University students’ intentions to learn artificial intelligence: the roles of supportive environments and expectancy–value beliefs

“Our study provides evidence that both supportive environments and expectancy–value beliefs are crucial factors in fostering students’ intentions to learn AI, which is consistent with previous research (Kelly et al., 2023; Teo & Zhou, 2014; Venkatesh et al., 2003).”

“…gain more experience with technology, the influence of significant others is expected to diminish, with individuals relying more on their own past experiences to shape their perceptions of technology, rather than the opinions of others (Venkatesh & Davis, 2000; Venkatesh & Morris, 2000).”

“According to the criteria recommended by Chen (2007), ΔCFI ≤ − 0.01, ΔRMSEA ≤ 0.015, and ΔSRMR ≤ 0.03 were the criteria for metric invariance, while ΔCFI ≤ − 0.01, ΔRMSEA ≤ 0.015, and ΔSRMR ≤ 0.01 were the criteria for scalar invariance.”

“In addition to the direct effects, we also examined whether expectancy–value beliefs mediated the influence of supportive environments on intentions to learn AI via Sobel tests (MacKinnon et al., 2002).”

“Existing research has indicated that students’ expectancy and value beliefs are domain-specific, demonstrating that high levels of expectancy and value beliefs in one subject may not necessarily translate to high levels in another subject (Wigfield & Eccles, 2000).”

“However, existing research on AI education has mostly focused on the curriculum and technical aspects (Salas-Pilco & Yang, 2022; Zawacki-Richter et al., 2019).”

“…gain more experience with technology, the influence of significant others is expected to diminish, with individuals relying more on their own past experiences to shape their perceptions of technology, rather than the opinions of others (Venkatesh & Davis, 2000; Venkatesh & Morris, 2000).”

“Therefore, SEVT provides a solid theoretical foundation for exploring the relationship among supportive environments, expectancy–value beliefs, and students’ intentions to learn AI.”

“…in AI seems to corroborate prior research showing a male advantage in STEM-related subjects (e.g., Wang & Degol, 2017), but seems go against prior research showing male disadvantage in other school subjects (e.g., King & Ganotice, 2014; King, 2016; Korper-shoek et al., 2021; Voyer & Voyer, 2014).”

“The male advantage in AI seems to corroborate prior research showing a male advantage in STEM-related subjects (e.g., Wang & Degol, 2017), but seems go against prior research showing male disadvantage in other school subjects (e.g., King & Ganotice, 2014; King, 2016; Korper-shoek et al., 2021;…”

“The number of latent profiles is determined through a combination of statistical criteria (Lubke & Muthén, 2005).”

“While past studies have demonstrated the importance of motivational factors in individuals’ intentions to learn AI (see Kelly et al., 2023, for a review), most of them have combined various types of motivational factors into a single model without explicitly applying a specific motivational theory…”

“In existing research, variable-centered approaches such as structural equation modeling (SEM) and regression modeling have been widely used to explore factors that influence students’ intentions to learn AI (e.g., Chatterjee & Bhattacharjee, 2020).”

“Supportive social norms are defined as the extent to which a student perceives other members of his/her social group want them to learn more about AI (Sohn & Kwon, 2020).”

“As the demand for AI skills continues to increase, it is crucial for higher education institutions to future-proof their graduates by motivating them to learn about AI (Bates et al., 2020).”

“Both latent mean differences and LPA analysis demonstrated that male students perceive higher levels of supportive environments and expectancy–value beliefs in learning AI (Papastergiou & Solomonidou, 2005; Qazi et al., 2022; Terzis & Economides, 2011).”

“Existing research has indicated that students’ learning is facilitated when their peers, teachers, and school leaders support their learning through positive social norms (King et al., 2019; King & McInerney, 2014; Skinner et al., (2022).”

“From a practical perspective, one way to create supportive norms is to offer AI-related enrichment lessons (Chai et al., 2020; Kong et al., 2021).”

“However, existing research on AI education has mostly focused on the curriculum and technical aspects (Salas-Pilco & Yang, 2022; Zawacki-Richter et al., 2019).”

“First, our findings highlight the importance of creating a supportive environment that emphasizes the importance of AI and offers adequate technology resources and technical assistance to foster students’ intentions to learn AI ( Teo, 2010).”

“Our study shows that, compared to their senior peers, junior students were more likely to fall into the profile of high support and expectancy–value belief and were more influenced by supportive social norms (Hauk et al., 2018; Venkatesh & Davis, 2000).”

