Lifelong learning predicting artificial intelligence literacy: A hierarchical multiple linear regression analysis

Aylin KİRİŞÇİ-SARIKAYA USBED, 7(13), 2025, Sonbahar / Fall 455 For citation / Atıf için: KİRİŞÇİ-SARIKAYA, A. (2025). Lifelong learning predicting artificial intelligence literacy: A hierarchical multiple linear regression analysis. Uluslararası Sosyal Bilimler ve Eğitim Dergisi – USBED 7(13), 455–482. https://doi.org/10.5281/zenodo.17092946, https://dergipark.org.tr/tr/pub/usbed Lifelong learning predicting artificial intelligence literacy: A hierarchical multiple linear regression analysis Aylin KİRİŞÇİ-SARIKAYA Ph.D; İzmir Democracy University, Education Faculty, Department of Educational Sciences, İzmir, Türkiye E-mail: [email protected] ORCID: 0000-0001-7443-8433 Makale Türü / Article Type: Araştırma Makalesi / Research Article Gönderilme Tarihi / Submission Date: 05/08/2025 Revizyon Tarihleri / Revision Dates: 06/08/2025 (Editor c.), 17.08.2025 (Major r.) Kabul Tarihi / Accepted Date: 10/09/2025 Etik Beyan / Ethics Statement ü Ethics Committee Approval of the article was given by Izmir Democracy University's dated 09.04.2025 and document numbered 2025/04-14. Araştırmacıların çalışmaya katkısı / Researchers' contribution to the study ü Author contribution: Wrote the article, collected the data, and analyzed/reported the results (100%). Çıkar çatışması / Conflict of interest ü The author declares that there is no potential conflict of interest in this study. Benzerlik / Similarity ü This study was scanned in the iThenticate program. The final similarity rate is 6%. Lifelong learning predicting artificial intelligence literacy: A hierarchical multiple linear regression analysis USBED, 7(13), 2025, Sonbahar / Fall 456 Lifelong learning predicting artificial intelligence literacy: A hierarchical multiple linear regression analysis Abstract This study investigated the relationship between preservice teachers’ lifelong learning (LLL) tendencies and their artificial intelligence (AI) literacy. It aimed to understand whether a more substantial commitment to LLL contributes to developing essential AI competencies among future educators. A total of 318 preservice teachers from various universities and departments in Türkiye participated in the study. The results revealed significant positive correlations between LLL tendencies and overall AI literacy, as well as with each AI literacy subdimension, namely awareness, usage, evaluation, and ethics. The results suggested that preservice teachers with higher LLL tendencies tend to be more AI literate. Hierarchical multiple linear regression analysis was utilized to investigate whether demographic variablesgender, year of study, ICT competency, and AI tool usageand LLL tendencies predicted AI literacy. ICT competency was found to be a significant predictor in the first model, and in the second model, LLL significantly improved the predictive power. As a result, ICT competency and LLL showed statistically significant predictive effects on preservice teachers’ AI literacy. These findings indicate the importance of improving preservice teachers’ LLL tendency to enhance AI literacy in their professional learning. Keywords: Professional learning, Artificial intelligence, Lifelong learning, Preservice teachers EXTENDED ABSTRACT Introduction In today’s rapidly changing educational environment, it is crucial for preservice teachers (PSTs) to not only understand traditional teaching practices but also to grasp the emerging technologies. As generative AI tools become more common, PSTs are expected to integrate these technologies into their instruction effectively and to be AI literate. One potential way to promote AI literacy among them could be by examining their orientation toward lifelong learning (LLL). By cultivating LLL, PSTs can develop the skills necessary to adapt to technological advancements and employ AI-driven teaching methods (UNESCO, para. 1, 2025). This study explored the association between PSTs’ LLL tendency and AI literacy. Various studies, such as Hagger et al. (2008), Sahin et al. (2010), and Sunthonkanokpong and Murphy (2019), highlighted the significance of LLL in adapting to the ever-evolving educational landscape. However, there is a gap in understanding the relationship between LLL and the PSTs’ AI literacy. Furthermore, LLL research on AI literacy is still in its early stages and needs more investigation (Laupichler et al., 2022). Therefore, this study provided evidence for the hypothesis that PSTs who exhibit greater LLL tendencies are more likely to become AI literate, and the study aimed to put forth how cultivating LLL may support future educators’ development of AI literacy by examining this relationship. Conceptual and Theoretical Framework LLL is a broad concept that changes and evolves over time. It was first used in the late 1960s (Vislie, 2008). In its traditional usage, the term predominantly referred to the processes and structures designed to facilitate learning in adulthood and across the span of an individual’s professional life. In time LLL policies and therefore its scope entails more holistic features that encompass formal, non-formal, and informal learning, each contributing to continuous personal and professional development across the lifespan (Laal, 2011). This perspective highlights the multifaceted nature of LLL which occurs not only in traditional educational settings but also through everyday experiences and workplace engagement. Therefore, the essential role of LLL in contemporary society cannot be overstated, particularly in navigating the complexities of a rapidly changing world in the age of AI. LLL serves not only to enhance PSTs’ capabilities but also to foster active citizenship. As AI increasingly integrates into education, equipping future educators with the LLL mindset seems essential for improving growth, professional excellence, and overall well-being. Aylin KİRİŞÇİ-SARIKAYA USBED, 7(13), 2025, Sonbahar / Fall 457 AI literacy is about competencies and abilities that enable individuals to understand, interact with, and critically evaluate AI technologies and their implications (Laupichler et al., 2022; Samoili et al., 2021; Shiri, 2024; Wang et al., 2023). Wang et al. (2023, 1324) conceptualized AI literacy through four core dimensions, as “awareness, usage, evaluation, and ethics”. Overall, AI literacy is conceptualized as a multifaceted and evolving concept that encompasses understanding AI principles, being able to use and interact with AI technologies, critically evaluating their capabilities and limitations, and considering their ethical and societal implications. The rise of digital technologies and AI has fostered highly interconnected learning ecosystems, where the integration of online and offline platforms enables continuous, personalized