Promoting Inclusive Accounting Education through the Integration of Stem Principles for a Diverse Classroom

Adebiyi, Olubukola Omolara; Olabanji, Samuel Oladiipo; Olaniyi, Oluwaseun Oladeji

doi:10.9734/ajess/2023/v49i41196

Cited by 8 publications

(4 citation statements)

References 25 publications

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“…Among these, tools like ChatGPT, Claude, Bard, Copilot, and Copy.ai have emerged as significant players, each with unique capabilities and user interfaces. While these tools offer numerous benefits, including enhanced efficiency, personalized experiences, and innovative solutions across various sectors, their widespread adoption raises critical questions and challenges [11,12].…”

Section: Introductionmentioning

confidence: 99%

Proliferation of AI Tools: A Multifaceted Evaluation of User Perceptions and Emerging Trend

Marquis,

Oladoyinbo,

Olabanji

et al. 2024

AJARR

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The rapid advancement of artificial intelligence (AI) technologies, epitomized by tools like ChatGPT, Claude, Bard, Copilot, and Copy AI, has significantly reshaped various professional landscapes. This study aimed to assess the impact of these AI tools on professional performance, job dynamics, and societal perceptions. Amidst their benefits in enhancing efficiency and introducing novel capabilities, these tools also pose challenges concerning job displacement, ethical implications, and societal balance. Data from 1623 professionals across diverse industries were analyzed to assess AI tool utilization, functionality, user satisfaction, and perceived impacts. The results indicate that AI tools substantially enhance professional efficiency and are vital in diverse tasks including data analysis and decision-making. However, they also significantly affect traditional job roles, underscoring the urgency for workforce adaptation and skill development. Notably, the study unveils a generational gap in AI adoption, with younger users showing higher engagement compared to older cohorts, suggesting a digital divide. The study’s novelty lies in its comprehensive analysis of AI tool impacts across multiple professions, highlighting ethical and societal challenges. Concerns about AI-induced job displacement, privacy, and ethical use were evident, calling for responsible AI integration. The study advocate for targeted reskilling programs to equip the workforce for an AI-driven future and ethical guidelines to ensure AI tools' responsible development and use. This research contributes to the understanding of AI’s role in modern professional settings and offers strategic insights for policymakers, educators, and industry leaders. Emphasizing a balanced approach, the study urges for AI deployment that maximizes benefits while addressing potential risks and societal concerns.

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Section: Introductionmentioning

confidence: 99%

Proliferation of AI Tools: A Multifaceted Evaluation of User Perceptions and Emerging Trend

Marquis,

Oladoyinbo,

Olabanji

et al. 2024

AJARR

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show abstract

“…As cloud computing continues to evolve, so do the tactics of cybercriminals, making traditional security measures often inadequate. This necessitates advanced solutions, such as AI-driven user behavior analysis, which promises a more dynamic and effective approach to identifying and mitigating security threats in cloud environments [6].…”

Section: Literature Reviewmentioning

confidence: 99%

AI-Driven Cloud Security: Examining the Impact of User Behavior Analysis on Threat Detection