“…study is conducted within the Chinese higher education context, where the government has identified AI as a key area of focus and a part of the national strategy, prioritizing investments in AI research and development, education and training, and infrastructure development (Wu et al., 2020).”

“Our study provides evidence that both supportive environments and expectancy–value beliefs are crucial factors in fostering students’ intentions to learn AI, which is consistent with previous research (Kelly et al., 2023; Teo & Zhou, 2014; Venkatesh et al., 2003).”

“While studies conducted in other countries, such as the UK and Belgium (Udeozor et al., 2023), Thailand (Ngamp-ornchai & Adams, 2016), and Lebanon (Tarhini et al., 2015), have demonstrated the importance of supportive environments in influencing students’ intentions to learn technologies, most of…”

“We identified three subgroups of students that are not visible in variable-centered research (e.g., Chai et al., 2020, 2022b; Chatterjee & Bhat-tacharjee, 2020).”

“Both latent mean differences and LPA analysis demonstrated that male students perceive higher levels of supportive environments and expectancy–value beliefs in learning AI (Papastergiou & Solomonidou, 2005; Qazi et al., 2022; Terzis & Economides, 2011).”

“Previous research has indicated that these beliefs are malleable and can be nurtured through appropriate interventions, such as connecting tasks to valued identities and helping students reflect on what pre-pares them for future success (Rosenzweig et al., 2022).”

“We identified three subgroups of students that are not visible in variable-centered research (e.g., Chai et al., 2020, 2022b; Chatterjee & Bhat-tacharjee, 2020).”

“Previous research has indicated that students tend to have stronger intentions to learn AI if they have higher levels of self-efficacy and if they perceive it as useful (Chai et al., 2020, 2022a; Ni & Cheung, 2023).”

“This finding is consistent with previous research, as students tend to devote more time and energy to learning activities when they have confidence in mastering and perceive important (Huang et al., 2020; Park, 2009).”

“Furthermore, SEVT acknowledges the situated nature of expectancy and value beliefs and sheds light on the interplay between internal motivation and external environmental factors on individual learning (Eccles & Wigfield, 2023).”

“Both latent mean differences and LPA analysis demonstrated that male students perceive higher levels of supportive environments and expectancy–value beliefs in learning AI (Papastergiou & Solomonidou, 2005; Qazi et al., 2022; Terzis & Economides, 2011).”

“Existing research has indicated that students’ learning is facilitated when their peers, teachers, and school leaders support their learning through positive social norms (King et al., 2019; King & McInerney, 2014; Skinner et al., (2022).”

“…been used to examine student learning in various subjects, such as literacy (Li et al., 2023; Yeung et al., 2022), STEM (Fong et al., 2021; Wang et al., 2023; Wille et al., 2020), and foreign language (Li et al., 2022), few studies have explored their influence on students’ intentions to learn AI.”

“Although the critical role of supportive environments and expectancy–value beliefs in learning AI has been highlighted, most of these studies focused on their direct effects on students’ intentions to learn AI (e.g., Chai et al. 2022b; Kelly et al., 2023).”

“Although SEVT has been used to examine student learning in various subjects, such as literacy (Li et al., 2023; Yeung et al., 2022), STEM (Fong et al., 2021; Wang et al., 2023; Wille et al., 2020), and foreign language (Li et al., 2022), few studies have explored their influence on students’…”

“…in AI seems to corroborate prior research showing a male advantage in STEM-related subjects (e.g., Wang & Degol, 2017), but seems go against prior research showing male disadvantage in other school subjects (e.g., King & Ganotice, 2014; King, 2016; Korper-shoek et al., 2021; Voyer & Voyer, 2014).”

“Although this research paradigm has been widely used in existing technology research (e.g., Chatter-jee & Bhattacharjee, 2020; Staddon, 2020), it limited our ability to identify best practices that could guide future practice.”

“…been used to examine student learning in various subjects, such as literacy (Li et al., 2023; Yeung et al., 2022), STEM (Fong et al., 2021; Wang et al., 2023; Wille et al., 2020), and foreign language (Li et al., 2022), few studies have explored their influence on students’ intentions to learn AI.”

“While studies conducted in other countries, such as the UK and Belgium (Udeozor et al., 2023), Thailand (Ngamp-ornchai & Adams, 2016), and Lebanon (Tarhini et al., 2015), have demonstrated the importance of supportive environments in influencing students’ intentions to learn technologies, most of…”

“Intentions have been widely used in educational technology (e.g., Ifinedo, 2018; Kwok & Yang, 2017) as a proxy for actual behaviors.”

