一种基于ChatBot驱动的软件开发环境

随着现代软件开发复杂度的增加,提高开发效率和团队协作的需求日益迫切。本文探讨基于ChatBot驱动的软件开发环境及其在实际开发中的应用。通过引入RocketChat、StackStorm和Hubot构建新的软件开发环境。在该环境中,ChatBot在信息共享、任务自动化和流程开发驱动方面发挥了重要作用。通过实际项目实践和用户调查发现,在新环境中的ChatBot虽然存在一定局限性,但为开发者带来了效率和协作的提升。基于ChatBot的软件开发环境可以为开发团队提供一个高度可扩展和具有实践指导意义的团队协作方案。

The Impact of Semi-Supervised Learning on the Performance of Intelligent Chatbot System

Artificial intelligent based dialog systems are getting attention from both business and academic communities.The key parts for such intelligent chatbot systems are domain classification,intent detection,and named entity recognition.Various supervised,unsupervised,and hybrid approaches are used to detect each field.Such intelligent systems,also called natural language understanding systems analyze user requests in sequential order:domain classification,intent,and entity recognition based on the semantic rules of the classified domain.This sequential approach propagates the downstream error;i.e.,if the domain classification model fails to classify the domain,intent and entity recognition fail.Furthermore,training such intelligent system necessitates a large number of user-annotated datasets for each domain.This study proposes a single joint predictive deep neural network framework based on long short-term memory using only a small user-annotated dataset to address these issues.It investigates value added by incorporating unlabeled data from user chatting logs into multi-domain spoken language understanding systems.Systematic experimental analysis of the proposed joint frameworks,along with the semi-supervised multi-domain model,using open-source annotated and unannotated utterances shows robust improvement in the predictive performance of the proposed multi-domain intelligent chatbot over a base joint model and joint model based on adversarial learning.

EP-Bot: Empathetic Chatbot Using Auto-Growing Knowledge Graph

People occasionally interact with each other through conversation.In particular,we communicate through dialogue and exchange emotions and information from it.Emotions are essential characteristics of natural language.Conversational artificial intelligence is an integral part of all the technologies that allow computers to communicate like humans.For a computer to interact like a human being,it must understand the emotions inherent in the conversation and generate the appropriate responses.However,existing dialogue systems focus only on improving the quality of understanding natural language or generating natural language,excluding emotions.We propose a chatbot based on emotion,which is an essential element in conversation.EP-Bot(an Empathetic PolarisX-based chatbot)is an empathetic chatbot that can better understand a person’s utterance by utilizing PolarisX,an autogrowing knowledge graph.PolarisX extracts new relationship information and expands the knowledge graph automatically.It is helpful for computers to understand a person’s common sense.The proposed EP-Bot extracts knowledge graph embedding using PolarisX and detects emotion and dialog act from the utterance.Then it generates the next utterance using the embeddings.EP-Bot could understand and create a conversation,including the person’s common sense,emotion,and intention.We verify the novelty and accuracy of EP-Bot through the experiments.

Vik: A Chatbot to Support Patients with Chronic Diseases

Background: Chatbots are easy to use and simulate a human conversation through text or voice via smartphones or computers. In the field of health, chatbots can improve patient information, monitoring, or treatment adherence. Method: The objective of this article is to describe how a chatbot dedicated to disease monitoring and support of patients can interact with them and how data are exploited to be safe. Results: Wefight designed a chatbot named Vik to empower patients with cancers or chronic diseases and their relatives via personalized text messages. Natural Language Processing models were used. We built several Vik for each disease. Each Vik has its contents, its own NLP model and interacts its way with the patient. Conclusion: Conversational agents may help patients with minor health concerns without seeing a real physician. If the quality of these softwares is not thoroughly assessed, they could be dangerous. If chatbots are effective and safe, they could be prescribed like a drug to improve patient information, monitoring, or treatment adherence.

