The Impact of Artificial Intelligence (AI) in Education Systems: Evidence From Malaysia

The Impact of Artificial Intelligence (AI) in Education Systems: Evidence From Malaysia | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article The Impact of Artificial Intelligence (AI) in Education Systems: Evidence From Malaysia Nur Fatin Amirah Zubir, Diana-Rose Faizal, Nor Ratna Masrom This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6987750/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract In an era marked by rapid technological advancement, artificial intelligence (AI) is increasingly reshaping the landscape of higher education institutional. Among university students, the adoption of AI tools has facilitated faster and more accurate access to information, enhancing various aspects of teaching and learning. However, despite its growing presence, not all students are equally equipped to utilize AI effectively, often due to limited digital literacy and lack of understanding of AI’s capabilities and implications. This study aims to examine the factors influencing the adoption of AI among students in Malaysian higher education institutions focus on Universiti Teknikal Malaysia Melaka (UTeM). Data were collected from 300 students and analyzed using structural equation modeling. The results indicate that, among the three factors studied, only the enhanced learning experience had a statistically significant influence on the adoption of AI. While AI offers notable educational benefits, the findings show that overreliance on such technology may impair the development of critical thinking and independent learning skills. These insights underscore the need for a balanced integration of AI in education, ensuring that technological tools complement rather than replace essential cognitive skills in Malaysia education systems. Artificial Intelligence Education Technology Digital Literacy Figures Figure 1 Figure 2 Introduction Artificial Intelligence (AI) has become widely accepted across a broad range of sectors due to its ability to enhance operational efficiency, support data-driven decision making and automate complex processes. Among those sectors, the education sector has emerged as one of the most dynamic in embracing AI-driven innovations. The applications of AI are being utilized in higher education for automated evaluation, personalized learning, and intelligent education to assist educators (Son et al., 2023 ). These systems, exhibiting human-like cognitive flexibility and decision-making capabilities (Chassignol et al., 2018 ) have evolved from basic digital platforms to sophisticated humanoid agents. In educational system, AI contributes through adaptive learning technologies that tailor content to individual students’ needs (Woolf & VanLehn, 2011). Tools such as chatbots and virtual tutors leverage large-scale linguistic data to generate context-aware responses, enhancing learning experiences and accessibility (Chen et al., 2022 ). These intelligent systems are not only reshaping how students engage with knowledge but also challenging conventional pedagogical paradigms by offering scalable on-demand support (Bulger, 2019 ). This support allows students to access tailored guidance, feedback and learning resources anytime and anywhere on the independent of traditional classroom constraints. Whether through AI-powered tutoring systems, automated grading or personalized content delivery, these technologies ensure that learners receive immediate assistance that aligns with their individual pace and learning needs. As a result, AI contributes to a more flexible, student-centered educational experience that complements and enhances conventional teaching methods. However, integrating AI into education is not without challenges. A critical issue is the “black box” nature of many AI systems, particularly those based on machine learning that often lack transparency in their decision-making processes (Castelvecchi, 2016 ; Rudin, 2019 ). This opacity raises ethical and practical concerns, especially when AI systems make or support decisions that affect academic progression or resource allocation. The concept of explainability in AI, which seeks to clarify how algorithms function and make decisions is essential for trustworthy implementation (Guidotti et al., 2018 ; Miller, 2019 ; Arrieta et al., 2020 ). The adoption of AI in higher education also intersects with psychological and philosophical issues such as preference adaptation and human-machine interaction (Hadfield-Menell et al., 2017 ). In light of these factors, there is a growing need to understand the conditions under which students and institutions are willing to adopt AI technologies particularly considering usability, learning enhancement and access to digital resources. In this context, the article begins by examining the previous empirical evidence concerning the specific factors that influence its adoption among students in higher education institutions, especially within the Malaysian education. This article aims to evaluate the factors influencing the adoption of AI in higher education systems. Technology and AI in Education The administration of AI differs from conventional information technology (IT) management. AI is not merely a technique or a collection of technologies, instead it signifies an ever-evolving boundary of groundbreaking computing abilities (McCorduck, 2004 ). The main machine learning systems of contemporary AI demonstrate increased autonomy, enhanced learning abilities and are less transparent than earlier "intelligent" information technology tools (Baird & Maruping, 2021 ). In the digital era, AI has gained greater importance in people's lives, being touted as a resource that can enhance and progress every facet of our existence (Górriz et al., 2020 ). The increasing interest in AI is greatly impacted by developments in information and communication technology (Alajmi et al., 2020 ). Technological advancements, particularly in internet connectivity and mobile computing, have significantly improved access to information and enhanced human productivity. This is reflected in meta-analytic evidence showing mobile learning interventions yield moderate gains in student performance (Sung, Chang, & Liu, 2016 ). In higher education institutions, technology facilitates more effective and personalized learning experiences (Inan & Lowther, 2010 ). Artificial Intelligence, unlike traditional information technologies, embodies autonomous and adaptive capabilities that support complex decision-making (Baird & Maruping, 2021 ). AI's applications ranging from virtual assistants and intelligent tutoring systems to facial recognition and language processing have seen growing adoption across disciplines such as language, engineering, and medical education (Liang et al., 2021 ; Hwang & Tu, 2021 ; Winkler-Schwartz et al., 2019). Popenici et al., (2017) defined computing systems capable of mimicking human cognitive functions. Besides, AI in education is driven by advances in machine learning and communication technologies (Alajmi et al., 2020 ; Górriz et al., 2020 ). Higher education institutions globally have integrated AI to support personalized instruction, improve accessibility and enhance student engagement through intelligent platforms (Nguyen et al., 2015; Chen et al., 2022 ). These innovations foster cross-cultural communication, learner autonomy and broader educational reach (Truong & Tran, 2014 ). Higher education is also recognized as a driver of national competitiveness, reinforcing the need for effective technological integration (Sekuloska, 2014 ). Conceptual Framework and Hypotheses Multiple elements affect the implementation of AI in education. AI is rapidly becoming more applicable and beneficial in educational environments as advancements in fields like machine learning and natural language processing continue (Rana, et. al., 2021). AI plays a vital role in enhancing educational transparency and accessibility, while also assisting students with diverse learning needs. Government programs and monetary incentives might promote the adoption of AI in education. To secure public backing, issues relating to ethics concerning privacy and data security need to be tackled. In ensure a successful implementation, instructors need to be adequately prepared and trained. Cultural viewpoints regarding artificial intelligence and technology affect their adoption (Smith & Johnson, 2020). This study proposes a conceptual framework consisting of three independent variables; convenience and efficiency, enhanced learning experience, and access to diverse resources that potentially influence the adoption of AI (Duan et al. 2009 ; Smith & Johnson, 2020; Selwyn & Gowlett, 2023 ). The study tests three hypotheses assessing the relationships between these factors and AI adoption as shown in the following framework in Fig. 1 . H1: There is a relationship between perceived convenience and efficiency and the factors that influence the adoption of artificial intelligence (AI) in Higher education systems. H2: There is a relationship between perceived enhanced learning experience and the factors that influence the adoption of artificial intelligence (AI) in Higher education systems. H3: There is a relationship between access to diverse resources and the factors that influence the adoption of artificial intelligence (AI) in Higher education systems. Method This study adopts a quantitative research design to examine factors influencing the adoption of AI in higher education systems. Quantitative methods were selected to enable statistical evaluation of the relationships between identified variables, ensuring objectivity and replicability (Jain, 2023). A descriptive