot (intervention group) a document about fertility and preconception health (control group 1) or a document about an irrelevant topic (control group 2) Participants' scores on the Cardiff Fertility Knowledge Scale and the State Trait Anxiety Inventory, their intentions to optimize preconception behaviours, e.g. taking folic acid, and the free text feedback provided by chatbot users were assessed.  A repeated measures analysis of variance showed significant fertility knowledge gains after the intervention in the intervention group +9.1 points) and control group 1 +14.9 points) but no significant change in control group 2 +1.1 points) Post test increases in the intentions to optimize behaviours were significantly higher in the intervention group than in control group 2, and were similar to those in control group 1. Post test state anxiety scores were significantly lower in the intervention group than in control group 1 and control group 2. User feedbacks about the chatbot suggested technical limitations, e.g. low comprehension of users' words, and pros and cons of using the chatbot, e.g. convenient versus coldness.  Providing fertility education using a chatbot improved fertility knowledge and intentions to optimize preconception behaviour without increasing anxiety, but the improvement in knowledge was small. Further technical development and exploration of personal affinity for technology is required.

title: Automatic Question Generation from Children's Stories for Companion Chatbot
abstract: With the rising trends of dialog system, interactions with questions become a vital human machine interface in realizing intentions and generating adequate feedback, thereby establishing question generation as an important and interesting research topic. In the education domain, question generation is also useful for reducing time and effort for teachers, as well as parents. Extensive research has been conducted on question generation recently but little focused on question generation in Chinese. In addition, previous papers examining question generation in Chinese, have been poorly reviewed due to the clumsy handling of sentence complexities of Chinese. In this article, we focus on developing a question generation technique based on children's tales. For the training and testing dataset, approximately one hundred articles from the website, Er Tong Gu Shi (http:/www.yfes.tn.edu.tw/yfesvi/story.htm) were used. To achieve better quality of the questions automatically generated, approaches and methods were carefully chosen in this system. The results show that questions generated from our system are mostly acceptable, which reflects the main objective of our system.

title: Zero coding Chatbot for blended learning: Basics in kick starting your own bot!
abstract: Zero coding Chatbot for blended learning: Basics in kick starting your own bot! Abstract Designing an on campus blended learning experience can be challenging. On one hand, teachers are challenged by motivating the students to engage with the online learning materials. While on the other hand, providing meaningful and prompt feedback requires tremendous teachers' time and attention to their students. Despite that, there are a number of tools and applications available for teachers to use to overcome these challenges. But without hands on design experience, teachers are usually not fully empowered to develop their own online courseware. One of the popular tools that can be used to address these challenges is the intelligent tutoring system (ITS) ITS is one of the early forms of artificial intelligence that is being applied in the field of education. These systems are usually paired with pedagogical agents or a virtual avatar where students can communicate or received some form of guidance. These systems provide learners with personalized feedback and guide them throughout the virtual environment (Ahn, et. al. 2017) However, creating a pedagogical agent requires complex computer programming skills and it is usually built from scratch to fit the intended educational purpose (Schroeder Adesope, 2012) This makes it difficult for teachers to adapt existing systems or to attempt in creating a similar version. But with the recent AI developments, like neural networks and natural language processing, companies like Google, Amazon, or IBM, offers coding free platforms that can enable a non technical person to create a chatbot that can fit into their own needs. Using these chatbot platforms, The University of Hong Kong (HKU) through Technology Enriched Learning Initiative, leverages on its experiences in integrating this technology to better enhance the learning experience of the students. In this workshop, we would like to share our experience gained from developing a set of chatbots for an online blended learning environment. These chatbots will gamify the learning experience in a science and technology common core course in HKU. In the next 55 minutes, we will address the following issues: How can we design chatbots without a strong technical background? What are the processes involved in developing a chatbot (i.e. intents, entities, and dialogs) How can chatbots be used for facilitating course operations and inquiry based learning? Through the workshop, attendees will identify what and how chatbots can be designed for their own blended or fully online courses. Key workshop activities Introduction Blended learning in HKU HKU Open edX What is a chatbot and why bother? Basic authoring Setting up your IBM account IBM Watson Basics Advanced authoring Creating your intents Creating your entities Creating your dialog Wrap up Intended participants People who are planning to develop chatbot to support their blended or fully online courses People who are interested in understanding the essentials of chatbot development and EdTech Maximum number of attendants: 15 Required equipment Projector with a connected computer Computers (One per participant) Good internet connections Participants need to create one IBMID account References Schroeder, N. Adesope, O. (2012) A case for the use of pedagogical agents in online learning environments. Journal of Teaching and Learning with Technology 1 (2) 43 47. Retrieved from https:/jotlt.indiana.edu/article/view/2126/3045 Ahn J. et al. (2017) Wizard's Apprentice: Cognitive Suggestion Support for Wizard of Oz Question Answering. In: Andre E. Baker R. Hu X. Rodrigo M. du Boulay B. (eds) Artificial Intelligence in Education. AIED 2017.

title: MathBot: A Personalized Conversational Agent for Learning Math
abstract: Online math education often lacks key features of in person instruction, such as personalized feedback. To emulate such interactivity, we developed MathBot, a rule based chatbot that explains math concepts, provides practice questions, and offers tailored feedback. We evaluated MathBot through three Amazon Mechanical Turk studies in which participants learned about arithmetic sequences. In the first study, we found that more than 40% of our participants indicated a preference for learning with MathBot over videos and written tutorials from Khan Academy that covered similar material. Although more participants preferred the Khan Academy materials, our results point to demand for alternative forms of online education. The second study measured learning gains, and found that MathBot produced comparable gains to the Khan Academy videos and tutorials. Combined with the findings of our first study, these results indicate that conversational agents can appeal to a substantial proportion of the population without sacrificing learning. Finally, in the third study, we integrated a contextual bandit algorithm into MathBot to experiment with different personalization strategies. Compared to a randomized A/B experiment, we found that the contextual bandit learned a similarly effective pedagogical policy at a lower cost. Our findings suggest that personalized conversational agents are Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from permissions@acm.org.

