Inteligencia artificial, innovación y transformación: una mirada interdisciplinaria

Chapters

• Herramientas de inteligencia artificial para mejorar los estilos de aprendizaje activo y teórico en alumnos de Licenciatura en Informática
  Héctor Luis López López, Cielo Estrella Meza González
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     Chapter abstract / See

  This chapter analyzes the impact of Artificial Intelligence (AI) on software
  development among students at the Faculty of Informatics Mazatlán, specifically
  in the Bachelor’s programs in Informatics and Information Systems Engineering.
  A quantitative approach was used through a 10-question Likert-type survey,
  administered to one hundred and forty students via Google Forms. The findings
  indicate that a high percentage of students have used AI tools in their software
  development tasks and perceive improvements in their code quality. However,
  while these tools help reduce errors and understand programming patterns, only
  a minority fully trust chatbot suggestions. It is concluded that AI tools provide
  significant support for students in software development but do not entirely replace
  human intervention, suggesting the need for more effective integration strategies in
  programming education.

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• Enhancing Virtual Education: A Narrative Review of AI Technologies for Automated Instructor Creation
  Michael Keari Omwenga , Linety Juma, Rubén González Vallejo, Fredrick Kayusi
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  The research on how educators use machine learning techniques and artificial intelligence (AI) applications to study virtual education is summarized in this chapter. According to this study, AI gives teachers numerous opportunities to improve their lesson planning (e.g., by explaining students’ needs and teaching teachers with them), acceptance (e.g., by providing instant feedback and allowing teachers to interfere), and assessment (e.g., by using programmed essay scoring).
  Academicians are involved in many aspects of the development of AI technology, as it was also observed. Serving as role models for AI algorithms and supporting AI research by confirming the accuracy of AI automated evaluation systems are two examples of these jobs.
  Two tasks that can be performed by an artificial instructor, including automating the process of generating personalized educational content and automating the process of generating additional explanation of concepts are presented. Both tasks essentially rely on the theory that educational content is fundamentally a program that can be discounting as written or verbal composites of didactic and meaningful statements. Examples show that there has been some success with generating effective responses or lectures similar to that by a real instructor. An argument made is that these technologies by themselves can be capable of automatically creating the gist of lesson plans, assessments, lectures, dialogues, or influencing group dynamics in a way that promotes effective learning outcomes. The study’s suggestions for future development as well as a variety of roles and data sources in using AI to teach real-world lessons.

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• IA y emprendimientos: un enfoque empresarial
  John Edisson García Peñaloza, Johanna Luceni Loaiza Vera, José Eustasio Rivera Montes, Norma Constanza García Peñaloza
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  Artificial intelligence (AI) has emerged as a disruptive force within the business  cosystem, redefining business models, value creation, and competitiveness in  entrepreneurial ventures. This chapter examines the intersection between AI and entrepreneurship from a multidimensional perspective that integrates scientific, technological, economic, and sociocultural paradigms in order to provide a rigorous and applicable analysis. Its central purpose is to explore how AI transforms entrepreneurial activity and to identify opportunities, challenges, and strategies for its effective adoption in business contexts. The chapter seeks to offer both a conceptual and practical framework enabling entrepreneurs and managers to make informed decisions in a constantly evolving digital environment. The analysis is grounded in a critical review of academic literature, case studies, and global trend assessments, combining economic theory with real-world business applications. An argumentative approach is employed to link technological innovation with commercial viability. AI not only optimizes processes but also generates new paradigms in venture creation, ranging from data-driven startups to algorithm-enabled scalable business models. However, its implementation requires overcoming technical, ethical, and regulatory barriers. The success of entrepreneurial initiatives in the age of AI depends on strategic integration that balances innovation, sustainability, and sociocultural adaptation.

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• Periodismo en la era de la inteligencia artificial generativa: los algoritmos en la construcción de contenidos posmediáticos
  William Ricardo Zambrano Ayala, Ana Tulia Sánchez Reyes, Henry Rubiano Daza
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     Chapter abstract / See

  Applications of generative artificial intelligence (GAI) are transforming the production, distribution, and consumption of news, giving rise to a complex interaction between technology and journalistic practices. The evolution of the media landscape, driven by rapid advances in electronics, computing, and digital technologies, has reshaped both journalistic practice and the dissemination of content across multiple channels, particularly on digital platforms. GAI has enabled more agile information management within digital environments, expanding possibilities for access and interaction with specific segments of society. In light of this context, the study set out the following research objective: to analyze the impact of automation, algorithms, and generative artificial intelligence on the creation, production, distribution, and consumption of content in the digital media outlets El Tiempo, La W Radio, and Noticias Caracol Televisión, the most widely consulted news sources in Colombia. To achieve this objective, a mixed-methods approach was employed, combining descriptive analysis and content analysis, supported by interviews, focus groups, and surveys conducted with directors, editors, journalists, and users of these media organizations. The findings indicate that, in the news outlets analyzed, content dissemination supported by GAI lacks a solid investigative and factual foundation. Instead, it tends to offer rapid interpretations of events aimed at immediate publication, often without depth. Immediacy, automated content, algorithmic logic, and click-driven metrics prevail over source verification and the appropriate use of interactive languages, including hypertextual, hypermedia, multimedia, and transmedia formats. The study concludes that the use of GAI and algorithms in media content creation is associated with a reduced level of investigative and fieldwork reporting, insufficient source verification, an increase in misinformation, information overload lacking social or public value, limited creativity, repetitive narrative formats, and a disregard for ethical and deontological principles.

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• Inteligencia artificial e investigación científica: una estrategia para el avance de las ciencias
  William Alejandro Orjuela Garzón, Juan Manuel Andrade Navia, Jonh Jairo Méndez Arteaga, Verenice Sánchez Castillo
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  The integration of large language models and generative artificial intelligence (AI) is radically transforming the way scientific research is developed, analyzed, and documented across multiple disciplines. This chapter aims to examine the principal areas, trends, and impacts associated with the use of AI in the research process as a fundamental strategy for scientific advancement. The study is grounded in a systematic literature review conducted in accordance with PRISMA guidelines, drawing on the Web of Science and Scopus databases within a ten-year observation window. Through a screening and eligibility process supported by the Bibliometrix software, a final sample of 233 records was selected for quantitative and qualitative analysis. The findings identify four essential dimensions within the scientific production cycle: scientific discovery (including literature review and hypothesis generation), data management (particularly big data processing), academic writing, and scientific communication. The chapter concludes that, although AI significantly enhances productivity and interdisciplinary collaboration, its implementation  presents critical challenges related to ethics, algorithmic bias, and academic integrity. It underscores that these tools should function as complements to human cognition rather than substitutes for the rigor of the scientific method.

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