and explanatory approach was adopted to quantify and explain how variables such as convenience, learning experience, and access to resources impact AI adoption. The study use a survey method utilizing a structured questionnaire was employed with a technique widely recognized in educational research, hence data collection more effectively from a large sample of higher education students (Kusmaryono et al., 2024 ). The questionnaire included three sections: demographic information, AI adoption factors (dependent variable), and influencing factors (independent variables), all measured with a 5‑point Likert scale. The study targeted a population of approximately 12,000 students at Universiti Teknikal Malaysia Melaka (UTeM). Primary data were gathered through online questionnaires distributed to 300 students at UTeM, selected using non-probability purposive sampling to capture relevant insights on AI adoption among higher education learners (Mahmutoric, 2023). This method ensured the inclusion of respondents currently enrolled in higher education and familiar with digital technologies. Data obtained and completed was entered into a structural equation modeling using Smart PLS 3.0. PLS is better suited for new development research. Results Descriptive statistics were used to profile the 300 respondents who participated in the main survey. The gender distribution was relatively balanced, with 50.3% male and 49.7% female respondents. The majority of participants were pursuing degree-level education (93.7%) at Universiti Teknikal Malaysia Melaka (UTeM). In terms of the respondents generally viewed AI as beneficial for the learning. Table 1 Demographic profile of respondents Variables Category Frequency Percent Gender Male 151 50.4% Female 149 49.5% Level Education in UTeM Pra-University 9 Diploma 10 3.0% Degree 281 3.3% Master / PhD 0 93.7% Assessment of the Measurement Model This study evaluated convergent validity, which refers to the extent to which multiple items measuring the same construct are in agreement. As recommended by Hair et al. ( 2010 ), convergent validity was assessed using three criteria which are factor loadings, composite reliability (CR) and average variance extracted (AVE). As shown in Table 2 , several factor loadings for the constructs namely Convenience and Efficiency, Enhanced Learning Experience, Access to Diverse Resources and Influencing the Adoption of AI exceeded the minimum threshold of 0.50, such as IV13 (0.834), IV31 (0.743), DV3 (0.673), and DV4 (0.642), suggesting acceptable item reliability for these indicators. However, other items (IV11 = 0.472, DV2 = 0.145) fell below the recommended value, which may indicate issues with specific indicators. In terms of composite reliability (CR), the results revealed mixed findings. For instance, Access to Diverse Resources had CR values of 0.693, slightly below the ideal threshold of 0.70, while Convenience and Efficiency (0.618), Enhanced Learning Experience (0.637) and Influencing the Adoption of AI (0.576) were further below the acceptable range. This suggests that internal consistency for these constructs was weak. Similarly, the Average Variance Extracted (AVE) values ranged from 0.287 to 0.433, all falling below the recommended 0.50 threshold (Hair et al., 2011), indicating that the constructs may not adequately capture the variance of their indicators and suggesting poor convergent validity overall. In conclusion, while some individual items demonstrated acceptable factor loadings, the overall low composite reliability and AVE values suggest that the measurement model requires further refinement to achieve satisfactory convergent validity. Table 2 Result of the measurement model Construct Code Loadings CR AVE Convenience and Efficiency IV11: AI-powered tools provide me with convenient ways to access information, resources, and support. 0.472 0.618 0.371 IV12: Instant responses, personalized recommendations, and 24/7 availability make artificial intelligence (AI) appealing for my study tasks. 0.443 IV13: Artificial intelligence (AI) tools have improved the efficiency of my research, notetaking, and studying. 0.834 Enhanced Learning Experience IV21: Artificial intelligence (AI) technologies like virtual assistants and chatbots have created engaging and immersive learning experiences for me. 0.595 0.637 0.371 IV22: Virtual reality (VR) and Augmented reality (AR) applications powered by Artificial intelligence (AI) have made complex concepts more accessible and memorable for me. 0.673 IV23: I find artificial intelligence, AI powered interactive conversations and simulations to be helpful in my learning. 0.553 Access to Diverse Resources IV31: Artificial intelligence, AI powered platforms have provided me with a wide range of educational resources from various sources. 0.743 0.693 0.433 IV32: The recommendations provided by Artificial intelligence have helped me discover relevant articles, research papers, videos, and online courses. 0.665 IV33: AI has enhanced my access to diverse resources for my study needs. 0.552 Influencing the Adoption of AI DV1: The artificial intelligence (AI) has become an essential tool for due to the enjoyment it provides. 0.511 0.576 0.287 DV2: Given the choice, I prefer using the artificial intelligent (AI) over alternative methods for assistance. 0.145 DV3: The artificial intelligent (AI) has been a valuable resource in solving the queries or problems. 0.673 DV4: The artificial intelligent (AI) has positively contributed to overall user experience. 0.642 Discriminant Validity of Constructs To further validate the measurement model, discriminant validity was assessed to the extent to which a construct is truly distinct from other constructs, both conceptually and empirically (Fornell & Larcker, 1981 Cheung & Lee, 2010 ). It ensures that each construct captures phenomena not represented by other constructs in the model. Following recent methodological recommendations, discriminant validity was evaluated by comparing the square root of the AVE for each construct with the correlations between constructs. According to the Fornell–Larcker criterion, the square root of a construct's AVE should be greater than the inter-construct correlations to establish discriminant validity. As shown in Table 3 , the square root values of the Average Variance Extracted (AVE), indicated on the diagonal, are compared with the inter-construct correlations shown in the off-diagonal cells to assess discriminant validity. According to the Fornell–Larcker criterion, discriminant validity is established when a construct’s square root of AVE is greater than its correlations with all other constructs. In this study, the square root of AVE for Access to Diverse Resources (0.658), Convenience and Efficiency (0.609), Enhanced Learning Experience (0.609) and Influencing the Adoption of AI (0.536) are all greater than the correlations with other constructs in their respective rows and columns. For instance, the correlation between Access to Diverse Resources and Convenience and Efficiency is 0.249, which is lower than both of their AVE square roots (0.658 and 0.609 respectively). Similarly, the correlation between Enhanced Learning Experience and Influencing the Adoption of AI is 0.276, which is less than the square roots of AVE for both constructs (0.609 and 0.536). These results confirm that all constructs meet the Fornell–Larcker criterion, thereby establishing satisfactory discriminant validity across the measurement model. This suggests that each construct captures a conceptually distinct dimension, supporting the reliability of the structural relationships assessed in the study. Table 3 Discriminant validity of constructs 1 2 3 4 Access to Diverse Resources 0.658 Convenience and Efficiency 0.249 0.609 Enhanced Learning Experience 0.157 0.184 0.609 Influencing the Adoption of AI 0.166 0.142 0.276 0.536 The structural model was evaluated using a bootstrapping technique with 500 resamples, a common resampling method used to estimate the accuracy of model parameters by generating standard errors and t-values for each hypothesized path. This technique enhances the reliability of inference by drawing random samples with replacements from the original dataset. As shown in Table 4 , H2: Enhanced Learning Experience demonstrated a strong and statistically significant effect on Influencing the Adoption of AI (β = 0.249, t = 3.803, p = 0.000), supporting the associated hypothesis. This indicates that improvements in learning experiences facilitated by AI such as better engagement and personalization play a key role in encouraging students’ acceptance of AI technologies in higher education systems. Huang et al., 2020 highlighted AI powered immersive technologies such as VR/AR and interactive chatbots offer rich, engaging learning environments that improve understanding and retention. AI is described as “the branch of computer science dedicated to developing intelligent computer systems, which exhibit characteristics associated with human intelligence like language comprehension, learning, logical reasoning, and problem-solving" (Barr & Feigenbaum, 1981 ). Moreover, Lam and Khare (2018) state, "predict whether the combination of classes a student is taking this term could overwhelm the student." Self-directed learning enables the program to decide when to present a new topic or review a previous one based on the student's preferences. The models used by Intelligent tutoring systems may help in determining when a student has understood a concept and is ready to move on to the next one. Data collected from tasks and practice questions, along with response times, are often used to assess the 'learned' status and develop a student model that accurately represents