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The research landscape on generative artificial intelligence: A bibliometric analysis of generative adversarial networks (GANs)
(RELX Group (Netherlands), 2024-01-01)
1
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Performance de chat GPT en el planteamiento y resolución de una figura geométrica: Trapecio Isósceles
(2025-11-01)
From an exploratory diagnostic approach, we describe relationships, trends and regularities in an isosceles trapezium and its sequential solving guided by the generative Artificial Intelligence (AIg) Chat GPT (free version). Information is obtained from three questions asked to Chat GPT. The answers given are also transcribed and their validity is verified. The method used is Socratic (Maieutics). The analysis is systemic. There are guidelines to elaborate and apply a prompt (set of instructions) that will generate interactions in the classroom and that will favour the development and verification of (new) knowledge based on the effective participation of each player, inviting - in a natural way - teamwork. Knowing that posing and solving problem situations promotes effective work between peers and the development of soft skills
3
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Improving adaptive large neighborhood search: an evaluation of parallel approaches with deep learning integration
(Springer Science+Business Media, 2026-02-01)
This paper introduces a hybrid optimization framework that enhances vehicle routing by integrating Parallel Adaptive Large Neighborhood Search (PALNS) with deep generative modeling. Our method uses Variational Autoencoders (VAEs) to extract latent representations of routing patterns, which dynamically guide neighborhood selection during the search. Unlike conventional heuristics that rely on handcrafted rules, our approach learns from historical solution data to balance exploration and exploitation. We conduct extensive computational experiments on benchmark CVRP instances and real routing data, showing significant improvements in solution quality and steeper convergence curves compared to standalone ALNS and other metaheuristics. While there is modest runtime overhead, the latency is consistent and within practical bounds. The results also highlight interpretability of latent features and their contribution to dynamic search behavior. This work bridges machine learning and large-scale combinatorial optimization, with practical implications for logistics and supply chain applications.
1
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HOW ATTITUDE SHAPES GENERATIVE AI ADOPTION INTENTIONS AMONG GRADUATE STUDENTS
(RELX Group (Netherlands), 2026-01-01)
2
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Global AI Cultures
(Association for Computing Machinery, 2025-08-01)
How a cultural focus can empower generative artificial intelligence.
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Addressing challenges and constructing a blueprint for effective generative AI integration in business operations
(Taylor & Francis, 2025-01-01)
This study addresses the critical challenges and proposes a comprehensive blueprint for effectively integrating Generative Artificial Intelligence (GenAI) into business operations. GenAI has emerged as a transformative force, offering significant competitive advantages to early adopters. However, a substantial gap remains in understanding the technical, organisational, and governance challenges associated with GenAI implementation. This research utilises a mixed-methods approach, incorporating a systematic literature review and expert interviews to develop a blueprint for deploying GenAI in organisations. The blueprint emphasises the alignment of GenAI initiatives with business objectives, the establishment of responsible governance framework, and the development of a technical infrastructure. It also highlights the decision-making process regarding the use of low-code/no-code platforms versus pro-code environments, as well as the impact of GenAI on both customer and employee experiences. Additionally, the study underscores the importance of organisational readiness, change management, and continuous improvement to foster a culture that embraces AI-driven innovation. By providing detailed insights into both technical and organisational aspects, this research bridges existing gaps and offers practical guidance for companies seeking to leverage GenAI to enhance their competitive edge. The findings contribute to the broader discourse on GenAI integration, supporting the strategic and operational scalability of GenAI within various industries.
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