智能助产术教学法——以“智能苏格拉底会话机器人”教学实践为例

当前,生成式人工智能与学生的人机会话主要是“知识讲述”型会话关系,这会影响学生的高阶思维能力发展。解决这一问题的关键是,如何将人机“知识讲述”型会话关系,转变为“知识转化”型会话关系。为此,研究者以助产术理论、ChatGPT、学习分析和腾讯QQ工具为基础,探索智能助产术教学法的学习发生机制,开发智能苏格拉底会话机器人,构建智能助产术教学模式。本研究采用准实验方法,以“远程教育学”课程为教学内容,以教育技术学专业本科生为对象,开展以智能会话机器人支持的教学实验。实验结果表明,智能助产术教学与直接使用ChatGPT的教学相比,能显著提升学生的问题解决能力、创新能力和协作学习能力,但是对学习绩效、批判性思维能力和自我效能感的影响不显著。为提高教学效果,研究者需提升计算机的系统算力,开发批判性思维学习支架,构建人机适切性互动机制,研制自我效能感提升策略。

Artificial Intelligence-Enabled Chatbots in Mental Health:A Systematic Review

Clinical applications of Artificial Intelligence(AI)for mental health care have experienced a meteoric rise in the past few years.AIenabled chatbot software and applications have been administering significant medical treatments that were previously only available from experienced and competent healthcare professionals.Such initiatives,which range from“virtual psychiatrists”to“social robots”in mental health,strive to improve nursing performance and cost management,as well as meeting the mental health needs of vulnerable and underserved populations.Nevertheless,there is still a substantial gap between recent progress in AI mental health and the widespread use of these solutions by healthcare practitioners in clinical settings.Furthermore,treatments are frequently developed without clear ethical concerns.While AI-enabled solutions show promise in the realm of mental health,further research is needed to address the ethical and social aspects of these technologies,as well as to establish efficient research and medical practices in this innovative sector.Moreover,the current relevant literature still lacks a formal and objective review that specifically focuses on research questions from both developers and psychiatrists in AI-enabled chatbotpsychologists development.Taking into account all the problems outlined in this study,we conducted a systematic review of AI-enabled chatbots in mental healthcare that could cover some issues concerning psychotherapy and artificial intelligence.In this systematic review,we put five research questions related to technologies in chatbot development,psychological disorders that can be treated by using chatbots,types of therapies that are enabled in chatbots,machine learning models and techniques in chatbot psychologists,as well as ethical challenges.

Mining the Chatbot Brain to Improve COVID-19 Bot Response Accuracy

People often communicate with auto-answering tools such as conversational agents due to their 24/7 availability and unbiased responses.However,chatbots are normally designed for specific purposes and areas of experience and cannot answer questions outside their scope.Chatbots employ Natural Language Understanding(NLU)to infer their responses.There is a need for a chatbot that can learn from inquiries and expand its area of experience with time.This chatbot must be able to build profiles representing intended topics in a similar way to the human brain for fast retrieval.This study proposes a methodology to enhance a chatbot’s brain functionality by clustering available knowledge bases on sets of related themes and building representative profiles.We used a COVID-19 information dataset to evaluate the proposed methodology.The pandemic has been accompanied by an“infodemic”of fake news.The chatbot was evaluated by a medical doctor and a public trial of 308 real users.Evaluationswere obtained and statistically analyzed tomeasure effectiveness,efficiency,and satisfaction as described by the ISO9214 standard.The proposed COVID-19 chatbot system relieves doctors from answering questions.Chatbots provide an example of the use of technology to handle an infodemic.