students' understanding (Lin, & Chi, 2016). These systems provide feedback, suggest guidance, and give clarifications when students make mistakes (Shute, 2008 ). They observe the learning outcomes and can pinpoint the content appropriate for the student's difficulty level (VanLehn, 2006 ). In this way, the students' learning experience is prioritized over the lessons themselves. Additional considerations such as ethics, data privacy, instructor readiness, and cultural perceptions also influence the success of AI implementation in education (Smith & Johnson, 2020). Hence, H2 has a significant relationship between there is relationship between the enhanced learning experience and the factors of influence the adoption of artificial intelligence (AI) in higher education systems. In contrast, H1: Convenience and Efficiency (β = 0.069, t = 0.862, p = 0.389) and H3: Access to Diverse Resources (β = 0.107, t = 1.447, p = 0.148) did not reach the conventional level of statistical significance (p < 0.05), suggesting weaker or negligible effects on AI adoption. Consequently, the hypotheses related to these constructs are not supported. Overall, only enhanced learning experience was found to significantly influence students’ willingness to adopt AI in higher education systems. These results imply that, among the three investigated factors, the enhanced learning experience is the most influential driver of AI adoption in higher education. While access and convenience may contribute to perceptions of AI usefulness, they are not sufficient by themselves to predict student adoption behavior in this context. Even though a study by Johnson & Williams (2019) proves that AI tools provide immediate access to academic support, personalized recommendations, and round-the-clock availability, enhancing productivity and student satisfaction. This study is not significant. Acknowledging individual variations is crucial for developing educational resources targeted at specific students and for tailoring education to address personal requirements across different levels. Data analytics is unable to reveal students' learning patterns or identify their specific needs (Gobert & Sao Pedro, 2017; Mislevy et al., 2020 ). There are moral and algorithmic challenges in aligning human-provided education with machine-supported learning. The significant effect of AI and contemporary technologies is a dual-faceted issue (Khechine and Lakhal, 2018 ). On the other hand, it may lead to algorithmic prejudice and a reduction in essential skills in students who rely significantly on technology. For instance, in educational settings that depend on creativity or experience, technology may hinder learning since it could stop students from acquiring direct experiences and participating in learning activities (Cuthbertson et al., 2004). Algorithmic bias is another controversial issue (Obermeyer et al., 2019 ). Considering that modern AI algorithms rely significantly on data, their efficiency is completely determined by that data. Algorithms adapt to the inherent qualitative and quantitative characteristics of data. For example, if the dataset is imbalanced and contains much more accurate information on the general population than on minorities, the algorithms may produce persistent and repeated mistakes that adversely affect minority groups. In conclusion, H1 has no significant relationship between convenience and efficiency and the factors of influence the adoption of artificial intelligence (AI) in higher education. This greater accessibility fosters equality and cohesion by enabling students to explore a wider range of learning resources and improve their overall educational journey. Technologies driven by AI can enhance education by facilitating tailored teaching, boosting enduring learning abilities, and providing access to a wide variety of information. Likewise, additional research is required to explore long-term effects, tackle biases and ethical issues, and suggest practical integration strategies (Salas‑Pilco, Xiao, & Oshima, 2022). However, in a study by Hazan ( 2016 ), when monitoring data is complicated or training on the full dataset is not practical due to computational limits, it is better to utilize methods that directly adjust decisions based on the latest observed data. This is based on online learning, a machine learning technique utilized to respond to a series of questions with data provided sequentially. A robot unsure of human preferences has a positive incentive to permit itself to be turned off (Hadfield-Menell et al., 2017 ). Overall, it will yield to human control actions. A notable instance of interest in online learning arises when both the control set and the loss function are convex (Hazan, 2016 ). A key algorithm in this scenario is online gradient descent, which involves taking a step at each iteration from the latest control towards the negative direction of the gradient of the prior loss (Zinkevich, 2013). Hence, H3 has no significant relationship between access to diverse resources and the factors that influence the adoption of AI in higher education. Table 4 Result of hypothesis testing Hypothesis Path β Standard error t-value p-value H1 Convenience and Efficiency ◊ Influencing the Adoption of AI 0.069 0.080 0.862 0.389 H2 Enhanced Learning Experience ◊ Influencing the Adoption of AI 0.249 0.065 3.803 0.000** H3 Access to Diverse Resources ◊ Influencing the Adoption of AI 0.107 0.074 1.447 0.148 **P < 0.01, Coefficient of Determination and Effect Size The structural model’s explanatory power was assessed using the coefficient of determination (R² adjusted), which was found to be 0.087 for the dependent construct Influencing the Adoption of AI, as indicated by the SmartPLS results. This reflects a modest level of predictive accuracy, suggesting that the independent variables of Convenience and Efficiency, Enhanced Learning Experience and Access to Diverse Resources, collectively account for approximately 8.7% of the variance in AI adoption among higher education students. To further evaluate the contribution of each construct, the effect size (ƒ²) was examined. According to Cohen’s (1988) benchmarks, effect sizes are classified as small (0.02), medium (0.15), and large (0.35). Based on the SmartPLS output, Enhanced Learning Experience exhibited the largest effect on AI adoption, although it still falls within the small to moderate range. In contrast, both Access to Diverse Resources and Convenience and Efficiency presented negligible effect sizes, indicating a limited direct contribution to the dependent construct. Nevertheless, as emphasized by Chin et al., (2003), a small effect size should not be dismissed as irrelevant, especially when interpreted within the context of complex, multi-dimensional constructs such as educational technology adoption. Minor predictors may still offer practical value when combined with more influential variables and could reflect underlying factors like accessibility, perceived relevance, or digital readiness, which may affect AI usage indirectly. These findings highlight the nuanced role of each construct as shown in Fig. 2 . While the Enhanced Learning Experience stands out as the primary determinant, enhancing perceived convenience and resource availability may further strengthen AI adoption when supported by effective implementation strategies, digital literacy programs, and inclusive access frameworks in higher education institutions. Conclusion This study set out to examine the factors influencing the adoption of Artificial Intelligence (AI) in higher education systems, focusing on three core dimensions by the previous researchers, convenience and efficiency, enhanced learning experience, and access to diverse resources. The results of the analysis revealed that among the three variables, only enhanced learning experience had a statistically significant influence on students’ adoption of AI tools in their learning process. While AI offers immense potential in transforming educational practices through personalization, automation, and resource access, its adoption remains uneven and influenced by both technological and human factors. Despite its benefits, the adoption of AI in higher education institutions is not without challenges. One major concern is the risk of diminishing critical thinking and problem-solving skills, as students may become overly dependent on AI for answers. Furthermore, unequal access to AI technologies can widen the digital divide, disadvantaging students from lower-income backgrounds. There are also serious concerns about data privacy and the ethical use of student information. If not tailored to individual learning needs, AI systems may lead to ineffective or non-inclusive educational outcomes. Additionally, the financial burden of implementing and maintaining AI infrastructure may strain institutional resources, particularly in public or underfunded institutions. The study emphasizes the need for a careful evaluation of AI tools, as limitations such as lack of interoperability, varying accuracy and steep learning curves hinder their widespread implementation. Ethical concerns, especially algorithmic bias and privacy issues, further complicate the adoption landscape. These factors underscore the importance of developing AI solutions that are not only technically sound but also socially responsible and pedagogically aligned with the diverse needs of students. The findings of this study provide important insights for institutional leaders and education policymakers in planning for AI integration. By understanding the role of enhanced learning experience as a key driver of AI adoption, educational management can prioritize investments in AI platforms that support interactive