learning across diverse settings and timeframes (UNESCO, 2025). In coherence with this, the studies conducted in the literature primarily focus on how AI and AI literacy influence LLL. For instance, Pu et al. (2024) explored the influence of AI-related learning and digital literacy on LLL outcomes in China. They underscored the role of AI technologies in fostering effective LLL. In another study, the key factors influencing the adoption and effectiveness of AI tools—like ChatGPT—in supporting LLL in educational and workplace settings were analyzed (Ahn, 2024). The study results indicated that if AI tools are practical, individuals experience greater benefits, positively influencing academic and job performance. Similarly, Asad and Aijaz (2025) found that generative AI positively influences continuous learning and skill enhancement among students of higher education institutions in Pakistan. However, little is known about the PSTs’ LLL tendency’s role in AI literacy so far. Therefore, this study addressed a critical gap by investigating the predictive role of LLL tendencies on AI literacy among PSTs. These novel insight could reveal how initial teacher education programs can be designed to promote continuous professional learning and effectively integrate AI technologies into teaching practices. Method Employed as a quantitative research design, this study aimed to investigate the predictive role of LLL tendencies on AI literacy among PSTs. Accordingly, the subsequent hypotheses were formulated: H1: PSTs with higher LLL tendencies will report significantly higher levels of AI literacy. H2: Gender, year of study, ICT competency level, frequency of AI tool usage, and LLL tendencies will significantly predict AI literacy levels among PSTs. H3: LLL tendency is a significant predictor of AI literacy even after controlling the demographic factors. The sample of the current study was composed of 318 PSTs from various departments and universities in Türkiye. The convenience sampling strategy was adopted to select the participants of the study. The questionnaire used in this research consisted of three parts, which were demographic information form, the “Artificial Intelligence Literacy Scale (AILS)” developed by Wang et al. (2023), and the “Lifelong Learning Scale (WielkLLS)” produced by Wielkiewicz and Meuwissen (2014). Findings Following the validation of the data's suitability and examining the assumptions required for hierarchical multiple regression analysis the data analysis started. Accordingly, the skewness and kurtosis values of AILS and LLL suggested that the data distributions did not substantially deviate from normality. Following this, correlation analysis was conducted. The results showed that WielkLLS was positively and significantly correlated with the overall AILS score (r = 0.398, p < .001). Later, a hierarchical multiple linear regression analysis was conducted. Two models were tested. Model I included only demographic variables, which were gender, year of study, ICT competency, and AI tool usage. The first model explained 21% of the variance in AI literacy [F (4, 313) =20.846, p < .001], indicating that the level of ICT competency significantly predicted AI literacy (β=0.535, p < .001). Gender, year of study, and AI tool usage were insignificant predictors (p > .05), suggesting no meaningful differences in AI literacy based on those variables alone. Afterward, Model II added the LLL tendency (WielkLLS) to the demographic predictors, and it was revealed that an additional factor explained a further 10% variance in AI literacy [Rchange=0.10, Fchange (1, 312) =45.227, p < .001]. The model’s explanatory power increased to R2=0.310, with an adjusted R2 of 0.299. Both ICT competency (β =0.463, p < .001) and WielkLLS (β =0.322, p < .001) were significant predictors. Conclusion, Discussion, and Recommendations The findings revealed that PSTs’ LLL tendencies were significantly associated with their AI literacy levels. The strong positive correlations observed between the WielkLLS and the overall AILS score underscored the integral role of LLL in developing AI-related competencies. These results align with previous research emphasizing that Lifelong learning predicting artificial intelligence literacy: A hierarchical multiple linear regression analysis USBED, 7(13), 2025, Sonbahar / Fall 458 LLL supports individuals in adapting to technological advancements and integrating emerging tools such as AI into their learning and professional practices (Asad & Aijaz, 2025; Laupichler et al., 2022; Pu et al., 2024). Specifically, the findings suggested that PSTs who exhibited a strong disposition toward LLL were more likely to be aware of, effectively use, critically evaluate, and ethically engage with AI technologies. This highlights the importance of embedding LLL principles within teacher education programs to better prepare future educators for the evolving demands of AI-integrated educational environments (Bozkurt et al., 2023; Schmidt-Hertha, 2025; UNESCO, 2025). There are several key implications for practitioners, policymakers, and researchers. Teacher education programs should integrate LLL activities like peer coaching, self-directed project work, and professional learning communities throughout their curricula. This integration would enhance PSTs’ commitment to continuous professional growth. Such models can promote LLL and support the development of competencies related to AI literacy. Since ICT competency significantly predicted AI literacy, several resources should be allocated to strengthen digital infrastructures and deliver comprehensive ICT professional development as a precursor to effective AI integration. From a policy perspective, teacher education curricula should include structured opportunities to develop AI literacy and LLL competencies through project-based learning, AI-focused workshops, and reflective digital portfolios. Educational policymakers should support the integration of AI literacy as a core component of teacher standards and provide sustained professional development pathways to ensure future educators remain adaptive in a rapidly changing technological landscape. By institutionalizing these strategies, initial teacher preparation programs can contribute to building a more agile, ethical, and digitally competent teaching workforce. INTRODUCTION The digitalization in education has accelerated with the latest developments in technology. The incorporation of artificial intelligence (AI) across numerous fields, including education has accompanied this transformation with the widespread use of big data, online storage, and machine learning (Kumar & Thakur, 2012; Zhai et al., 2021). AI is often used to personalize learning, automate administrative and assessment tasks, and support intelligent tutoring systems (Cukurova & Miao, 