“Existing research has indicated that students’ learning is facilitated when their peers, teachers, and school leaders support their learning through positive social norms (King et al., 2019; King & McInerney, 2014; Skinner et al., (2022).”

“Aside from individual beliefs, the environment also plays a critical role in learning (King, 2022; King et al., 2019; King & McInerney, 2014).”

“Demographic factors, such as field of study, gender, and year level might be critical factors given that previous studies have shown that students’ intentions toward technology differ due to their varied learning experiences (Owens & Lilly, 2017).”

“Studies that use a person-centered approach, such as latent profile analysis (LPA), have found the existence of different subgroups of individuals characterized by distinct expectancy–value beliefs (Schweder & Raufelder, 2022).”

“Existing research has indicated that students’ learning is facilitated when their peers, teachers, and school leaders support their learning through positive social norms (King et al., 2019; King & McInerney, 2014; Skinner et al., (2022).”

“For example, Mei (2019) found that senior students have higher levels of perceived usefulness than junior students.”

“To achieve this, the Chinese Ministry of Education released the “Artificial Intelligence Innovation Action Plan for Institutions of Higher Education” in 2018, which urges higher education institutions to create an environment that enables students to learn AI by establishing infrastructure and curricula that support A…”

“…in AI seems to corroborate prior research showing a male advantage in STEM-related subjects (e.g., Wang & Degol, 2017), but seems go against prior research showing male disadvantage in other school subjects (e.g., King & Ganotice, 2014; King, 2016; Korper-shoek et al., 2021; Voyer & Voyer, 2014).”

“Although SEVT has been used to examine student learning in various subjects, such as literacy (Li et al., 2023; Yeung et al., 2022), STEM (Fong et al., 2021; Wang et al., 2023; Wille et al., 2020), and foreign language (Li et al., 2022), few studies have explored their influence on students’…”

“Given the transformative influence of AI on future workplaces, international organizations such as the OECD (Vincent-Lancrin & Van der Vlies, 2020), the World Economic Forum (2023), UNICEF (Lemaignan et al., 2021), and UNESCO (Miao et al., 2021) have acknowledged the importance of AI-related skills in the 21st century.”

“For instance, Orji (2010) found that social science students are more influenced by supportive social norms, while science students place greater importance on perceived usefulness.”

“…SEVT has been used to examine student learning in various subjects, such as literacy (Li et al., 2023; Yeung et al., 2022), STEM (Fong et al., 2021; Wang et al., 2023; Wille et al., 2020), and foreign language (Li et al., 2022), few studies have explored their influence on students’ intentions to…”

“…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…”

“Undergraduates with a high degree of technological self-efficacy are more likely to explore new AI tools as they are released into the public domain (Kong et al., 2021; Obenza et al., 2024; Wang et al., 2023).”

“Building upon this foundation, B. Wang et al. (2023) examined the intricate interplay between digital and AI technologies by leveraging Wilson et al.’s (2015) technological-cognitive-ethical model model alongside the KSAVE framework within the Information and Communication Technologies (ICT) domain.”

“Moreover, these students often display more positive emotions towards AI, recognizing its potential as a collaborative part - ner rather than a mere tool (Hew et al., 2023 ; Wang et al., 2023a, b ).”

“However, large-scale theory-driven studies on university students’ perceptions of learning AI seem to be lacking (Hornberger et al., 2023; Wang et al., 2023).”

“Echoing this sentiment, Wang et al. (2023) maintain that students who perceive their learning contexts as more supportive are notably more eager to engage in AI-enhanced learning, illustrating the positive correlation between supportive educational environments and the willingness to adopt AI for…”

“The Motivational Model of Eccles and Wigfield, 2002 (simplified) Guided by the situated expectancy–value theory, Wang et al. (2023) By identifying critical factors that promote or inhibit students’ intentions to learn AI, these studies aim to provide valuable insights for higher education…”

“…technologies not only gauges their value but also unveils their potential to address learning needs, underscoring the significance of factors that drive consistent adoption and influence uptake (Algerafi et al., 2023; Chan & Hu, 2023; Foroughi et al., 2023; Joshi et al., 2021; Wang et al., 2023).”

“Another concept closely linked to understanding the relationship between self-efficacy and individual attitudes towards AI is the level of competency individuals possess with AI (Naiseh & Shukla, 2023; Wang et al., 2023).”

“To gauge the supportiveness of the environments for learning with AI, we adapted the scale by Wang et al. (2023).”

“Third, teachers need to educate students about the value of AI, which can positively influence students’ attitudes towards AI (Jatileni et al., 2023; Wang et al., 2023), thereby reducing their AI anxiety and promoting their use behavior (Kwak et al., 2022).”

