Artificial intelligence (AI) is increasingly used to support financial reporting fraud detection, yet algorithmic accuracy alone does not determine how effective AI proves to be in practice. This study develops and tests an integrated model in which Auditor Trust in AI Systems mediates the effects of AI Predictive Capability, Explainable AI, Data Quality, and AI Governance and Internal Control Quality on Perceived Financial Reporting Fraud Detection Effectiveness. The model draws on human–AI trust theory, the automation–augmentation perspective, and agency theory. Data from 450 U.S. auditing and financial reporting professionals were collected by structured questionnaire and analysed using Partial Least Squares Structural Equation Modelling in SmartPLS 4. All four antecedents significantly predicted Auditor Trust in AI Systems (R² = .697), which in turn strongly predicted fraud detection effectiveness (R² = .524; β = .724, p < .001) and significantly mediated the effects of all four antecedents. The findings show that AI-enabled fraud detection is a socio-technical outcome that delivers value only when systems are explainable, built on sound data, embedded in appropriate governance structures and trusted by their users. The study contributes an integrated framework linking technological, informational, organisational and behavioural conditions, with practical guidance for audit firms and regulators.
JEL classification numbers: M41; M42; O33; G30.
Keywords: artificial intelligence, financial reporting fraud detection, auditor trust, explainable AI, data quality, AI governance.
Geometry represents a demanding mathematical domain requiring spatial visualization, abstract reasoning, and structured cognitive strategies. This qualitative systematic literature review examines empirical evidence regarding digital educational game implementations for enhancing students' geometry problem-solving skills. Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) framework, relevant peer-reviewed studies published between 2015 and 2025 were retrieved from major academic databases using targeted keywords. Fourteen empirical articles satisfied the inclusion criteria and were analyzed across game genres, pedagogical designs, and learning outcomes. The synthesis reveals that interactive platforms, visual modeling software, and mobile educational applications foster problem-solving proficiency by supporting dynamic spatial manipulation, algorithmic reasoning, task persistence, and collaborative inquiry. Challenge-driven game mechanics mitigate cognitive overload by translating abstract geometric principles into intuitive exploratory spaces. Consequently, game-based learning serves as an effective instructional methodology for advancing geometric problem-solving competencies. Future educational research must explore adaptive learning architectures, artificial intelligence integration, and tailored scaffolding mechanics to address diverse student aptitudes across spatial domains and longitudinal mathematical development. ABSTRAK Geometri merupakan ranah matematika yang menuntut dan membutuhkan visualisasi spasial, penalaran abstrak, serta strategi kognitif yang terstruktur. Tinjauan pustaka sistematis kualitatif (qualitative systematic literature review) ini mengkaji bukti empiris mengenai implementasi permainan edukasi digital (digital educational games) untuk meningkatkan keterampilan pemecahan masalah (problem-solving skills) geometri siswa. Mengikuti kerangka kerja Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA), studi-studi peer-reviewed relevan yang diterbitkan antara tahun 2015 dan 2025 dikumpulkan dari basis data akademik utama menggunakan kata kunci yang ditargetkan. Sebanyak empat belas artikel empiris memenuhi kriteria inklusi dan dianalisis berdasarkan genre permainan, desain pedagogis, serta capaian pembelajaran. Sintesis ini mengungkapkan bahwa platform interaktif, perangkat lunak pemodelan visual, dan aplikasi edukasi seluler mendorong kemahiran pemecahan masalah dengan mendukung manipulasi spasial dinamis, penalaran algoritmik, ketekunan tugas, dan inkuiri kolaboratif. Mekanika permainan berbasis tantangan (challenge-driven game mechanics) memitigasi beban kognitif berlebih (cognitive overload) dengan menerjemahkan prinsip-prinsip geometri abstrak ke dalam ruang eksplorasi yang intuitif. Akibatnya, pembelajaran berbasis permainan (game-based learning) berfungsi sebagai metodologi instruksional yang efektif untuk memajukan kompetensi pemecahan masalah geometris. Penelitian pendidikan di masa depan harus mengeksplorasi arsitektur pembelajaran adaptif (adaptive learning architectures), integrasi kecerdasan buatan (artificial intelligence), dan mekanika scaffolding yang disesuaikan untuk mengatasi keragaman bakat siswa di seluruh ranah spasial serta perkembangan matematika longitudinal.
