Understanding AI Chatbot adoption in education: PLS-SEM analysis of user behavior factors

Md Rabiul Hasan , Nahian Ismail Chowdhury , Md Hadisur Rahman , Md Asif Bin Syed , JuHyeong Ryu
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Abstract

The integration of Artificial Intelligence (AI) into education is a recent development, with chatbots emerging as a noteworthy addition to this transformative landscape. As online learning platforms rapidly advance, students need to adapt swiftly to excel in this dynamic environment. Consequently, understanding the acceptance of chatbots, particularly those employing Large Language Models (LLM) such as Chat Generative Pretrained Transformer (ChatGPT), Google Bard, and other interactive AI technologies, is of paramount importance. Investigating how students accept and view chatbots is essential to directing their incorporation into Industry 4.0 and enabling a smooth transition to Industry 5.0's customized and human-centered methodology. However, existing research on chatbots in education has overlooked key behavior-related aspects, such as Optimism, Innovativeness, Discomfort, Insecurity, Transparency, Ethics, Interaction, Engagement, and Accuracy, creating a significant literature gap. To address this gap, this study employs Partial Least Squares Structural Equation Modeling (PLS-SEM) to investigate the determinant of chatbots adoption in education among students, considering the Technology Readiness Index and Technology Acceptance Model. Utilizing a five-point Likert scale for data collection, we gathered a total of 185 responses, which were analyzed using R-Studio software. We established 12 hypotheses to achieve its objectives. The results showed that Optimism and Innovativeness are positively associated with Perceived Ease of Use and Perceived Usefulness. Conversely, Discomfort and Insecurity negatively impact Perceived Ease of Use, with only Insecurity negatively affecting Perceived Usefulness. Furthermore, Perceived Ease of Use, Perceived Usefulness, Interaction and Engagement, Accuracy, and Responsiveness all significantly contribute to the Intention to Use, whereas Transparency and Ethics have a negative impact on Intention to Use. Finally, Intention to Use mediates the relationships between Interaction, Engagement, Accuracy, Responsiveness, Transparency, Ethics, and Perception of Decision Making. These findings provide insights for future technology designers, elucidating critical user behavior factors influencing chatbots adoption and utilization in educational contexts.
了解人工智能聊天机器人在教育领域的应用:用户行为因素的 PLS-SEM 分析
人工智能(AI)与教育的结合是最近才出现的,而聊天机器人则是这一变革中值得关注的新成员。随着在线学习平台的快速发展,学生需要迅速适应,以便在这一动态环境中脱颖而出。因此,了解学生对聊天机器人的接受程度至关重要,尤其是那些采用大型语言模型(LLM)的聊天机器人,如 Chat Generative Pretrained Transformer(ChatGPT)、Google Bard 和其他交互式人工智能技术。调查学生如何接受和看待聊天机器人,对于引导学生将聊天机器人融入工业 4.0 并顺利过渡到工业 5.0 的定制和以人为本的方法至关重要。然而,关于教育领域聊天机器人的现有研究忽略了与行为相关的关键方面,如乐观、创新、不适、不安全、透明、道德、互动、参与和准确性,从而造成了重大的文献空白。为了填补这一空白,本研究采用偏最小二乘法结构方程模型(PLS-SEM)研究学生在教育领域采用聊天机器人的决定因素,同时考虑了技术准备指数和技术接受模型。我们使用五点李克特量表进行数据收集,共收集到 185 个回答,并使用 R-Studio 软件对其进行了分析。为实现研究目标,我们提出了 12 项假设。结果显示,乐观和创新与 "感知易用性 "和 "感知有用性 "呈正相关。相反,"不舒适 "和 "不安全 "对 "感知易用性 "有负面影响,只有 "不安全 "对 "感知有用性 "有负面影响。此外,"感知易用性"、"感知有用性"、"互动与参与"、"准确性 "和 "响应性 "都对 "使用意向 "有显著促进作用,而 "透明度 "和 "道德规范 "则对 "使用意向 "有负面影响。最后,使用意向对互动、参与、准确性、响应性、透明度、道德和决策感知之间的关系起到了中介作用。这些发现为未来的技术设计者提供了启示,阐明了影响聊天机器人在教育环境中的采用和使用的关键用户行为因素。
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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