generative ai

Automating Deception: AI s Evolving Role in Romance Fraud

The increasing sophistication of AI-based tools is transforming the landscape of online deception. Fraudsters have long relied on pre-scripted messages and structured manipulation techniques to exploit victims, but AI offers new opportunities to scale and refine these operations. Generative AI has already been exploited for various cyber-enabled crimes, and recent studies indicate that there is a growing interest in leveraging AI for social engineering and financial fraud. The development of industrialised romance fraud, as seen in large-scale scam networks in West Africa and Southeast Asia, indicates that AI could not only enable new fraudsters but further streamline existing operations. Yet while LLMs excel at identifying the procedural elements of romance fraud, they still struggle to sustain these deceptive patterns over extended interactions. This asymmetry creates a technical opportunity for defensive applications, particularly in detecting AI-generated content across the romance fraud lifecycle.

Exploratory Study on Stakeholders' Perspectives on the Integration of Generative Artificial Intelligence in Science Education - Supplementary Material

This supplementary file provides the audit trail of the Reflexive Thematic Analysis conducted in the study. It documents the analytical process across all phases of the analysis, enhancing the transparency, rigor, and trustworthiness of the research. The material includes: (i) familiarization notes developed during the initial engagement with the focus group data; (ii) a representative sample of the initial inductive codes, together with working definitions and illustrative extracts; (iii) the development of candidate themes and the evolution of codes into thematic structures; and (iv) decisions made during theme review and refinement, including their methodological rationale. The document also details the use of AI-assisted conceptual clustering as a supportive tool during theme generation, while maintaining researcher-led interpretation in accordance with Reflexive Thematic Analysis principles. Overall, the supplementary material provides a comprehensive account of the analytical pathway from raw data to the final thematic framework presented in the article.

From AlphaGo to ChatGPT

AI is a transformative force in the digital age. The swift rise of generative AI technology, particularly with ChatGPT launched in Nov 2022, has sparked global interest. We hold technology to be best understood as a quasi-social movement that is mobilising symbolic and material resources for societal change. We are tracing this mobilisation effort for AI on three variables: the flow of media salience and shifts in topicality and public awareness. Over the period 2000-2023, the paper traces the salience of AI relative to other tech issues such as nuclear power, genetics and climate change. For the period 2015-2023 we analyse key themes and public awareness of AI and how they shift over time. Results show similarities and differences between the UK and Korea, reflecting the different impact of two landmark events. While AlphaGo 2016 shocked Korea into FOMO, fear of the Nation missing out on new technology, ChatGPT 2023 alarmed the UK about TOOC, a potential technology-out-of-control. The paper shows how 2015-2023 is likely to be another hype cycle of AI coming to a turning point. AI is a global challenge but played out to local logic. The AI challenge is confronted with different dispositions in the UK and in Korea despite similar geo-political positions on AI.

Artificial Intelligence and the Teaching of Indian Writing in English: Possibilities, Problems, and Emerging Directions

Artificial Intelligence (AI) is becoming part of everyday academic life. It is now used to explain difficult ideas, providelanguage support, generate questions, offer feedback, and assist students in locating information. Much of the discussionabout AI in education, however, has centred on science, technology, professional training, and assessment. Its place inliterary studies has received less sustained attention. This paper considers how AI may be used in the teaching of IndianWriting in English in higher education. It follows a qualitative, conceptual approach and draws on research related to AI ineducation, generative AI, digital humanities, literary teaching, and postcolonial studies. The paper argues for a careful andlimited use of AI. It may help students approach historical background, cultural references, difficult vocabulary, andunfamiliar literary contexts, but it cannot take the place of reading, interpretation, classroom conversation, or the teacher’sjudgement. The discussion refers to works by Raja Rao, Salman Rushdie, Amitav Ghosh, Arundhati Roy, Jhumpa Lahiri, andChetan Bhagat to show how AI-supported activities might be used in the literature classroom. At the same time, the paperexamines serious concerns: fabricated information, academic dishonesty, cultural and algorithmic bias, unequal access,privacy, and the danger of replacing close reading with ready-made summaries. A human-centred model is proposed inwhich AI offers preliminary assistance, while students verify information, return to the literary text, and develop their owninterpretations. The paper concludes that AI can be useful in the teaching of Indian Writing in English only when it remainssubordinate to literary judgement, cultural awareness, and independent thought.