COVID-19 Lockdown in India: An Experimental Study on Promoting Mental Wellness Using a Chatbot during the Coronavirus

India imposed the largest lockdown in the world in response tofight the spread of the Novel Coronavirus disease(COVID-19)from 19 March till 31 May 2020.The onset of the pandemic left the general public feeling psycho-socially distressed,helpless,and anxious.The researcher developed a Messenger supported Chatbot,based on the broaden and build model,to cater to the healthy general public to promote positivity and mental well-being.31 participants between 22 and 45 years old consensually took a pre-test,Chatbot intervention,and post-test.The Chatbot provided guided activities out of which positive affirmations,meditation,and exercises were mostly used.The qualitative data from the study shows that the majority of the participants strongly feel positivity is within themselves and that the tool provided a self-help approach to be me well,mentally during the lockdown.The intervention helped significantly reducing symptoms of psychosocial distress in six of the individual’s post-chatbot interventions.Participants’impressions of the tool suggest more preponderant opportunities for future research in technology-driven mental health support.

基于生成式人工智能技术的对话机器人能促进在线协作学习绩效吗?

近年来,人工智能技术迅猛发展,对教育教学产生了巨大的冲击和影响,最引人注目的是基于生成式人工智能技术的对话机器人的兴起。为进一步提高在线协作学习绩效,研究旨在检验基于生成式人工智能技术的对话机器人对在线协作学习绩效、批判性思维意识及其知识激活量和贡献度的影响。研究邀请大学生参与在线协作学习,综合采用定量和定性相结合的研究方法,深度分析对话机器人的影响。研究结果表明,基于生成式人工智能技术的对话机器人不仅在其知识激活量、贡献度方面明显优于传统媒体,而且也能够显著提升在线协作学习绩效和批判性思维意识。文章深入分析和讨论了研究结果,并提出若干启示和建议。

CO-BOT:An Intelligent Technique for Designing a Chatbot for Initial COVID-19 Test

The coronavirus(nCOV-19),which was discovered,has now spread around the world.However,managing the flow of a large number of cases has proven to be a significant issue for hospitals or healthcare professionals.It is becoming increasingly challenging to speak with a medical expert after the epidemic’s initial wave has passed,particularly in rural areas.Thus,it becomes clear that a Chatbot that is well-designed and implemented can assist patients who are located far away by advocating preventive actions,and viral updates in various cities,and minimising the psychological harm brought on by dread.In this study,a sophisticated Chabot’s design for diagnosing individuals who have been exposed to COVID-19 is presented,along with recommendations for immediate safety measures.Additionally,when symptoms grow serious,this virtual assistant makes contact with specialised medical professionals.

智能群体多维感知机器人支持的人机协同研学教学模式--以“孔子”大语言模型会话机器人的研发与教学为例

在大语言模型教育应用中,人机会话反馈形式单一问题一直未得到较好解决,这严重影响了学生的高阶思维能力培养。开发具有智能群体多维感知能力的会话机器人,是解决这一问题的重要途径。为此,研究者以群体感知、同伴反馈和研究性学习理论为基础,利用文心一言、飞书开发平台和学习分析等技术,开发了智能群体多维感知会话机器人“孔子”,构建了人机协同研学教学模式。教学实验以“现代教育技术应用”课程为教学内容,采用准实验研究设计,邀请数学专业学生参与实验。结果表明,与直接使用文心一言的教学相比,人机协同研学教学模式能显著提升学生的问题解决能力、批判性思维能力、创新能力和外在学习动机,但对学习绩效、内在动机和协作学习能力效果欠佳。为了提高教学效果,研究者需提高多维感知评价精准性、感知维度全面性、情境学习多样性和教师教学技能。

旅游网站机器人客服身份披露和服务属性对顾客满意度的交互影响研究

基于技术的发展,旅游网站机器人客服对话质量显著提高,在互动方面和人工客服具有高度相似性。顾客是否知晓机器人客服的真实身份会影响满意度。然而,现有研究较少结合服务属性探究旅游网站机器人客服身份披露对顾客满意度的影响机制。基于此,文章利用两个实验讨论了机器人客服身份披露和服务属性对顾客满意度的交互作用及内在机制。结果表明:在享乐属性的服务中,无身份披露(vs.有身份披露)的机器人客服更能提高顾客的满意度;在实用属性的服务中,身份披露对满意度作用不显著。同时,能力感知在其中发挥中介作用。研究结论不仅丰富了与机器人客服身份披露相关的研究,也为旅游网站机器人客服身份披露提供了理论指导。