2024; Luckin et al., 2016). This technological innovation is a part of the Fourth Industrial Revolution. In the field of education, it is identified as the “Fourth Education Revolution” and is characterized with the changing roles and responsibilities of teachers and students. (Zhai et al., 2021; Zhai, 2024). In this new learning ecosystem, preservice teachers (PSTs) must acquire the necessary AI-related knowledge and skills, and they should enhance their AI literacy in order to effectively perform in future teaching environments (Cukurova & Miao, 2024; Pokrivcakova, 2023; Zhang et al., 2023). AI and the AI literacy are critical topics on the agendas of numerous nations. Dedicated governmental units have been established to address the issue. In parallel, Türkiye has prioritized digitalization and AI within its strategic framework. The establishment of the Digital Transformation Office under the presidency demonstrates this commitment, positioning itself as a leader in integrating AI into various state functions (Türkiye Cumhuriyeti Cumhurbaşkanlığı Dijital Dönüşüm Ofisi, 2025). AI technologies have been particularly emphasized in the education sector, leading to enhancements in the content offered through the Aylin KİRİŞÇİ-SARIKAYA USBED, 7(13), 2025, Sonbahar / Fall 459 Education Information Network (EBA). Additionally, an innovative and flexible AI-supported individual learning platform, MEBI, has been introduced for secondary school students, allowing for completely personalized learning experiences (Ministry of National Education, 2025). In March 2025, the Artificial Intelligence and Big Data Applications Department was established by the Ministry of National Education within the General Directorate of Innovation and Education Technologies. This center will conduct research and development studies to ensure that teachers, students, and parents can safely use AI-supported systems. The rise and widespread availability of generative AI tools and numerous AI-supported applications highlight the necessity for effectively utilizing these technologies. A strong understanding of AI is regarded as crucial in the age of generative AI (Bozkurt, 2024; Bozkurt et al., 2023; Laupichler et al., 2022; Low et al., 2025; Ng et al., 2021). Similarly, for PSTs AI literacy is crucial to integrate AI into future classrooms. They can comprehend, engage with, and critically assess AI technologies and their implications when they are AI literate (Laupichler et al., 2022; Shiri, 2024). One potential way to promote AI literacy of PSTs could be by examining their orientation toward lifelong learning (LLL). PSTs’ attitudes and willingness to LLL and professional development may significantly influence their AI literacy and capacity, and therefore, they may more effectively adapt to emerging AI-driven educational practices. In the current age, “the learning ecosystem is interconnected, employing both online and offline resources to enable learning to take place anywhere, anytime, via individualized pathways” (UNESCO, 2025, para.1). By actively engaging in LLL, PSTs would be better at keeping pace with rapid AI innovations and evolving pedagogical demands. Thus, this study explores the relationship between PSTs' LLL tendency and AI literacy. Though the significance of LLL for educators and PSTs is highly studied and well-established (e.g., Hagger et al., 2008; Sunthonkanokpong & Murphy, 2019; Sahin et al., 2010), its interplay with AI literacy remains underexplored. In addition, AI literacy in LLL research is an emerging and new subject and requires further analysis (Laupichler et al., 2022). Thus, this study explores the association between PSTs’ LLL tendency and AI literacy. Although the importance of LLL for teachers and PSTs is widely recognized (e.g., Hagger et al., 2008; Sahin et al., 2010; Sunthonkanokpong & Murphy, 2019), little is known about how it interacts with AI literacy. Furthermore, LLL research on AI literacy is still in its early stages and needs more investigation (Laupichler et al., 2022). Therefore, this study provides evidence for the hypothesis that PSTs who exhibit greater LLL tendencies are Lifelong learning predicting artificial intelligence literacy: A hierarchical multiple linear regression analysis USBED, 7(13), 2025, Sonbahar / Fall 460 more likely to become AI literate, and the study aims to put forth how cultivating LLL may support future educators’ development of AI literacy by examining this relationship. CONCEPTUAL FRAMEWORK Lifelong Learning LLL is a broad concept that changes and evolves over time. It was first used in the late 1960s (Vislie, 2008). In its traditional usage, the term predominantly referred to the processes and structures designed to facilitate learning in adulthood and across the span of an individual’s professional life. Jarvis (2004) emphasized the complexity of the concept and highlighted that LLL encompasses both individual, lifelong personal learning and institutionalized, formal education. In this regard LLL occurs not only within educational systems but also across noneducational sectors, such as workplaces and communities. There have been various policy documents published on LLL. For example, in its report, the Organization for Economic Cooperation and Development (OECD, 2021) emphasized LLL and positioned it as a foundational pillar for future societal development. Accordingly, LLL plays a significant role both in knowledge economy, employment and democratic values with social cohesion (Cummins & Kunkel, 2015; OECD, 2021). OECD underscores the potential of LLL as a strategic tool for addressing the challenges of globalization, technological change, and demographic shifts. Delors Report introduced a comprehensive framework for lifelong education, rising on four pillars of learning which are “learning to know, learning to do, learning to live together, and learning to be” (Delors, 1996). These pillars, rather than focusing only on the acquisition of knowledge or skills, emphasize the intellectual, social, emotional and agentic development of the whole person across the lifespan. The 2030 Agenda for Sustainable Development, adopted by United Nations (UN) member states, includes 17 goals aiming to foster a better future for all (UN, 2015). The sustainable development goal four (SDG4) specifically highlights the need for quality education and LLL opportunities for everyone. Additionally, LLL is highlighted as one of the key tools for achieving many other SDG targets, such as LLL could help in eliminating poverty and reducing inequality by helping people gain important skills and knowledge to boost their chances of finding work and earning better income. According to the seventh and the latest report of International Conferences on Adult Education called CONFINTEA (UNESCO Institute for Lifelong Learning, 2022), representatives