Introduction 介绍

Artificial intelligence (AI) has been applied in various industries, such as healthcare (Topol, 2019), finance (Königstorfer & Thalmann, 2020), the transportation industry (Iyer, 2021), and education (Zhai et al., 2021). Generative AI (GenAI) is a subset of AI that has tremendous potential to revolutionize human-AI interactions and solve complex problems within educational settings (Russell & Norvig, 2016). ChatGPT, a type of GenAI, was released in November 2022 (Schulman et al., 2022), it has impressive capabilities to generate coherent and contextually appropriate responses that closely mimic human-like communication with an advanced language model based on the Generative Pre-trained Transformer (GPT) architecture. This has sparked significant interest in academic and industry circles (Agrawal et al., 2022; Chui et al., 2022; Cotton et al., 2023; Mucharraz y Cano et al., 2023) as well as among the public (Nah et al., 2023). It has potentials to provide personalized learning experiences and tailor instructional content to individual students’ needs and abilities (Chan & Lee, 2023; Chassignol et al., 2018; Crompton & Burke, 2023). It can also foster collaboration and peer interaction by generating context-aware prompts and responses, creating a dynamic learning environment that fosters engagement and deeper understanding (Zawacki-Richter et al., 2019).
人工智能（AI）已应用于各种行业，如医疗保健（Topol，2019）、金融（Königstorfer & Thalmann，2020）、运输行业（Iyer，2021）和教育（Zhai et al.， 2021）。 生成式AI （GenAI）是人工智能的一个子集，具有巨大的潜力来革新人类与人工智能的互动并解决教育环境中的复杂问题（Russell & Norvig），2016年）。ChatGPT 是 GenAI 的一种，于 2022 年 11 月发布（Schulman et al.， 2022），它具有令人印象深刻的能力，可以生成连贯且上下文适当的响应，这些响应与基于生成式预训练转换器 （GPT） 架构的高级语言模型紧密模拟类似人类的交流。这引发了学术界和工业界的极大兴趣（Agrawal et al.， 2022;Chui et al.， 2022;Cotton 等人，2023 年;Mucharraz y Cano等人，2023 年）以及公众（No 等人，2023 年）。它具有提供个性化学习体验的潜力，并根据个别学生的需求和能力定制教学内容（Chan & Lee，2023;Chassignol等人，2018 年;Crompton & Burke， 2023）。它还可以通过生成上下文感知的提示和响应来促进协作和同伴互动，创造一个充满活力的学习环境，促进参与和更深入的理解（Zawacki-Richter et al.， 2019）。

However, the emergence and upgrades of GenAI have also brought new challenges for both teaching and learning (Chan & Hu, 2023). This calls for a human-centered approach to education (UNESCO, 2023) as higher education institutions adapt to this changing landscape. It is crucial to understand students’ intentions to use GenAI tools and ensure students are adequately prepared for their personal and professional pursuits in this fast-paced world (Chan, 2023a, 2023b). Intention, an important indicator of human behaviour (Ajzen, 2002), has been widely studied in educational technology (e.g., Ifinedo, 2018) but often within general contexts such as AI learning intention (Chai et al., 2021; Wang et al., 2023) or in non-GenAI settings, for example, AI-enabled automatic scoring applications (Fu et al., 2020) and AI teaching assistants (Kim et al., 2020). Since the adaptation of GenAI in higher education is still in the exploratory stage, past research on AI cannot fully illustrate students’ perceptions in GenAI-specific context.
然而，GenAI 的出现和升级也为教学和学习带来了新的挑战（Chan & 胡，2023 年）。这需要采取以人为本的教育方法（联合国教科文组织，2023 年），因为高等教育机构正在适应这一不断变化的环境。了解学生使用 GenAI 工具的意图并确保学生在这个快节奏的世界中为他们的个人和职业追求做好充分准备至关重要（Chan， 2023a， 2023b）。意图是人类行为的重要指标（Ajzen，2002 年），已在教育技术中得到广泛研究（例如，Ifinedo，2018 年），但通常在人工智能学习意图等一般背景下（Chai et al.， 2021;Wang et al.， 2023） 或在非 GenAI 环境中，例如，支持 AI 的自动评分应用程序（Fu et al.， 2020）和 AI 教学助理（Kim et al.， 2020）。由于 GenAI 在高等教育中的适应仍处于探索阶段，过去对 AI 的研究无法完全说明学生在 GenAI 特定背景下的看法。

Therefore, this study aims to address student-centered factors influencing their intentions towards GenAI by using the Expectancy Value Theory (EVT) to understand students’ preliminary knowledge and their perceptions of using the newly launched technology. EVT posits that individual’s motivation to engage in a behaviour is influenced by their expectations of success and the value they place on the behaviour (Wigfield, 1994; Wigfield & Eccles, 2000), working as a framework to examine students’ intentions and perceptions from knowledge, perceived value, and perceived cost of the technology in the early stage. To facilitate educators and policymakers to make informed decisions about the integration of GenAI into higher education, the research will validate an instrument specifically designed for GenAI context and further explore the following questions:
因此，本研究旨在通过使用期望值理论 （EVT） 来了解学生的初步知识和他们对使用新推出的技术的看法，从而解决影响他们对 GenAI 意图的以学生为中心的因素。EVT 假设个人从事某种行为的动机受到他们对成功的期望和他们对行为的重视的影响（Wigfield，1994 年;Wigfield & Eccles， 2000），作为一个框架，从知识、感知价值和早期阶段的技术感知成本中检查学生的意图和感知。为了促进教育工作者和政策制定者就将 GenAI 整合到高等教育中做出明智的决策，该研究将验证专为 GenAI 环境设计的工具，并进一步探讨以下问题：

  1. 1.

    Is there a correlation between students’ knowledge of and familiarity with GenAI and their intention to use GenAI?
    学生对 GenAI 的了解和熟悉程度与他们使用 GenAI 的意图之间是否存在相关性？

  2. 2.

    Is there a correlation between students’ perceived value of using GenAI and their intention to use AI?
    学生使用 GenAI 的感知价值与他们使用 AI 的意图之间是否存在相关性？

  3. 3.

    Is there a correlation between students’ perceived cost of using GenAI and their intention to use AI?
    学生使用 GenAI 的感知成本与他们使用 AI 的意图之间是否存在相关性？

Literature on student’s perception of AI and GenAI
关于学生对 AI 和 GenAI 的看法的文献

There has been a growing interest in students’ perceptions of AI in education ranging from general AI use to specific applications such as AI teaching assistants and ChatGPT. In Chan and Hu (2023), student voices from a survey involving 399 Hong Kong undergraduates and postgraduates across disciplines revealed five benefits and six challenges of GenAI in education. Perceived advantages included personalized learning, writing help, and research abilities. Yet, concerns around accuracy, privacy, ethics, and its impact on personal growth, career, and societal values were voiced.
人们对学生对教育中 AI 的看法越来越感兴趣，从一般 AI 使用到 AI 助教和 ChatGPT 等特定应用程序。在 Chan 和 胡 （2023） 中，一项涉及 399 名香港跨学科本科生和研究生的调查中，学生的声音揭示了 GenAI 在教育中的五大好处和六大挑战。感知优势包括个性化学习、写作帮助和研究能力。然而，人们对准确性、隐私、道德及其对个人成长、职业和社会价值观的影响表示担忧。

Zou et al. (2020) employed a sequential explanatory mixed-methods design, which comprised of a survey assessing student perceptions of their current usage and effectiveness of AI-English Language Learning apps for speaking skills enhancement, followed by qualitative interviews to elucidate and interpret the findings from the questionnaire. The sample included 113 Year 1 and Year 2 English for Academic Purposes (EAP) students from an English-speaking university in China. The primary findings reveal that participants expressed positive opinions regarding AI technology’s role in developing speaking skills, albeit with certain limitations, such as the absence of personalization and feedback. The potential implications of this study suggest that AI technology may serve as a valuable tool for supporting EAP students in improving their speaking skills.
Zou et al. （2020） 采用了顺序解释性混合方法设计，其中包括一项调查，评估学生对他们当前使用 AI-英语语言学习应用程序的看法以及提高口语技能的有效性，然后进行定性访谈以阐明和解释问卷的结果。样本包括来自中国一所英语大学的 113 名 1 年级和 2 年级学术英语 （EAP） 学生。主要调查结果显示，参与者对 AI 技术在发展口语技能方面的作用表达了积极的看法，尽管存在一定的局限性，例如缺乏个性化和反馈。这项研究的潜在意义表明，人工智能技术可能成为支持 EAP 学生提高口语技能的宝贵工具。