高等教育中如何应用ChatGPT类生成式人工智能——联合国教科文组织《高等教育中的ChatGPT和人工智能:快速入门指南》解析

ChatGPT引发的浪潮在为高等教育带来变革与创新的同时,也使高等教育面临着新问题与新挑战。为此,联合国教科文组织于2023年4月发布《高等教育中的ChatGPT和人工智能:快速入门指南》,用以阐释ChatGPT如何应用于高等教育。通过对该指南的深入解读,详细分析了ChatGPT在高等教育中的教学、科研、管理、社区参与等方面的应用变革,并从学术诚信、监管问题、隐私问题、认知偏差、性别与多样化、可访问性、商业化等七个方面探讨了ChatGPT给高等教育带来的挑战和伦理问题。最后,提出高等教育机构如何使用ChatGPT的可行性建议:谨慎而有创造性地使用ChatGPT、构建理解和管理ChatGPT的能力、进行人工智能审核等,旨在为我国高等教育机构应用ChatGPT可能面临的机遇和挑战提供经验和启示,并为推动ChatGPT向可持续、公正和合乎伦理的方向发展提供借鉴与参考。

共建共治共享视域下不同智能程度聊天机器人的效果评估

党的二十大报告强调要建设人人有责、人人尽责、人人享有的社会治理共同体。新一代人工智能迅猛发展使得社会治理智能化受到高度关注。通过智能技术赋能,提升政府公共服务、社会治理的数字化智能化水平的重要场景之一是采用智能聊天机器人为公众拓展参与途径、助力社会治理共同体提供可能。基于此,本文从相关理论基础和实践应用出发,明确智能技术应用所体现出的智能性的内涵,通过调查实验法设计被试间实验,从实证角度出发分析智能性对于公众在线参与的影响,对实践应用场景中如何实施智能技术提供对策建议。

护生对大语言模型聊天机器人使用体验的质性研究

目的了解护生对大语言模型聊天机器人的使用体验,为规范使用大语言模型聊天机器人提供参考。方法采用描述性质性研究方法,以目的抽样法选取15名来自4所医学院校的护生进行半结构式访谈。使用内容分析法对访谈资料进行分析、归纳并提炼主题。结果共提炼出4个主题:使用体验感多样化;使用场景差异化;使用潜在问题;自我感知需求和期待。结论护理教育和管理者应引领护生树立正确使用大语言模型价值观,制定相关使用准则规范其使用,还须加强培训和护理伦理建设,促进大语言模型在护理教育中的创新融合。

生成式人工智能聊天机器人:重新定义图书馆用户服务

生成式人工智能聊天机器人将会引起图书馆行业用户服务的重大变革。文章讨论了GPT技术下的聊天机器人以及国内外图书馆关于聊天机器人的应用历史及现状,通过对比传统聊天机器人解读生成式人工智能聊天机器人在图书馆行业中的应用优势、场景及机遇,并从生成式人工智能聊天机器人自身局限性、落地我国图书馆行业的阻碍、图书馆管理等三方面分析了它给我国图书馆行业带来的挑战。

人工智能聊天机器人助力眼科和科学论文写作

人工智能聊天机器人ChatGPT是使用深度学习技术、能对自然语言输入产生类似人类反应的一种大型语言模型(LLMs)。它由OpenAI公司在2022.11开发,属于“生成预训练转换器(GPT)”模型家族的一种,目前可为公众所用。ChatGPT能够捕捉人类语言的细微差别和复杂性,生成适当的、与上下文相关的响应。它可以帮助医务人员完成各种任务,如研究、诊断、患者监护和医学教育,从确定研究课题到协助临床和实验室诊断,了解各自领域的新进展和科学写作。ChatGPT在眼科已吸引了越来越多的关注和广泛应用。然而,目前在这些任务中使用ChatGPT和其他人工智能工具仍存在一定的局限性、伦理和法律问题,如可信度、剽窃、版权侵犯和偏见。未来的研究将集中在开发新的方法来减轻这些局限性,同时发挥ChatGPT在医疗等相关方面的积极作用。

