Techno-solutionism (Ts) amplifies academic integrity issues endemic to using Generative AI in Open and Distance education (ODE). It (Ts) induces in Higher education (HE) the disposition that technology can and should be employed in every aspect of teaching, learning, and assessment. The prevalence of Ts in ODE and the consequence of undermining academic integrity is found in the surge in published papers. A 2023 study by Nature of over 1600 scientists reports that nearly 30% use GenAI to write papers, (...) and 5% use it in grant applications. A 2024 Originalityai report on 13,000 analysed papers on arXiv indicates a sudden increase of 200.42%, 44.68% and 22.04%, respectively, in Computer Science, Physics and Mathematics in papers published in Jan. 2019 and those published in Nov. 2023. Apr. 2024 paper on arXiv notes Computer Science to be the most prolific in LLM usage with up to 17.5% increase. The problem this study addresses is balancing the challenges and opportunities of GenAI in ODE. Is there a limit to AI use in ODE? How can we upsurge academic integrity in the age of AI? How might we rethink the culture of publish or perish in the age of AI? Adopting the scoping review methodology and discourse analysis, this paper analyses the gains and pains of AI in ODE. Leveraging the critical theory of technology, it recommends clear green and red lines on using AI in teaching, research, and assessment. (shrink)

The industrial proliferation of AI necessitates robust contextual transparency, often lacking in current reporting. This paper introduces a framework for comprehensive reporting of AI, GenAI, and Agentic AI, moving beyond performance metrics. It prioritizes data provenance, algorithmic clarity, operational context, and decision rationale, crucial for trust and accountability. Data provenance ensures integrity, algorithmic clarity demystifies operations, operational context situates performance, and XAI elucidates outputs. For GenAI, transparency on model architecture, training data, and ethics is paramount. Agentic AI requires (...) insights into reinforcement learning, multiagent interactions, and safety. This framework, encompassing data, algorithmic, operational, and ethical aspects, advocates for standardized reporting, stakeholder engagement, and continuous monitoring. Addressing data privacy, system complexity, and lack of standards requires collaboration. Future research includes automated tools and international standards, aligned with IEEE standards. Contextual reporting is crucial for responsible AI, trust, and regulatory navigation, bridging AI sophistication and societal comprehension, promoting ethical standards. (shrink)

The fundamental purpose of software testing is to develop new test case sets that demonstrate the software product's deficiencies. Upon preparation of the test cases, the Test Oracle delineates the expected program behavior for each scenario. The application's correct functioning and its properties will be assessed by prioritizing test cases and running its components, which delineate inputs, actions, and outputs. The prioritization methods include initial ordering, random ordering, and reverse ranking based on fault detection capabilities. software application development often used (...) a test suite, which was less well recognized as a suite for validating software correctness. Each test case set in the suite had distinct instructions and goals based on the system and its configuration that were evaluated. This article presents a Generative AI artificial neural network model for automated software testing based on particle swarm optimization. Generative AI is the method by which computers use existing data, including text, audio/video files, images, and code, to produce new material. An artificial neural network (ANN) is a complex adaptive system capable of altering its internal structure in response to the information it processes. Precisely manipulate the connection and its weight to achieve optimal accuracy. The PSO is a heuristic, population-based global optimization method. (shrink)

Acceleration ethics addresses the tension between innovation and safety in artificial intelligence. The acceleration argument is that risks raised by innovation should be answered with still more innovating. This paper summarizes the theoretical position, and then shows how acceleration ethics works in a real case. To begin, the paper summarizes acceleration ethics as composed of five elements: innovation solves innovation problems, innovation is intrinsically valuable, the unknown is encouraging, governance is decentralized, ethics is embedded. Subsequently, the paper illustrates the acceleration (...) framework with a use-case, a generative artificial intelligence language tool developed by the Canadian telecommunications company Telus. While the purity of theoretical positions is blurred by real-world ambiguities, the Telus experience indicates that acceleration AI ethics is a way of maximizing social responsibility through innovation, as opposed to sacrificing social responsibility for innovation, or sacrificing innovation for social responsibility. (shrink)

Early artificial intelligence research was dominated by intellectualist assumptions, producing explicit representation of facts and rules in “good old-fashioned AI”. After this approach foundered, emphasis shifted to deep learning in neural networks, leading to the creation of Large Language Models which have shown remarkable capacity to automatically generate intelligible texts. This new phase of AI is already producing profound social consequences which invite philosophical reflection. This paper argues that Charles Peirce’s philosophy throws valuable light on genAI’s capabilities first with (...) regard to meaning, then knowledge and truth. Firstly, I explore how Peirce’s icon/index/symbol distinction illuminates the functioning of genAI. I argue that genAI’s engineers have skilfully captured a form of symbolicity, but no other sign-kind. In lacking indexical signs, LLMs lack connection with, and accountability to, particular worldly objects. In lacking iconic signs, LLMs are insufficiently disciplined by structural – most notably logical – relationships. Then I argue that GenAI’s astounding stream of articulate, truth-semblant, yet worthless texts issues a timely reckoning to modern philosophy’s representational realism. By contrast, Peirce’s pragmatism scaffolds a rich relational realism (Gili and Maddalena 2022), which shows how meaningful concepts, and a grasp of truth, can only occur across multiple cognitive systems who are simultaneously richly related with one another, and a shared environment in which they continually act and receive feedback, within a logical space of reasons. As Peirce himself noted, “Mere knowledge, though it be systematized, may be a dead memory; while by science we all habitually mean a living and growing body of truth”. (shrink)

