AI-powered ventures: How digital competencies shape entrepreneurial success

The integration of artificial intelligence (AI) and digital competency is revolutionizing financial analysis, altering traditional methods and requiring professionals to adapt to new technological paradigms. The rapid evolution of AI-driven tools has significantly improved financial modeling, risk assessment, and predictive analytics. However, these advancements also present challenges, including the need for enhanced digital skills, ethical considerations, and regulatory adaptations. This study explores the impact of AI on financial analysis and how digital competency is shaping the future of financial professionals. The primary objective of this research is to assess the transformation of financial analysis due to AI adoption and the increasing importance of digital competency. This research employs a mixed-methods approach, integrating qualitative and quantitative analysis. A systematic literature review of AI applications in financial analysis is conducted, supplemented by expert interviews with financial professionals, data scientists, and policymakers. Additionally, empirical data is collected through surveys measuring AI adoption, digital competency levels, and its impact on financial decision-making. Statistical analysis is applied to evaluate the effectiveness of AI tools in enhancing accuracy, efficiency, and risk management. Findings indicate that AI-driven financial analysis significantly improves accuracy, speed, and predictive capabilities while reducing human errors and operational costs. The adoption of AI requires financial professionals to develop new competencies, including proficiency in machine learning, data visualization, and algorithmic risk assessment. However, challenges such as data security concerns, resistance to change, and regulatory constraints must be addressed. The study underscores the importance of continuous learning and digital training programs to equip professionals with the necessary skills for AI-enhanced financial analysis. Future research should focus on refining AI-driven financial analysis models, addressing ethical implications, and developing regulatory frameworks for AI in finance.

ObjectiveThis study investigates the factors influencing physicians’ acceptance and adoption of artificial intelligence (AI) technologies in clinical practice, integrating the Theory of Planned Behavior (TPB) and the Technology Acceptance Model (TAM), while also examining the mediating role of trust.MethodsA structured survey was conducted among 414 physicians assessing their perceptions of AI technologies using constructs from TPB, TAM, and trust-related factors. Partial Least Squares Structural Equation Modeling (PLS-SEM) was employed for data analysis.ResultsFindings confirm that TPB and TAM effectively explain physicians’ AI acceptance, with TPB exhibiting a stronger predictive power compared to TAM. Trust emerged as a critical determinant in AI adoption, fully mediating the relationship between perceived behavioral control (p < 0.001), subjective norms (p < 0.05), perceived usefulness (p < 0.001), ease of use (p < 0.001), and behavioral intention. Notably, perceived ease of use (p < 0.001) had the strongest direct impact on trust, while perceived usefulness (p < 0.001) significantly influenced behavioral intention. Attitude toward AI showed a significant effect (p < 0.01). Subjective norms and perceived behavioral control had weaker direct influences (p < 0.05 and p = 0.07, respectively).ConclusionTrust plays a pivotal role in AI adoption, shaping physicians’ acceptance beyond traditional TPB and TAM factors. Healthcare administrators, policymakers, and technology developers should focus on enhancing trust by improving AI transparency, interpretability, and user-friendly design.