ABSTRACT The rapid development of generative artificial intelligence, particularly language models such as GPT-4, has transformed human–machine interaction through the use of prompts across various contexts. However, the adaptive capabilities of these models introduce security challenges, particularly those involving prompt injection, output manipulation, behavioral constraint bypassing, and potential data breaches. This study aims to analyze the security dimensions of prompt-based interaction with GPT-4 and to identify its vulnerabilities and the effectiveness of the implemented mitigation mechanisms. A case study method was employed through several stages, including the design and testing of various types of manipulative prompts, observation of model responses, identification of deviations from security constraints, and evaluation of the available security filters and behavioral restrictions. The findings indicate that security filters and behavioral constraints implemented in GPT-4 can reduce certain risks associated with harmful interactions, but they do not completely prevent more sophisticated prompt-engineering strategies. These vulnerabilities demonstrate that the security of language-model-based systems cannot rely solely on built-in protection mechanisms but requires layered risk mitigation strategies, continuous security evaluation, and improved user awareness. The study concludes that strengthening security frameworks, conducting systematic testing against manipulative scenarios, and promoting safe and ethical AI usage practices are necessary to improve the resilience of prompt-based AI systems. ABSTRAK Perkembangan kecerdasan buatan generatif, khususnya model bahasa seperti GPT-4, telah mengubah pola interaksi manusia dan mesin melalui penggunaan perintah (prompt) dalam berbagai konteks. Namun, kemampuan model dalam memahami dan menghasilkan respons secara adaptif menimbulkan tantangan keamanan, terutama terkait injeksi perintah, manipulasi keluaran, pengabaian batasan perilaku, dan potensi pelanggaran data. Penelitian ini bertujuan menganalisis dimensi keamanan interaksi berbasis perintah pada GPT-4 serta mengidentifikasi kerentanan dan efektivitas mekanisme mitigasi yang diterapkan. Penelitian menggunakan metode studi kasus dengan tahapan berupa perancangan dan pengujian berbagai jenis perintah manipulatif, pengamatan terhadap respons model, identifikasi bentuk penyimpangan dari batasan keamanan, serta evaluasi terhadap filter dan mekanisme pembatasan perilaku yang tersedia. Hasil penelitian menunjukkan bahwa penerapan filter keamanan dan pembatasan perilaku pada GPT-4 mampu mengurangi sebagian risiko interaksi berbahaya, tetapi belum sepenuhnya mencegah strategi rekayasa perintah yang lebih kompleks. Kerentanan tersebut menunjukkan bahwa keamanan sistem berbasis model bahasa tidak hanya bergantung pada mekanisme perlindungan bawaan, tetapi juga memerlukan strategi mitigasi berlapis, evaluasi keamanan secara berkelanjutan, dan peningkatan kesadaran pengguna. Penelitian ini menyimpulkan bahwa penguatan kerangka kerja keamanan, pengujian terhadap skenario manipulatif, dan penerapan praktik penggunaan AI yang aman dan etis diperlukan untuk meningkatkan ketahanan sistem AI berbasis perintah.
Herman Santoni· ACADEMIA Jurnal Inovasi Rise...· 0 citations
Digital transformation drives the reformation of modern educational governance as conventional models often burden teachers with administrative tasks. This study aims to analyze the integration of digitalization in lesson planning to improve the effectiveness of instructional management in the Society 5.0 era. The method used is a qualitative descriptive literature review of reputable scientific publications indexed by Scopus and Sinta from 2018 to 2025. The results indicate that digitalization shifts the function of planning from mere document compliance to an adaptive, data-analytic strategic instrument. The utilization of Learning Management Systems (LMS), Educational Management Information Systems (EMIS), cloud computing, and artificial intelligence is proven to significantly reduce educators' manual workloads. Furthermore, the digital ecosystem facilitates real-time instructional monitoring and evaluation, strengthens multi-stakeholder collaboration, and enhances the accuracy of educational managerial decision-making. The study concludes that digital planning is a fundamental pillar in realizing effective, inclusive, and flexible learning governance. Equitable infrastructure support and sustainable teacher digital competency development are necessary to maximize implementation across all national educational levels. ABSTRAK Transformasi digital mendorong reformasi tata kelola pendidikan modern karena model konvensional sering membebani guru secara administratif. Penelitian ini bertujuan menganalisis integrasi digitalisasi dalam perencanaan pembelajaran guna meningkatkan efektivitas manajemen instruksional di era Society 5.0. Metode yang digunakan adalah kajian literatur deskriptif kualitatif terhadap publikasi ilmiah bereputasi terindeks Scopus dan Sinta pada rentang tahun 2018–2025. Hasil kajian menunjukkan bahwa digitalisasi menggeser fungsi perencanaan dari sekadar kepatuhan dokumen menjadi instrumen strategis berbasis data analitik yang adaptif. Pemanfaatan Learning Management System (LMS), Educational Management Information System (EMIS), cloud computing, serta artificial intelligence terbukti mampu memangkas beban kerja manual pendidik secara signifikan. Selain itu, ekosistem digital memfasilitasi monitoring dan evaluasi instruksional secara real-time, memperkuat kolaborasi multisektoral, serta meningkatkan akurasi pengambilan keputusan manajerial pendidikan. Penelitian menyimpulkan bahwa perencanaan digital merupakan pilar fundamental dalam mewujudkan tata kelola pembelajaran yang efektif, inklusif, dan fleksibel. Diperlukan dukungan infrastruktur merata serta peningkatan kompetensi digital pendidik yang berkelanjutan untuk memaksimalkan implementasi sistem ini pada seluruh jenjang satuan pendidikan nasional.