什么样的聊天机器人更能促进学习?——基于60项实验与准实验研究的元分析

随着人工智能技术的迅猛发展,特别是大语言模型的兴起,应用聊天机器人辅助学生学习成为研究热点。相关研究表明,聊天机器人能有效激发学生的学习动机,提高学习投入度、自我效能感和学业成就等。然而,究竟什么样的聊天机器人更能促进学习,如何才能有效地应用聊天机器人,仍是亟待解决的问题。本研究采用元分析方法,系统分析近十年国内外应用聊天机器人促进学生学习的60项实证研究。结果表明,聊天机器人对学生学习效果有中等程度的正向影响,反馈类型、交互模态等交互设计对学习效果有显著的调节作用。不同年级、不同学习方式对聊天机器人的应用效果存在显著差异,但不同学科和学习环境无显著差异。文章最后展望了聊天机器人的未来研究方向。

图书馆AI聊天机器人持续使用意愿研究——基于情感体验的中介

[目的/意义]探究用户持续使用图书馆AI聊天机器人意愿的影响因素,更好地理解用户的持续性思维,并为未来学习生态下图书馆AI聊天机器人的进一步发展带来启示。[方法/过程]基于使用与满足理论和“刺激-机体-反应”框架,建立图书馆AI聊天机器人用户持续使用意愿概念模型。采用问卷调查法收集数据,再结合PLS-SEM和fsQCA混合研究方法对模型中所含变量与假设进行验证。[结果/结论]研究结果表明,享乐性(娱乐和逃避)、社会性(社交存在)和功利性(便利性和信息咨询)3种类型的满足对情感体验(敬畏体验和情感参与)有显著正向影响,其中逃避对敬畏体验影响最大,社交存在对情感参与影响最大;情感体验对持续使用意愿有显著正向影响,其中敬畏体验对持续使用意愿影响最大;情感体验作为作用机制影响了用户满足感和使用意愿。

人工智能聊天机器人在新生儿黄疸居家监测中的应用分析

目的探讨人工智能(artificial intelligence,AI)聊天机器人在新生儿黄疸居家监测中的应用价值。方法选取2021年11月—2022年3月在重庆市妇幼保健院产科出生的700例黄疸新生儿,按照随机数字表分为对照组与观察组,每组350例。对照组监护人接受常规宣教,观察组监护人在接受常规宣教基础上联合AI聊天机器人干预,随访观察1个月。比较2组监护人情况(黄疸知识掌握情况、情绪状态与护理满意度)和新生儿情况(黄疸出现时间、黄疸消退时间、二次黄疸住院发生率、生长发育情况)。对微信群聊天内容进行词云分析。结果干预后1个月,与对照组监护人比较,观察组监护人的黄疸知识调查问卷各项目得分较高,抑郁自评量表和焦虑自评量表分值更低,护理满意度评分更高(均P<0.05)。与对照组新生儿比较,观察组新生儿黄疸消退时间更短,二次黄疸住院发生率较低,干预后1个月的体质量、智力发育指数量表分值和运动发育指数量表分值均较高(均P<0.05)。微信群聊天内容词云热点包括预约、黄疸出现时间、黄疸消退时间、黄疸症状等。结论在新生儿黄疸的居家监测中,AI聊天机器人能显著提高监护人对黄疸的认识,促进新生儿黄疸消退和生长发育,同时减轻监护人的不良情绪,提高满意度,并通过微信群掌握监护人的疑虑。