The integration of Artificial Intelligence (AI) into personal finance and wealth management has fundamentally reshaped financial behaviors and decision-making processes. The primary objective of this study is to evaluate the role of AI in influencing personal financial behaviors and wealth management outcomes. Specifically, it aims to determine how AI adoption, investment, and usage impact personal savings and net worth. This study adopts a quantitative approach, utilizing secondary data from trusted sources such as Our World in Data and the Federal Reserve Bank of St. Louis. The dataset spans from 2010 to 2022, capturing trends over a significant period of AI development and adoption. A multivariate regression model is employed to examine the relationships between the dependent variables, Personal Savings Rate and Change in Net Worth, and independent variables such as AI adoption rate, AI investment, and household debt-to-income ratio. Descriptive statistics, correlation analysis, and stationarity tests are conducted to ensure data reliability and model validity. Diagnostic checks, including heteroskedasticity tests and Durbin-Watson statistics, further validate the robustness of the results. The study reveals that AI adoption positively influences personal savings by encouraging disciplined financial behaviors, consistent with the findings of prior research. However, its impact on wealth accumulation is less direct, with AI investment showing a surprising negative association with changes in net worth. This indicates inefficiencies in resource allocation or lag effects in the benefits of large-scale AI investments. Traditional economic factors, such as household debt and spending habits, continue to play significant roles in shaping financial outcomes, highlighting the enduring influence of non-technological determinants. The study also underscores the role of macroeconomic variables, such as unemployment, in moderating AI’s impact, with precautionary savings behaviors emerging during periods of economic uncertainty. Based on the findings, several actionable recommendations emerge. For individuals, the adoption of AI-driven tools that promote financial literacy and track spending can enhance savings and improve overall financial health. Financial institutions should prioritize user-centric designs in AI platforms, ensuring accessibility and functionality for diverse demographics. Policymakers are encouraged to support initiatives that bridge disparities in AI adoption, such as digital literacy programs and affordable access to financial technologies. Moreover, strategic investment in AI tools that address wealth management complexities, such as portfolio optimization and risk assessment, is critical for improving long-term financial outcomes. Originality This study contributes to the growing body of literature on AI in finance by offering a dual focus on personal savings and wealth management. Unlike previous studies that often treat these domains independently, this research provides an integrated perspective, highlighting both the synergies and divergences in AI’s impact. The findings on the nuanced relationship between AI investment and financial outcomes offer a fresh lens for evaluating the effectiveness of technological advancements. Furthermore, the study’s emphasis on traditional economic factors alongside AI-related variables underscores its originality in bridging the gap between technological innovation and foundational economic principles. This approach provides a robust framework for future research and practical applications in finance.

Artificial Intelligence (AI) has the potential to reshape family medicine by enhancing clinical, educational, administrative, and research operations. Despite AI's transformative potential, its adoption is inconsistent, and strategic frameworks remain limited. This study explores current AI adoption, organizational policies, integration priorities, and budget allocations within family medicine departments. A survey of 218 family medicine department chairs in the US and Canada was conducted via SurveyMonkey from August 13 to September 20, 2024, as part of the Council of Academic Family Medicine (CAFM) Educational Research Alliance (CERA) omnibus project. Survey questions assessed current and planned AI utilization, presence of formal departmental or organizational policies (defined as written guidelines, strategic plans, or frameworks), integration priorities, and budget allocations. Data were analyzed using Chi-square tests, Wilcoxon Rank Sum tests, and Kruskal-Wallis tests, with a primary focus on bivariate comparisons. The survey achieved a 50.9% response rate (111/218). Current AI use was reported by 56.9% (62/109), while 37.6% (41/109) indicated formal organizational policies. Primary goals for AI integration included improving clinical operations (52.3%), administrative streamlining (16.5%), educational applications (11.9%), and research (4.6%). Budget allocations were minimal (median, 0%; mean 2.4%), though departmental budgets likely underestimate actual institutional investment in AI. Departments reporting AI use had significantly more full-time equivalent faculty (median, 40.0 vs 25.5, P = .023). Geographic and chair demographics were not significantly associated with differences in AI adoption. AI integration in family medicine departments is viewed as essential, though current adoption is limited by uncertain strategic planning and minimal departmental budget allocations, potentially reflecting reliance on centralized institutional information technology (IT) investments. While AI is widely viewed as important, structured policy frameworks and implementation strategies are still developing. Further research is essential to guide policy development and strategic investment to ensure AI's safe, efficient, and effective integration into family medicine.

This study presents a systematic literature review (SLR) examining the interplay between artificial intelligence (AI) integration, teachers’ digital competence, and transformational leadership and management in educational contexts. A total of 45 peer-reviewed studies (2015–2025) were analyzed, retrieved from major academic databases including Scopus, Web of Science, ERIC, and Google Scholar, following a structured selection process inspired by PRISMA guidelines. The findings indicate that successful AI integration in education extends beyond technological infrastructure and is strongly influenced by human and organizational factors. In particular, teachers’ digital competence emerges as a critical enabler of effective implementation, while transformational leadership and management jointly shape strategic direction, organizational culture, and the operational conditions necessary for innovation. Leadership contributes to vision-building and motivation, whereas management ensures coordination, resource allocation, and sustainability of digital initiatives. The review also identifies key challenges, including gaps in professional development, resistance to change, and inequalities in access to digital resources. At the same time, it highlights significant opportunities, such as personalized learning, data-informed decision-making, and enhanced administrative efficiency. This study contributes to the literature by proposing an integrated socio-technical perspective that connects AI technologies with human capabilities and organizational processes. It also identifies critical research gaps and offers directions for future research. The findings provide practical implications for policymakers, school leaders, and educators seeking to implement coherent and sustainable digital transformation strategies in the AI era.