Haensch et al. (2023) analyzed TikTok the social media content to better understand how students perceive and use ChatGPT. The findings suggest that students are interested in using ChatGPT for various tasks, but there is also a concern about its potential impact on academic integrity. The study highlights the need for educators to consider how they incorporate or regulate AI technologies like ChatGPT in universities to raise awareness among students about ethical considerations when using AI technologies. More AI regulatory in education information on governance, pedagogical and operational are found in Chan (2023a, 2023b).
Haensch 等人（2023 年）分析了 TikTok 的社交媒体内容，以更好地了解学生如何感知和使用 ChatGPT。研究结果表明，学生对使用 ChatGPT 完成各种任务感兴趣，但也担心它对学术诚信的潜在影响。该研究强调，教育工作者需要考虑如何在大学中整合或监管 ChatGPT 等 AI 技术，以提高学生在使用人工智能技术时对道德考虑的认识。更多关于治理、教学和运营的人工智能教育监管信息见 Chan （2023a， 2023b）。

A recent study in India (Kumar & Raman, 2022) surveyed 682 students enrolled in full-time business management programmes to gather their opinions on the usage of AI in various aspects of higher education, including the teaching learning process, admission process, placement process, and administrative process. Students generally had positive perceptions of AI usage in higher education, particularly in administrative and admission processes. However, they were more hesitant about AI being used as a partial replacement for faculty members in the teaching–learning process. The study also found that students’ prior exposure to AI influenced their perceptions.
印度最近的一项研究（Kumar & Raman，2022）调查了682名参加全日制商业管理课程的学生，以收集他们对人工智能在高等教育各个方面的使用的看法，包括教学学习过程、录取过程、安置过程和行政过程。学生普遍对人工智能在高等教育中的使用持积极看法，尤其是在行政和录取过程中。然而，他们对人工智能在教学过程中被用作教职员工的部分替代品更加犹豫。该研究还发现，学生之前接触 AI 会影响他们的感知。

Several studies on students’ perceptions of AI adopt the Technology Acceptance Model (TAM). TAM posits that the perceived usefulness (PU) and perceived ease of use (PEOU) of a technology are key determinants of its acceptance and use (Abdullah & Ward, 2016; Davis, 1989). Using this model, Kim et al. (2020) investigated students’ perceptions of the usefulness of and ease of communication with AI teaching assistants in the United States. The study included 321 college students, and the findings suggest that perceived usefulness and ease of communication with an AI teaching assistant positively predict favorable attitudes, which consequently leads to stronger intention to adopt AI teaching assistant-based education. Students who perceived positively with AI teaching assistants mentioned an increase in efficiency and convenience in online education. However, some students also expressed concerns about the lack of human interaction and the potential for errors or technical glitches. Another example is Hu’s (2022) study that examined the factors affecting students’ use of an AI-supported smart learning environment system and found that perceived ease of use and perceived usefulness influenced students’ behavioural intention.
一些关于学生对 AI 的看法的研究采用了技术接受模型 （TAM）。TAM 假设技术的感知有用性 （PU） 和感知易用性 （PEOU） 是其接受和使用的关键决定因素（Abdullah & Ward，2016 年;Davis， 1989）。使用这个模型，Kim et al. （2020） 调查了美国学生对 AI 助教的有用性和与人工智能助教交流的便利性的看法。该研究包括 321 名大学生，研究结果表明，感知到的有用性和与 AI 助教的交流便利性可以积极预测积极的态度，从而导致采用基于 AI 助教的教育的意愿更强。对 AI 助教持积极看法的学生提到在线教育的效率和便利性有所提高。然而，一些学生也对缺乏人际互动以及可能出现错误或技术故障表示担忧。另一个例子是 胡 （2022） 的研究，该研究考察了影响学生使用人工智能支持的智能学习环境系统的因素，发现感知的易用性和感知的有用性会影响学生的行为意图。

Bonsu and Baffour-Koduah (2023) explored the perceptions and intentions of Ghanaian higher education students towards using ChatGPT, using a mixed-method approach guided by the Technology Acceptance Model (TAM) with a sample size of 107 students. The study found that although there was no significant relationship between students’ perceptions and their intention to use ChatGPT, students expressed the intention to use and supported its adoption in education, given their positive experiences. Social media was identified as a key source of students’ knowledge about ChatGPT, and they perceived more advantages than disadvantages of using it in higher education.
Bonsu 和 Baffour-Koduah （2023） 使用以技术接受模型 （TAM） 为指导的混合方法，样本量为 107 名学生，探讨了加纳高等教育学生对使用 ChatGPT 的看法和意图。研究发现，尽管学生的看法与他们使用 ChatGPT 的意图之间没有显着关系，但鉴于他们的积极体验，学生们表达了使用和 Support 将其用于教育的意图。社交媒体被认为是学生了解 ChatGPT 的关键来源，他们认为在高等教育中使用社交媒体的好处多于坏处。

Gado et al. (2022) used an integrated model based on TAM and the unified theory of acceptance and use of technology (UTAUT) to investigate psychology students’ acceptance of and intention to use AI in German universities. Perceived usefulness, perceived social norm, and attitude towards AI were shown to predict intention to use AI; however, perceived ease of use was found to have no significant influence on intention to use. Although perceived knowledge of AI did not have a significant impact on attitude, it showed a relationship with intention to use. In Raffaghelli et al.’s (2022) study in Spain, the UTAUT model was adopted to examine students’ reaction to an early warning system, an AI tool that monitors student progress and detects students who are at risk of failing, in a fully online university. Their results show low expected effort in the tool’s usage was correlated with high perceived usefulness. Students’ perception of the tool also changed over time with the post-usage survey showing a lower acceptance level than the pre-usage survey.
Gado 等人（2022 年）使用基于 TAM 和技术接受和使用统一理论 （UTAUT） 的综合模型来调查心理学学生在德国大学对人工智能的接受和意图。感知的有用性、感知的社会规范和对 AI 的态度被证明可以预测使用 AI 的意图;然而，发现感知的易用性对使用意向没有显着影响。尽管感知到的 AI 知识对态度没有显着影响，但它显示出与使用意向的关系。在 Raffaghelli 等人（2022 年）在西班牙的研究中，采用了 UTAUT 模型来检查学生对早期预警系统的反应，早期预警系统是一种人工智能工具，可以监控学生的进步并检测有不及格风险的学生，在完全在线的大学中。他们的结果表明，该工具使用中的低预期工作量与高感知有用性相关。学生对该工具的看法也随着时间的推移而变化，使用后调查显示接受水平低于使用前调查。

Students’ intention to use AI-driven language models like ChatGPT in India was also explored by Raman et al. (2023). This study, framed by Rogers’ Perceived Theory of Attributes and based on Expectancy-Value Theory (EVT), aimed to explore the factors that determine university students’ intentions to use ChatGPT in higher education. A sample of 288 students participated in the study, which focused on five factors of ChatGPT adoption: Relative Advantage, Compatibility, Ease of Use, Observability, and Trialability. The results revealed that all five factors significantly influenced ChatGPT adoption, with students perceiving it as innovative, compatible, and user-friendly. The potential implications of this study suggest that students are open to using AI-driven language models like ChatGPT in their education and perceive them as valuable resources for independent learning.
Raman 等人（2023 年）也探讨了学生在印度使用 ChatGPT 等人工智能驱动语言模型的意图。本研究以罗杰斯的感知属性理论为框架，基于期望价值理论 （EVT），旨在探讨决定大学生在高等教育中使用 ChatGPT 的意图的因素。该研究的样本有 288 名学生参与，该研究侧重于采用 ChatGPT 的五个因素：相对优势、兼容性、易用性、可观察性和可试用性。结果显示，所有五个因素都显着影响了 ChatGPT 的采用，学生认为它具有创新性、兼容性和用户友好性。这项研究的潜在意义表明，学生对在教育中使用 ChatGPT 等人工智能驱动的语言模型持开放态度，并将其视为独立学习的宝贵资源。

In the Netherlands, a study (Abdelwahab et al., 2023) was conducted using a survey completed by 95 students from 27 higher education institutions. The survey questions were categorized into four factors based on a conceptual framework, including students’ awareness of AI, teacher’s skills in AI teaching, teaching facilities for AI, and the AI curriculum. Respondents were asked to provide their answers using various methods, such as a 5-point Likert scale, ranking, yes or no, or open-response answers. Business students in the Netherlands have expressed concerns regarding their higher education institutions’ readiness to prepare them for AI work environments. They feel that the institutions are ill-equipped or have not fully utilized their resources to provide adequate AI-related training. There is an urgent need to update the curriculum and educational facilities for AI work environments and provide more comprehensive training and education on AI-related topics.
在荷兰，一项研究（Abdelwahab et al.， 2023）使用来自 27 所高等教育机构的 95 名学生完成的调查进行。调查问题根据概念框架分为四个因素，包括学生对 AI 的认识、教师的 AI 教学技能、AI 教学设施和 AI 课程。受访者被要求使用各种方法提供他们的答案，例如 5 分李克特量表、排名、是或否或开放式回答。荷兰的商科学生对他们的高等教育机构是否准备好为 AI 工作环境做好准备表示担忧。他们认为这些机构设备不足或没有充分利用其资源来提供足够的 AI 相关培训。迫切需要更新 AI 工作环境的课程和教育设施，并提供有关 AI 相关主题的更全面的培训和教育。