Fitri Sri Mulyani, Maman Suryaman· ACADEMIA Jurnal Inovasi Rise...· 0 citations
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The integration of generative artificial intelligence into officer education represents a significant innovation that challenges doctrines, practices, and pedagogical strategies. At the same time, it demands a balance between the potential of generative artificial intelligence and the cadets' autonomy to critically analyse and correlate the knowledge transmitted. In this exploratory-descriptive study, a questionnaire was administered to first-year cadets at the Portuguese Military Academy. These students were from the Portuguese Army Officer Courses and the Portuguese National Republican Guard.
Adriana Filipa Gameiro Martins· Zenodo (CERN European Organi...· 0 citations
The integration of generative artificial intelligence into officer education represents a significant innovation that challenges doctrines, practices, and pedagogical strategies. At the same time, it demands a balance between the potential of generative artificial intelligence and the cadets' autonomy to critically analyse and correlate the knowledge transmitted. In this exploratory-descriptive study, a questionnaire was administered to first-year cadets at the Portuguese Military Academy. These students were from the Portuguese Army Officer Courses and the Portuguese National Republican Guard.
Adriana Filipa Gameiro Martins· Zenodo (CERN European Organi...· 0 citations
FUNDAMENTALS OF MACHINE LEARNING At the heart of digital transformation lies machine learning, a pivotal technology that shapes the course of innovation. Essentially, machine learning enables computers to learn from data and make independent decisions without needing specific programming instructions .For individuals aiming to explore machine learning and data science, grasping its fundamental concepts is a crucial first step. This foundational knowledge is essential for tackling real-world problems with appropriate models and techniques, thereby facilitating accurate evaluation, problem-solving, and fostering creativity in the field. Understanding Machine Learning In today's digital landscape, machines can perform tasks previously thought to require human intelligence. But how do they achieve this? The answer resides in machine learning. Machine learning is a subset of computer science dedicated to developing methods that allow computers to learn autonomously from data rather than through direct programming. These methods are referred to as algorithms. In simple terms, machine learning allows computers to extract insights from data and make informed decisions based on those insights. For example, it’s similar to training a computer to recognize faces in photos, understand spoken language, translate between languages, or play complex games like chess or Go without receiving explicit programming for these functions. Distinction Between Machine Learning, Deep Learning, and Artificial Intelligence Machine learning serves as a facet of artificial intelligence (AI) focused on designing algorithms that enable computers to gain knowledge from data independently. Deep learning represents a specialized segment within machine learning that employs neural networks to identify complex patterns in datasets. These artificial neural networks mimic the functioning of the human brain. It is often stated that such a network can approximate any mathematical function, greatly enhancing its capacity for learning. Artificial intelligence encompasses a wider array of methodologies aimed at emulating various aspects of human intelligence; this category includes both machine learning and deep learning as specific strategies within it. Data science combines multiple disciplines by utilizing scientific methods and machine learning algorithms to extract insights and knowledge from both structured and unstructured datasets. Capabilities of Machine Learning Machine learning empowers computers to detect patterns in datasets, understand languages, identify objects in images or videos, provide recommendations, and predict future outcomes based on historical data. Indeed, this technology is revolutionizing numerous industries due to its ability to analyze large volumes of information and extract meaningful insights. Below are some significant applications: Key Principles of Machine Learning Machine learning has extensive real-world applications owing to the variety of methodologies employed. Nonetheless, the essential principles underlying machine learning remain constant across different algorithms and uses.The fundamental components supporting machine learning include data, algorithms, training processes, testing methods, and evaluation techniques. These elements are crucial for constructing models capable of generalizing effectively to new or unseen data.