Olabanji,

Marquis,

Adigwe

et al. 2024

AJRCoS

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This study explores the comparative effectiveness of AI-driven user behavior analysis and traditional security measures in cloud computing environments. It specifically examines their accuracy, speed, and predictive capabilities in detecting and responding to cyber threats. As reliance on cloud-based solutions intensifies, the integration of Artificial Intelligence (AI) and machine learning into cloud security has become increasingly vital. The research focuses on how AI-driven security systems, with their advanced pattern recognition and anomaly detection, compare to traditional methods in identifying deviations from standard user behaviors in cloud settings. Employing a quantitative approach, the study utilizes a detailed survey strategy, targeting cybersecurity professionals across multiple industries, including finance, healthcare, information technology, retail, and government sectors. The survey, comprising both closed-ended and Likert-scale questions, is designed to elicit nuanced responses on the perceptions and experiences of these professionals regarding AI-driven versus traditional security methods in cloud environments. The data, collected from a purposive sample of 243 cybersecurity personnel, is analyzed using multiple regression analysis. This analysis facilitates an understanding of the impact of different security systems on the efficacy of threat detection and response in cloud contexts. The results indicate that while both AI-driven and traditional methods significantly improve threat detection accuracy, traditional methods show a slight edge. Conversely, AI-driven systems demonstrate notably superior predictive capabilities and overall enhanced security performance. These findings suggest the necessity of a hybrid security strategy in cloud computing. Such an approach would combine the advanced capabilities of AI, particularly in predictive analytics and adaptability, with the rapid and reliable responses of traditional methods. This integrated strategy is proposed to effectively address the unique challenges posed by the dynamic and complex nature of cloud-based cyber threats. This study provides valuable insights for both businesses and IT professionals on the effective integration of AI-driven security measures in cloud environments. It highlights the evolving role of AI in cloud security and the importance of maintaining a balance between innovative AI approaches and established traditional methods to create a robust, comprehensive cloud security framework.

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“…This white paper also underscores the importance of identity protection and how their system adapts as applications and devices shift to the cloud. Moreover, EyeLock's introduction of new reference designs for iris authentication solutions indicates ongoing advancements and adaptability in cloud-based biometric systems [24,25]. However, while these reports provide insights into specific products, they might lack a broader industry perspective, often focusing on individual solutions rather than a comprehensive analysis of the field.…”

Section: Accuracy Variability Across Cloud Platformsmentioning

confidence: 99%

AI for Identity and Access Management (IAM) in the Cloud: Exploring the Potential of Artificial Intelligence to Improve User Authentication, Authorization, and Access Control within Cloud-Based Systems

Olabanji,

Olaniyi,

Adigwe

et al. 2024

AJRCoS

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This comprehensive study explores the integration and effectiveness of Artificial Intelligence (AI) in Identity and Access Management (IAM) within cloud environments. It primarily focuses on how AI can enhance user authentication, authorization, and access control, addressing the challenges and possibilities in cloud computing. The study adopts a mixed-methods approach, employing both quantitative and qualitative analyses. A survey involving 582 cybersecurity experts provides insights into the current state and potential of AI in IAM, while multiple regression analysis examines the impact of various factors on system effectiveness. Four hypotheses are explored: the impact of hardware and software configurations on system accuracy (H1), the influence of computational environments on reliability (H2), the role of demographic factors in user acceptance (H3), and the effect of technological enhancements on system performance and acceptance (H4). Findings indicate significant correlations between these factors and the effectiveness of AI in IAM. Notably, hardware configurations and security concerns influence system accuracy; computational environment variations affect system reliability; demographic factors impact user acceptance; and enhancements such as user feedback, advancements in AI technology, continuous learning algorithms, and system transparency improve performance and acceptance. These insights underscore the need for advanced hardware, standardized software, user-centric design, and continuous improvement in AI technologies for effective IAM in cloud environments. The study provides actionable recommendations for cloud service providers and developers, emphasizing the importance of involving users in development processes, ensuring transparency, and adopting adaptive algorithms. Future research directions include longitudinal studies on the impact of technological advancements and exploring demographic-specific responses to AI-integrated IAM solutions.

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Promoting Inclusive Accounting Education through the Integration of Stem Principles for a Diverse Classroom

Cited by 8 publications

References 25 publications

Proliferation of AI Tools: A Multifaceted Evaluation of User Perceptions and Emerging Trend

Proliferation of AI Tools: A Multifaceted Evaluation of User Perceptions and Emerging Trend

AI-Driven Cloud Security: Examining the Impact of User Behavior Analysis on Threat Detection

AI for Identity and Access Management (IAM) in the Cloud: Exploring the Potential of Artificial Intelligence to Improve User Authentication, Authorization, and Access Control within Cloud-Based Systems

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