Conversational and generative artificial intelligence and human–chatbot interaction in education and research

IF 3.1 4区管理学 Q2 MANAGEMENT

International Transactions in Operational Research Pub Date : 2024-07-31 DOI:10.1111/itor.13522

Ikpe Justice Akpan, Yawo M. Kobara, Josiah Owolabi, Asuama A. Akpan, Onyebuchi Felix Offodile

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引用次数: 0

Abstract

Artificial intelligence (AI) as a disruptive technology is not new. However, its recent evolution, engineered by technological transformation, big data analytics, and quantum computing, produces conversational and generative AI (CGAI/GenAI) and human‐like chatbots that disrupt conventional operations and methods in different fields. This study investigates the scientific landscape of CGAI and human–chatbot interaction/collaboration and evaluates use cases, benefits, challenges, and policy implications for multidisciplinary education and allied industry operations. The publications trend showed that just 4% (n = 75) occurred during 2006–2018, while 2019–2023 experienced astronomical growth (n = 1763 or 96%). The prominent use cases of CGAI (e.g., ChatGPT) for teaching, learning, and research activities occurred in computer science (multidisciplinary and AI; 32%), medical/healthcare (17%), engineering (7%), and business fields (6%). The intellectual structure shows strong collaboration among eminent multidisciplinary sources in business, information systems, and other areas. The thematic structure highlights prominent CGAI use cases, including improved user experience in human–computer interaction, computer programs/code generation, and systems creation. Widespread CGAI usefulness for teachers, researchers, and learners includes syllabi/course content generation, testing aids, and academic writing. The concerns about abuse and misuse (plagiarism, academic integrity, privacy violations) and issues about misinformation, danger of self‐diagnoses, and patient privacy in medical/healthcare applications are prominent. Formulating strategies and policies to address potential CGAI challenges in teaching/learning and practice are priorities. Developing discipline‐based automatic detection of GenAI contents to check abuse is proposed. In operational/operations research areas, proper CGAI/GenAI integration with modeling and decision support systems requires further studies.

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教育和研究中的对话式和生成式人工智能以及人与聊天机器人的互动

人工智能（AI）作为一种颠覆性技术并不新鲜。然而，在技术变革、大数据分析和量子计算的推动下，人工智能最近的发展产生了对话式和生成式人工智能（CGAI/GenAI）以及类人聊天机器人，颠覆了不同领域的传统操作和方法。本研究调查了 CGAI 和类人聊天机器人交互/协作的科学前景，并评估了多学科教育和相关行业运营的用例、效益、挑战和政策影响。论文发表趋势显示，2006-2018年期间的论文发表率仅为4%（n = 75），而2019-2023年则经历了天文数字般的增长（n = 1763或96%）。CGAI（如 ChatGPT）在教学、学习和研究活动中的突出用例出现在计算机科学（多学科和人工智能；32%）、医疗/保健（17%）、工程（7%）和商业领域（6%）。知识结构表明，商业、信息系统和其他领域的多学科知名人士之间开展了强有力的合作。专题结构突出了CGAI的重要用例，包括在人机交互、计算机程序/代码生成和系统创建中改善用户体验。CGAI对教师、研究人员和学习者的广泛用途包括教学大纲/课程内容生成、测试辅助工具和学术写作。在医疗/保健应用中，滥用和误用（剽窃、学术诚信、侵犯隐私）以及错误信息、自我诊断危险和病人隐私等问题十分突出。制定战略和政策以应对 CGAI 在教学和实践中可能遇到的挑战是当务之急。建议开发基于学科的 GenAI 内容自动检测功能，以防止滥用。在操作/运行研究领域，CGAI/GenAI 与建模和决策支持系统的适当整合需要进一步研究。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

International Transactions in Operational Research OPERATIONS RESEARCH & MANAGEMENT SCIENCE-

CiteScore

7.80

自引率

12.90%

发文量

146

审稿时长

>12 weeks

期刊介绍： International Transactions in Operational Research (ITOR) aims to advance the understanding and practice of Operational Research (OR) and Management Science internationally. Its scope includes: International problems, such as those of fisheries management, environmental issues, and global competitiveness International work done by major OR figures Studies of worldwide interest from nations with emerging OR communities National or regional OR work which has the potential for application in other nations Technical developments of international interest Specific organizational examples that can be applied in other countries National and international presentations of transnational interest Broadly relevant professional issues, such as those of ethics and practice Applications relevant to global industries, such as operations management, manufacturing, and logistics.