from more than 140 countries underscored their Aylin KİRİŞÇİ-SARIKAYA USBED, 7(13), 2025, Sonbahar / Fall 461 dedication to the SDGs by acknowledging the pivotal role of adult education as an integral aspect of LLL and collectively pledged to actualize LLL. In time LLL policies and therefore its scope entails more holistic features that encompass formal, non-formal, and informal learning, each contributing to continuous personal and professional development across the lifespan (Laal, 2011). This perspective highlights the multifaceted nature of LLL which occurs not only in traditional educational settings but also through everyday experiences and workplace engagement. According to another recent international report, Making lifelong learning a reality: a handbook, (UNESCO Institute for Lifelong Learning [UIL], 2022), LLL is a comprehensive concept that connects learning with everyday life across all ages, settings, and methods. It emphasizes that learning should not be seen as limited to childhood or early adulthood but as a continuous process throughout a person's life. Therefore, the essential role of LLL in contemporary society cannot be overstated, particularly in navigating the complexities of a rapidly changing world in the age of AI. LLL serves not only to enhance PSTs’ capabilities but also to foster active citizenship. As AI increasingly integrates into education, equipping future educators with the LLL mindset seems essential for improving growth, professional excellence, and overall well-being. Artificial Intelligence Literacy Since the term of AI was first used by McCarthy, Minsky, Rochester and Shannon in a study in 1955 (McCarthy et al., 1955), the definition of the term has been extended and diversified. For example, the Joint Research Centre of the European Commission addressed both core domains and broader themes of AI and proposed an operational definition (Samoili et al., 2021). According to OECD’s definition: “An AI system is a machine-based system that, for explicit or implicit objectives, infers, from the input it receives, how to generate outputs such as predictions, content, recommendations, or decisions that can influence physical or virtual environments. Different AI systems vary in their levels of autonomy and adaptiveness after deployment” (OECD, 2024, para. 14). Generative AI represents a cutting-edge segment of AI technology, showcasing systems that mirror human language abilities. These systems are usually trained using sophisticated techniques such as deep learning and neural networks, which allow them to analyze and process vast amounts of data (Bozkurt & Sharma, 2023). As a result, they can understand context, Lifelong learning predicting artificial intelligence literacy: A hierarchical multiple linear regression analysis USBED, 7(13), 2025, Sonbahar / Fall 462 generate coherent text, and transform information into creative ways (Bozkurt & Sharma, 2023; Laupichler et al., 2022; Low et al., 2025). AI literacy is about competencies and abilities that enable individuals to understand, interact with, and critically evaluate AI technologies and their implications (Laupichler et al., 2022; Samoili et al., 2021; Shiri, 2024; Wang et al., 2023). AI literacy is one of the technological literacies, along with media literacy (Potter, 2018), data literacy (Gummer & Mandinach, 2015), computational literacy (diSessa, 2018), and scientific literacy (Laugksch, 2000); and it is closely related to digital literacy and AI education (Ng et al., 2021; Shiri, 2024). Many contemporary literacies are interrelated and do not exist as distinct categories, but they are useful for comprehending conceptualization (Stordy, 2015) emphasizes that many contemporary literacies are interconnected and do not exist as exclusive categories, but they are valuable for understanding conceptualization. Therefore, AI literacy includes important elements of a larger conceptual framework focused on AI competency. For example, Ng et al. (2021) outlined several essential elements of AI literacy that are based on Bloom's taxonomy. Educators should focus on them to enhance learners' AI competencies. These components include knowledge and understanding, application and use, evaluation and creation, and ethical considerations. Cetindamar et al. (2022) identified four sets of skills linked with AI literacy for employees, namely technology-related, work-related, human-machine-related, and learningrelated capabilities. And Wang et al. (2023, 1324) conceptualized AI literacy through four core dimensions, as “awareness, usage, evaluation, and ethics”. Overall, AI literacy is conceptualized as a multifaceted and evolving concept that encompasses understanding AI principles, being able to use and interact with AI technologies, critically evaluating their capabilities and limitations, and considering their ethical and societal implications. Preservice Teachers’ Lifelong Learning and Artificial Intelligence Literacy Developing a strong understanding of AI has become crucial for PSTs in the age of AI (Bozkurt, 2024; Bozkurt et al., 2023; Laupichler et al., 2022). They should be better equipped to keep pace with rapid technological innovations and evolving learning ecosystem demands. It is important to understand the current state of AI and its potential advancements in future. This foundational understanding can be integrated into undergraduate teacher training programs. Therefore, future educators should be supported to navigate an increasingly digital learning environment better and provided and upskilled via ongoing professional development opportunities to utilize AI in their teaching practices (Schmidt-Hertha, 2025). Investigating the Aylin KİRİŞÇİ-SARIKAYA USBED, 7(13), 2025, Sonbahar / Fall 463 relationship between PSTs’ LLL tendency and AI literacy and revealing the predictive effects on AI literacy could be regarded as one of the important steps that can enhance the initial teacher training programs. In the context of LLL, the rise of digital technologies and AI has fostered highly interconnected learning ecosystems, where the integration of online and offline platforms enables continuous, personalized learning across diverse settings and timeframes (UNESCO, 2025). In coherence with this, the studies conducted in the literature primarily focus on how AI and AI literacy influence LLL. For instance, Pu et al. (2024) explored the influence of AI-related learning and digital literacy on LLL outcomes in China. They underscored the role of AI technologies in fostering effective LLL. In another study, the key factors influencing the adoption and effectiveness of AI tools—like ChatGPT—in supporting LLL in educational and workplace settings were analyzed (Ahn, 2024). The study results indicated that if AI tools are practical, individuals experience greater benefits, positively