This research explores the quickly changing field of generative artificial intelligence (GAI) chatbots in higher education, an industry that is undergoing major technological changes. AI chatbots, such as ChatGPT, HuggingChat, and Google Bard, are becoming more and more common in a variety of sectors, including education. Their acceptance is still in its early phases, with a variety of prospects and obstacles. However, their potential in higher education is particularly noteworthy, providing lecturers and students with affordable, individualized support. Creating a comprehensive framework to aid the usage of generative AI chatbots in higher education institutions (HEIs) is the aim of this project. The Generative AI Chatbots Acceptance Model (GAICAM) is the result of this study's synthesis of elements from well-known frameworks, including the TAM, UTAUT2, TPB, and others along with variables like optimism, innovativeness, discomfort, insecurity, and others. Using a research method that encompasses a comprehensive analysis of extant literature from databases such as IEEE, ACM, ScienceDirect, and Google Scholar, the study aims to comprehend the implications of AI Chatbots on higher education and pinpoint critical elements for their efficacious implementation. Peer-reviewed English-language publications published between 2020 and 2023 with a focus on the use of AI chatbots in higher education were the main focus of the search criteria. The results demonstrate how much AI chatbots can do to improve student engagement, streamline the educational process, and support administrative and research duties. But there are also clear difficulties, such as unfavorable student sentiments, doubts about the veracity of material produced by AI, and unease and nervousness with new technologies.

翻译：本研究聚焦于高等教育领域快速变革的生成式人工智能（GAI）聊天机器人技术——一个正经历重大技术变革的行业。人工智能聊天机器人（如ChatGPT、HuggingChat和Google Bard）已日益普及至包括教育在内的多个领域，但其应用仍处于起步阶段，伴随多样化机遇与挑战。然而，其在高等教育中的潜力尤为突出，能够为学生和教师提供经济且个性化的支持。本项目旨在构建一个综合性框架，以促进生成式人工智能聊天机器人在高等教育机构中的实施，最终提出了“生成式人工智能聊天机器人接受模型”（GAICAM）。该模型整合了TAM、UTAUT2、TPB等成熟框架的要素，并融合了乐观、创新性、不适感、不安全感等变量。研究采用系统文献综述方法，对IEEE、ACM、ScienceDirect及Google Scholar等数据库中的现有文献进行全面分析，旨在理解人工智能聊天机器人对高等教育的影响，并识别其有效实施的关键要素。检索标准聚焦于2020至2023年间发表的、关注人工智能聊天机器人在高等教育中应用的同行评审英文文献。研究表明，人工智能聊天机器人在提升学生参与度、优化教育流程以及支持行政与研究任务方面具有显著潜力。但同时也暴露出诸多挑战，包括学生的消极情绪、对AI生成内容真实性的质疑，以及面对新技术时的不安与焦虑。