Purpose

Generative Artificial Intelligence (Gen-AI) has provided new opportunities and challenges in using educational environments for students’ interaction and knowledge acquisition. Based on the expectation–confirmation theory, this paper aims to investigate the effect of different constructs associated with Gen-AI on engagement, satisfaction and word-of-mouth.

Design/methodology/approach

We collected data from 508 students in the UK using Qualtrics, a prominent online data collection platform. The conceptual framework was analysed through structural equation modelling.

Findings

The findings show that Gen-AI expectation formation and Gen-AI quality help to boost Gen-AI engagement. Further, we found that active engagement positively affects Gen-AI satisfaction and positive word of mouth. The mediating role of Gen-AI expectation confirmation between engagement and the two outcomes, satisfaction and positive word of mouth, was also confirmed. The moderating role of cognitive processing in the relationship between Gen-AI quality and engagement was found.

Originality/value

This paper extends the Expectation-Confirmation Theory on how Gen-AI can enhance students’ engagement and satisfaction. Suggestions for future research are derived to advance beyond the confines of the current study and to capture the development in the use of AI in education.

中文翻译：

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生成式 AI 参与、质量和期望形成之间的联系：期望-确认理论的应用

 目的

生成式人工智能 （Gen-AI） 为利用教育环境进行学生互动和知识获取提供了新的机遇和挑战。基于期望-确认理论，本文旨在研究与 Gen-AI 相关的不同结构对参与度、满意度和口碑的影响。

设计/方法/方法

我们使用著名的在线数据收集平台 Qualtrics 收集了英国 508 名学生的数据。通过结构方程建模对概念框架进行分析。

 发现

研究结果表明，Gen-AI 期望形成和 Gen-AI 质量有助于提高 Gen-AI 的参与度。此外，我们发现积极参与对 Gen-AI 的满意度和积极的口碑有积极影响。Gen-AI 期望确认在参与度与满意度和积极口碑这两个结果之间的中介作用也得到了证实。发现认知加工在 Gen-AI 质量和参与度之间的关系中起调节作用。

 原创性/价值

本文扩展了 Gen-AI 如何提高学生参与度和满意度的期望-确认理论。对未来研究的建议旨在超越当前研究的范围，并捕捉人工智能在教育中的应用发展。