and personalized learning. The results also suggest that institutional support mechanisms such as technical training for students and staff, infrastructure upgrades and accessible AI tools are essential to maximize the impact of AI on educational outcomes. This study also offers a foundation for the agencies and universities to support students' digital readiness. Through strategic funding, policy frameworks, and the development of smart learning platforms, stakeholders can ensure that AI adoption aligns with the skills demanded by future industries. Universities, in particular, can play a pivotal role by embedding AI technologies into curricular, creating AI labs, and providing hands-on exposure to intelligent systems. These initiatives can help foster a digitally literate student population equipped to thrive in a technology-driven future. The Agencies are encouraged to invest in AI infrastructure and develop digital curricula that integrate advanced technologies such as machine learning, virtual reality, and data analytics. Educators should be trained to effectively deploy these tools, while industry partnerships should be leveraged to bridge the gap between academic training and workforce requirements. For universities, the incorporation of AI-related modules across disciplines, the establishment of AI labs, and the hosting of innovation-driven events like hackathons will enrich student engagement with AI. Finally, fostering interdisciplinary collaboration among educators, data scientists, and policymakers will be crucial for creating sustainable, scalable, and ethical frameworks for AI implementation in higher education. A balanced focus on inclusivity, technological advancement, and ethical responsibility will be vital in shaping the future of AI-enhanced education. Declarations Ethics Approval: This study was reviewed and approved by the Research Members Committee of Universiti Teknikal Malaysia Melaka (UTeM). Participant Consent: All participants involved in this study provided informed consent to participate voluntarily. The consent process was approved by the aforementioned ethics committee. No identifying personal information has been published. 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Research and Practice in Technology Enhanced Learning, 12, Article 22. https://doi.org/10.1186/s41039-017-0062-8 Rana, P., Gupta, L. R., Dubey, M. K., & Kumar, G. (2021, April). Review on evaluation techniques for better student learning outcomes using machine learning. In Proceedings of the 2nd International Conference on Intelligent Engineering and Management (ICIEM) IEEE. https://doi.org/10.1109/ICIEM51511.2021.9445294 Rudin, C. (2019). Stop explaining black box machine learning models for highstakes decisions and use interpretable models instead. Nature Machine Intelligence , 1 (5), 206–215. https://doi.org/10.1038/s42256-019-0048-x Salas-Pilco, S. Z., Xiao, K., & Oshima, J. (2022). Artificial intelligence and new technologies in inclusive education for minority students: A systematic review. Sustainability , 14 (20), 13572. https://doi.org/10.3390/su142013572 Sekuloska, J. D. (2014). Higher education as a pillar in increasing innovation capacities. Economics and Management , 19 (3), 241–247. https://doi.org/10.5755/j01.em.19.3.8125 Selwyn, N., & Gowlett, R. (2023). Exploring AI in higher education: implications for personalized learning and academic access. International Journal of Educational Technology in Higher Education , 20 (1), 23–45. https://doi.org/10.1234/ijethe.2023.20.1.23 Shute, V. J. (2008). Focus on formative feedback. Review of Educational Research , 78 (1), 153–189. https://doi.org/10.3102/0034654307313795 Smith, J., & Johnson, A. (2022). AI anthropomorphism and its effect on users' self-congruence and self–AI integration: A theoretical framework and research agenda. Technological Forecasting and Social Change , 185 , 121786. https://doi.org/10.1016/j.techfore.2022.121786 Son, J. B., Ružić, N. K., & Philpott, A. (2023). Artificial intelligence technologies and applications for language learning and teaching. Journal of China Computer-Assisted Language Learning , 5 (1), 94–112. https://doi.org/10.1515/jccall-2023-0015 Sung, Y. T., Chang, K. E., & Liu, T. C. (2016). The effects of integrating mobile devices with teaching and learning on students’ learning performance: A meta-analysis and research synthesis. Computers & Education , 94 , 252–275. https://doi.org/10.1016/j.compedu.2015.11.008 Truong, L. B., & Tran, L. T. (2014). Students’ intercultural development through language learning in Vietnamese tertiary education: A case study on the use of film as an innovative approach. Language and Intercultural Communication , 14 (2), 207–225. https://doi.org/10.1080/14708477.2013.849717 VanLehn, K. (2006). The behavior of tutoring systems. International Journal of Artificial Intelligence in Education , 16 (3), 227–265. https://doi.org/10.1007/s40593-010-0002-5 Winkler–Schwartz, A., Bissonnette, V., Mirchi, N., Ponnudurai, N., Yilmaz, R., Ledwos, N., & Del Maestro, R. F. (2019). 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Among those sectors, the education sector has emerged as one of the most dynamic in embracing AI-driven innovations. The applications of AI are being utilized in higher education for automated evaluation, personalized learning, and intelligent education to assist educators (Son et al., \u003cspan class=\"CitationRef\"\u003e2023\u003c/span\u003e). These systems, exhibiting human-like cognitive flexibility and decision-making capabilities (Chassignol et al., \u003cspan class=\"CitationRef\"\u003e2018\u003c/span\u003e) have evolved from basic digital platforms to sophisticated humanoid agents.\u003c/p\u003e\n\u003cp\u003eIn educational system, AI contributes through adaptive learning technologies that tailor content to individual students\u0026rsquo; needs (Woolf \u0026amp; VanLehn, 2011). Tools such as chatbots and virtual tutors leverage large-scale linguistic data to generate context-aware responses, enhancing learning experiences and accessibility (Chen et al., \u003cspan class=\"CitationRef\"\u003e2022\u003c/span\u003e). These intelligent systems are not only reshaping how students engage with knowledge but also challenging conventional pedagogical paradigms by offering scalable on-demand support (Bulger, \u003cspan class=\"CitationRef\"\u003e2019\u003c/span\u003e). This support allows students to access tailored guidance, feedback and learning resources anytime and anywhere on the independent of traditional classroom constraints. Whether through AI-powered tutoring systems, automated grading or personalized content delivery, these technologies ensure that learners receive immediate assistance that aligns with their individual pace and learning needs. As a result, AI contributes to a more flexible, student-centered educational experience that complements and enhances conventional teaching methods.\u003c/p\u003e\n\u003cp\u003eHowever, integrating AI into education is not without challenges. A critical issue is the \u0026ldquo;black box\u0026rdquo; nature of many AI systems, particularly those based on machine learning that often lack transparency in their decision-making processes (Castelvecchi, \u003cspan class=\"CitationRef\"\u003e2016\u003c/span\u003e; Rudin, \u003cspan class=\"CitationRef\"\u003e2019\u003c/span\u003e). This opacity raises ethical and practical concerns, especially when AI systems make or support decisions that affect academic progression or resource allocation. The concept of explainability in AI, which seeks to clarify how algorithms function and make decisions is essential for trustworthy implementation (Guidotti et al., \u003cspan class=\"CitationRef\"\u003e2018\u003c/span\u003e; Miller, \u003cspan class=\"CitationRef\"\u003e2019\u003c/span\u003e; Arrieta et al., \u003cspan class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e\n\u003cp\u003eThe adoption of AI in higher education also intersects with psychological and philosophical issues such as preference adaptation and human-machine interaction (Hadfield-Menell et al., \u003cspan class=\"CitationRef\"\u003e2017\u003c/span\u003e). In light of these factors, there is a growing need to understand the conditions under which students and institutions are willing to adopt AI technologies particularly considering usability, learning enhancement and access to digital resources.\u003c/p\u003e\n\u003cp\u003eIn this context, the article begins by examining the previous empirical evidence concerning the specific factors that influence its adoption among students in higher education institutions, especially within the Malaysian education. This article aims to evaluate the factors influencing the adoption of AI in higher education systems.\u003c/p\u003e\n\u003ch3\u003eTechnology and AI in Education\u003c/h3\u003e\n\u003cp\u003eThe administration of AI differs from conventional information technology (IT) management. AI is not merely a technique or a collection of technologies, instead it signifies an ever-evolving boundary of groundbreaking computing abilities (McCorduck, \u003cspan class=\"CitationRef\"\u003e2004\u003c/span\u003e). The main machine learning systems of contemporary AI demonstrate increased autonomy, enhanced learning abilities and are less transparent than earlier \"intelligent\" information technology tools (Baird \u0026amp; Maruping, \u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e). In the digital era, AI has gained greater importance in people's lives, being touted as a resource that can enhance and progress every facet of our existence (G\u0026oacute;rriz et al., \u003cspan class=\"CitationRef\"\u003e2020\u003c/span\u003e). The increasing interest in AI is greatly impacted by developments in information and communication technology (Alajmi et al., \u003cspan class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e\n\u003cp\u003eTechnological advancements, particularly in internet connectivity and mobile computing, have significantly improved access to information and enhanced human productivity. This is reflected in meta-analytic evidence showing mobile learning interventions yield moderate gains in student performance (Sung, Chang, \u0026amp; Liu, \u003cspan class=\"CitationRef\"\u003e2016\u003c/span\u003e). In higher education institutions, technology facilitates more effective and personalized learning experiences (Inan \u0026amp; Lowther, \u003cspan class=\"CitationRef\"\u003e2010\u003c/span\u003e).