A study involved 102 physics students from a German university who evaluated ChatGPT responses to introductory physics questions (Dahlkemper et al., 2023). This study aimed to evaluate how physics students perceive the linguistic quality and scientific accuracy of ChatGPT responses to physics questions. The study used a survey instrument based on the Unified Theory of Acceptance and Use of Technology (UTAUT), and included three statements about students’ expectations of AI performance and their attitudes towards AI in general. The items were answered on a 5-point Likert scale. The UTAUT model (Venkatesh et al., 2003) identifies four key factors that influence technology adoption: performance expectancy, effort expectancy, social influence, and facilitating conditions. The key findings suggest that while students generally perceived the linguistic quality of ChatGPT responses positively, they were more critical of the scientific accuracy. Additionally, students who had prior experience with AI were more likely to have positive attitudes towards AI in general.
一项研究涉及来自德国大学的 102 名物理学生，他们评估了 ChatGPT 对物理入门问题的回答（Dahlkemper 等人，2023 年）。本研究旨在评估物理学生如何看待 ChatGPT 对物理问题回答的语言质量和科学准确性。该研究使用了基于技术接受和使用统一理论 （UTAUT） 的调查工具，并包括关于学生对 AI 性能的期望以及他们对 AI 的总体态度的三项陈述。这些项目以 5 分李克特量表回答。UTAUT 模型 （Venkatesh et al.， 2003） 确定了影响技术采用的四个关键因素：绩效预期、努力预期、社会影响力和促进条件。主要发现表明，虽然学生普遍对 ChatGPT 回答的语言质量持积极态度，但他们对科学准确性持批评态度。此外，以前有 AI 经验的学生总体上更有可能对 AI 持积极态度。

Several factors have been identified in the literature as influencing students’ intention of using GenAI in education. Familiarity with AI technologies, personal innovativeness, and perceived usefulness have been shown to positively affect students’ attitudes toward AI (Chassignol et al., 2018). Furthermore, perceived ease of use, which relates to the user-friendliness of AI tools, has been found to be a crucial determinant of students’ willingness to adopt AI technologies (Venkatesh et al., 2003). Table 1 shows some previous studies on students’ perceptions of AI and ChatGPT Our study intends to employ the expectancy-value theory (EVT) to investigate the correlation between students’ intention to use GenAI and their knowledge, familiarity, perceived value, and cost of GenAI. The EVT postulates that self-efficacy and perceived value affect technology adoption. While the more commonly used TAM views that perceived ease of use (self-efficacy) impacts perceived usefulness (utility value), and perceived usefulness (utility value) influences behavioural intention in a cascade mechanism; the EVT presents both expectancy (self-efficacy) and perceived value as having a direct impact on technology adoption in a concurrent manner (Backfisch et al., 2021a, 2021b). In addition, the concept of “cost” in the EVT, which is the sacrifice, effort, and the negative aspects of engaging in an activity (Wigfield & Eccles, 1992) is either overlooked or underdeveloped in models such as TAM and UTAUT. Although the EVT has been used in many areas, there has been limited research into the predictive utility of this framework in relation to students’ attitudes and intention towards GenAI use. Hence, this study aimed to investigate whether students’ expectancy and their perceived value of GenAI concurrently affect their intention to adopt GenAI. It is hoped that the findings of this study would shed light on the factors that influence students’ adoption of GenAI and provide an alternative perspective on how the relationship between student perception and intention to adopt GenAI can be understood.
文献中已经确定了几个影响学生在教育中使用 GenAI 的意图的因素。对 AI 技术的熟悉程度、个人创新能力和感知有用性已被证明对学生对 AI 的态度产生积极影响（Chassignol et al.，2018）。此外，与人工智能工具的用户友好性有关的感知易用性已被发现是学生是否愿意采用人工智能技术的关键决定因素（Venkatesh et al.，2003）。表 1 显示了以前关于学生对 AI 和 ChatGPT 的看法的一些研究我们的研究打算采用期望值理论 （EVT） 来调查学生使用 GenAI 的意图与他们的知识、熟悉度、感知价值和 GenAI 成本之间的相关性。EVT 假设自我效能感和感知价值会影响技术采用。虽然更常用的 TAM 观点认为感知易用性（自我效能感）会影响感知有用性（效用值），而感知有用性（效用值）在级联机制中影响行为意图;EVT 将期望（自我效能感）和感知价值同时呈现为对技术采用产生直接影响（Backfisch 等人，2021a，2021b）。 此外，EVT中的“成本”概念，即牺牲、努力和参与活动的消极方面（Wigfield & Eccles，1992）在TAM和UTAUT等模型中要么被忽视，要么发展不足。尽管 EVT 已用于许多领域，但关于该框架与学生对 GenAI 使用的态度和意图相关的预测效用的研究有限。 因此，本研究旨在调查学生的期望和他们对 GenAI 的感知价值是否同时影响他们采用 GenAI 的意愿。希望这项研究的结果能够阐明影响学生采用 GenAI 的因素，并为如何理解学生感知与采用 GenAI 的意愿之间的关系提供另一种视角。

Table 1 Some previous studies on students’ perceptions of AI and ChatGPT
表 1 关于学生对 AI 和 ChatGPT 感知的一些先前研究
Full size table 全尺寸表格

Expectancy-value theory and other frameworks
期望值理论和其他框架

In the previous section, different frameworks such as TAM, UTAUT to explore student perception of AI have been mentioned. For our study, Expectancy-Value Theory (EVT) will be used. EVT posits that individuals’ decisions to engage in a particular activity or task are influenced by their expectations of success (expectancy) and the perceived value they attach to that activity (value).
在上一节中，提到了不同的框架，例如 TAM、UTAUT 来探索学生对 AI 的看法。在我们的研究中，将使用期望值理论 （EVT）。EVT 假设个人从事特定活动或任务的决定受到他们对成功的期望（期望）和他们对该活动的感知价值（价值）的影响。

Expectancy refers to an individual’s belief in their ability to succeed in a task, while value encompasses several components, such as attainment value, intrinsic value, utility value, and cost (Wigfield & Eccles, 2000). When examining students’ intention to use GenAI, this framework can be utilized to address the research questions as follows:
期望是指个人对他们成功完成任务的能力的信念，而价值包含几个组成部分，如成就价值、内在价值、实用价值和成本（Wigfield & Eccles， 2000）。在检查学生使用 GenAI 的意图时，可以使用此框架来解决以下研究问题：

RQ1: Is there a correlation between students’ knowledge of and familiarity with GenAI and their intention to use GenAI?
RQ1：学生对 GenAI 的了解和熟悉程度与他们使用 GenAI 的意图之间是否存在相关性？

According to the expectancy-value theory, students’ knowledge and familiarity with GenAI may influence their expectancy beliefs. The more familiar and knowledgeable students are with the technology such as how they generate outputs, the higher their expectancy beliefs may be, leading to a higher likelihood of adopting GenAI in their learning processes (Wigfield & Eccles, 2000). Previous research has also shown a positive correlation between students’ knowledge of technology and their intention to use it such as via the UTAUT model (Venkatesh et al., 2003).
根据期望值理论，学生对 GenAI 的知识和熟悉程度可能会影响他们的期望信念。学生对技术（如他们如何产生输出）越熟悉和了解越多，他们的期望信念可能就越高，从而导致在他们的学习过程中采用GenAI的可能性更高（Wigfield & Eccles，2000）。以前的研究还表明，学生的技术知识与他们使用技术的意图之间存在正相关关系，例如通过 UTAUT 模型（Venkatesh et al.， 2003）。

RQ 2: Is there a correlation between students’ perceived value of using GenAI and their intention to use AI?
RQ 2：学生使用 GenAI 的感知价值与他们使用 AI 的意图之间是否存在相关性？