influencing academic and job performance. Similarly, Asad and Aijaz (2025) found that generative AI positively influences continuous learning and skill enhancement among students of higher education institutions in Pakistan. However, little is known about the PSTs’ LLL tendency’s role in AI literacy so far. Therefore, this study addressed a critical gap by investigating the predictive role of LLL tendencies on AI literacy among PSTs. These novel insights could reveal how initial teacher education programs can be designed to promote continuous professional development and effectively integrate AI technologies into teaching practices. METHOD Research Model and Hypotheses Employed as a quantitative research design, this study aimed to investigate the predictive role of LLL tendencies on AI literacy among PSTs. Accordingly, the subsequent hypotheses were formulated to test the survey data statistically through correlation and hierarchical multiple linear regression analyses. Hypothesis 1: PSTs with higher LLL tendencies will report significantly higher levels of AI literacy, including the subdimensions of AI literacy (awareness, usage, evaluation, and ethical understanding of AI tools). PSTs with strong LLL tendencies would be more likely to seek new information, experiment with AI tools, and reflect on their ethical and practical implications. In this context, it is essential Lifelong learning predicting artificial intelligence literacy: A hierarchical multiple linear regression analysis USBED, 7(13), 2025, Sonbahar / Fall 470 correlations among AILS subdimensions imply that PSTs who are more aware of AI also tend to use it more actively, evaluate it more critically, and consider ethical issues more carefully. In conclusion, a hierarchical multiple linear regression analysis was conducted to investigate the predictive effects of gender, year of study, ICT competency level, AI tool usage, and WielkLLS on AI literacy. The results are shown in Table 4. Table 4 Hierarchical Multiple Linear Regression Analysis Results in the Predictive Role of Various Variables on AI Literacy Model/ Variables B SE β t p 95% CI for β LL UL Model I Constant 50.064 2.605 19.215 <0.001 -2.120 -0.872 Demographic Gender -0.082 0.983 -0.009 -0.084 0.933 -0.235 0.247 Year of study 0.473 0.579 0.052 0.817 0.414 -0.096 0.199 ICT competency 4.824 0.595 0.535 8.114 <0.001 0.408 0.662 AI tool usage -0.590 0.533 -0.065 -1.108 0.269 -0.166 0.048 R2 0.210 Adjusted R2 0.200 ΔR² 0.210 F for Change in R2 20.846*** Model II Constant 34.847 3.327 10.475 <0.001 -1.845 -0.794 Demographic Gender 0.104 0.921 0.012 0.113 0.910 -0.193 0.244 Year of study 0.331 0.542 0.037 0.611 0.542 -0.080 0.155 ICT competency 4.174 0.565 0.463 7.389 <0.001 0.342 0.590 AI tool usage -0.524 0.499 -0.058 -1.051 0.294 -0.157 0.054 WielkLLS 0.348 0.052 0.322 6.725 <0.001 0.207 0.440 R2 0.310 Adjusted R2 0.299 ΔR² 0.100 F for Change in R2 45.227*** Note. Gender: 1 for females, 2 for males. CI = confidence interval; LL = lower limit; UL = upper limit. ***p < .001 Table 4 presents the results of a hierarchical regression analysis examining the predictors of AI literacy. Two models were tested. Model I included only gender, year of study, ICT competency, and AI tool usage. The first model explained 21% of the variance in AI literacy [F (4, 313) =20.846, p < .001], indicating that the level of ICT competency significantly predicted AI literacy (β=0,535, p < .001). According to Cohen’s (1988) guideline, this represents a medium-to-large effect size (f² = 0.27). Gender, year of study, and AI tool usage were insignificant predictors (p > .05), suggesting no meaningful differences in AI literacy based on those variables alone. Afterward, Model II added the LLL tendency (WielkLLS) to the predictors, and it was revealed that an additional factor explained a further 10% variance in Aylin KİRİŞÇİ-SARIKAYA USBED, 7(13), 2025, Sonbahar / Fall 471 AI literacy [Rchange=0.100, Fchange (1, 312) =45.227, p < .001], which is a moderate effect size (f² = 0.15). The model’s explanatory power increased to R2=0.310, with an adjusted R2 of 0.299. In this final model, both ICT competency (β =0.463, p < .001) and WielkLLS (β =0.322, p < .001) were significant positive predictors, while AI tool usage and demographics remained nonsignificant. DISCUSSION The findings revealed that PSTs’ LLL tendencies, as measured by the WielkLLS, were significantly associated with their AI literacy levels. The strong positive correlations observed between the WielkLLS and the overall AILS score underscored the integral role of LLL in developing AI-related competencies. These results align with previous research emphasizing that LLL supports individuals in adapting to technological advancements and integrating emerging tools such as AI into their learning and professional practices (Asad & Aijaz, 2025; Laupichler et al., 2022; Pu et al., 2024). Specifically, the findings suggested that PSTs who exhibited a strong disposition toward LLL were more likely to be aware of, effectively use, critically evaluate, and ethically engage with AI technologies. This highlights the importance of embedding LLL principles within teacher education programs to better prepare future educators for the evolving demands of AI-integrated educational environments (Bozkurt et al., 2023; Schmidt-Hertha, 2025; UNESCO, 2025). Model I of the hierarchical regression showed that demographic variables explained 21% of the variance in AI literacy with ICT competency emerging as a significant predictor (β = 0.535, p < .001) meaning ICT competency and LLL tendency are strong and significant predictors of AI literacy among PSTs. In contrast, gender, year of study, and AI tool usage were not significant predictors, indicating that these factors alone do not account for meaningful differences in AI literacy levels. While prior AI education efforts were primarily centered on university-level learners due to the advanced programming competencies required, more recent developments in age-appropriate tools and applications have made AI literacy increasingly accessible to a broader demographic, including younger learners (Ng et al., 2021; Yim, 2024). Aligned with this, the current study found no significant prediction of the year of study on AI literacy, meaning exposure to AI is not dependent on student’s academic progression. Integrating AI into everyday technologies and the availability of user-friendly learning tools may contribute to a more uniform distribution Lifelong learning predicting artificial intelligence literacy: A hierarchical multiple linear regression analysis USBED, 7(13), 2025, Sonbahar / Fall 472 of AI literacy across PSTs, regardless of how far along they are in their studies (Williams et al., 2024). Literature frequently highlights that gender influences technology acceptance and use (Fietta