\u003c/p\u003e\n\u003cp\u003eArtificial Intelligence, unlike traditional information technologies, embodies autonomous and adaptive capabilities that support complex decision-making (Baird \u0026amp; Maruping, \u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e). AI's applications ranging from virtual assistants and intelligent tutoring systems to facial recognition and language processing have seen growing adoption across disciplines such as language, engineering, and medical education (Liang et al., \u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e; Hwang \u0026amp; Tu, \u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e; Winkler-Schwartz et al., 2019). Popenici et al., (2017) defined computing systems capable of mimicking human cognitive functions. Besides, AI in education is driven by advances in machine learning and communication technologies (Alajmi et al., \u003cspan class=\"CitationRef\"\u003e2020\u003c/span\u003e; G\u0026oacute;rriz et al., \u003cspan class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e\n\u003cp\u003eHigher education institutions globally have integrated AI to support personalized instruction, improve accessibility and enhance student engagement through intelligent platforms (Nguyen et al., 2015; Chen et al., \u003cspan class=\"CitationRef\"\u003e2022\u003c/span\u003e). These innovations foster cross-cultural communication, learner autonomy and broader educational reach (Truong \u0026amp; Tran, \u003cspan class=\"CitationRef\"\u003e2014\u003c/span\u003e). Higher education is also recognized as a driver of national competitiveness, reinforcing the need for effective technological integration (Sekuloska, \u003cspan class=\"CitationRef\"\u003e2014\u003c/span\u003e).\u003c/p\u003e\n\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\n\u003ch2\u003eConceptual Framework and Hypotheses\u003c/h2\u003e\n\u003cp\u003eMultiple elements affect the implementation of AI in education. AI is rapidly becoming more applicable and beneficial in educational environments as advancements in fields like machine learning and natural language processing continue (Rana, et. al., 2021). AI plays a vital role in enhancing educational transparency and accessibility, while also assisting students with diverse learning needs. Government programs and monetary incentives might promote the adoption of AI in education. To secure public backing, issues relating to ethics concerning privacy and data security need to be tackled. In ensure a successful implementation, instructors need to be adequately prepared and trained. Cultural viewpoints regarding artificial intelligence and technology affect their adoption (Smith \u0026amp; Johnson, 2020).\u003c/p\u003e\n\u003cp\u003eThis study proposes a conceptual framework consisting of three independent variables; convenience and efficiency, enhanced learning experience, and access to diverse resources that potentially influence the adoption of AI (Duan et al. \u003cspan class=\"CitationRef\"\u003e2009\u003c/span\u003e; Smith \u0026amp; Johnson, 2020; Selwyn \u0026amp; Gowlett, \u003cspan class=\"CitationRef\"\u003e2023\u003c/span\u003e). The study tests three hypotheses assessing the relationships between these factors and AI adoption as shown in the following framework in Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eH1: There is a relationship between perceived convenience and efficiency and the factors that influence the adoption of artificial intelligence (AI) in Higher education systems.\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eH2: There is a relationship between perceived enhanced learning experience and the factors that influence the adoption of artificial intelligence (AI) in Higher education systems.\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eH3: There is a relationship between access to diverse resources and the factors that influence the adoption of artificial intelligence (AI) in Higher education systems.\u003c/em\u003e\u003c/p\u003e\n\u003c/div\u003e"},{"header":"Method","content":"\u003cp\u003eThis study adopts a quantitative research design to examine factors influencing the adoption of AI in higher education systems. Quantitative methods were selected to enable statistical evaluation of the relationships between identified variables, ensuring objectivity and replicability (Jain, 2023). A descriptive and explanatory approach was adopted to quantify and explain how variables such as convenience, learning experience, and access to resources impact AI adoption. The study use a survey method utilizing a structured questionnaire was employed with a technique widely recognized in educational research, hence data collection more effectively from a large sample of higher education students (Kusmaryono et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). The questionnaire included three sections: demographic information, AI adoption factors (dependent variable), and influencing factors (independent variables), all measured with a 5‑point Likert scale.\u003c/p\u003e \u003cp\u003eThe study targeted a population of approximately 12,000 students at Universiti Teknikal Malaysia Melaka (UTeM). Primary data were gathered through online questionnaires distributed to 300 students at UTeM, selected using non-probability purposive sampling to capture relevant insights on AI adoption among higher education learners (Mahmutoric, 2023). This method ensured the inclusion of respondents currently enrolled in higher education and familiar with digital technologies. Data obtained and completed was entered into a structural equation modeling using Smart PLS 3.0. PLS is better suited for new development research.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eDescriptive statistics were used to profile the 300 respondents who participated in the main survey. The gender distribution was relatively balanced, with 50.3% male and 49.7% female respondents. The majority of participants were pursuing degree-level education (93.7%) at Universiti Teknikal Malaysia Melaka (UTeM). In terms of the respondents generally viewed AI as beneficial for the learning.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n \u003cdiv class=\"colspec\" align=\"left\"\u003e\u003cbr\u003e\u003c/div\u003e\n \u003ctable id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eDemographic profile of respondents\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eVariables\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCategory\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eFrequency\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePercent\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGender\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e151\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e50.4%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e149\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e49.5%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"4\" align=\"left\"\u003e\n \u003cp\u003eLevel Education in UTeM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePra-University\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDiploma\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.0%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDegree\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e281\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.3%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMaster / PhD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e93.7%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003ch3\u003eAssessment of the Measurement Model\u003c/h3\u003e\n\u003cp\u003eThis study evaluated convergent validity, which refers to the extent to which multiple items measuring the same construct are in agreement. As recommended by Hair et al. (\u003cspan class=\"CitationRef\"\u003e2010\u003c/span\u003e), convergent validity was assessed using three criteria which are factor loadings, composite reliability (CR) and average variance extracted (AVE). As shown in Table \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e, several factor loadings for the constructs namely Convenience and Efficiency, Enhanced Learning Experience, Access to Diverse Resources and Influencing the Adoption of AI exceeded the minimum threshold of 0.50, such as IV13 (0.834), IV31 (0.743), DV3 (0.673), and DV4 (0.642), suggesting acceptable item reliability for these indicators. However, other items (IV11\u0026thinsp;=\u0026thinsp;0.472, DV2\u0026thinsp;=\u0026thinsp;0.145) fell below the recommended value, which may indicate issues with specific indicators.