title: Social Interaction with a Conversational Agent: An Exploratory Study
abstract: Conversational agents that display many human qualities have become a valuable method business uses to communicate with online users to supply services or products, to help in online order process or to search the Web. The gaming industry and education may benefit from this type of interface. In this type of chats, users could have different alternatives: text display, photo of a real person, or a cartoon drawing and others. This is an exploratory study that reviews five randomly chosen conversations that an animated chatbot has with Web users. The character simulates human gestures, but they are stylized to reproduce animation standards. The goal of this exploratory study is to provide feedback that will help designers to improve the functionality of the conversational agent, identify useraEUR(tm)s needs, define future research, and learn from previous errors. The methodology used was qualitative content analysis

title: The Use of AI Chatbot as An Assistant Tool for SW Education

title: Feasibility and Constraints in Applying an AI Chatbot to English Education

title: Acceptability of artificial intelligence (AI) led chatbot services in healthcare: A mixed methods study
abstract: Artificial intelligence (AI) is increasingly being used in healthcare. Here, AI based chatbot systems can act as automated conversational agents, capable of promoting health, providing education, and potentially prompting behaviour change. Exploring the motivation to use health chatbots is required to predict uptake; however, few studies to date have explored their acceptability. This research aimed to explore participants' willingness to engage with AI led health chatbots.  The study incorporated semi structured interviews (N 29) which informed the development of an online survey (N 216) advertised via social media. Interviews were recorded, transcribed verbatim and analysed thematically. A survey of 24 items explored demographic and attitudinal variables, including acceptability and perceived utility. The quantitative data were analysed using binary regressions with a single categorical predictor.  Three broad themes: 'Understanding of chatbots' 'AI hesitancy' and 'Motivations for health chatbots' were identified, outlining concerns about accuracy, cyber security, and the inability of AI led services to empathise. The survey showed moderate acceptability (67% correlated negatively with perceived poorer IT skills OR 0.32 [CI95%:0.13 0.78] and dislike for talking to computers OR 0.77 [CI95%:0.60 0.99] as well as positively correlated with perceived utility OR 5.10 [CI95%:3.08 8.43] positive attitude OR 2.71 [CI95%:1.77 4.16] and perceived trustworthiness OR 1.92 [CI95%:1.13 3.25]  Most internet users would be receptive to using health chatbots, although hesitancy regarding this technology is likely to compromise engagement. Intervention designers focusing on AI led health chatbots need to employ user centred and theory based approaches addressing patients' concerns and optimising user experience in order to achieve the best uptake and utilisation. Patients' perspectives, motivation and capabilities need to be taken into account when developing and assessing the effectiveness of health chatbots.

title: Factors Influencing Adoption Intention of AI Powered Chatbot for Public Transport Services within a Smart City
abstract: Cities are growing in size in all parts of the world.This puts a burden on the public transport infrastructure and creates numerous issues such as overcrowding, delayed services, and commuter dissatisfaction. Government authorities in various countries including India are adopting smart city concepts to overcome these problems. Smart cities are characterized by their extensive use of emerging technologies for managing various citizen services. Accordingly, researchers of this paper studied the adoption intention of Artificial Intelligence (AI) powered Chabot by smart city citizens for delivering anytime, anywhere, and automated public transport information services. These services can include finding routes and schedules, buying tickets, registering complaints,or collecting feedback from commuters. The study employed an extended UTAUT model to measure the adoption intention. A primary survey is conducted using a structured questionnaire and the data is analyzed using the structural equation modeling technique.The findings from this study suggest that performance expectancy, effort expectancy, social influence, facilitating conditions, anthropomorphism, and trust directly affectthe adoption intention of the Chatbot. The proposed Chatbot solution in this study has social implications in terms of attracting more citizens to use public transport instead of their private vehicles thereby reducing congestion, travel delays, and climate pollution. The study also provides vital insights to public transport officials and policymakers while designing or upgrading public transport information systems in a developing country like India. This study makes a novel contribution to literature as it empirically validates the intention to use AI powered Chatbot for public transport in the context of a large developing country like India.

title: AI Assisted Chatbot for E Commerce to Address Selection of Products from Multiple Products
abstract: Artificial Intelligence has been used nowadays increasingly to develop and advance numerous fields and industries, including finance, healthcare, education, transportation, and more. Machine Learning is a subset of AI techniques which includes various algorithms that gives machines the ability to learn from data or while interacting with the world without being explicitly programmed. E commerce websites are trending nowadays due to online shopping makes customer's life easier. Similar to this, Chatter Robots i.e. Chatbots are providing better customer service through Internet. A chatbot is a computer or software program for simulating intelligent conversations with human using rules or artificial intelligence for machine. Users can interact with the chatbot program via conversational interface through written or spoken text as input. With the help of E commerce website sellers can reach to larger audience and with the help of chatbots, sales can be increased by personal interaction with the users. Chatbots will welcome a user to conversation, guide to customer to make purchase which will reduce customer's struggle. Chatbots will ask customers all the relevant questions to find the perfect fit, style, and color for them. Chatbots are the future of marketing and customer support. Chatbots are one such means of technology which helps humans in a lot of ways, by helping them in e commerce to increase sales whilst providing great customer satisfaction.