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.”

“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;…”

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

“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).”

“…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 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).”

“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).”

“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).”

“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).”

“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).”

“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).”

“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).”

“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).”

“…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.”

“…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’…”

“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).”

“…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 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.”

“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).”

“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).”

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

“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…”

“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.”