Kapil Shukla, Dr. BHARGAV PADHYA· Zenodo (CERN European Organi...· 0 citations
FUNDAMENTALS OF MACHINE LEARNING At the heart of digital transformation lies machine learning, a pivotal technology that shapes the course of innovation. Essentially, machine learning enables computers to learn from data and make independent decisions without needing specific programming instructions .For individuals aiming to explore machine learning and data science, grasping its fundamental concepts is a crucial first step. This foundational knowledge is essential for tackling real-world problems with appropriate models and techniques, thereby facilitating accurate evaluation, problem-solving, and fostering creativity in the field. Understanding Machine Learning In today's digital landscape, machines can perform tasks previously thought to require human intelligence. But how do they achieve this? The answer resides in machine learning. Machine learning is a subset of computer science dedicated to developing methods that allow computers to learn autonomously from data rather than through direct programming. These methods are referred to as algorithms. In simple terms, machine learning allows computers to extract insights from data and make informed decisions based on those insights. For example, it’s similar to training a computer to recognize faces in photos, understand spoken language, translate between languages, or play complex games like chess or Go without receiving explicit programming for these functions. Distinction Between Machine Learning, Deep Learning, and Artificial Intelligence Machine learning serves as a facet of artificial intelligence (AI) focused on designing algorithms that enable computers to gain knowledge from data independently. Deep learning represents a specialized segment within machine learning that employs neural networks to identify complex patterns in datasets. These artificial neural networks mimic the functioning of the human brain. It is often stated that such a network can approximate any mathematical function, greatly enhancing its capacity for learning. Artificial intelligence encompasses a wider array of methodologies aimed at emulating various aspects of human intelligence; this category includes both machine learning and deep learning as specific strategies within it. Data science combines multiple disciplines by utilizing scientific methods and machine learning algorithms to extract insights and knowledge from both structured and unstructured datasets. Capabilities of Machine Learning Machine learning empowers computers to detect patterns in datasets, understand languages, identify objects in images or videos, provide recommendations, and predict future outcomes based on historical data. Indeed, this technology is revolutionizing numerous industries due to its ability to analyze large volumes of information and extract meaningful insights. Below are some significant applications: Key Principles of Machine Learning Machine learning has extensive real-world applications owing to the variety of methodologies employed. Nonetheless, the essential principles underlying machine learning remain constant across different algorithms and uses.The fundamental components supporting machine learning include data, algorithms, training processes, testing methods, and evaluation techniques. These elements are crucial for constructing models capable of generalizing effectively to new or unseen data.
Kapil Shukla, Dr. BHARGAV PADHYA· Zenodo (CERN European Organi...· 0 citations
This seven-volume series examines how artificial intelligence is reshaping India’s economy, institutions and development pathways. Volume I establishes the conceptual foundations of AI, traces the evolution of the Indian economy, evaluates digital transformation and positions AI within the Fourth Industrial Revolution. It presents AI as a socio-technical system whose outcomes depend on infrastructure, institutions, data, skills and accountability. Volume II analyses AI’s contribution to economic growth, productivity and sectoral transformation across agriculture, manufacturing, services, MSMEs and international trade. Volume III examines banking, financial inclusion, FinTech, digital payments, UPI, blockchain and digital currency, emphasising security, consumer protection and meaningful financial participation. Volume IV studies employment, the future of jobs, skill development, higher education, women’s participation and rural employment. It argues that workforce outcomes depend on whether AI complements human capability, supports lifelong learning and distributes productivity gains fairly. Volume V evaluates digital governance, smart government, public administration, taxation, judicial systems, cybersecurity and data protection. It stresses legality, transparency, human oversight, appeal mechanisms and democratic accountability. Volume VI considers inclusive development through tribal development, healthcare, education, rural transformation, poverty reduction and the Sustainable Development Goals. It highlights multilingual access, community participation, cultural relevance, data protection and equitable service delivery. Volume VII explores AI startups, innovation ecosystems, digital entrepreneurship, research and development and India @2047. Across 40 chapters, the series distinguishes technological adoption from performance and measurable impact. Its 1,003 tables and 396 figures organise evidence, trends, comparisons, risks and policy alternatives. The central conclusion is that India does not face a choice between adopting AI and protecting people. It must choose between unmeasured adoption and evidence-governed adoption. AI can advance growth, innovation, inclusion and state capacity when supported by reliable institutions, representative data, human capability, environmental responsibility, continuous validation and enforceable rights. Its purpose is inclusive, resilient, sustainable and human-centred national development.