The concept of Generative Artificial Intelligence (GenAI) is ubiquitous in the public and semi-technical domain, yet rarely defined precisely. We clarify main concepts that are usually discussed in connection to GenAI and argue that one ought to distinguish between the technical and the public discourse. In order to show its complex development and associated conceptual ambiguities, we offer a historical-systematic reconstruction of GenAI and explicitly discuss two exemplary cases: the generative status of the Large Language Model BERT (...) and the differences between protein structure predictions from AlphaFold 2 and 3. Our analysis shows that there is no unique and unambiguous definition of GenAI based on a purely technical account of the term. Following this conclusion, we argue that the public discourse is not simply a less complex way of speaking, but instead transcends its technical basis. As a means to structure this newly emerging discussion landscape we introduce a non-exhaustive list of four central aspects of GenAI: (multi-)modality, interaction, flexibility, and productivity. These dimensions constitute a first step towards defining GenAI beyond its technical basis. (shrink)

The potential of using personalized Large Language Models (LLMs) or “generative AI” (GenAI) to enhance productivity in academic research, as highlighted by Porsdam Mann and colleagues (Porsdam Mann...

The pace of technological change continues to outstrip the evolution of democratic institutions, creating an urgent need for innovative approaches to democratic reform. However, the experimentation bottleneck - characterized by slow speed, high costs, limited scalability, and ethical risks - has long hindered progress in democracy research. This paper proposes a novel solution: employing generative artificial intelligence (GenAI) to create synthetic data through the simulation of digital homunculi, GenAI-powered entities designed to mimic human behavior in social contexts. By (...) enabling rapid, low-risk experimentation with alternative institutional designs, this approach could significantly accelerate democratic innovation. I examine the potential of GenAI-assisted research to mitigate current limitations in democratic experimentation, including the ability to simulate large-scale societal interactions and test complex institutional mechanisms. While acknowledging potential risks such as algorithmic bias, reproducibility challenges, and AI alignment issues, I argue that the benefits of synthetic data are likely to outweigh their drawbacks if implemented with proper caution. To address existing challenges, I propose a range of technical, methodological, and institutional adaptations. The paper concludes with a call for interdisciplinary collaboration in the development and implementation of GenAI-assisted methods in democracy research, highlighting their potential to bridge the gap between democratic theory and practice in an era of rapid technological change. (shrink)

This participatory study, conducted in an introductory psychology class, recounts self-reflections of 22 undergraduate students and their instructor engaging in an GenAI-mediated storyboard generation process. It relies on Gordon Pask’s conversation theory, structuring out the nature of interactions between students, instructor, and GenAI, and then uses a qualitative narrative to describe these conversational feedback loops constituting the creation of draft and final storyboards. Results suggest students engaged in cyclical feedback driven processes to master their creations, used elements of (...) photography related to the relationships between objects in frame, image processing, and cultural objects/themes to generate narratives. Issues were faced with generating text on images, and the consistency and style of generated frames as they interacted with GenAI tools. This study provides guidelines to university teaching professionals to design effective activities mediated by text-to-image GenAI. (shrink)

Concerns around Generative Artificial Intelligence (GenAI) in higher education have so far largely centred on assessment integrity, resulting in fundamental questions about students’ broader engagement with these tools remaining underexplored. This paper reports on the findings of a survey that forms part of a wider study, comprising the first empirical investigation of GenAI use by university students as a method of engaging with their academic readings. Our survey of 101 students shows that over half of all students surveyed (...) used GenAI to some degree as a method of engaging with their unit readings. Our findings suggest that students turn to these tools in response to time constraints and conceptual difficulties, while maintaining complex attitudes toward their use: most welcome their availability, yet few report trusting or relying on GenAI-generated interpretations or summaries of texts. Importantly, our data reveals substantial demographic variations in usage patterns, with international students and those taking subjects as electives showing significantly higher rates of AI use to assist with reading. This suggests GenAI tools may be serving as important mediators for attempting to overcome epistemic barriers to learning, particularly for students who face additional linguistic or disciplinary challenges. (shrink)