Value is a crucial component of the expectancy-value framework, and it is hypothesised that students who perceive higher value in using GenAI will be more likely to adopt it (Wigfield & Eccles, 2000). Studies have shown that perceived usefulness and perceived ease of use are significant determinants of technology acceptance (Davis, 1989; Teo, 2009; Venkatesh et al., 2003). Maheshwari’s (2021) study also highlights the impact of institutional support and perceived enjoyment on students’ intentions to continue studying courses online. Specifically, the perceived value components that influence these intentions are:
价值是期望-价值框架的关键组成部分，据假设，在使用GenAI中感知到更高价值的学生将更有可能采用它（Wigfield & Eccles，2000）。研究表明，感知有用性和感知易用性是技术接受度的重要决定因素（Davis，1989 年;Teo， 2009;Venkatesh et al.， 2003）。Maheshwari （2021） 的研究还强调了机构支持和感知享受对学生继续在线学习课程的意愿的影响。具体来说，影响这些意图的感知价值组成部分是：

  • Attainment value which refers to the belief that engaging in a behavior will lead to an important goal or outcome. For example, students who believe that using GenAI will improve their academic performance or digital competence may be more likely to use it.
    成就值，指的是相信从事某项行为将导致重要的目标或结果。例如，认为使用 GenAI 会提高他们的学习成绩或数字能力的学生可能更有可能使用它。

  • Intrinsic value refers to the personal enjoyment or satisfaction that a person derives from engaging in a behavior. For example, students who enjoy exploring new technologies or feeling comfortable using GenAI due to the anonymity.
    内在价值是指一个人从参与某种行为中获得的个人享受或满足感。例如，由于匿名性，喜欢探索新技术或对使用 GenAI 感到舒适的学生。

  • Utility value refers to the belief that engaging in a behavior will lead to practical benefits, such as improved skills or knowledge. For example, students who believe that using GenAI will help them save time or provide them with unique feedback may be more likely to use it.
    效用价值是指相信参与某种行为会带来实际好处，例如提高技能或知识。例如，认为使用 GenAI 会帮助他们节省时间或为他们提供独特反馈的学生可能更有可能使用它。

RQ 3: Is there a correlation between students’ perceived cost of using GenAI and their intention to use AI?
RQ 3：学生使用 GenAI 的感知成本与他们使用 AI 的意图之间是否存在相关性？

Cost refers to the negative aspects or barriers associated with engaging in a particular behavior such as effort, time, undermining the value of education, limiting social interactions, or hindering the development of holistic competencies (Chan & Hu, 2023; Chan, 2023b).
成本是指与从事特定行为相关的消极方面或障碍，例如努力、时间、破坏教育价值、限制社交互动或阻碍整体能力的发展（Chan & 胡，2023 年;Chan，2023b）。

Cost can be seen as a factor that influences an individual’s motivation and intention to engage in a behavior. If students perceive the costs of using GenAI to outweigh its benefits, they may be less likely to adopt the technology. Previous research has shown that perceived barriers, such as cost, can negatively affect students’ intentions to use technology in education (Chan & Hu, 2023; Flake et al., 2015; Regmi & Jones, 2020; Stüber, 2018). The expectancy-value framework has been widely used in educational research to examine students’ motivation, learning, and achievement (Cheng et al., 2020; Sin et al., 2022).
成本可以被视为影响个人从事某种行为的动机和意图的因素。如果学生认为使用 GenAI 的成本超过了它的好处，他们就不太可能采用这项技术。先前的研究表明，感知到的障碍，如成本，会对学生在教育中使用技术的意愿产生负面影响（Chan & 胡，2023 年;Flake et al.， 2015;Regmi & Jones， 2020;Stüber，2018 年）。期望值框架已广泛用于教育研究，以检查学生的动机、学习和成就（Cheng et al.， 2020;Sin et al.， 2022）。

Why expectancy-value theory?
为什么是期望值理论？

The Expectancy-Value Theory (EVT) is widely used and has been adopted across various domains. It is chosen as the theoretical framework for this study over other models such as the Unified Theory of Acceptance and Use of Technology (UTAUT), Technology Acceptance Model (TAM) and Theory of Planned Behavior (TPB) because EVT specifically focuses on the factors that drive individuals’ motivation and decision-making processes related to their choices, goals, and performance, which is a major focus of this study. While other models like UTAUT, TAM and TPB offer valuable insights into technology acceptance and adoption, they do not fully capture the motivational factors that are central to EVT.
期望值理论 （EVT） 被广泛使用并已应用于各个领域。它被选为本研究的理论框架，而不是其他模型，例如技术接受和使用统一理论 （UTAUT）、技术接受模型 （TAM） 和计划行为理论 （TPB），因为 EVT 特别关注驱动个人动机和决策过程的因素，与他们的选择、目标和表现相关， 这是本研究的一个主要重点。虽然 UTAUT、TAM 和 TPB 等其他模型为技术接受和采用提供了有价值的见解，但它们并没有完全捕捉到 EVT 的核心激励因素。

EVT is considered more suitable for this study because it takes into account the perceived value and cost associated with using GenAI, which are critical factors in determining students’ intentions to use such technology. Moreover, EVT also emphasizes the role of students’ knowledge and familiarity with GenAI, which is an essential aspect of this study’s research questions.
EVT 被认为更适合本研究，因为它考虑了与使用 GenAI 相关的感知价值和成本，这是决定学生使用此类技术意图的关键因素。此外，EVT 还强调学生的知识和熟悉 GenAI 的作用，这是本研究研究问题的一个重要方面。

Methodology 方法论

The main purpose of this study was to examine the correlations between students’ intention to use GenAI (e.g. ChatGPT) and their knowledge, perceived value, and perceived cost of using the technology, subsequent to the validation of the questionnaire developed upon EVT.
本研究的主要目的是在验证基于 EVT 开发的问卷之后，检查学生使用 GenAI（例如 ChatGPT）的意图与他们的知识、感知价值和使用该技术的感知成本之间的相关性。

Sampling 采样

The methodology for this study employed a cross-sectional survey design using an online questionnaire to gather data on students’ familiarity, knowledge, perceived value, perceived costs, and intention regarding the use of GenAI technologies in teaching and learning at universities in Hong Kong. In Nov 2022, these universities were confronted with the unexpected intrusion of GenAI, just like other industries. This disruption led to varying policies among universities, with some allowing students to use ChatGPT while others temporarily banned the technology due to perceived risks (Mok, 2023). The research was also inspired by the need for long-term solutions to address the challenges posed by GenAI by investigating its potential value and cost among students.
本研究的方法采用横断面调查设计，使用在线问卷来收集有关学生对在香港大学使用 GenAI 技术的熟悉度、知识、感知价值、感知成本和意图的数据。2022 年 11 月，这些大学与其他行业一样，面临着 GenAI 的意外入侵。这种中断导致大学之间的政策不同，一些大学允许学生使用 ChatGPT，而另一些大学则由于感知到的风险而暂时禁止该技术（Mok，2023 年）。该研究还受到对长期解决方案的需求的启发，通过调查 GenAI 在学生中的潜在价值和成本来应对 GenAI 带来的挑战。

Convenience sampling was applied, wherein the participants were selected based on their accessibility and willingness to participate. To reach the participants, the questionnaire was distributed throughout March of 2023 via a bulk email sent to all the students studying in a university in Hong Kong, including undergraduate and postgraduate students from STEM and non-STEM disciplines. While this approach may not ensure a representative sample of the target population, it allows for the efficient collection of data from a large group of respondents. In total, 405 (out of 460 total responses) participants (as summarized in Table 2) provided valid information for data analyses with an average age of 23.87 years, consisting of 51.4% males (n = 208) and 48.6% females (n = 197). These participants were at different levels of degrees, with 50.9% (n = 206) at an undergraduate level and 49.1% (n = 199) at a postgraduate level. They also had various academic backgrounds, with 52.8% (n = 214) from STEM majors such as engineering, science, and architecture, and 45.7% from non-STEM majors such as education, business, law, medicine, and arts.
应用了便利抽样，其中参与者是根据他们的可访问性和参与意愿来选择的。为了联系参与者，问卷于 2023 年 3 月通过批量电子邮件分发给在香港大学学习的所有学生，包括 STEM 和非 STEM 学科的本科生和研究生。虽然这种方法可能无法确保目标人群的代表性样本，但它允许从一大群受访者那里有效地收集数据。总共有 405 名（总共 460 份回复）参与者（如表 2 所示）为数据分析提供了有效信息，平均年龄为 23.87 岁，其中 51.4% 为男性 （n = 208） 和 48.6% 为女性 （n = 197）。这些参与者处于不同的学位水平，其中 50.9% （n = 206） 处于本科阶段，49.1% （n = 199） 处于研究生阶段。他们也具有不同的学术背景，其中 52.8% （n = 214） 来自工程、科学和建筑等 STEM 专业，45.7% 来自教育、商业、法律、医学和艺术等非 STEM 专业。