Background: The integration of Artificial Intelligence (AI) into higher education presents both opportunities and challenges, potentially exacerbating existing digital divides. In Nepal, disparities in digital access and skills persist, but limited research exists on how these divides manifest in the perceived usage and attitudes toward AI tools among graduate students, a key group for national AI adoption. Objectives: This study aimed to investigate the dimensions of the digital divide in the use of AI tools among graduate students in Nepal, focusing on their perceptions, confidence, and trust, and to examine potential variations based on demographic factors such as gender and field of study. Methods: A quantitative, descriptive-explanatory study was conducted with 226 graduate students from various disciplines within Kathmandu Valley, selected via simple random sampling. Data were collected through a structured questionnaire. Reliability was confirmed with a Cronbach's Alpha of .750, and construct validity was established through factor analysis. Data were analyzed using descriptive statistics and an independent samples t-test. Findings: Results indicated generally positive perceptions of AI’s utility, with students acknowledging awareness of beneficial tools. However, a significant confidence and trust gap was identified, with notable portions expressing neutrality or doubt regarding the correctness of AI information and their own confidence in using AI for academic work. No statistically significant gender difference in perceptions was found. Variation was observed across academic disciplines, suggesting field-specific relevance as a potential factor. Conclusion: The study concludes that the digital divide in Nepal’s AI era is evolving beyond basic access into a second-level divide characterized by disparities in digital competence, critical evaluation skills, and trust in AI systems. Demographic factors like gender appear less influential than discipline-specific exposure and practical, critical literacy. Implications: The findings underscore the need for educational policies and pedagogical strategies that move beyond providing access to focus on developing AI literacy, critical thinking, and discipline-specific competencies to ensure equitable and effective AI adoption in higher education.

Purpose This study aims to investigate the interplay between artificial intelligence (AI) integration, organizational digital culture, human resource management (HRM) practices and employee sustainable performance in luxury hotels in Malaysia. It seeks to elucidate how AI adoption influences organizational dynamics, shapes HRM practices and impacts employee sustainable performance over time. Design/methodology/approach Using a quantitative approach, survey questionnaires derived from prior research were utilized. Analysis using G*Power software determined an appropriate sample size, with psychometric evaluation validating scale development. Statistical analyses using Statistical Package for Social Sciences (SPSS) 28.0 and SmartPLS 4 confirmed data reliability and validity. Findings Out of the five hypotheses, three were supported. A positive relationship was found between AI adoption and employee sustainable performance, highlighting AI’s potential to enhance productivity and job satisfaction. However, the relationship between AI adoption and organizational digital culture was not supported. On the other hand, HRM practices positively influenced employee sustainable performance. In addition, organizational digital culture was positively associated with employee sustainable performance, underscoring the role of digital fluency in driving workforce productivity. Conversely, AI failed to moderate the relationship between HRM practices and employee sustainable performance. Research limitations/implications The study’s focus on luxury hotels in Malaysia and its reliance on cross-sectional data, suggesting the need for longitudinal designs and diverse organizational contexts in future research. Comparative studies across sectors and countries could offer insights into variations in AI adoption practices and their impact on organizational performance. Originality/value This study contributes to theoretical frameworks by empirically examining complex relationships between AI integration, HRM practices, organizational digital culture and employee performance, emphasizing the importance of considering organizational context and cultural factors in understanding the implications of AI adoption for sustainable performance enhancement.