This paper provides a comprehensive review of the Vasicek model and its applications in finance, categorizing the literature into four key areas: Vasicek model applications, Monte Carlo simulations, negative interest rates and risk, and deep learning for financial time series. To provide deeper insights, a synthesis chart and chronological analysis are included to highlight significant trends and contributions. Building upon this foundation, we employ Generative Adversarial Networks (GANs) and Variational Autoencoders (VAEs) to generate synthetic future interest rate data. These generated (...) projections are then integrated as inputs into the Vasicek model, dynamically adjusting its parameters with the support of AI-driven synthetic data. Additionally, we propose the use of publicly available financial information, gathered via public-facing large language models (LLMs) like ChatGPT, to assess whether the models project trends in line with real-world data. Specifically, we will query ChatGPT to analyze 50 key questions related to interest rates, risk management strategies, inflation, and economic indicators from institutions like the Federal Reserve and banks. Our approach also leverages publicly available information to refine model outputs, reducing reliance on assumptions and emphasizing the alignment of AI-generated noise with observable market behaviors. By integrating these real-world insights, we aim to ensure that our models remain both innovative and grounded in current economic realities. Ultimately, this framework combines advanced generative AI models, such as GANs and VAEs, with human oversight and economic data, providing a robust, adaptable, and forward-thinking approach to financial risk modeling. Keywords: GANs, VAEs, Vasicek, GenAI, Financial Risk Modeling. (shrink)

This paper presents a review and propose framework for training older financial services employees (age 45+) in Generative AI applications. As banks rapidly adopt AI tools, our research identifies specific barriers facing older workers including technological anxiety, interface complexity, and knowledge retention challenges. We conclude that older workers require approximately 30-40% more training time than younger colleagues but achieve comparable proficiency with appropriate support. Key success factors include: (1) peer mentoring systems pairing tech-savvy junior employees with senior staff, (2) simplified (...) interfaces removing unnecessary technical options, and (3) job-specific practice scenarios rather than abstract exercises. This paper further explores the critical need for training older adults in Generative AI (GenAI). While GenAI offers transformative potential across various sectors, ensuring equitable access and its adoption requires addressing the specific challenges faced by older populations. These challenges include digital literacy gaps, concerns about data privacy and security, and the need for user-friendly interfaces especially for older population who might be largely non-technical. The paper examines recent literature and key considerations for developing effective GenAI training programs for older adults, emphasizing the importance of foundational digital skills, accessible language, personalized learning, and ongoing support. Additionally, this study highlights the digital divide faced by older adults, emphasizing the need for structured AI training programs. Furthermore, it analyzes future projections of GenAI's impact, highlighting the necessity of upskilling and reskilling the workforce, including older individuals, to bridge the emerging GenAI skills gap. The paper categorizes and quantifies the types of sources used to support its claims, providing a comprehensive overview of the current state of research and expert opinion on this topic with tables, graphics and charts. By addressing the unique needs of older learners and preparing for the future of GenAI, we can foster digital inclusion and empower all members of society to benefit from this transformative technology. This paper also examines the impact of Generative AI (GenAI) and Agentic AI on the financial services sector, with a specific focus on workforce training and upskilling. Key findings from litreature indicate that by 2027, 80\% of the engineering workforce will require AI-related upskilling (Gartner) and AI-driven automation can reduce manual data tasks by up to 80% (West Monroe). For example, in banking, AI adoption has led to tangible productivity gains, such as Capitec Bank employees saving over one hour per week using AI tools (as suggested by recent reports). The paper categorizes and quantifies recent AI adoption trends, workforce transformation data, and financial efficiency metrics to provide a comprehensive condensed overview of the evolving AI landscape in financial services based on recent reports. (shrink)

Trust is the foundation for learning, and we must not allow ignorance of this new technologies, like Generative AI, to disrupt the relationship between students and educators. As a first step, we need to actively engage with AI tools to better understand how they can help us in our work.

The processes of computation and automation that produce digitized objects have displaced the concept of an image once conceived through optical devices such as a photographic plate or a camera mirror that were invented to accommodate the human eye. Computational images exist as information within networks mediated by machines. They are increasingly less about what art history understands as representation or photography considers indexing and more an operational product of data processing. Through genealogical, theoretical, and practice-based investigation, this dissertation project (...) traces a lineage of computation through images from early cybernetics to contemporary machine learning under algorithmic capitalist conditions of political economy. Objects include mundane examples such as smartphone cameras to sublime examples such as the imaging of black holes. It articulates new conditions and forms of perception and surveillance that result from contemporary technological development in computation. The project builds on theorists of computational media such as Friedrich Kittler, Wendy Hui Kyong Chun, Jussi Parikka, Shane Denson, and Alexander Galloway to examine the interplay between perception, computation, and epistemology. Further theoretical contribution builds on the work of Luciana Parisi and Jacques Ranciére’s articulation of aesthetics, and Michel Foucault’s gesture towards a heterotopia. Finally, it draws from Donna Haraway’s metaphors of the cyborg and sympoiesis to frame the capacity of art to transfigure the limitations of digital network culture into new possibilities for being. Here, it curates practices by Danielle Brathwaite-Shirley, Harun Farocki, Morehshin Allahyari, and Pierre Huyghe. (shrink)

We are pleased to feature a video interview with Jonathan Sim, where he shares his ongoing journey of integrating artificial intelligence (AI) in his teaching, the challenges encountered along the way, and what educators can do to get their students to meaningfully engage with AI tools like ChatGPT to enhance their learning.