Table 2 Demographic information
表 2 人口统计信息
Full size table 全尺寸表格

Instrument for analyses 分析仪器

The questionnaire was specifically designed for the purposes of this study. It was informed by a review of relevant literature and existing questionnaires on teachers’ and students’ perceptions of educational technologies based on the EVT framework (e.g., Backfisch et al., 2021a, 2021b; Ball et al., 2019; Chen, 2011; Ranellucci et al., 2020). Since there are no currently available questionnaires on students’ perceptions of GenAI which are based on the EVT framework, some of the questionnaire items in this study were adapted from other similar instruments. For example, in Chen’s (2011) questionnaire on students’ acceptance of e-learning, one of the items measuring students’ e-learning performance expectancy is “If I use the CUS (Cyber University System), I will increase my chances of getting more competence”. To measure students’ GenAI attainment value, the item was adapted as “I believe generative AI technologies such as ChatGPT can improve my digital competence” (Q8). Further, most of the items were constructed to reflect issues and challenges associated with GenAI use within the EVT framework. For instance, Q20 “Generative AI technologies such as ChatGPT will hinder my development of generic or transferable skills such as teamwork, problem-solving, and leadership skills” and Q21 “I can become over-reliant on generative AI technologies” were designed to measure the perceived cost of GenAI, that is, the potential risks posed by GenAI cautioned by authors such as Bai et al. (2023) and Nah et al. (2023). To ensure that the questionnaire items were relevant and clear, pilot studies were conducted before the formal data collection. The questionnaire was modified based on the feedback received during the pilot study to ensure its accuracy and clarity.
该问卷是专门为本研究目的而设计的。它通过回顾相关文献和现有问卷来了解基于 EVT 框架的教师和学生对教育技术的看法（例如，Backfisch 等人，2021a、2021b;Ball等人，2019 年;Chen， 2011;Ranellucci等人，2020 年）。由于目前没有基于 EVT 框架的关于学生对 GenAI 的看法的问卷，因此本研究中的一些问卷项目是从其他类似工具改编而来的。例如，在 Chen's （2011） 关于学生接受电子学习的问卷中，衡量学生电子学习表现预期的项目之一是“如果我使用 CUS（网络大学系统），我将增加获得更多能力的机会”。为了衡量学生的 GenAI 成就值，该项目被改编为“我相信 ChatGPT 等生成式 AI 技术可以提高我的数字能力”（Q8）。此外，大多数项目的构建是为了反映与 EVT 框架内 GenAI 使用相关的问题和挑战。例如，Q20“ChatGPT 等生成式 AI 技术将阻碍我开发通用或可转移技能，例如团队合作、解决问题和领导技能”和 Q21“我可能会过度依赖生成式 AI 技术”旨在衡量 GenAI 的感知成本，即 Bai 等人（2023 年）和 No 等人（2023 年）等作者警告的 GenAI 带来的潜在风险).为确保问卷项目的相关性和清晰度，在正式数据收集之前进行了试点研究。 根据试点研究期间收到的反馈对问卷进行了修改，以确保其准确性和清晰度。

The instrument consisted of four main sections: knowledge of GenAI, perceived value of using GenAI, perceived cost of using GenAI, and intention to use GenAI. The items and factors are already grounded in the EVT framework (Wigfield & Eccles, 2000), which has been well-established in previous research on technology adoption (Venkatesh et al., 2003). Table 3 shows the factors, their corresponding questionnaire items, and the analysis methods used and Table 4 shows the survey items. The participants’ opinions were assessed using 23 five-point Likert scale questions (Q1 as frequency scale; Q2-Q23, with response options ranging from 1-Strongly Disagree to 5-Strongly Agree). This allowed the participants to express their level of agreement or uncertainty on each statement.
该工具由四个主要部分组成：GenAI 知识、使用 GenAI 的感知价值、使用 GenAI 的感知成本以及使用 GenAI 的意图。这些项目和因素已经在EVT框架中奠定了基础（Wigfield & Eccles， 2000），这在之前的技术采用研究中已经得到了很好的确立（Venkatesh等人，2003）。表 3 显示了因素、相应的问卷项目和使用的分析方法，表 4 显示了调查项目。使用 23 个五点李克特量表问题（Q1 作为频率量表;Q2-Q23，回答选项范围从 1-非常不同意到 5-非常同意）。这允许参与者表达他们对每个陈述的同意程度或不确定性。

Table 3 Summary of factors and questionnaire items
表 3 因素和问卷项目汇总
Full size table 全尺寸表格
Table 4 Descriptive analysis results (N = 405)
表 4 描述性分析结果 （N = 405）
Full size table 全尺寸表格

Rationale for analyses 分析理由

The analyses were conducted in three stages. The first stage focused on descriptive analyses of the responses to show the normality of the data and to reveal participants’ perceptions mainly by comparing means. The second stage involved the validation of each factor as specified in the EVT section (Table 3), where the validity and reliability of the scales were tested. The final stage analysed the correlations between students’ knowledge of and familiarity with GenAI, students’ perceived value of using GenAI, students’ perceived cost of using GenAI, and their intention to use AI, which aligned with the research questions.
分析分三个阶段进行。第一阶段侧重于对反应的描述性分析，以显示数据的正态性，并主要通过比较手段来揭示参与者的看法。第二阶段涉及验证 EVT 部分（表 3）中指定的每个因素，其中测试了量表的有效性和可靠性。最后阶段分析了学生对 GenAI 的了解和熟悉程度、学生使用 GenAI 的感知价值、学生使用 GenAI 的感知成本以及他们使用 AI 的意图之间的相关性，这与研究问题一致。

Regarding the validation stage (i.e. stage 2), due to the strong theoretical basis of EVT, it was decided to use only Confirmatory Factor Analysis (CFA) without using Exploratory Factor Analysis (EFA), following the calculation of Cronbach’s alpha. According to Brown (2006), CFA is a hypothesis-driven method that allows for direct testing of the proposed factor structure. This method will enable us to focus on hypothesis testing and confirming the hypothesized factor structure, rather than exploring new and unknown factor structures that EFA would provide. Moreover, the use of CFA in this study can be considered a parsimonious approach, ensuring that our research findings are concise and easier to interpret. For example, in the context of our study, we will be able to assess whether the survey items measuring Students’ Knowledge of AI (Q2-Q6) indeed load onto a single factor, as theorized. The validity of constructs was measured by Average variance extracted (AVE), composite reliability (CR) (Fornell & Larcker, 1981; Hair et al., 2015), and Heterotrait-monotrait (HTMT) ratio (Henseler et al., 2015).
关于验证阶段（即第 2 阶段），由于 EVT 具有强大的理论基础，决定在计算 Cronbach 的 alpha 之后仅使用验证因子分析 （CFA），而不使用探索性因子分析 （EFA）。根据 Brown （2006） 的说法，CFA 是一种假设驱动的方法，允许直接测试所提出的因子结构。这种方法将使我们能够专注于假设检验和确认假设的因子结构，而不是探索 EFA 将提供的新和未知的因子结构。此外，在本研究中使用 CFA 可以被视为一种简洁的方法，确保我们的研究结果简洁易懂。例如，在我们的研究背景下，我们将能够评估衡量学生人工智能知识（Q2-Q6）的调查项目是否确实像理论上的那样加载到单个因素上。结构的有效性是通过平均方差提取（AVE）、复合可靠性（CR）（Fornell & Larcker，1981;Hair et al.， 2015） 和异质性-单性状 （HTMT） 比率 （Henseler et al.， 2015）。

The analyses in the research were made through IBM SPSS 27 and IBM AMOS 28. The missing data were imputed by the multiple imputation procedure in SPSS.
研究中的分析是通过 IBM SPSS 27 和 IBM AMOS 28 进行的。缺失数据由 SPSS 中的多重插补程序进行插补。

Results 结果

Stage 1: descriptive analysis
第 1 阶段：描述性分析

The survey study was conducted among students from Hong Kong to explore their perceptions of using GenAI technologies like ChatGPT for teaching and learning in higher education. The use frequency and familiarity with GenAI technologies among participants varied (never = 33.6%; rarely = 22.0%; sometimes = 28.9%; often = 9.6%; always = 5.9%) based on Q1 (“I have used generative AI technologies like ChatGPT”). With a mean as low as 2.32 (see Table 2), Q1 demonstrated that many participants had limited user experience with GenAI by the date the research was conducted.
这项调查研究在香港学生中进行，旨在了解他们对使用 ChatGPT 等 GenAI 技术进行高等教育教学的看法。根据第一季度（“我使用过 ChatGPT 等生成式 AI 技术”），参与者对 GenAI 技术的使用频率和熟悉程度各不相同（从不 = 33.6%;很少 = 22.0%;有时 = 28.9%;经常 = 9.6%;总是 = 5.9%）。Q1 的平均值低至 2.32（见表 2），表明许多参与者在研究进行之日对 GenAI 的用户体验有限。