\u003c/p\u003e\n\u003cp\u003eIn terms of composite reliability (CR), the results revealed mixed findings. For instance, Access to Diverse Resources had CR values of 0.693, slightly below the ideal threshold of 0.70, while Convenience and Efficiency (0.618), Enhanced Learning Experience (0.637) and Influencing the Adoption of AI (0.576) were further below the acceptable range. This suggests that internal consistency for these constructs was weak. Similarly, the Average Variance Extracted (AVE) values ranged from 0.287 to 0.433, all falling below the recommended 0.50 threshold (Hair et al., 2011), indicating that the constructs may not adequately capture the variance of their indicators and suggesting poor convergent validity overall. In conclusion, while some individual items demonstrated acceptable factor loadings, the overall low composite reliability and AVE values suggest that the measurement model requires further refinement to achieve satisfactory convergent validity.\u0026nbsp;\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n \u003ctable id=\"Tab2\" border=\"1\"\u003e\n \u003ccaption\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eResult of the measurement model\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eConstruct\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCode\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eLoadings\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCR\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eAVE\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eConvenience and Efficiency\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIV11: AI-powered tools provide me with convenient ways to access information, resources, and support.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.472\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.618\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.371\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIV12: Instant responses, personalized recommendations, and 24/7 availability make artificial intelligence (AI) appealing for my study tasks.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.443\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIV13: Artificial intelligence (AI) tools have improved the efficiency of my research, notetaking, and studying.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.834\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eEnhanced Learning Experience\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIV21: Artificial intelligence (AI) technologies like virtual assistants and chatbots have created engaging and immersive learning experiences for me.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.595\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.637\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.371\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIV22: Virtual reality (VR) and Augmented reality (AR) applications powered by Artificial intelligence (AI) have made complex concepts more accessible and memorable for me.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.673\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIV23: I find artificial intelligence, AI powered interactive conversations and simulations to be helpful in my learning.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.553\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eAccess to Diverse Resources\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIV31: Artificial intelligence, AI powered platforms have provided me with a wide range of educational resources from various sources.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.743\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.693\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.433\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIV32: The recommendations provided by Artificial intelligence have helped me discover relevant articles, research papers, videos, and online courses.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.665\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIV33: AI has enhanced my access to diverse resources for my study needs.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.552\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eInfluencing the Adoption of AI\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDV1: The artificial intelligence (AI) has become an essential tool for due to the enjoyment it provides.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.511\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.576\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.287\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDV2: Given the choice, I prefer using the artificial intelligent (AI) over alternative methods for assistance.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.145\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDV3: The artificial intelligent (AI) has been a valuable resource in solving the queries or problems.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.673\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDV4: The artificial intelligent (AI) has positively contributed to overall user experience.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.642\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003ch3\u003eDiscriminant Validity of Constructs\u003c/h3\u003e\n\u003cp\u003eTo further validate the measurement model, discriminant validity was assessed to the extent to which a construct is truly distinct from other constructs, both conceptually and empirically (Fornell \u0026amp; Larcker, \u003cspan class=\"CitationRef\"\u003e1981\u003c/span\u003e Cheung \u0026amp; Lee, \u003cspan class=\"CitationRef\"\u003e2010\u003c/span\u003e). It ensures that each construct captures phenomena not represented by other constructs in the model. Following recent methodological recommendations, discriminant validity was evaluated by comparing the square root of the AVE for each construct with the correlations between constructs. According to the Fornell\u0026ndash;Larcker criterion, the square root of a construct\u0026apos;s AVE should be greater than the inter-construct correlations to establish discriminant validity.\u003c/p\u003e\n\u003cp\u003eAs shown in Table \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e, the square root values of the Average Variance Extracted (AVE), indicated on the diagonal, are compared with the inter-construct correlations shown in the off-diagonal cells to assess discriminant validity. According to the Fornell\u0026ndash;Larcker criterion, discriminant validity is established when a construct\u0026rsquo;s square root of AVE is greater than its correlations with all other constructs.\u003c/p\u003e\n\u003cp\u003eIn this study, the square root of AVE for Access to Diverse Resources (0.658), Convenience and Efficiency (0.609), Enhanced Learning Experience (0.609) and Influencing the Adoption of AI (0.536) are all greater than the correlations with other constructs in their respective rows and columns. For instance, the correlation between Access to Diverse Resources and Convenience and Efficiency is 0.249, which is lower than both of their AVE square roots (0.658 and 0.609 respectively). Similarly, the correlation between Enhanced Learning Experience and Influencing the Adoption of AI is 0.276, which is less than the square roots of AVE for both constructs (0.609 and 0.536).\u003c/p\u003e\n\u003cp\u003eThese results confirm that all constructs meet the Fornell\u0026ndash;Larcker criterion, thereby establishing satisfactory discriminant validity across the measurement model. This suggests that each construct captures a conceptually distinct dimension, supporting the reliability of the structural relationships assessed in the study.\u0026nbsp;\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n \u003ctable id=\"Tab3\" border=\"1\"\u003e\n \u003ccaption\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eDiscriminant validity of constructs\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eAccess to Diverse Resources\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.658\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eConvenience and Efficiency\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.249\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.609\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eEnhanced Learning Experience\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.157\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.184\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.609\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eInfluencing the Adoption of AI\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.166\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.142\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.276\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.536\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eThe structural model was evaluated using a bootstrapping technique with 500 resamples, a common resampling method used to estimate the accuracy of model parameters by generating standard errors and t-values for each hypothesized path. This technique enhances the reliability of inference by drawing random samples with replacements from the original dataset.\u003c/p\u003e\n\u003cp\u003eAs shown in Table \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e, H2: Enhanced Learning Experience demonstrated a strong and statistically significant effect on Influencing the Adoption of AI (\u0026beta;\u0026thinsp;=\u0026thinsp;0.249, t\u0026thinsp;=\u0026thinsp;3.803, p\u0026thinsp;=\u0026thinsp;0.000), supporting the associated hypothesis. This indicates that improvements in learning experiences facilitated by AI such as better engagement and personalization play a key role in encouraging students\u0026rsquo; acceptance of AI technologies in higher education systems. Huang et al., \u003cspan class=\"CitationRef\"\u003e2020\u003c/span\u003e highlighted AI powered immersive technologies such as VR/AR and interactive chatbots offer rich, engaging learning environments that improve understanding and retention.