IntroductionThis study examines the integration of Artificial Intelligence (AI)-based tools into university-level accounting education in Medellín, Colombia. The objective was to identify the factors that influence students' intention to adopt these technologies, using theoretical frameworks such as the Theory of Planned Behavior (TPB) and the Technology Acceptance Model (TAM).MethodsAn experimental methodology was applied, which included the development of an educational video incorporating AI and an accounting simulator. A total of 105 students participated in the study and completed a Likert-type questionnaire designed to evaluate constructs including attitude, perceived usefulness, ease of use, subjective norm, and behavioral control.ResultsThe analysis revealed that perceived ease of use significantly influenced both perceived usefulness and behavioral control. Additionally, subjective norm had an impact on attitude and intention to use. However, perceived usefulness did not translate into favorable attitudes toward AI adoption, indicating a gap between the functionalities students recognized and their expectations of the technology.DiscussionThe findings highlight the importance of contextualizing AI functionalities within educational settings. They suggest the need for pedagogical strategies that align technological tools with students' expectations and foster a more receptive environment toward digital innovation in accounting education.

The integration of Artificial Intelligence (AI) into professional development offers transformative opportunities for enhancing employee learning, performance, and competence. However, the determinants driving AI-supported staff development remain underexplored. This study investigates the key factors influencing employees’ behavioral intention to use AI systems for professional growth within organizational contexts. Drawing on a unified conceptual model that integrates the Technology Acceptance Model (TAM) and Social Cognitive Theory (SCT), the research examines how individual, organizational, and contextual enablers facilitate AI adoption in learning and development programs. Data was collected from 312 employees across diverse organizations participating in structured professional development initiatives and analyzed using Structural Equation Modeling (SEM). The results reveal that digital selfefficacy, management support for AI-related training, motivation for self-development, and perceived relevance of AI content are the most significant predictors of employees’ intention to engage in AI-enabled professional learning. The study advances theory by extending traditional technology adoption frameworks with motivational and contextual dimensions, offering a practice-centered understanding of AI adoption in employee development. The findings also underscore the importance of designing relevance-driven, confidence-building, and leadership-supported AI learning strategies to achieve sustainable digital transformation in workforce development.

Purpose The purpose of this paper is to identify and explain the organizational conditions under which artificial intelligence adoption in universities leads to structural change rather than incremental adaptation. By integrating Luhmann’s theory of decision premises with Argyris and Schön’s concept of organizational learning loops, the study conceptualizes artificial intelligence (AI) adoption as a process mediated by institutional structures and mechanisms of invisibilization and proposes strategies to foster double-loop learning that enable universities to surface and address organizational paradoxes, thereby creating the conditions for meaningful transformation in teaching, research and governance. Design/methodology/approach This conceptual study develops an analytical framework combining Luhmann’s theory of decision premises (programs, communication channels and personnel) with Argyris and Schön’s distinction between single-loop and double-loop learning to examine how universities process AI adoption. The approach synthesizes literature from organizational sociology, higher education studies and paradox theory to explain how contradictions are mediated by institutional structures and managed through mechanisms of invisibilization. The framework is applied analytically to the context of AI in teaching, research and governance, identifying conditions under which contradictions escalate into paradoxes that destabilize decision premises and create opportunities for structural change. Findings The study shows that universities often integrate AI within existing decision premises, containing contradictions through mechanisms of invisibilization, reframing contradictions as technical adjustments, recasting innovation as continuity and suspending role redefinitions, sustaining single-loop learning and organizational stability. Structural change through double-loop learning occurs when external pressures, such as regulatory mandates and funding constraints, converge with internal tensions in academic culture, governance and faculty roles, escalating contradictions into paradoxes that destabilize decision premises. The analysis posits that transformation depends on reconfiguring program premises toward reflexivity, redesigning communication channels for deliberative governance and redefining personnel premises to integrate AI-related expertise into formal authority structures. Research limitations/implications As a conceptual analysis, the study does not include empirical testing, which limits the ability to generalize findings across institutional contexts. Future research should apply and refine the proposed framework through comparative and longitudinal studies of AI adoption in universities, examining variations across governance models, regulatory environments and disciplinary cultures. The framework offers a basis for analyzing how decision premises mediate technological change, highlighting the need for research that investigates the interaction between external pressures, internal tensions and invisibilization mechanisms. Such work can inform both theory development in organizational change and the design of policies that foster reflexive, transformative AI integration. Practical implications The framework offers university leaders and policymakers strategies to foster transformative AI adoption by making organizational contradictions visible and actionable. Institutions can reconfigure program premises to align AI initiatives with mission and values, redesign communication channels to integrate AI within participatory governance and redefine personnel premises to incorporate AI-related expertise into formal authority structures. These interventions can help balance efficiency gains with academic autonomy, transparency and epistemic diversity. Policymakers can use the framework to design regulatory and funding mechanisms that incentivize reflexive adaptation rather than superficial compliance, thereby creating conditions for sustainable organizational change in teaching, research and governance. Social implications By framing AI adoption in universities as an organizational learning challenge, the study highlights its potential societal impact beyond technical efficiency. Universities play a central role in shaping knowledge production, professional formation, and public trust in expertise. AI integration that prioritizes reflexivity, inclusivity and participatory governance can strengthen these societal functions, fostering equitable access to high-quality education and preserving epistemic diversity. Conversely, uncritical adoption risks reinforcing managerial logics that marginalize academic voices and narrow the social purposes of higher education. The framework encourages institutions to engage with AI in ways that support democratic accountability and socially responsive knowledge systems. Originality/value This paper offers a novel conceptual framework linking Luhmann’s theory of decision premises with Argyris and Schön’s organizational learning loops to explain how AI adoption in universities is mediated by institutional structures. By introducing the concept of invisibilization mechanisms, reframing contradictions as technical adjustments, recasting innovation as continuity and suspending role redefinitions, the study advances understanding of why AI often reinforces stability rather than triggering structural change. It also extends organizational change theory in higher education by specifying conditions under which contradictions escalate into paradoxes and by proposing targeted strategies to foster double-loop learning that enable transformative, reflexive integration of AI technologies.