As summarized by Table 4, S.D., skewness, and kurtosis values indicate a normal distribution of the dataset, which allows further calculations in stage 2 and stage 3. To better understand participants’ opinions towards Q2-Q23, means were interpreted by referring to the Likert Scale interval recommended by Pimentel (2010), where a point mean falls into the range from 1.00 to 1.80 can be regarded as strongly disagree, 1.81 to 2.60 as disagree, 2.61 to 3.40 as neutral, 3.41 to 4.20 as agree, and 4.21 to 5.00 as strongly agree.
如表 4 所示，S.D.、偏度和峰度值表示数据集的正态分布，这允许在第 2 阶段和第 3 阶段进行进一步计算。为了更好地了解参与者对第 2 季度至第 23 季度的看法，参考 Pimentel （2010） 推荐的李克特量表区间来解释平均值，其中 1.00 到 1.80 范围内的点平均值可以被视为非常不同意，1.81 到 2.60 可以被视为不同意，2.61 到 3.40 是中性，3.41 到 4.20 是同意，4.21 到 5.00 是非常同意。

Thus, based on the means of Q22 (mean = 3.92) and Q23 (mean = 3.86), many of the participants believed that the integration of generative AI technologies in higher education will have a positive impact on teaching and learning in the long run and they envision integrating generative AI technologies into their teaching practices in the future. They showed an overall agreement perception of the statements regarding their knowledge of ChatGPT (Q2-Q6), with means ranging from 4.15 to 3.8. Additionally, they also recognized the value (Q7-Q17) of ChatGPT in various teaching, learning, and working occasions; except for Q11 (“I think generative AI technologies such as ChatGPT can help me become a better writer.”) and Q10 (“I can ask questions to generative AI technologies such as ChatGPT that I would otherwise not voice out to my teacher.”), with means at 3.31 and 3.38, which tended to be more neutral. Whereas students’ responses on the cost of using generative AI were more neutral, with means settling between 2.85 and 3.15.
因此，根据 Q22（平均值 = 3.92）和 Q23（平均值 = 3.86）的平均值，许多参与者认为，从长远来看，将生成式 AI 技术整合到高等教育中将对教学产生积极影响，他们设想将生成式 AI 技术整合到未来的教学实践中。他们对 ChatGPT 知识的陈述（Q2-Q6）表现出总体一致的看法，均值范围为 4.15 到 3.8。此外，他们还认识到 ChatGPT 在各种教学、学习和工作场合中的价值 （Q7-Q17）;除了 Q11（“我认为 ChatGPT 等生成式 AI 技术可以帮助我成为一名更好的作家”）和 Q10（“我可以向 ChatGPT 等生成式 AI 技术提出问题，否则我不会向我的老师提出这些问题”），均值分别为 3.31 和 3.38，它们往往更加中性。而学生对使用生成式 AI 的成本的回答则更加中立，均值稳定在 2.85 到 3.15 之间。

Such responses may imply the consensus value of ChatGPT amongst the majority of the participants. They did have a certain level of understanding and concerns over the limitations of generative AI technology in terms of handling complex tasks. Meanwhile, the participants recognized the technology’s attainment value such as improving efficiency, and its foreseeable utility value in the workplace as well as the long-term impacts on the learning outcomes (for example, academic performance, creativity- and emotion-related competencies).
这样的回答可能意味着 ChatGPT 在大多数参与者中的共识价值。他们确实对生成式 AI 技术在处理复杂任务方面的局限性有一定程度的理解和担忧。同时，参与者认识到该技术的成就价值，例如提高效率，及其在工作场所的可预见效用价值，以及对学习成果的长期影响（例如，学习成绩、创造力和情感相关能力）。

Stage 2: reliability and validity of the scales
第 2 阶段：量表的可靠性和有效性

Driven by the theory of EVT, the questionnaire aimed to understand students’ perceptions of using GenAI with regard to knowledge, value, and cost. To measure the reliability of the constructs, Cronbach’s alpha coefficient was first calculated to test the internal consistency. Cronbach’s alphas for the 5 knowledge, 11 perceived value, and 4 perceived cost items are 0.812, 0.876, and 0.746. As shown in Table 5, the Cronbach alpha values are all greater than or closer to 0.7, indicating an acceptable internal consistency within the three scales.
在 EVT 理论的驱动下，该问卷旨在了解学生对使用 GenAI 在知识、价值和成本方面的看法。为了测量结构的可靠性，首先计算了 Cronbach 的 alpha 系数以测试内部一致性。5 个知识、11 个感知价值和 4 个感知成本项目的 Cronbach Alpha 分别为 0.812、0.876 和 0.746。如表 5 所示，Cronbach alpha 值都大于或接近 0.7，表明三个尺度内存在可接受的内部一致性。

Table 5 Cronbach alpha coefficient results (Total N = 405)
表 5 Cronbach alpha 系数结果（总 N = 405）
Full size table 全尺寸表格

CFA tests were then conducted to test the construct reliability of the knowledge, perceived value (with three sub-scales), and perceived cost. Since χ2 is very sensitive to sample size and its p-value will tend to be small in a big sample, we decided to report the χ2/df ratio instead as recommended by Schermelleh-Engel et al. (2003). The results in Table 6 indicate a good model fit regarding students’ knowledge, student-perceived value, and student-perceived cost with all less than 3 (Schermelleh-Engel et al., 2003), the root mean square error of approximation (RMSEA) all lower than 0.07 (Steiger, 2007) and standardized root mean square residual (SRMR) lower than 0.080 (Hu & Bentler, 1999). TLI of perceived value (= 0.948) are less than 0.95, yet still higher than 0.90 and its CFI is higher than 0.95.
然后进行 CFA 测试以测试知识的结构可靠性、感知价值（具有三个子量表）和感知成本。由于 χ2 对样本量非常敏感，并且其 p 值在大样本中往往很小，因此我们决定按照 Schermelleh-Engel 等人（2003 年）的建议报告 χ2/df 比率。表 6 中的结果表明，在学生的知识、学生感知价值和学生感知成本方面，模型拟合良好，均小于 3（Schermelleh-Engel 等人，2003 年），近似均方根误差 （RMSEA） 均低于 0.07（Steiger，2007 年），标准化均方根残差 （SRMR） 低于 0.080（胡 & Bentler，1999 年 ).感知价值的 TLI （= 0.948） 小于 0.95，但仍高于 0.90，其 CFI 高于 0.95。

Table 6 CFA results (N = 405)
表 6 CFA 结果 （N = 405）
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Average variance extracted (AVE), composite reliability (CR) (Fornell & Larcker, 1981; Hair et al., 2014), and Heterotrait-monotrait (HTMT) ratio (Henseler et al., 2015) were further used to examine the convergent validity and discriminant validity. AVE and CR were calculated based on the factor loading of the items and the HTMT ratios were generated with the “HTMT plugin” developed by Gaskin et al. (2023).
平均方差提取（AVE），复合可靠性（CR）（Fornell & Larcker，1981;Hair et al.， 2014） 和异质性状-单性状 （HTMT） 比率 （Henseler et al.， 2015） 进一步用于检验收敛效度和判别效度。AVE 和 CR 是根据项目的因子载荷计算的，HTMT 比率是使用 Gaskin 等人（2023 年）开发的 “HTMT 插件 ”生成的。

As shown in Table 7, though some of the AVE values are lesser and closer to 0.50, yet with the CR value of the knowledge scale, value scale, and cost scale as 0.822, 0.763, and 0.873 accordingly, which exceed the cutoff point of 0.60, it may be able to conclude that the convergent validity is acceptable (Fornell & Larcker, 1981). The values of HTMT ratios (see Table 8) further denote good discriminant validity, as all the values range from 0.660 to 0.002, lower than the threshold of 0.85 (Henseler et al., 2015).
如表7所示，尽管一些AVE值较小且接近0.50，但是知识尺度、价值尺度和成本尺度的CR值分别为0.822、0.763和0.873，超过了0.60的临界点，可能可以得出收敛效度可以接受的结论（Fornell & Larcker， 1981 年）。HTMT 比率的值（见表 8）进一步表示良好的判别效度，因为所有值的范围都在 0.660 到 0.002 之间，低于 0.85 的阈值（Henseler et al.， 2015）。

Table 7 Convergent validity results (N = 405)
表 7 收敛效度结果 （N = 405）
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Table 8 Discriminant validity results (N = 405)
表 8 判别有效性结果 （N = 405）
Full size table 全尺寸表格