et al., 2022; Ong & Lai, 2006; Schepman & Rodway, 2022; World Economic Forum, 2024). Various studies have reported gender-based differences in technology acceptance and perception of AI, with female students being more influenced by factors such as computer selfefficacy, awareness of AI, ease of use, and positive expectations towards technology (Cachero et al., 2025; Ong & Lai, 2006; World Economic Forum, 2024; Yim, 2024). Moreover, previous research indicates that female students are more anxious and have little interest in using technology (Dai et al., 2020; Sindermann et al., 2020; World Economic Forum, 2024). However, the findings of this study contradict this assumption. Kaya et al. (2024) did not find any statistically significant gender differences in understanding AI concepts or attitudes toward AI, like the results of the current study. The difference between the findings of the previous studies and this study could reflect the current trend in rich technology use and familiarity across genders. The increased integration of AI technologies into everyday life and education may contribute to more equitable processes. In addition to this common trend, the characteristics of the participant group in the current study could explain the deficiency of a statistically significant gender prediction on AI literacy. The participant PSTs attend faculties and take a special education program and, therefore, possess a certain level of academic readiness. This shared educational background may minimize differences among general populations and lead to more uniform levels of AI literacy across genders. The current study exhibited that the frequency of AI tool usage was not a significant predictor. This finding could mean that AI tool usage alone does not account for meaningful differences in the AI literacy levels of PSTs. This result contradicts previous studies that emphasized that higher engagement with technologies enables people to understand and utilize that technology (Celik, 2023; Kale et al., 2018; Ung et al., 2022) and digital inequalities significantly influence digital literacies (Celik, 2023; Wang & Wu, 2021). ICT competency is a type of skill that falls under digital competencies (UNESCO, 2018; FaydaKinik, 2023). The current study exhibited that ICT competency is a significant predictor of AI literacy for PSTs. This finding emphasizes the role of general digital competency in shaping individuals’ ability to engage with recent technologies (Hava & Babayigit, 2025; Pu et al., 2024; Shiri, 2024). Strong ICT abilities can be a basis for growing AI integration in education, as Aylin KİRİŞÇİ-SARIKAYA USBED, 7(13), 2025, Sonbahar / Fall 473 demonstrated by Hava and Babayigit (2025). According to their findings, digital competencies substantially predicted teachers’ AI-related technical, pedagogical, and content knowledge (AITPACK). Accordingly, people who are eager and skillful in using ICTs are better at evaluating and understanding AI-powered systems. Celik (2023) found that teachers with higher technological knowledge of AI could better assess the ethical implications and decisions of AIrelated tools such as chatbots, intelligent tutoring systems, dashboards, and automated scoring software. After controlling demographic variables and ICT competency, LLL tendency even remained a significant predictor of AI literacy (β = 0.322, p < .001) in Model II, indicating a moderate and unique contribution to PSTs’ AI literacy levels. This final model, therefore, suggests that PSTs who exhibit stronger tendencies toward LLL are more likely to have higher levels of AI literacy, and demographic characteristics such as gender, year of study, and self-reported AI tool usage did not significantly contribute to the model. This finding coincides with previous studies that provide an increasing link between LLL and AI literacy. For instance, Humble (2023), in his scoping review, highlighted the nurturing readiness theme in the face of rapid technological advancements by noting that LLL is essential to preparing individuals, particularly educators, for an AI-integrated future. Similar to this, Ng (2021) stressed the importance of developing adult learning programs to integrate AI literacy into education, especially for teachers who do not have any prior expertise. Laupichler et al. (2022) also supported this claim and noted that various studies have conceptualized AI literacy as a multifaceted and evolving construct, and AI literacy links with adult education, higher education, and LLL. Their review has shown that AI literacy involves technical knowledge and reflective, evaluative, and adaptive competencies. These competencies are named as the core elements of LLL. This finding also connects well with the current research. In addition to these, earlier research revealed that AI literacy requires continuous professional learning. Teachers need to keep up with the latest developments to provide contemporary and effective teaching and overcome technological and structural barriers (Gong et al., 2020; Vazhayil et al., 2019). Both preservice and in-service teachers must keep pace with technological advancements and the demands of new educational ecosystems. In the context of the Fourth Education Revolution (Zhai, 2024), educators with strong AI literacy can enhance instruction and support students through AI-integrated education (Cavalcanti et al., 2021; Xu, 2021). These findings highlight AI literacy’s dynamic nature and its close relationship with LLL and reinforce the current study’s result. Lifelong learning predicting artificial intelligence literacy: A hierarchical multiple linear regression analysis USBED, 7(13), 2025, Sonbahar / Fall 474 Implications The current study provides several key implications for practitioners, policymakers, and researchers. Teacher education programs should integrate LLL activities like peer coaching, self-directed project work, and professional learning communities throughout their curricula. This integration would enhance PSTs’ commitment to continuous professional growth. Such models can promote LLL and support the development of competencies related to AI literacy. Since ICT competency significantly predicted AI literacy, several resources should be allocated to strengthen digital infrastructures and deliver comprehensive ICT professional development as a precursor to effective AI integration. From a policy perspective, teacher education curricula should include structured opportunities to develop AI literacy and LLL competencies through project-based learning, AI-focused workshops, and reflective digital portfolios. Educational policymakers should support the integration of AI literacy as a core component of teacher standards and provide sustained professional development pathways to ensure future educators remain adaptive in a rapidly