Algorithmic Sovereignty in Digital Banking

Digital banks increasingly use machine learning and generative AI inside decisions that affect access to credit, fraud interventions, financial-crime controls, customer support and internal operations. The principal governance problem is not that regulators have demanded a single form of ‘explainable AI’. It is that a regulated firm must be able to identify which legal obligations apply to a particular use of a system, allocate responsibility, constrain execution, preserve evidence and respond when the model, data, product or law changes. This paper calls the capability to do so algorithmic sovereignty: the institution’s practical ability to govern automated decisions throughout their lifecycle rather than merely consume opaque outputs.The paper develops a UK–EU framework for converting regulatory sources into versioned obligations, testable controls and decision-level evidence. It corrects two common overstatements. First, the EU AI Act does not classify every banking model as high-risk, and the United Kingdom has not created a general AI Act for financial services. Secondly, deterministic rules or formal proofs do not make a system legally compliant by themselves. They can prove properties of an encoded specification, but the specification, facts, institutional process and legal interpretation remain contestable.A hybrid architecture is proposed: probabilistic systems perform perception, prediction and drafting; a deterministic policy layer evaluates structured facts against versioned rules; a separate evidence layer records inputs, model and policy versions, reasons, overrides and outcomes. Logic-based and formal methods—including Datalog, policy-as-code languages, SMT solvers and proof assistants—are useful in selected parts of this control plane. They are not a replacement for statistical models, legal judgment, data governance or meaningful human review. The result is a narrower but more defensible thesis: compliance can become partially executable and continuously testable, provided the organisation treats code as one controlled representation of law rather than law itself.

GLAT-CN: Nationwide Validation of the Chinese Generative AI Literacy Assessment Test among Higher Education Students

FACULTY AI READINESS AND THE DIGITAL DIVIDE IN UZBEKISTAN'S EXPANDING PRIVATE HIGHER EDUCATION SECTOR

Background: In the 2025/2026 academic year, the number of non-state higher educational organizations in Uzbekistan reached ninety-seven, highlighting the rapid expansion and competitive nature of private tertiary education. While infrastructure investment is growing, the professional readiness of faculty members to integrate Artificial Intelligence (AI) tools into teaching and research remains uneven, threatening to create an institutionaldigitaldivide.Methods: This study examines eighty-two full-time faculty members from twelve private universities using a descriptive, non-experimental quantitative approach. Data was collected via standardized digital literacy rubrics to assess proficiency across three key domains: generative AI pedagogy, automated research workflows, and algorithmic evaluation.Results: The empirical data demonstrates that while seventy-one percent of younger faculty (under thirty-five years old) demonstrate high proficiency in employing AI tools for syllabus generation, sixty-eight percent of senior faculty (over fifty years old) exhibit strong resistance or low literacy, preferring traditional teaching methods. Furthermore, regional branches of private institutions face a significant digital divide due to inadequate computing infrastructure and lack of localized training.Conclusion: The structural expansion of private higher education in Uzbekistan outpaces faculty digital capabilities. To mitigate this digital divide, institutions must transition from basic technology procurement to systematic, structured AI literacy programs for academic staff. [1]

More Than a Voice: The Role of Embodiment in LLM-Based Reading Tutors for Children

Children’s engagement in reading practice is strongly influenced by social and affective factors, yet many digital reading tools lack the ability to support meaningful social interaction. In this work, we investigate how embodiment in generative AI tutors is associated with children’s social, perceptual, and emotional responses during reading activities. We developed two versions of a reading tutor powered by a Large Language Model capable of dynamically generating reading content: (1) an embodied agent with a visual avatar and (2) a non-embodied agent. In a within-subject study with children, we evaluated the impact of embodiment using eye-tracking (visual attention), self-reported measures (social presence and emotional valence), and automated facial emotion recognition. Results showed that the embodied agent was associated with higher levels of visual attention to the task and increased perceived social presence. While self-reported emotional valence did not differ significantly between conditions, the embodied agent elicited a higher proportion of positive emotional expressions during the interaction, with a moderate effect size. These findings suggest that visual embodiment may influence how children attend to and perceive AI-based tutors, supporting a more socially oriented interaction. By complementing functional feedback with responsive visual cues, embodied agents may promote engagement and observable affective responses during learning activities, highlighting their potential for educational human–AI interaction.