\u003c/p\u003e\n\u003cp\u003eAI is described as \u0026ldquo;the branch of computer science dedicated to developing intelligent computer systems, which exhibit characteristics associated with human intelligence like language comprehension, learning, logical reasoning, and problem-solving\u0026quot; (Barr \u0026amp; Feigenbaum, \u003cspan class=\"CitationRef\"\u003e1981\u003c/span\u003e). Moreover, Lam and Khare (2018) state, \u0026quot;predict whether the combination of classes a student is taking this term could overwhelm the student.\u0026quot; Self-directed learning enables the program to decide when to present a new topic or review a previous one based on the student\u0026apos;s preferences. The models used by Intelligent tutoring systems may help in determining when a student has understood a concept and is ready to move on to the next one. Data collected from tasks and practice questions, along with response times, are often used to assess the \u0026apos;learned\u0026apos; status and develop a student model that accurately represents students\u0026apos; understanding (Lin, \u0026amp; Chi, 2016). These systems provide feedback, suggest guidance, and give clarifications when students make mistakes (Shute, \u003cspan class=\"CitationRef\"\u003e2008\u003c/span\u003e). They observe the learning outcomes and can pinpoint the content appropriate for the student\u0026apos;s difficulty level (VanLehn, \u003cspan class=\"CitationRef\"\u003e2006\u003c/span\u003e). In this way, the students\u0026apos; learning experience is prioritized over the lessons themselves. Additional considerations such as ethics, data privacy, instructor readiness, and cultural perceptions also influence the success of AI implementation in education (Smith \u0026amp; Johnson, 2020). Hence, H2 has a significant relationship between there is relationship between the enhanced learning experience and the factors of influence the adoption of artificial intelligence (AI) in higher education systems.\u003c/p\u003e\n\u003cp\u003eIn contrast, H1: Convenience and Efficiency (\u0026beta;\u0026thinsp;=\u0026thinsp;0.069, t\u0026thinsp;=\u0026thinsp;0.862, p\u0026thinsp;=\u0026thinsp;0.389) and H3: Access to Diverse Resources (\u0026beta;\u0026thinsp;=\u0026thinsp;0.107, t\u0026thinsp;=\u0026thinsp;1.447, p\u0026thinsp;=\u0026thinsp;0.148) did not reach the conventional level of statistical significance (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05), suggesting weaker or negligible effects on AI adoption. Consequently, the hypotheses related to these constructs are not supported. Overall, only enhanced learning experience was found to significantly influence students\u0026rsquo; willingness to adopt AI in higher education systems.\u003c/p\u003e\n\u003cp\u003eThese results imply that, among the three investigated factors, the enhanced learning experience is the most influential driver of AI adoption in higher education. While access and convenience may contribute to perceptions of AI usefulness, they are not sufficient by themselves to predict student adoption behavior in this context. Even though a study by Johnson \u0026amp; Williams (2019) proves that AI tools provide immediate access to academic support, personalized recommendations, and round-the-clock availability, enhancing productivity and student satisfaction. This study is not significant. Acknowledging individual variations is crucial for developing educational resources targeted at specific students and for tailoring education to address personal requirements across different levels. Data analytics is unable to reveal students\u0026apos; learning patterns or identify their specific needs (Gobert \u0026amp; Sao Pedro, 2017; Mislevy et al., \u003cspan class=\"CitationRef\"\u003e2020\u003c/span\u003e). There are moral and algorithmic challenges in aligning human-provided education with machine-supported learning. The significant effect of AI and contemporary technologies is a dual-faceted issue (Khechine and Lakhal, \u003cspan class=\"CitationRef\"\u003e2018\u003c/span\u003e). On the other hand, it may lead to algorithmic prejudice and a reduction in essential skills in students who rely significantly on technology. For instance, in educational settings that depend on creativity or experience, technology may hinder learning since it could stop students from acquiring direct experiences and participating in learning activities (Cuthbertson et al., 2004). Algorithmic bias is another controversial issue (Obermeyer et al., \u003cspan class=\"CitationRef\"\u003e2019\u003c/span\u003e). Considering that modern AI algorithms rely significantly on data, their efficiency is completely determined by that data. Algorithms adapt to the inherent qualitative and quantitative characteristics of data. For example, if the dataset is imbalanced and contains much more accurate information on the general population than on minorities, the algorithms may produce persistent and repeated mistakes that adversely affect minority groups. In conclusion, H1 has no significant relationship between convenience and efficiency and the factors of influence the adoption of artificial intelligence (AI) in higher education.\u003c/p\u003e\n\u003cp\u003eThis greater accessibility fosters equality and cohesion by enabling students to explore a wider range of learning resources and improve their overall educational journey. Technologies driven by AI can enhance education by facilitating tailored teaching, boosting enduring learning abilities, and providing access to a wide variety of information. Likewise, additional research is required to explore long-term effects, tackle biases and ethical issues, and suggest practical integration strategies (Salas‑Pilco, Xiao, \u0026amp; Oshima, 2022). However, in a study by Hazan (\u003cspan class=\"CitationRef\"\u003e2016\u003c/span\u003e), when monitoring data is complicated or training on the full dataset is not practical due to computational limits, it is better to utilize methods that directly adjust decisions based on the latest observed data. This is based on online learning, a machine learning technique utilized to respond to a series of questions with data provided sequentially. A robot unsure of human preferences has a positive incentive to permit itself to be turned off (Hadfield-Menell et al., \u003cspan class=\"CitationRef\"\u003e2017\u003c/span\u003e). Overall, it will yield to human control actions. A notable instance of interest in online learning arises when both the control set and the loss function are convex (Hazan,\u0026nbsp;\u003cspan class=\"CitationRef\"\u003e2016\u003c/span\u003e). A key algorithm in this scenario is online gradient descent, which involves taking a step at each iteration from the latest control towards the negative direction of the gradient of the prior loss (Zinkevich, 2013). Hence, H3 has no significant relationship between access to diverse resources and the factors that influence the adoption of AI in higher education.\u003cbr\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n \u003ctable id=\"Tab4\" border=\"1\"\u003e\n \u003ccaption\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eResult of hypothesis testing\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eHypothesis\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePath\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u0026beta;\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eStandard error\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003et-value\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ep-value\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eH1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eConvenience and Efficiency \u0026loz; Influencing the Adoption of AI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.069\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.080\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.862\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.389\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eH2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEnhanced Learning Experience \u0026loz; Influencing the Adoption of AI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.249\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.065\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.803\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.000**\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eH3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAccess to Diverse Resources \u0026loz; Influencing the Adoption of AI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.107\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.074\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.447\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.148\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"6\"\u003e**P\u0026thinsp;\u0026lt;\u0026thinsp;0.01,\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\n \u003ch2\u003eCoefficient of Determination and Effect Size\u003c/h2\u003e\n \u003cp\u003eThe structural model\u0026rsquo;s explanatory power was assessed using the coefficient of determination (R\u0026sup2; adjusted), which was found to be 0.087 for the dependent construct Influencing the Adoption of AI, as indicated by the SmartPLS results. This reflects a modest level of predictive accuracy, suggesting that the independent variables of Convenience and Efficiency, Enhanced Learning Experience and Access to Diverse Resources, collectively account for approximately 8.7% of the variance in AI adoption among higher education students.