Behavioral intentions for learning AI
TRA, proposed by Fishbein and Ajzen (1975), asserts that the variables of people's actual behavior can be precisely and immediately determined by their behavioral intention to perform that behavior. The core elements of TRA are: behavioral intention, defined as people's beliefs about their future willingness to perform a particular behavior (Fishbein and Ajzen 1975). Ajzen (1991) further explained that behavioral intention “indicates how hard people are willing to try or how much effort they are willing to make to perform the behavior” ( p. 181). Now, as a factor, behavioral intention has been widely used in various fields (e.g., Bin et al. 2020; Davis 1989; Davis et al. 1989; Kyndt et al. 2011; LaCaille 2013). For example, Davis (1989) and Davis et al. (1989) proposed in TAM that users' behavioral intentions to use a new system directly determine the adoption of that system.As another example, the variable intention to learn is recognized as a “proximate determinant of participation in learning activities” (Kyndt et al. 2011, p. 214).
Since AI is a new and updated brand of advanced technology, many teachers feel that they do not have enough knowledge or skills to actually use it or teach it successfully (Celik et al. 2022; Chounta et al. 2021; Chiu and Chai 2020; Huang et al. 2020). To address this issue and equip teachers with the necessary knowledge and skills, it is important to increase teachers' readiness for AI learning. However, As Chai et al. (2021) argued that the factors behavioral intention to learnAlthough supported by the TRA, it remains under-discussed and thoroughly researched in AI education. This study operationally conceptualized behavioral intention to learn AI as people's beliefs about their future willingness to learn AI.term Behavioral intention to learn AI describes K-12 teachers’ beliefs about the components of AI and their future willingness to learn how to apply AI in education (Chai et al. 2021). When teachers have a high behavioral intention to learn AI, they are more likely to engage in different types of professional learning activities that involve AI knowledge and skills.
Awareness of the Use of AI for Social Good (PAIS)
The first TRA suggests that people's attitudes toward a particular behavior can significantly predict their intentions for related behaviors (Fishbein and Ajzen 1975). Specifically, attitude toward a particular behavior refers to “the extent to which a person has a favorable or unfavorable evaluation or evaluation of the behavior in question” (Azjen 1991, p. 188). This type of attitude generally occurs when people judge the consequences of their actions (Fishbein and Ajzen 1975). People can have positive attitudes toward an action if they believe its consequences are beneficial (Fishbein and Ajzen 1975).
The fair use of AI can have various consequences, which can benefit not only the users themselves but also society. Recently, calls for applying AI in the area of social good have been increasing (Cowls et al. 2021; Floridi et al. 2021; Tomašev et al. 2020).term AI for social welfare It was also introduced to explain the phenomenon of “leveraging AI technology to deliver socially beneficial outcomes” (Cowls et al. 2021, p. 111). Educational researchers recommend that the idea of AI for social good should be incorporated into K-12 school curricula (e.g., Chiu and Chai 2020, Lin and Van Brummelen 2021). By doing so, teachers and students can realize that the use of AI can bring great benefits to others and society, and have a positive attitude toward AI learning (Chai et al. . 2021). In fact, Chai et al. (2021) said that the factors are; pie, one of the important but often neglected aspects of attitudes towards AI learning. Furthermore, considering the influence of attitudes on behavioral intentions (Fishbein and Ajzen 1975), if people can perceive the benefits of using AI for society, they will be extrinsically motivated and have strong behavioral intentions to learn AI. It can be assumed that they will have it. . However, to our knowledge, this relationship has never been demonstrated among teachers. We formulate the following hypothesis.
H1:K-12 teachers' PAIS directly influences teachers' behavioral intentions to learn AI.
Self-efficacy in AI learning
Added Ajzen (1985, 1991) perceived behavioral control We proposed to TRA and proposed TPB. TPB complements it perceived behavioral control It is also an important determinant of behavioral intentions (Ajzen 1985, 1991), which describes “people's perceptions of the ease or difficulty of performing the behavior of interest” (Ajzen 1991, p . 183). In particular, Ajzen (1991) noted that perceived behavioral control is “most compatible with his Bandura (1977, 1982) concept of perceived self-efficacy” (p. 184). Bandura (1982), in social cognitive theory, states that self-efficacy “is concerned with judgments of how well one can carry out a course of action necessary to cope with a future situation” (p. 122). I suggested.
Previous studies have confirmed the influence of self-efficacy on behavioral intentions to learn (e.g., Evans et al. 2020; Lin et al. 2018; Kumar et al. 2020). For example, Kumar et al. (2020) demonstrated the direct impact of mobile learning self-efficacy on mobile learning intentions.Based on the TPB, this study Self-efficacy in AI learning We further hypothesize the following as their perceptions of the ease or difficulty of learning and understanding basic AI knowledge and concepts.
H2: K-12 teachers' self-efficacy in learning AI directly influences their behavioral intention to learn AI.