Stage 3: correlations among the variables
第 3 阶段：变量之间的相关性

The correlations between students’ knowledge, perceived value, and the perceived cost of using GenAI were analyzed using bivariate correlation with Pearson’s correlation coefficient (r). Pearson’s correlation coefficient is used to measure the linear relationship between factors derived from EVT and students’ intention to use GenAI in higher education for a sample of 405 participants. The results (see Table 9) suggested a relatively high and positive correlation between student-perceived value (r = 0.606, p < 0.001) and students’ intention to use. The three subscales—attainment value (r = 0.587, p < 0.001), intrinsic value (r = 0.459, p < 0.001), and utility value (r = 0.506, p < 0.001)—were also positively correlated with students’ intention to use GenAI.
使用带有 Pearson 相关系数 （r） 的双变量相关性分析学生的知识、感知价值和使用 GenAI 的感知成本之间的相关性。Pearson 相关系数用于衡量 EVT 得出的因素与学生在高等教育中使用 GenAI 的意向之间的线性关系，样本为 405 名参与者。结果（见表 9）表明学生感知值 （r = 0.606，p < 0.001） 与学生 的使用意向之间存在相对较高的正相关关系。三个分量表——成就值 （r = 0.587， p < 0.001）、内在值 （r = 0.459， p < 0.001） 和效用值 （r = 0.506， p < 0.001） ——也与学生使用 GenAI 的意图呈正相关。

Table 9 Correlation analysis results (Pearson’s correlation coefficient) (N = 405)
表 9 相关性分析结果（Pearson 相关系数）（N = 405）
Full size table 全尺寸表格

The correlations between students’ past use frequency (r = 0.339), students’ knowledge (r = 0.178), student-perceived cost (r =  − 0.295) of GenAI, and their intention to use were more moderate but still significant. Compared with knowledge, the connection between the student-perceived cost of using generating AI and students’ intention to use GenAI was stronger, though in a negative way.
学生过去使用频率 （r = 0.339）、学生知识 （r = 0.178）、学生感知成本 （r = − 0.295） 和他们的使用意向之间的相关性较为适中，但仍然显著。与知识相比，学生感知到的使用生成 AI 的成本与学生使用 GenAI 的意图之间的联系更强，尽管是负面的。

Discussion 讨论

The findings from Table 7 demonstrates that EVT-related factors, such as knowledge, perceived value (including attainment, intrinsic, and utility values), and perceived cost, are all significantly correlated with students’ intention to use GenAI in higher education. This finding resonates with Kim et al.’s (2020) and Hu’s (2022) studies that students’ perceived ease of use and perceived usefulness of AI tools positively affect their intention to use the tools. The perceived value has the strongest positive correlation with intention to use, while the perceived cost has a weak negative correlation.
表 7 的结果表明，EVT 相关因素，例如知识、感知价值（包括成就、内在和效用价值）和感知成本，都与学生在高等教育中使用 GenAI 的意图显着相关。这一发现与 Kim 等人 （2020） 和 胡 （2022） 的研究产生了共鸣，即学生感知到的易用性和感知到的人工智能工具有用性对他们使用这些工具的意图产生了积极影响。感知价值与使用意向的正相关最强，而感知成本的负相关较弱。

The student-perceived value of GenAI emerged as the most significant factor influencing their intention to utilize such technologies in an educational context. The majority of participants acknowledged the potential advantages of GenAI in the workplace and its capacity to enhance learning outcomes, encompassing the improvement of academic performance and the development of digital competence. Moreover, students identified utility value in aspects such as increased efficiency, provision of personalized and immediate feedback, and facilitation of idea generation. Similarly, Gado et al. (2022) and Hu (2022) found that perceived usefulness of AI had a significant relationship with students’ intention to use AI. It shows that a positive perception of how GenAI can assist or benefit students’ academic work and their future professional life is key to its adoption.
学生对 GenAI 的感知价值成为影响他们在教育环境中使用此类技术意图的最重要因素。大多数参与者承认 GenAI 在工作场所的潜在优势及其提高学习成果的能力，包括提高学习成绩和发展数字能力。此外，学生们在提高效率、提供个性化和即时反馈以及促进想法产生等方面确定了实用价值。同样，Gado 等人（2022 年）和 胡（2022 年）发现，人工智能的感知有用性与学生使用人工智能的意图有显著关系。它表明，对 GenAI 如何帮助或有益于学生的学术工作和他们未来的职业生活的积极看法是其采用的关键。

The correlation analysis between students’ knowledge of GenAI and their intention to use it revealed a statistically significant, albeit weak relationship. Previous experience with GenAI, on the other hand, had a moderate correlation with intention to use. While Dahlkemper et al (2023) showed that students who had prior experience with AI tended to have a positive attitude towards AI, our study was able to demonstrate a significant relationship between students’ frequency of GenAI use and their intention to use GenAI tools. The findings suggest that in addition to providing students with basic knowledge about GenAI, such as its definition, limitations, and benefits, it is also important to create opportunities for students to utilise GenAI or integrate its use in their university life to encourage adoption of the technology.
学生对 GenAI 的了解与他们使用它的意图之间的相关性分析揭示了具有统计学意义但较弱的关系。另一方面，以前使用 GenAI 的经验与使用意向有中等相关性。虽然 Dahlkemper 等人（2023 年）表明，以前有过 AI 经验的学生往往对 AI 持积极态度，但我们的研究能够证明学生使用 GenAI 的频率与他们使用 GenAI 工具的意图之间存在显着关系。研究结果表明，除了为学生提供有关 GenAI 的基本知识（例如其定义、局限性和好处）外，为学生创造利用 GenAI 的机会或将其使用整合到他们的大学生活中以鼓励采用该技术也很重要。

As perceived cost was negatively correlated with the intention to use, it suggests that reducing the perceived costs associated with the use of GenAI could potentially increase students’ intention to use it. Compared to previous studies which utilised TAM to explore the relationship between perceived usefulness, perceived ease of use, and intention to use (e.g., Bonsu & Baffour-Koduah, 2023; Hu, 2022; Kim et al., 2020), this EVT-based study shows that perceived cost is also an important factor affecting students’ intention to use GenAI. As shown in the responses to the questionnaire, students were concerned that the use of GenAI could undermine the value of a university education, deprive them of the opportunities to interact with others, and hinder the development of transferable skills. To tackle these apprehensions, the study advises fostering social and experiential learning (Chan, 2022) as well as promoting interpersonal interactions within higher education environments.
由于感知成本与使用意向呈负相关，因此表明降低与使用 GenAI 相关的感知成本可能会增加学生使用它的意愿。与之前利用TAM探索感知有用性、感知易用性和使用意向之间的关系的研究相比（例如，Bonsu & Baffour-Koduah，2023; 胡， 2022;Kim et al.， 2020），这项基于 EVT 的研究表明，感知成本也是影响学生使用 GenAI 意愿的重要因素。正如对问卷的回答所示，学生们担心使用 GenAI 可能会破坏大学教育的价值，剥夺他们与他人互动的机会，并阻碍可转移技能的发展。为了解决这些担忧，该研究建议促进社交和体验式学习（Chan，2022 年），并在高等教育环境中促进人际互动。

Implications 影响

The implications of this study, which employed a validated instrument grounded in Expectancy-Value Theory (EVT) to evaluate student perceptions of GenAI in higher education, are multifaceted and have far-reaching consequences for researchers, educators, and educational institutions alike.
这项研究采用了基于期望值理论 （EVT） 的经过验证的工具来评估学生在高等教育中对 GenAI 的看法，其影响是多方面的，对研究人员、教育工作者和教育机构等都有深远的影响。

First and foremost, our study contributes to current understanding of factors that influence students’ acceptance and intention to use GenAI by highlighting the role of cognitive (knowledge of GenAI) and affective (perception of value and cost) factors as well as previous experience of using GenAI. By identifying these factors and their relationships with intention to use AI, the study provides valuable insights for educators and institutions looking to foster AI adoption in higher education. By emphasizing the potential value of GenAI, addressing concerns related to perceived costs, and enhancing students’ knowledge about these technologies, institutions can develop strategies and interventions aimed at promoting positive attitudes towards AI and ultimately improving the learning experience for students.
首先，我们的研究通过强调认知（GenAI 的知识）和情感（对价值和成本的感知）因素的作用以及以前使用 GenAI 的经验，有助于当前理解影响学生接受和打算使用 GenAI 的因素。通过确定这些因素及其与使用 AI 的意图的关系，该研究为希望促进 AI 在高等教育中采用的教育工作者和机构提供了宝贵的见解。通过强调 GenAI 的潜在价值，解决与感知成本相关的问题，并增强学生对这些技术的了解，机构可以制定策略和干预措施，旨在促进对 AI 的积极态度，并最终改善学生的学习体验。