\u003c/p\u003e\n \u003cp\u003eTo further evaluate the contribution of each construct, the effect size (\u0026fnof;\u0026sup2;) was examined. According to Cohen\u0026rsquo;s (1988) benchmarks, effect sizes are classified as small (0.02), medium (0.15), and large (0.35). Based on the SmartPLS output, Enhanced Learning Experience exhibited the largest effect on AI adoption, although it still falls within the small to moderate range. In contrast, both Access to Diverse Resources and Convenience and Efficiency presented negligible effect sizes, indicating a limited direct contribution to the dependent construct.\u003c/p\u003e\n \u003cp\u003eNevertheless, as emphasized by Chin et al., (2003), a small effect size should not be dismissed as irrelevant, especially when interpreted within the context of complex, multi-dimensional constructs such as educational technology adoption. Minor predictors may still offer practical value when combined with more influential variables and could reflect underlying factors like accessibility, perceived relevance, or digital readiness, which may affect AI usage indirectly.\u003c/p\u003e\n \u003cp\u003eThese findings highlight the nuanced role of each construct as shown in Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e. While the Enhanced Learning Experience stands out as the primary determinant, enhancing perceived convenience and resource availability may further strengthen AI adoption when supported by effective implementation strategies, digital literacy programs, and inclusive access frameworks in higher education institutions.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis study set out to examine the factors influencing the adoption of Artificial Intelligence (AI) in higher education systems, focusing on three core dimensions by the previous researchers, convenience and efficiency, enhanced learning experience, and access to diverse resources. The results of the analysis revealed that among the three variables, only enhanced learning experience had a statistically significant influence on students\u0026rsquo; adoption of AI tools in their learning process. While AI offers immense potential in transforming educational practices through personalization, automation, and resource access, its adoption remains uneven and influenced by both technological and human factors.\u003c/p\u003e \u003cp\u003eDespite its benefits, the adoption of AI in higher education institutions is not without challenges. One major concern is the risk of diminishing critical thinking and problem-solving skills, as students may become overly dependent on AI for answers. Furthermore, unequal access to AI technologies can widen the digital divide, disadvantaging students from lower-income backgrounds. There are also serious concerns about data privacy and the ethical use of student information. If not tailored to individual learning needs, AI systems may lead to ineffective or non-inclusive educational outcomes. Additionally, the financial burden of implementing and maintaining AI infrastructure may strain institutional resources, particularly in public or underfunded institutions.\u003c/p\u003e \u003cp\u003eThe study emphasizes the need for a careful evaluation of AI tools, as limitations such as lack of interoperability, varying accuracy and steep learning curves hinder their widespread implementation. Ethical concerns, especially algorithmic bias and privacy issues, further complicate the adoption landscape. These factors underscore the importance of developing AI solutions that are not only technically sound but also socially responsible and pedagogically aligned with the diverse needs of students.\u003c/p\u003e \u003cp\u003eThe findings of this study provide important insights for institutional leaders and education policymakers in planning for AI integration. By understanding the role of enhanced learning experience as a key driver of AI adoption, educational management can prioritize investments in AI platforms that support interactive and personalized learning. The results also suggest that institutional support mechanisms such as technical training for students and staff, infrastructure upgrades and accessible AI tools are essential to maximize the impact of AI on educational outcomes.\u003c/p\u003e \u003cp\u003eThis study also offers a foundation for the agencies and universities to support students' digital readiness. Through strategic funding, policy frameworks, and the development of smart learning platforms, stakeholders can ensure that AI adoption aligns with the skills demanded by future industries. Universities, in particular, can play a pivotal role by embedding AI technologies into curricular, creating AI labs, and providing hands-on exposure to intelligent systems. These initiatives can help foster a digitally literate student population equipped to thrive in a technology-driven future.\u003c/p\u003e \u003cp\u003eThe Agencies are encouraged to invest in AI infrastructure and develop digital curricula that integrate advanced technologies such as machine learning, virtual reality, and data analytics. Educators should be trained to effectively deploy these tools, while industry partnerships should be leveraged to bridge the gap between academic training and workforce requirements. For universities, the incorporation of AI-related modules across disciplines, the establishment of AI labs, and the hosting of innovation-driven events like hackathons will enrich student engagement with AI.\u003c/p\u003e \u003cp\u003eFinally, fostering interdisciplinary collaboration among educators, data scientists, and policymakers will be crucial for creating sustainable, scalable, and ethical frameworks for AI implementation in higher education. A balanced focus on inclusivity, technological advancement, and ethical responsibility will be vital in shaping the future of AI-enhanced education.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch5\u003e\n \u003cp\u003eEthics Approval: This study was reviewed and approved by the Research Members Committee of Universiti Teknikal Malaysia Melaka (UTeM).\u003c/p\u003e\n \u003cp\u003eParticipant Consent: All participants involved in this study provided informed consent to participate voluntarily. The consent process was approved by the aforementioned ethics committee. No identifying personal information has been published.\u003c/p\u003e\n\u003c/h5\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eD.R conceived the study framework and led the design of the research. N.F.A developed the questionnaire and coordinated the data collection process. D.R and N.F.A performed the data analysis and visualization. D.R interpreted the findings and wrote the first draft of the manuscript. N.R contributed to the literature review. All authors reviewed, edited, and approved the final version of the manuscript.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAlajmi, Q., AlSharafi, M. A., \u0026amp; Abuali, A. (2020). Smart learning gateways for Omani HEIs towards educational technology: Benefits, challenges and solutions. \u003cem\u003eInternational Journal of Information Technology and Language Studies\u003c/em\u003e, \u003cem\u003e4\u003c/em\u003e(1), 12\u0026ndash;17.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eArrieta, A. B., D\u0026iacute;az-Rodr\u0026iacute;guez, N., Ser, D., Bennetot, J., Tabik, A., Barbado, S., Garc\u0026iacute;a, A., GilL\u0026oacute;pez, S., Molina, S., Benjamins, D., Chatila, R., R., \u0026amp; Herrera, F. (2020). 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AAAI Press.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Artificial Intelligence, Education, Technology, Digital Literacy","lastPublishedDoi":"10.21203/rs.3.rs-6987750/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6987750/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eIn an era marked by rapid technological advancement, artificial intelligence (AI) is increasingly reshaping the landscape of higher education institutional. Among university students, the adoption of AI tools has facilitated faster and more accurate access to information, enhancing various aspects of teaching and learning. However, despite its growing presence, not all students are equally equipped to utilize AI effectively, often due to limited digital literacy and lack of understanding of AI\u0026rsquo;s capabilities and implications. This study aims to examine the factors influencing the adoption of AI among students in Malaysian higher education institutions focus on Universiti Teknikal Malaysia Melaka (UTeM). Data were collected from 300 students and analyzed using structural equation modeling. The results indicate that, among the three factors studied, only the enhanced learning experience had a statistically significant influence on the adoption of AI. While AI offers notable educational benefits, the findings show that overreliance on such technology may impair the development of critical thinking and independent learning skills. These insights underscore the need for a balanced integration of AI in education, ensuring that technological tools complement rather than replace essential cognitive skills in Malaysia education systems.\u003c/p\u003e","manuscriptTitle":"The Impact of Artificial Intelligence (AI) in Education Systems: Evidence From Malaysia","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-07-02 09:33:10","doi":"10.21203/rs.3.rs-6987750/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"cac4e0f2-664b-4851-aa46-361a14d9c9d4","owner":[],"postedDate":"July 2nd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-10-09T05:39:00+00:00","versionOfRecord":[],"versionCreatedAt":"2025-07-02 09:33:10","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6987750","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6987750","identity":"rs-6987750","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}