“Despite both genders acknowledging the educational value of genAI [42], male students were more likely to act on these perceptions [19], a pattern supported by their perception of stronger institutional and social support [42].”

“Those who can access more resources related to AI exhibited higher levels of intrinsic motivation 43,46 .”

“In the context of generative AI (GenAI), GenAI self-ef fi cacy is described as individuals ’ con fi dence in their capacity to use GenAI tools, such as ChatGPT, for academic and research reasons (Wang et al., 2023).”

“• Involve a wider range of stakeholders, including students, teachers, administrators and policymakers in the research process (e.g., Chan & Hu, 2023; Wang et al., 2023). g., Banihashem et al., 2024).”

“…learning from high-performing AI-driven businesses fos - ters learning behaviors among students, enhancing professional readiness, while Expectancy-Value Theory underscores the motivational pull of perceived profes - sional gains from AI integration (Schunk, 2020; Wang et al., 2023a, b).”

“International organisations such as the OECD, UNESCO, and UNICEF have highlighted the importance of equipping the 21st-century workforce with AI-related skills [79].”

“This financial burden can hinder the usage of AI and limit its potential benefits for students and faculty Wang et al., 2023).”

“Similarly, the availability of resources, such as workshops, guides, and subscriptions to GAI tools, can enhance students’ access to and comfort with these technologies (Almaiah et al., 2022; Vuorikari et al., 2022; F. Wang et al., 2023).”

“Wang et al. (2023 ) demonstrated that when AI usage aligns with uni - versity students’ academic learning goals, it enhances their motivation based on attainment value.”

“This hypothesis is in line with recent evidence showing that university students’ intentions to master AI are fostered by supportive environments and their expectations of value to be derived from their efforts (Wang et al. 2023).”

“For instance, racially biased output may unintentionally alienate or favour certain groups of students (Wang, 2023).”

“Research has shown that when tasks are perceived as unappealing or overly complex, individuals tend to exhibit a stronger reliance on AI, viewing it as a means to ease the task burden and enhance efficiency [37, 38].”

“Therefore, during the past several years, there has been a proliferation of academic contributions and ideas on educational AI governance (Wang et al. , 2023; Crompton & Burke, 2023).”

“Another study of 494 university students, Wang et al. (2023) found that college students’ perceptions and intentions to learn AI are powerfully shaped by supportive environments and expectancy beliefs.”

“With the growing attention to AI education, recent research investigated the motivational factors that influence students ’ learning intention and experiences (Chai et al., 2021, 2022; Wang et al., 2023).”

“Although AI-related courses are available, they are often elective rather than mandatory (Wang et al., 2023), requiring students to actively choose to enroll.”

“Previous research has demonstrated that when students possess high learning self-efficacy and believe that technology has practical value in enhancing personal competence, they tend to exhibit stronger learning intention 54,55 .”

“Efforts to provide AI-focused training workshops [77] and create supportive environments [78] are crucial for reducing barriers to AI adoption.”

“For instance, several scholars indicated that students with reduced technical capacity in AI applications were more likely to utilise the applications smoothly (F. Wang et al. 2023).”

“In this study, AI self-e ffi cacy was de fi ned as an individual ’ s con fi dence in their ability to learn and use AI and was measured by four items (Bandura 1997; Hong 2022; Wang et al. 2023).”

“…levels of expectancy-value beliefs are more likely to invest greater effort, engage more deeply, and allocate more time to learning activities, ultimately leading to higher levels of learning performance (Bong 2001; Guo et al. 2017; Wang et al. 2023a, 2023b; Wigfield, Eccles, and Möller 2020).”

“The scale used in this study is based on Wang et al. ( 2023), which was adapted from Chai et al. (2020), and includes two dimensions: Social Support Norms (SN) and Facilitating Conditions (FC).”

“Intention to learn was developed by Chai et al. [18] and has been validated among students and teachers in several studies [20,44].”

“Visual models depicting these theoretical relations show social expectations and norms impacting expectancies and values (e.g., Gladstone et al., 2022; Naylor et al., 2021; Skinner & Raine, 2022; Wang et al., 2023).”

“The views or perspectives of others will influence the actions a person will take (Mustafa et al., 2023; F. Wang et al., 2023).”

“However, existing research has demonstrated the importance of learning environments and motivational beliefs in fostering students’ intentions to learn about new technology (Wang et al., 2023).”

“Wang et al. (2023) investigaron las intenciones de los estudiantes universitarios para aprender IA y el papel de los entornos de apoyo y las creencias de expectativa-valor.”