Artificial intelligence (AI) is increasingly integrated into higher education, yet how different purposes of AI use influence student creativity remains underexplored. In particular, little is known about the mediating role of digital competencies and the moderating role of students' attitudes toward AI. Drawing on Social Cognitive Theory, this study examines how AI use for learning and AI use for entertainment relate to student creativity through digital competencies, and how attitudes toward AI condition these relationships. Data were collected from 271 undergraduate students majoring in Traditional Chinese Medicine in China and analyzed using PLS-SEM and moderated mediation analysis. The results show that both learning-oriented and entertainment-oriented AI use positively relate to digital competencies, which in turn enhance student creativity. Digital competencies fully mediate the relationship between AI use for learning and creativity and partially mediate the relationship between AI use for entertainment and creativity. Moreover, attitudes toward AI play a dual moderating role: positive attitudes strengthen the effect of entertainment-oriented AI use but weaken the effect of learning-oriented AI use on digital competencies. This study contributes to the literature by distinguishing different purposes of AI use, identifying digital competencies as a key explanatory mechanism, and revealing the nuanced role of attitudes toward AI in shaping creativity outcomes. It also offers practical implications for designing AI-supported educational practices in specialized domains such as Traditional Chinese Medicine.

Background: Preparedness for disasters and emergencies at individual, community and organizational levels could be more effective tools in mitigating (the growing incidence) of disaster risk and ameliorating their impacts. That is, to play more significant roles in disaster risk reduction (DRR). Preparedness efforts focus on changing human behaviors in ways that reduce people’s risk and increase their ability to cope with hazard consequences. While preparedness initiatives have used behavioral theories to facilitate DRR, many theories have been used and little is known about which behavioral theories are more commonly used, where they have been used, and why they have been preferred over alternative behavioral theories. Given that theories differ with respect to the variables used and the relationship between them, a systematic analysis is an essential first step to answering questions about the relative utility of theories and providing a more robust evidence base for preparedness components of DRR strategies. The goal of this systematic review was to search and summarize evidence by assessing the application of behavioral theories to disaster and emergency health preparedness across the world.Methods: The protocol was prepared in which the study objectives, questions, inclusion and exclusion criteria, and sensitive search strategies were developed and pilot-tested at the beginning of the study. Using selected keywords, articles were searched mainly in PubMed, Scopus, Mosby’s Index (Nursing Index) and Safetylit databases. Articles were assessed based on their titles, abstracts, and their full texts. The data were extracted from selected articles and results were presented using qualitative and quantitative methods.Results: In total, 2040 titles, 450 abstracts and 62 full texts of articles were assessed for eligibility criteria, whilst five articles were archived from other sources, and then finally, 33 articles were selected. The Health Belief Model (HBM), Extended Parallel Process Model (EPPM), Theory of Planned Behavior (TPB) and Social Cognitive Theories were most commonly applied to influenza (H1N1 and H5N1), floods, and earthquake hazards. Studies were predominantly conducted in USA (13 studies). In Asia, where the annual number of disasters and victims exceeds those in other continents, only three studies were identified. Overall, the main constructs of HBM (perceived susceptibility, severity, benefits, and barriers), EPPM (higher threat and higher efficacy), TPB (attitude and subjective norm), and the majority of the constructs utilized in Social Cognitive Theories were associated with preparedness for diverse hazards. However, while all the theories described above describe the relationships between constituent variables, with the exception of research on Social Cognitive Theories, few