changing technological landscape. By institutionalizing these strategies, initial teacher preparation programs can contribute to building a more agile, ethical, and digitally competent teaching workforce. Limitations and Further Considerations Several limitations of this study should be acknowledged. This research was based on quantitative data and methods. Although validated scales were used in the study, the findings were obtained based on self-reported data. Future studies on this subject may diversify the data and methods. Although the study’s sample size was sufficient for validity and reliability, a larger sample size would increase the generalizability. Although economical and time-efficient, convenience sampling, as a type of non-probability sampling method, is prone to selection bias. In the current study, this method resulted in a sample that is nonrepresentative of the gender distribution. The predominance of female participants is a crucial point of consideration, as research indicates that gender can play a role in technology acceptance and use (Cachero et al., 2025; Sindermann et al., 2020; World Economic Forum, 2024). As a result, the perspectives and experiences of male PSTs may not be fully reflected in this study. Researchers should target more balanced gender representation in future studies. Additionally, the current cross-sectional study drew data from only one context, which was PSTs. Future research could explore in-service teachers so that the positions of more Aylin KİRİŞÇİ-SARIKAYA USBED, 7(13), 2025, Sonbahar / Fall 475 experienced teachers in the profession can be revealed. Similar studies could have significant practical implications in fields where knowledge is rapidly evolving, such as health. Therefore, conducting similar research in these dynamic areas is also recommended. A further significant limitation of this study is its geographic restriction to PSTs in Türkiye. The findings are valuable within the Turkish context; however, they may not be generalizable to other countries due to substantial differences in educational systems, technological infrastructure, and socio-cultural factors that heavily influence teacher training and technology adoption. CONCLUSION AI-related tools are becoming widespread in the K-12 and higher education context. However, PSTs’ knowledge and skills in integrating AI in learning are little known. The LLL tendency of PSTs emphasizes their intellectual, social, emotional, and agentic development for addressing the challenges of a rapidly changing world in the age of AI. This study provided empirical evidence that PSTs with stronger LLL tendencies are significantly more likely to demonstrate higher levels of AI literacy. The findings indicated that LLL is positively associated with all dimensions of AI literacy—awareness, usage, evaluation, and ethics— highlighting the multifaceted benefits of fostering a LLL mindset among future educators. The hierarchical regression analysis further underscored the importance of ICT competency and LLL tendencies as significant predictors of AI literacy. These results suggested that developing PSTs’ commitment to continuous learning and enhancing their ICT skills can play a critical role in preparing them to engage with AI technologies in educational settings effectively. REFERENCES Ahn, H. Y. (2024). AI-powered e-learning for lifelong learners: impact on performance and knowledge application. Sustainability, 16(20), 9066. https://doi.org/10.3390/su16209066 Asad, M.M. & Aijaz, A. (2025). Influence of ChatGPT and generative AI on continuous learning and skill enhancement in Education 4.0: contextual insights from the higher education of Pakistan, The TQM Journal, Vol. ahead-of-print No. ahead-of-print. Retrieved April 08, 2025 from https://doi.org/10.1108/TQM-09-2024-0357 Bozkurt, A., Xiao, J., Lambert, S., Pazurek, A., Crompton, H., Koseoglu, S., Farrow, R., Bond, M., Nerantzi, C., Honeychurch, S., Bali, M., Dron, J., Mir, K., Stewart, B., Costello, E., Mason, J., Stracke, C. M., Romero-Hall, E., Koutropoulos, A.,…Jandrić, P. (2023). Lifelong learning predicting artificial intelligence literacy: A hierarchical multiple linear regression analysis USBED, 7(13), 2025, Sonbahar / Fall 476 Speculative futures on ChatGPT and generative artificial intelligence (AI): A collective reflection from the educational landscape. Asian Journal of Distance Education, 18(1), 53–130. https://doi.org/10.5281/zenodo.7636568 Bozkurt, A., & Sharma, R. C. (2023). Challenging the status quo and exploring the new boundaries in the age of algorithms: Reimagining the role of generative AI in distance education and online learning. Asian Journal of Distance Education, 18(1). https://asianjde.com/ojs/index.php/AsianJDE/article/view/714 Boztepe, O., & Demirtas, Z. (2016). The adaptation of lifelong learning scale into Turkish culture. Journal of Family Counseling and Education, 1(1), 10-17. https://doi.org/10.32568/jfce.238294 Cachero, C., Tomás, D. & Pujol, F. A. (2025). Gender bias in self-perception of artificial intelligence knowledge, impact, and support among higher education students: an observational study. ACM Transactions in Computer Education. (March 2025). Retrieved April 10, 2025 from https://doi.org/10.1145/3721295 Cavalcanti, A. P., Barbosa, A., Carvalho, R., Freitas, F., Tsai, Y. S., Gašević, D., & Mello, R. F. (2021). Automatic feedback in online learning environments: A systematic literature review. Computers and Education: Artificial Intelligence, 2, 100027. https://doi.org/10.1016/j.caeai.2021.100027 Celebi, C., Yilmaz, F., Demir, U., Karakus, F. (2023). Artificial intelligence literacy: An adaptation study. Instructional Technology and Lifelong Learning, 4(2), 291-306. https://doi.org/10.52911/itall.1401740 Celik, I. (2023). Exploring the determinants of artificial intelligence (Ai) literacy: Digital divide, computational thinking, cognitive absorption. Telematics and Informatics, 83, 102026. https://doi.org/10.1016/j.tele.2023.102026 Cetindamar, D., Kitto, K., Wu, M., Zhang, Y., Abedin, B., & Knight, S. (2022). Explicating AI literacy of employees at digital workplaces. Ieee Transactions on Engineering Management, 71, 810–823. https://doi.org/10.1109/TEM.2021.3138503 Creswell, J. W. (2014). Research design: Qualitative, quantitative, and mixed methods approaches. Sage Cukurova, M., & Miao, F. (2024). AI competency framework for teachers. UNESCO Publishing. https://unesdoc.unesco.org/ark:/48223/pf0000391104 Aylin KİRİŞÇİ-SARIKAYA USBED, 7(13), 2025, Sonbahar / Fall 477 Cummins, P., & Kunkel, S. (2015). A Global Examination of Policies and Practices for Lifelong Learning. New Horizons in Adult Education and Human Resource Development, 27(3), 3–17. https://doi.org/10.1002/nha3.20107 Dai, Y., Chai, C.-S., Lin, P.-Y., Jong, M. S.