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Research Article
Innovative Classroom Interconnection Platform for Teaching in Large University Groups
Issue:
Volume 13, Issue 5, October 2025
Pages:
167-176
Received:
11 August 2025
Accepted:
21 August 2025
Published:
15 September 2025
Abstract: The rapid increase in demand for higher education in developing countries remains a constant concern for decision makers who must address many organizational and teaching quality issues related to managing large groups of students. A recent study at the University of Lome showed that managing large groups, while taking into account their socioeconomic background and learning environment, requires in-person courses on a campus with well-equipped classrooms similar to smart classrooms, providing better conditions for both teachers and students. According to various studies, smart classrooms are becoming the preferred solution for teachers and learners to address demographic changes. In this paper, we propose a smart classroom interconnection platform for teaching large groups of students. The platform combines videoconferencing, lecture recording, and audience management technologies. It can integrate artificial intelligence resources, which are increasingly discussed in teaching materials. This solution aims to extend a classroom’s capacity to other classrooms across a university campus, thereby maintaining teacher-learner and learner-learner interactions. It consists of a main classroom (MC) and several remote classrooms (RC) linked together by a communication medium, with technical staff and tutors operating in two modes (unicast mode and full broadcast mode). It provides educational tools and learning spaces to allow numerous learners to access high-quality higher education and helps teachers effectively use pre-recorded video materials and improve them.
Abstract: The rapid increase in demand for higher education in developing countries remains a constant concern for decision makers who must address many organizational and teaching quality issues related to managing large groups of students. A recent study at the University of Lome showed that managing large groups, while taking into account their socioecono...
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Research Article
Rethinking AI Superintelligence Preparedness Through a Justice Lens
Achi Iseko*
Issue:
Volume 13, Issue 5, October 2025
Pages:
177-189
Received:
22 August 2025
Accepted:
3 September 2025
Published:
19 September 2025
Abstract: Debates on superintelligence preparedness have long been dominated by precautionary logics that emphasize speculative catastrophic risks. While these frameworks often present themselves as universal, they obscure justice-based perspectives and reinforce epistemic asymmetries between the Global North and South. This article interrogates the global discourse of “AI precaution” through a justice lens, drawing on qualitative analysis of policy documents and expert interviews conducted across diverse contexts, including Kenya, Brazil, India, South Africa, and the Philippines. The findings reveal three key dynamics. First, epistemic exclusion, where expertise and lived experience from the Global South are marginalized within precautionary imaginaries. Second, temporal asymmetry, as precautionary frameworks impose linear, universal timelines of risk that neglect locally situated priorities and temporalities. Third, distributive imbalance, whereby the burdens of precaution disproportionately fall on communities least responsible for the development of AI systems. To address these challenges, the paper introduces the concept of situated precaution as a pluralist alternative to dominant precautionary logics. Situated precaution foregrounds epistemic justice and temporal sovereignty, highlighting the importance of recognizing diverse knowledge systems, political contexts, and social vulnerabilities in shaping AI governance. By advancing this reframing, the article bridges critical theory, science and technology studies, and global policy debates, offering a framework for more equitable approaches to superintelligence preparedness. Ultimately, these reframing challenges dominant narratives that universalize risk while erasing alternative perspectives. It contributes to a broader rethinking of what it means to govern AI responsibly in a deeply unequal world, emphasizing the need for governance models that anticipate speculative futures while also addressing present injustices.
Abstract: Debates on superintelligence preparedness have long been dominated by precautionary logics that emphasize speculative catastrophic risks. While these frameworks often present themselves as universal, they obscure justice-based perspectives and reinforce epistemic asymmetries between the Global North and South. This article interrogates the global d...
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Research Article
Diversity as Ethical Infrastructure: Reimagining AI Governance for Justice and Accountability
Achi Iseko*
Issue:
Volume 13, Issue 5, October 2025
Pages:
190-204
Received:
27 August 2025
Accepted:
8 September 2025
Published:
25 September 2025
Abstract: Algorithmic bias remains a persistent ethical challenge in the deployment of artificial intelligence (AI) systems, particularly where opaque decision-making intersects with entrenched social inequities. While technical solutions such as fairness-aware algorithms and explainability tools have proliferated, the governance dimensions of AI ethics, especially the role of diversity in shaping oversight structures, remain undertheorized. This article introduces the Diversity-Centric AI Governance Framework (DCAIGF), a novel model that integrates cognitive diversity, intersectionality ethics, and cross-cultural regulatory alignment as foundational elements of inclusive AI oversight. Grounded in 65 semi-structured expert interviews, comparative case studies (Google and IBM), and policy analysis of key global frameworks (e.g., EU AI Act, UNESCO Recommendation on AI Ethics, OECD AI Principles), this study finds that homogenous governance structures often reproduce epistemic blind spots and normative monocultures. In contrast, diverse institutional architectures foster reflexivity, accountability, and ethical robustness across contexts. By conceptualizing diversity as ethical infrastructure rather than symbolic representation, DCAIGF advances four innovations: mandated cognitive pluralism, embedded intersectionality, hybrid legal adaptability, and modular implementation pathways. These features enable practical translation across public, private, and multilateral governance ecosystems. The paper contributes to AI ethics by offering a socio-technical, globally relevant, and empirically grounded model for institutional reform. It further proposes a policy agenda that links epistemic justice to regulatory legitimacy offering a pluralistic roadmap for addressing algorithmic bias beyond the limits of technical mitigation alone.
Abstract: Algorithmic bias remains a persistent ethical challenge in the deployment of artificial intelligence (AI) systems, particularly where opaque decision-making intersects with entrenched social inequities. While technical solutions such as fairness-aware algorithms and explainability tools have proliferated, the governance dimensions of AI ethics, esp...
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Research Article
On Fixed Point Results of Suzuki-Type Contractions on Controlled Metric Spaces with Applications
John Pamba*,
Isaac Daniel Tembo
Issue:
Volume 13, Issue 5, October 2025
Pages:
205-210
Received:
21 January 2025
Accepted:
11 April 2025
Published:
25 September 2025
Abstract: In this paper, we introduce the concept of Suzuki-type contractions on controlled metric spaces and prove a fixed point theory. This extends and generalises the already existing results of Suzuki-type contractions on b−metric spaces and extended b−metric spaces to controlled metric spaces. Some illustrative examples are presented in order to amplify our findings. It is shown that Suzuki-type contractions in the setting of controlled metric spaces provide greater generality and flexibility compared to the setting of metric spaces. We do this by constructing an example where a Suzuki-type contraction does not guarantee a fixed point in a standard metric space but does in a controlled metric space. In this setting, the control function in the controlled metric helps to stabilise iterative sequences in proving the fixed point theory and indeed in the application. Finally, our main result is applied to show the existence of a solution for the fredholm type integral equation. The results obtained in this paper contribute to the broader study of fixed point theory and its applications in mathematical analysis and applied sciences.
Abstract: In this paper, we introduce the concept of Suzuki-type contractions on controlled metric spaces and prove a fixed point theory. This extends and generalises the already existing results of Suzuki-type contractions on b−metric spaces and extended b−metric spaces to controlled metric spaces. Some illustrative examples are presented in order to amplif...
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