studies of other theories and models used path analysis to identify the interdependence relationships between the constructs described in the respective theories/models. Similarly, few identified how other mediating variables could influence disaster and emergency preparedness. Conclusions: The existing evidence on the application of behavioral theories and models to disaster and emergency preparedness is chiefly from developed countries. This raises issues regarding their utility in countries, particularly in Asisa and the Middle East, where cultural characteristics are very different to those prevailing in the Western countries in which theories have been developed and tested. The theories and models discussed here have been applied predominantly to disease outbreaks and natural hazards, and information on their utility as guides to preparedness for man-made hazards is lacking. Hence, future studies related to behavioral theories and models addressing preparedness need to target developing countries where disaster risk and the consequent need for preparedness is high. A need for additional work on demonstrating the relationships of variables and constructs, including more clearly articulating roles for mediating effects was also identified in this analysis.

The integration of Artificial Intelligence (AI) into organizational processes presents unique challenges for Small and Medium‐sized Enterprises (SMEs), particularly in fostering effective human‐AI collaboration. Unlike large corporations with extensive resources for AI adoption, SMEs require adaptable frameworks tailored to their specific constraints and operational needs. This paper introduces the novel Human‐AI Collaboration Maturity Model (HAIC‐MM), which is a systems engineering framework designed to assess, guide, and enhance AI integration within SMEs. Developed through the synthesis of AI maturity models, digital transformation frameworks, and human‐machine teaming research, HAIC‐MM identifies seven dimensions and 32 capabilities across five maturity levels that are essential for successful AI adoption in SME contexts. Empirical validation through survey analysis ( N = 100) confirmed the model's robustness. Subsequent focus group analyses ( N = 10, repeated across five sessions) further validated HAIC‐MM's practical utility and alignment with the operational realities of SMEs, emphasizing its relevance to everyday challenges faced by these organizations. Pilot testing with industry practitioners ( N = 3) confirmed the usability and usefulness of the final HAIC‐MM tool. HAIC‐MM provides SME leaders with a structured, human‐centered, and systematic approach to evaluate and cultivate human‐AI collaboration, addressing key areas such as resource optimization, workforce empowerment, ethical AI oversight, and adaptive organizational culture. This research contributes to AI‐enabled systems engineering by offering a practical framework for harmonizing human and AI capabilities within resource‐constrained environments, ultimately supporting SMEs in achieving sustainable and ethically grounded AI integration across the organization. Summary This paper introduces the Human‐AI Collaboration Maturity Model (HAIC‐MM), a framework designed to address the unique AI adoption challenges faced by Small and Medium‐sized Enterprises (SMEs). The model identifies critical dimensions and capabilities needed to foster effective collaboration between humans and AI systems. The model also defines five maturity levels within each capability, allowing a granular assessment within the holistic framework. HAIC‐MM provides a practical, step‐by‐step guide to assess and enhance AI integration for SMEs. The model emphasizes ethical AI oversight, workforce empowerment, and adaptive organizational culture, while addressing key challenges like resource constraints. HAIC‐MM represents a significant contribution to the fields of systems engineering and organizational behavior, offering researchers investigating socio‐technical systems, AI integration processes, and SME innovation strategies a rigorous framework for both theoretical advancement and practical implementation. With its focus on real‐world application, HAIC‐MM equips practitioners with actionable insights to build trust, optimize collaboration between human and AI capabilities, and achieve sustainable, ethically sound AI adoption, ensuring their organizations remain competitive in an increasingly digital economy.