-Y., Guo, Y., & Qin, J. (2020). Promoting students’ well-being by developing their readiness for the artificial intelligence age. Sustainability, 12(16), 6597. https://doi.org/10.3390/su12166597 Delors, J. (1996). Learning: The treasure within; report to UNESCO of the International Commission on Education for the Twenty-first Century. UNESCO. Retrieved April 08, 2025 from https://unesdoc.unesco.org/ark:/48223/pf0000109590 diSessa, A. A. (2018). Computational literacy and “the big picture” concerning computers in mathematics education. Mathematical Thinking and Learning, 20(1), 3–31. https://doi.org/10.1080/10986065.2018.1403544 Fayda-Kinik, F. S. (2023). The impact of digital competences on academic procrastination in higher education: a structural equation modeling approach. Pegem Journal of Education and Instruction, 13(3), 25–35. https://doi.org/10.47750/pegegog.13.03.03 Field, A. (2013). Discovering statistics using IBM SPSS statistics (4th ed.). Sage Publications Fietta, V., Zecchinato, F., Di Stasi, B., Polato, M., & Monaro, M. (2022). Dissociation between users’ explicit and implicit attitudes toward artificial intelligence: An experimental study. IEEE Transactions on Human-Machine Systems, 52(3), 481–489. https://doi.org/10.1109/THMS.2021.3125280 Gong, X., Tang, Y., Liu, X., Jing, S., Cui, W., Liang, J., & Wang, F. Y. (2020, October). K-9 artificial intelligence education in Qingdao: Issues, challenges and suggestions. In 2020 IEEE international Conference on networking, Sensing and control (ICNSC) (pp. 1–6). IEEE. https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9238087 González-Anleo, J. M., Delbello, L., Martínez-Gonzálo, J. M., & Gómez, A. (2024). Sociodemographic impact on the adoption of emerging technologies. Journal of Small Business Strategy, 34(2), 42–50. https://doi.org/10.53703/001c.122089 Gummer, E. S., & Mandinach, E. B. (2015). Building a conceptual framework for data literacy. Teachers College Record, 117(4), 1–22. https://doi.org/10.1177/016146811511700401 Hagger, H., Burn, K., Mutton, T., & Brindley, S. (2008). Practice makes perfect? Learning to learn as a teacher. Oxford review of education, 34(2), 159–178. https://doi.org/10.1080/03054980701614978 Lifelong learning predicting artificial intelligence literacy: A hierarchical multiple linear regression analysis USBED, 7(13), 2025, Sonbahar / Fall 478 Hava, K., & Babayigit, O. (2025). Exploring the relationship between teachers’ competencies in AI-TPACK and digital proficiency. Education and Information Technologies, 30, 3491–3508. https://doi.org/10.1007/s10639-024-12939-x Hong, W., Chan, F. K. Y., Thong, J. Y. L., Chasalow, L., & Dhillon, G. (2014). A framework and guidelines for context-specific theorizing in information systems research. Information Systems Research, 25(1), 111–136. http://hdl.handle.net/20.500.12424/4276068 Huck, S. W. (2012). Reading Statistics and Research (6th ed.). Pearson. Humble, N. (2023). A lifelong learning perspective on artificial intelligence: What do we need to know?. In ICERI2023 Proceedings (pp. 595–602). IATED. https://doi.org/10.21125/iceri.2023.0212 Jarvis, P. (2004). Adult education and lifelong learning: Theory and practice. Third edition. Routledge. Kacperski, C., Ulloa, R., Bonnay, D., Kulshrestha, J., Selb, P., & Spitz, A. (2025). Characteristics of ChatGPT users from Germany: Implications for the digital divide from web tracking data. PloS one, 20(1), e0309047. https://doi.org/10.1371/journal.pone.0309047 Kaya, F., Aydin, F., Schepman, A., Rodway, P., Yetisensoy, O & Kaya, M. D. (2024). The roles of personality traits, ai anxiety, and demographic factors in attitudes toward artificial intelligence, International Journal of Human–Computer Interaction, 40(2), 497–514. https://doi.org/10.1080/10447318.2022.2151730 Kumar, K., & Thakur, G. S. M. (2012). Advanced applications of neural networks and artificial intelligence: A review. International journal of information technology and computer science, 4(6), 57–68. https://doi.org/10.5815/ijitcs.2012.06.08 Kuo, I. H., Rabindran, J. M., Broadbent, E., Lee, Y. I., Kerse, N., Stafford, R. M., & MacDonald, B. A. (2009, September). Age and gender factors in user acceptance of healthcare robots. In RO-MAN 2009-The 18th IEEE International Symposium on Robot and Human Interactive Communication (pp. 214–219). IEEE. https://ieeexplore.ieee.org/document/5326292 Laal, M. (2011). Lifelong learning: What does it mean?. Procedia-Social and Behavioral Sciences, 28, 470–474. https://doi.org/10.1016/j.sbspro.2011.11.090 Aylin KİRİŞÇİ-SARIKAYA USBED, 7(13), 2025, Sonbahar / Fall 479 Laugksch, R. C. (2000). Scientific literacy: A conceptual overview. Science education, 84(1), 71–94. https://doi.org/10.1002/(SICI)1098-237X(200001)84:1<71::AIDSCE6>3.0.CO;2-C Laupichler, M. C., Aster, A., Schirch, J., & Raupach, T. (2022). Artificial intelligence literacy in higher and adult education: A scoping literature review. Computers and Education: Artificial Intelligence 3, 100101. https://doi.org/10.1016/j.caeai.2022.100101 Luckin, R., Holmes, W., Griffiths, M., & Forcier, L. B. (2016). Intelligence Unleashed: An Argument for AI in Education. Pearson. Retrieved April 18, 2025 from https://static.googleusercontent.com/media/edu.google.com/en//pdfs/IntelligenceUnleashed-Publication.pdf McCarthy, J., Minsky, M., Rochester, N., & Shannon, C. (1955). A proposal for Dartmouth Summer Project on artificial Intelligence. Retrieved April 28, 2025 from http://jmc.stanford.edu/articles/dartmouth/dartmouth.pdf Méndez-Suárez, M., Monfort, A., & Hervas-Oliver, J. L. (2023). Are you adopting artificial intelligence products? Social-demographic factors to explain customer acceptance. European Research on Management and Business Economics, 29(3), 100223. https://doi.org/10.1016/j.iedeen.2023.100223 Ng, D. T. K., Leung, J. K. L., Chu, S. K. W., & Qiao, M. S. (2021). Conceptualizing AI literacy: An exploratory review. Computers and Education: Artificial Intelligence, 2, 100041. https://doi.org/10.1016/j.caeai.2021.100041 Norman, G. (2010). Likert scales, levels of measurement and the “laws” of statistics. Advances in Health Sciences Education, 15(5), 625–632. https://doi.org/10.1007/s10459-0109222-y OECD (2021). OECD Skills Outlook 2021: Learning for Life. OECD Publishing. https://doi.org/10.1787/0ae365b4-en. OECD (2024). Compendium of Legal Instruments of the OECD Recommendation of the Council on Artificial Intelligence. Retrieved March 22, 2025 from https://legalinstruments.oecd.org/en/instruments/OECD-LEGAL-0449 Ong, C. S., & Lai, J. Y. (2006). Gender differences in perceptions and relationships among dominants of e-learning acceptance. Computers in Human Behavior, 22(5), 816–829. https://doi.org/10.1016/j.chb.2004.03.006 Pallant, J. (2020). SPSS Survival Manual: A step by step guide to data analysis using IBM SPSS (7th ed.). Routledge. https://doi.org/10.4324/9781003117452