VADITHE MALLIKARJUNA NAIK, Vanitha B, Goverdhan Reddy Lankala et al.· Zenodo (CERN European Organi...· 0 citations
This seven-volume series examines how artificial intelligence is reshaping India’s economy, institutions and development pathways. Volume I establishes the conceptual foundations of AI, traces the evolution of the Indian economy, evaluates digital transformation and positions AI within the Fourth Industrial Revolution. It presents AI as a socio-technical system whose outcomes depend on infrastructure, institutions, data, skills and accountability. Volume II analyses AI’s contribution to economic growth, productivity and sectoral transformation across agriculture, manufacturing, services, MSMEs and international trade. Volume III examines banking, financial inclusion, FinTech, digital payments, UPI, blockchain and digital currency, emphasising security, consumer protection and meaningful financial participation. Volume IV studies employment, the future of jobs, skill development, higher education, women’s participation and rural employment. It argues that workforce outcomes depend on whether AI complements human capability, supports lifelong learning and distributes productivity gains fairly. Volume V evaluates digital governance, smart government, public administration, taxation, judicial systems, cybersecurity and data protection. It stresses legality, transparency, human oversight, appeal mechanisms and democratic accountability. Volume VI considers inclusive development through tribal development, healthcare, education, rural transformation, poverty reduction and the Sustainable Development Goals. It highlights multilingual access, community participation, cultural relevance, data protection and equitable service delivery. Volume VII explores AI startups, innovation ecosystems, digital entrepreneurship, research and development and India @2047. Across 40 chapters, the series distinguishes technological adoption from performance and measurable impact. Its 1,003 tables and 396 figures organise evidence, trends, comparisons, risks and policy alternatives. The central conclusion is that India does not face a choice between adopting AI and protecting people. It must choose between unmeasured adoption and evidence-governed adoption. AI can advance growth, innovation, inclusion and state capacity when supported by reliable institutions, representative data, human capability, environmental responsibility, continuous validation and enforceable rights. Its purpose is inclusive, resilient, sustainable and human-centred national development.
VADITHE MALLIKARJUNA NAIK, Vanitha B, Goverdhan Reddy Lankala et al.· Zenodo (CERN European Organi...· 0 citations
ABSTRACTRansomware has evolved from opportunistic malware into a mature criminal business model thatblends rapid encryption, lateral movement, and data extortion. Traditional signature-based andrule-driven defenses struggle to keep pace with fast-changing ransomware variants, adversarialevasion, and the operational complexity of modern digital environments. This paper proposes anartificial intelligence (AI)–driven approach to enhance ransomware detection and response byintegrating behavior-based analytics, anomaly detection, supervised classification, and decisionsupport automation within a governance-aligned incident response workflow. Building on thebroader role of AI in cybersecurity defense mechanisms, the study develops a conceptualframework that links technical detection and response capabilities to national cybersecuritystrategy principles, critical infrastructure protection priorities, and organizational culturereadiness. The proposed architecture emphasizes continuous learning, context-aware riskscoring, and response orchestration designed to reduce time-to-detect and time-to-contain whilemaintaining policy compliance and operational resilience. The paper concludes with anevaluation blueprint using defensible metrics and a strategic alignment checklist to support realworld deployment.
Knight Gabriel· Zenodo (CERN European Organi...· 0 citations
The accounting profession has undergone significant technological and operational change over the past fifteen years, yet the early-career experience of staff accountants has remained comparatively unchanged. This article examines the changing balance of power in the accounting labor market, where historically low unemployment, retirements, a constrained talent pipeline, and persistent first-year turnover have increased the importance of retaining young accountants. It argues that compensation and workload alone do not explain early-career departures. Lack of meaningful feedback, mentorship, professional development, and a sense of connection to the purpose of the work can contribute substantially to turnover. The article also considers the impact of artificial intelligence and automation on entry-level accounting, contending that these technologies can increase the value and capabilities of young professionals rather than simply eliminate junior roles. Firms that use automation to accelerate staff development, while providing structured mentorship, timely feedback, and individualized development plans, may be better positioned to retain talent and build future leadership capacity. The article concludes that the talent shortage presents firms with an opportunity to reconsider how they develop and engage young accountants, emphasizing that relatively simple management practices can become important components of a long-term retention strategy.