In the current era where artificial intelligence technology plays an increasingly important role in education, teachers are increasingly interested in applying AI to enhance learning efficiency and assessment. However, the acceptance of AI in assessment remains diverse, both helping to make education more equal and effective. At the same time, some are concerned that AI may replace the role of teachers or cause negative impacts. This study aimed to create a causal model explaining the determinants of the use of artificial intelligence (AI) in assessing real-world online learning outcomes of teachers in basic education by integrating the conceptual frameworks of the Technology Acceptance Model (TAM) and the Theory of Planned Behavior (TPB). It covered both technology perception factors, namely Trust in AI, Barriers to AI Adoption, Technology Self-Efficacy, and planned behavioral factors, namely Attitude Toward Behavior, Subjective Norms, and Perceived Behavioral Control, to predict teachers’ behaviors to accept AI in real-world online assessments. The sample consisted of 260 basic education teachers, selected by multi-stage random sampling in schools that used online assessments. A five-point scale questionnaire was employed as a research tool which was tested for content validity and internal reliability. Structural Equation Modeling (SEM) was used as data analysis. The results showed that the model demonstrated excellent fit indices (GFI = 1.000, AGFI = 0.997, RMSEA = 0.000), and explained 79.1% of the variance in AI adoption behavior (R² = 0.791). The proposed causal model could explain the variance in AI usage behaviour significantly, where the variable of AI adoption in teachers’ real-world online assessment (AAB) was directly influenced by the variables of attitude toward AI use in assessment (ATB), social norms (SN), perceived behavioral control (PBC), AI trust (TA), and technology self-confidence (TSF), all of which were statistically significant. In addition, the high barriers to AI use had a negative effect, indicating that teachers were less likely to adopt AI in real-world online assessments. This finding indicates that teachers make rational decisions to accept technology based on perceived value, rather than social pressure. The promotion of AI should focus on developing teachers’ knowledge and skills, along with creating a supportive environment that reduces the difficulty of using such technology, and avoiding direct enforcement through orders or regulations. Future research should explore longitudinal trends and include contextual or institutional variables that may affect teachers’ decision-making regarding AI use.

The adoption of Artificial Intelligence (AI) in education is rapidly transforming the design and delivery of teaching, learning, and institutional management by enabling more adaptive, data-driven, and personalized educational experiences. This study synthesizes evidence from Scopus-indexed and ABDC-ranked research to examine the opportunities, challenges, and future directions associated with AI integration in educational contexts. AI-powered tools such as intelligent tutoring systems, learning analytics platforms, natural language processing applications, and automated assessment systems allow for real-time monitoring of learner performance and engagement, facilitating the delivery of customized content and feedback tailored to individual needs (Hwang &amp; Tu, 2021). These capabilities enhance student motivation, improve learning outcomes, and support the development of self-regulated learning skills. Furthermore, AI contributes to institutional efficiency by automating administrative tasks and enabling data-driven decision-making processes, thereby optimizing resource allocation and improving overall educational management (Zawacki Richter et al., 2019). Despite these advantages, the adoption of AI in education is accompanied by several critical challenges, including concerns related to data privacy, algorithmic bias, lack of transparency, and disparities in access to technological infrastructure (Williamson &amp; Eynon, 2020). Additionally, the successful implementation of AI depends on teacher readiness and digital competence, as educators play a crucial role in integrating AI tools into pedagogical practices (Scherer et al., 2021). Looking ahead, future developments such as explainable AI, human–AI collaboration, and the integration of AI with emerging technologies are expected to enhance the effectiveness and inclusivity of AI-driven educational systems (Ifenthaler &amp; Yau, 2020). Overall, the study concludes that while AI offers significant potential to transform education, its success depends on balancing technological innovation with ethical considerations, pedagogical alignment, and institutional preparedness.