Several previous studies have shown that two predictors of behavioral intention derived from the TPB, namely attitude toward behavior and perceived behavioral control (i.e., self-efficacy), are significantly correlated (e.g. : Coban and Atasoy 2019; Kao et al. 2020; Yada et al., 2018). For example, teachers' attitudes toward inclusive education were found to be significantly influenced by their self-efficacy in using inclusive practices (Yada et al. 2018). However, since there are few studies applying her TPB in AI education (Chai et al. 2021), the relationship between self-efficacy in AI learning and attitude toward using AI remains largely unexplored, especially among teachers. Not. Considering that PAIS is one of the important aspects of attitude towards the use of AI, we hypothesize the following.
H3: K-12 teachers' self-efficacy in AI learning directly influences PAIS.
AI literacy
Besides TPB, Fishbein and Ajzen (2010) pointed out that epistemic factors can serve as antecedents of attitudinal and control beliefs, which in turn may predict behavioral intentions. Specifically, cognitive factors typically describe people's concepts of knowledge or knowledge in a particular domain or field (Hofer and Pintrich 1997). In AI education, AI literacy is an important cognitive element (Chai et al. 2021) that summarizes people's knowledge and understanding of AI concepts and applications (Chai et al. 2021; Lin et al. 2021; Ng et al. 2021). Chai et al. (2021) defined AI literacy as people who “understand the components of AI and know how to apply AI to a variety of problems” (p. 90). Long and Magerko (2020) provided a more comprehensive definition of AI literacy. “A set of competencies that enable individuals to critically evaluate AI technologies; communicate and collaborate effectively with AI; and use AI as a tool online, at home, and at work” (p. 598). Although AI episteme is not explicitly highlighted in its definition, Long and Magerko (2020) point out that literacy has historically been associated with people's access to knowledge, and that AI knowledge is defined as AI literacy. suggested that it is an important element of
Previous studies have shown that the effects of literacy on attitudes toward specific behaviors (e.g., January 2018; Nam and Park, 2016) and self-efficacy (e.g., Khan and Idris, 2019; Pryor et al., 2016) Impact has been verified. Nevertheless, to our knowledge, no study on teachers' her AI learning has examined such effects. Based on notes from Fishbein and Ajzen (2010), we hypothesize the following.
H4: AI literacy of K-12 teachers directly impacts PAIS.
H5:K-12 teachers' AI literacy directly impacts their self-efficacy in AI learning.
Furthermore, considering the hypothesized effects of K-12 teachers' AI literacy on PAIS, self-efficacy, and behavioral intention to learn AI, the following indirect effects are formulated.
H6: K-12 teachers' AI literacy indirectly influences their behavioral intention to learn AI through PAIS.
H7:K-12 teachers' AI literacy indirectly influences their behavioral intention to learn AI through self-efficacy.
AI ethics awareness
When it comes to the proper learning and use of AI, ethics is an important issue that cannot be ignored (Borenstein and Howard 2021; Lin et al. 2021; Qin et al. 2020; Richards and Dignum 2019; Shih et al. 2021 ). Indeed, the uncertainties and risks of AI have caused widespread public concern (Jobin et al. 2019; Qin et al. 2020). In response to these concerns, a number of ethical principles have been developed to promote the appropriate understanding and use of AI (Jobin et al. 2019; Richards and Dignum 2019). Among them, transparency, responsibility, justice, and sustainability are four core AI ethical principles that are widely emphasized (Lin et al. 2021).
The term “awareness” refers to people's attentiveness, interest (careful or observant), and sensitivity regarding a particular issue or behavior (Sudarmadi et al. 2001). Lin et al. (2021) and Shih et al. (2021) pointed out that there is a strong positive link between AI ethics awareness and AI literacy. In fact, according to Long and Magerko's (2020) definition, an AI literate individual is able to critically evaluate her AI. Therefore, they are likely to pay close attention to and be concerned about the risks of AI and be aware of the ethical issues of AI. In addition, AI-savvy people usually have good knowledge and understanding about AI (Chai et al. 2021; Lin et al. 2021; Ng et al. 2021), so they are aware of the potential risks and limitations of AI. , you can also know and understand uncertainty. , and thereby recognize the ethical aspects of AI. However, the direct impact of AI ethics awareness on AI literacy has rarely been observed among K-12 teachers. We formulate the following hypothesis.
H8:K-12 teachers' AI literacy directly impacts their awareness of AI ethics.
Awareness may play an important role in attitude formation (Potas et al. 2022; Shuhaiber and Mashal 2019; Sweldens et al. 2014)footnote 1. For example, in the field of educational technology, Potas et al. (2022) found that young people's perceptions of technology addiction directly influenced their attitudes toward technology addiction. Therefore, it is reasonable to think that an individual's awareness of AI ethics may influence their attitude toward the use of AI. However, to our knowledge, this effect has not been confirmed. Considering that the factor PAIS is one of the most important attitudes towards the use of AI (Chai et al. 2021), we hypothesize the following.
H9:K-12 teachers' perceptions of AI ethics directly impact PAIS.
Furthermore, considering the hypothesized effects of AI literacy on awareness of AI ethics, PAIS, and behavioral intentions to learn AI, the following indirect effects are formulated:
H10: AI literacy of K-12 teachers indirectly influences PAIS through AI ethics awareness.
H11: K-12 teachers' perceptions of AI ethics indirectly influence their behavioral intentions to learn AI through the use of AI for social good.
Based on the aforementioned rationale, a conceptual research model is proposed (see Figure 1).