The double-edged sword of proactive GenAI: how it shapes user usage intention

Purpose With the emergence of generative AI (GenAI), proactive services that initiate actions on behalf of users without explicit prompts are becoming increasingly prevalent. Although such services can enhance human-AI interaction, emerging evidence suggests that GenAI proactivity may also elicit adverse user reactions. To reconcile these mixed findings, this research examines why and when GenAI's proactive services positively and negatively influence users' usage intention. Design/methodology/approach Drawing on mind perception theory, we develop a dual-pathway model proposing that increased proactivity simultaneously enhances perceived agency and diminishes perceived control. We further propose that task type (hedonic vs. utilitarian) determines the relative dominance of these two pathways. We test these predictions through two scenario-based experiments. Study 1 examines the dual-pathway model in a health assistant context, whereas Study 2 investigates the moderating role of task type in a shopping assistant context. Findings Study 1 demonstrates that high (vs. low) proactivity simultaneously increases perceived agency and reduces perceived control. Study 2 shows that task type moderates these effects: for the hedonic task, the positive pathway through perceived agency dominates, making high proactivity more favorable; for the utilitarian task, the negative pathway through perceived control prevails, making low proactivity more preferred. Originality/value This research advances the literature on GenAI's proactive services by demonstrating that their double-edged effects operate through parallel enabling and constraining pathways. We also extend mind perception theory by revealing the psychological costs of agency attribution. Practically, the findings offer actionable insights for firms seeking to tailor GenAI functionalities to enhance user experience and foster usage across diverse digital business contexts.

Generative AI: Beyond ChatGPT

Generative Artificial Intelligence (AI) has become an important area of computer science, changing the way people create, process, and interact with digital content. Although ChatGPT has made Generative AI widely known, its capabilities extend far beyond conversational systems. Generative AI includes several technologies, such as Large Language Models (LLMs), Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs), and Diffusion Models, which can generate text, images, audio, video, software code, and other forms of content. This paper examines the development of Generative AI and its applications in different fields. In education, it can support personalized learning and content generation, while in healthcare it can assist with medical imaging and drug research. In software development, Generative AI can support code generation and debugging. It is also being used in entertainment, business automation, and scientific research. However, the rapid growth of this technology has introduced several challenges, including inaccurate or misleading outputs, privacy concerns, copyright issues, bias in generated content, and security risks. These challenges highlight the importance of responsible development and use of Generative AI. This paper discusses how Generative AI is evolving beyond ChatGPT and explores its potential to support human creativity and problem-solving. It also emphasizes the need for reliable, transparent, secure, and responsible AI systems for future applications.

Hardware-Native Engineering Management: Reclaiming Manufacturing Discipline from Software-Derived Practice

Recent management doctrine is often associated with software-sector practices such as agile methods, sprint cycles, and minimum viable products. That account obscures an earlier manufacturing lineage. Many of the practices software popularized were first abstracted from hardware manufacturing. Scrum grew out of Takeuchi and Nonaka’s 1986 study of Honda, Canon, Fuji-Xerox, Toyota, and other manufacturers; kanban and just-in-time came from Toyota’s production system. Software borrowed these methods and adapted them to a medium in which rollback is cheap, feedback is quick, and most failures carry limited cost. During that adaptation, software practice reduced the emphasis on front-loaded discipline that the manufacturing originals retained for physical reasons. The lighter versions were later applied in hardware contexts as general management practice, often without sufficient attention to the constraints that physical work imposes. We argue that managing hardware with these lighter methods produces a predictable class of failure, and that two forces now make correction urgent: renewed investment in physical systems (energy, semiconductors, defence, robotics, and the physical plant of AI itself), while generative AI automates much of the software work whose practices were treated as universal. We identify the physical properties that separate the two domains, compare the resulting framework with established industrial standards, and propose five principles for hardware-native engineering management: simulation-first design, risk as a first-class metric, decision quality over speed, structured management of irreversibility, and dual-speed organizational architecture. We operationalize each principle with metrics and a maturity model, then test the framework’s diagnostic value through a structured retrospective analysis of three documented failures and one contrasting success.

Generative AI Use for Mental Health Support: Trends, Correlates, and Outcomes Among Canadian Students