Second, the study has implications for the design of educational curricula. The findings suggest that institutions should focus on fostering AI literacy, particularly knowledge and awareness of the benefits of GenAI as these two factors were found to be correlated with students’ intention to use GenAI in this study. Knowledge, a key component of AI literacy, can increase students’ confidence and readiness in using GenAI (Ng et al., 2021). In addition, having a good understanding of AI can reduce students’ apprehensions about GenAI (Jeffrey, 2020). There should also be opportunities for students to integrate the use of GenAI in individual and collaborative tasks to allow them to explore how GenAI can enhance their learning experience without compromising interaction with peers. By incorporating these elements into their curricula, institutions can ensure that students are ready to adopt GenAI and are equipped with the skills and knowledge necessary to make the most of AI in their academic pursuits and future careers.
其次，该研究对教育课程的设计有影响。研究结果表明，机构应专注于培养 AI 素养，特别是对 GenAI 好处的知识和认识，因为发现这两个因素与学生在本研究中使用 GenAI 的意图相关。知识是 AI 素养的关键组成部分，可以提高学生使用 GenAI 的信心和准备程度（Ng et al.， 2021）。此外，对 AI 有很好的了解可以减少学生对 GenAI 的担忧（Jeffrey，2020 年）。学生还应该有机会将 GenAI 的使用整合到个人和协作任务中，让他们探索 GenAI 如何在不影响与同龄人互动的情况下增强他们的学习体验。通过将这些元素纳入他们的课程，机构可以确保学生准备好采用 GenAI，并具备在他们的学术追求和未来职业生涯中充分利用 AI 所需的技能和知识。

The role of motivation in shaping students’ adoption of GenAI is apparent in this study as demonstrated by the correlations between students’ knowledge of GenAI, previous experience of using GenAI, perceived value, and intention to use GenAI. Wigfield (1994) refers to expectancy and value as motivational constructs that determine an individual’s decision to perform and persist in tasks. Students who have used AI applications and have sound knowledge of AI tend to have a positive view of the technology (Chen et al., 2022), thus resulting in high expectancies for success. Similarly, beliefs about the importance, usefulness, and value of a task mediate one’s motivation to participate in the task (Wigfield & Eccles, 1992). Hence, classroom pedagogy should integrate motivational strategies targeting at enhancing students’ expectancies for success and instiling positive value beliefs. Integration of GenAI in academic tasks should be personalised so that students can derive satisfaction and enjoyment from its use while realising its importance to them as students and as future-ready workers upon graduation. In addition, teachers can provide guidance and advice to students on how they may tackle the challenges posed by GenAI in a task to reduce their anxiety and apprehension about GenAI use.
在这项研究中，动机在塑造学生采用 GenAI 方面的作用是显而易见的，学生对 GenAI 的了解、以前使用 GenAI 的经验、感知价值和使用 GenAI 的意图之间的相关性证明了这一点。Wigfield （1994） 将期望和价值称为决定个人执行和坚持任务的决定的激励结构。使用过 AI 应用程序并对 AI 有深入了解的学生往往对这项技术持积极态度（Chen et al.， 2022），因此对成功抱有很高的期望。同样，对任务的重要性、有用性和价值的信念会调节一个人参与任务的动机（Wigfield & Eccles， 1992）。因此，课堂教学法应整合激励策略，旨在提高学生对成功的期望并灌输积极的价值观信念。将 GenAI 整合到学术任务中应该是个性化的，这样学生就可以从它的使用中获得满足感和乐趣，同时意识到它对他们作为学生和毕业后为未来做好准备的重要性。此外，教师可以向学生提供指导和建议，让他们如何应对 GenAI 在任务中带来的挑战，以减少他们对 GenAI 使用的焦虑和担忧。

Additionally, the development of a validated instrument based on EVT represents a significant contribution to the field. To date, there has been a lack of robust, theoretically grounded instruments to assess students’ attitudes towards GenAI adoption, making it challenging to systematically understand the factors that influence their intention to use the technologies. The EVT-based instrument addresses this gap in the literature and provides a strong foundation for future research and practice in this area.
此外，基于 EVT 的经过验证的仪器的开发代表了对该领域的重大贡献。迄今为止，一直缺乏强大的、有理论依据的工具来评估学生对 GenAI 采用的态度，这使得系统地了解影响他们使用这些技术意图的因素具有挑战性。基于 EVT 的工具解决了文献中的这一空白，并为该领域的未来研究和实践奠定了坚实的基础。

Limitation 限度

Despite the valuable insights provided by this study, it is important to acknowledge its limitations. Firstly, the sample size was restricted to 405 participants, which may not be fully representative of the larger student population. Additionally, the study was conducted at a single point in time, and thus, it may not account for potential changes in students’ attitudes and perceptions towards GenAI over time. The focus on higher education students also limits the generalizability of the findings to other age groups or educational contexts. Furthermore, the study primarily relied on self-reported data, which may be subject to response biases, such as social desirability or recall bias. Lastly, the study did not explore the influence of individual differences, such as cultural background, personal experiences, and learning styles, which could also impact students’ perceptions of GenAI.
尽管这项研究提供了有价值的见解，但重要的是要承认其局限性。首先，样本量仅限于 405 名参与者，这可能不能完全代表更大的学生群体。此外，该研究是在单个时间点进行的，因此，它可能无法解释学生对 GenAI 的态度和看法随着时间的推移而发生的潜在变化。对高等教育学生的关注也限制了研究结果对其他年龄组或教育背景的普遍性。此外，该研究主要依赖于自我报告的数据，这些数据可能会受到反应偏倚的影响，例如社会期望或回忆偏倚。最后，该研究没有探讨个体差异的影响，例如文化背景、个人经历和学习风格，这也可能会影响学生对 GenAI 的看法。

It is essential to acknowledge that skipping EFA in the validation can have some limitations, such as the potential to overlook alternative factor structures or issues with item loadings (Fabrigar et al., 1999). However, given the strong theoretical basis, prior research, and focus on hypothesis testing, using only CFA in this study can be considered a justifiable decision. To address potential concerns, it may be helpful to consider conducting additional validation studies in the future to further explore the factor structure and psychometric properties of the survey instrument.
必须承认，在验证中跳过 EFA 可能会有一些限制，例如可能会忽略替代因子结构或项目加载问题（Fabrigar et al.， 1999）。然而，鉴于强大的理论基础、先前的研究和对假设检验的关注，在本研究中仅使用 CFA 可以被认为是一个合理的决定。为了解决潜在的问题，考虑在未来进行额外的验证研究以进一步探索调查工具的因子结构和心理测量特性可能会有所帮助。

Future research should aim to address these limitations by employing larger and more diverse samples, conducting longitudinal studies, and examining the impact of individual differences on the relationship between perceived value, perceived cost, and the intention to use GenAI.
未来的研究应该旨在通过采用更大、更多样化的样本、进行纵向研究以及检查个体差异对感知价值、感知成本和使用 GenAI 的意图之间关系的影响来解决这些限制。

Conclusion 结论

This study explored students’ perceptions of GenAI using an EVT-based instrument. The findings reveal a significant correlation between students’ knowledge of GenAI, previous use of GenAI, perceived value, and intention to use, thus highlighting the role of motivation in shaping students’ decision to adopt GenAI. Educational initiatives to promote GenAI use should focus on enhancing expectancies for success and fostering positive value beliefs through personalised learning experience and strategies for mitigating GenAI risks. As GenAI is rapidly becoming a global trend and reshaping the practices of various industries, higher education has an important mission to prepare a future-ready workforce that is able to Malechwanzi et al. (2016). utilise and collaborate with GenAI effectively. To achieve this goal, students’ acceptance and willingness to adopt GenAI are crucial and there is an urgent need for research into this area. This study offers a validated instrument for measuring students’ perceptions of GenAI, which can be used by researchers for further studies of GenAI adoption.
本研究使用基于 EVT 的工具探讨了学生对 GenAI 的看法。研究结果揭示了学生对 GenAI 的了解、以前对 GenAI 的使用、感知价值和使用意向之间的显着相关性，从而突出了动机在塑造学生采用 GenAI 的决定中的作用。促进 GenAI 使用的教育举措应侧重于通过个性化的学习体验和降低 GenAI 风险的策略来提高对成功的期望和培养积极的价值信念。随着 GenAI 迅速成为一种全球趋势并重塑各个行业的实践，高等教育肩负着一项重要使命，即培养能够为未来做好准备的劳动力 Malechwanzi 等人（2016 年）。有效地利用 GenAI 并与之协作。为了实现这一目标，学生接受和愿意采用 GenAI 至关重要，迫切需要对这一领域进行研究。这项研究提供了一种经过验证的工具，用于测量学生对 GenAI 的看法，研究人员可以使用该工具进一步研究 GenAI 的采用情况。