Conferencia
Permanent URI for this collectionhttps://hdl.handle.net/11285/636053
Presentación o disertación realizada dentro de un congreso o evento similar, o como evento académico independiente, tales como: Conferencia inaugural, conferencia magistral, conferencia de clausura.
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- Harnessing open language models: a systematic literature review unleashing AI's potential for a smarter future(IEEE Xplore, 2025-01-01) García-López I.M., Ramirez-Montoya M.S., Molina-Espinosa J.M.; https://ror.org/03ayjn504This study provides a systematic literature review (SLR) on Open Large Language Models (OLLM), which are large-scale natural language processing (NLP) models with accessible source code, configuration, and training data for the community. Recent advances in supervised and unsupervised learning techniques have improved the accuracy and contextual capabilities of OLLMs, enabling advanced applications in conversational interaction and long-text analysis. This research explored the applications and socioeconomic impacts of OLLMs in various industries, such as healthcare, education, and business management, demonstrating how these models optimize the efficiency and personalization of different processes. The study also addresses the ethical and operational challenges associated with OLLMs, such as bias management, data privacy and security, decision-making transparency, and technological dependency. Strategies are proposed to mitigate these issues, including regular ethics audits and the adoption of explainable AI frameworks. Finally, the study emphasizes the importance of maintaining a balance between OLLMs and human skills, the need for robust governance frameworks to ensure the ethical and legal operation of these models, and the promotion of continuous innovation to expand their capabilities for a positive and lasting impact on society.
- Digital educational ecosystem with artificial intelligence: evaluation of complex and computational thinking(Springer Link, 2024-10-23) Valenzuela-Arvizu, S. Y., Ramírez-Montoya, M. S., & García-Peñalvo, F.J.; https://ror.org/03ayjn504Due to the social, work, and technological demands characterizing 21st-century society, developing complex and computational thinking skills in the university environment is essential. This document aims to present the current status of a research plan for a doctoral thesis that analyses how university students’ development of computational and complex thinking skills are correlated using training experiences in an AI driven digital ecosystem. This research utilizes a mixed method concurrent design with a triangulation strategy, that is, the collection of quantitative and qualitative data carried out simultaneously (QUAN-Qual). The expected results will enable the development of an evaluation prototype for educational ecosystems with integration of AI. The document is organized into six sections: a) introduction: context and motivation underlying this research project, b) the state of the art of the primary theoretical constructs, c) the hypothesis and research objectives, d) the methodology followed, e) the status of the thesis and f) expected contributions. The research project will contribute to educational innovation, generating valuable knowledge by designing an evaluation prototype for digital ecosystems integrated with AI focused on developing complex and computational thinking skills in university students.
- User experience in digital ecosystems with integration of Artificial Intelligence: a systematic literature mapping from 2010 to 2024(Springer Link, 2024-10-23) Valenzuela-Arvizu, S. Y., Ramírez-Montoya, M. S., & García-Peñalvo, F.J.; https://ror.org/03ayjn504Analyzing users’ experience in digital ecosystems (DE) is essential to ensure effective spaces capable of satisfying their needs and interests. The present study analyzed the publications in the Scopus and Web of Science (WoS) databases from 2010 to 2024 on the study topic of “user experience (UX) in DEs that integrate artificial intelligence (AI).” One hundred eighty-two published articles were reviewed using the Systematic Mapping methodology. Inclusion, exclusion, and quality criteria were applied to obtain the most relevant information. The results showed a) the preponderance of empirical research articles over theoretical/conceptual; b) the mixed methodology approach and the concurrent triangulation design were the most used; c) the main areas of interest were Health, Education, and Technology, which reflects in d) the contexts of the most cited articles and e) the areas of specialization of the main journals analyzed; f) the United States tops the list of the leading countries in this research topic, followed by China and Australia; and e) the studies emphasized assessing the usability and satisfaction with chatbots, the most prevalent and studied AI tool. This review provides a framework for identifying the state of the art of the research topic, making it possible to identify current and emerging research trends.
