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Permanent URI for this collectionhttps://hdl.handle.net/11285/345284
Artículo científico o editorial en una publicación periódica académica sujeto a revisión de pares. Cumple con los índices internacionales o bases de datos de amplia cobertura, como el listado del Current Contents, ISI WEB of Knowledge (http://isiknowledge.com/) e índice de revistas mexicanas de CONACYT (www.conacyt.mx/dac/revistas). Éstos indizan y resumen los artículos de revistas seleccionadas, en todas las áreas del saber.
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- Crafting personalized learning paths with AI for lifelong learning: a systematic literature review(2024-08-07) Bayly Castañeda, Karla Patricia; Ramírez Montoya, María Soledad; Morita Alexander, Adelina; https://ror.org/03ayjn504; https://ror.org/00v8fdc16The rapid evolution of knowledge requires constantly acquiring and updating skills, making lifelong learning crucial. Despite decades of artificial intelligence, recent advances promote new solutions to personalize learning in this context. The purpose of this article is to explore the current state of research on the development of artificial intelligence-mediated solutions for the design of personalized learning paths. To achieve this, a systematic literature review (SRL) of 78 articles published between 2019 and 2024 from the Scopus and Web or Science databases was conducted, answering seven questions grouped into three themes: characteristics of the published research, context of the research, and type of solution analyzed. This study identified that: (a) the greatest production of scientific research on the topic is developed in China, India and the United States, (b) the focus is mainly directed towards the educational context at the higher education level with areas of opportunity for application in the work context, and (c) the development of adaptive learning technologies predominates; however, there is a growing interest in the application of generative language models. This article contributes to the growing interest and literature related to personalized learning under artificial intelligence mediated solutions that will serve as a basis for academic institutions and organizations to design programs under this model.
- Complex competencies for leader education: artificial intelligence analysis in student achievement profiling(Taylor @ Francis Online, 2024-07-21) Ramírez Montoya, María Soledad; Morales Menendez, Ruben; Tworek, Michael; Escobar Díaz, Carlos Alberto; Tariq, Rasikh; Tenorio Sepúlveda, Gloria Concepción; https://ror.org/03ayjn504; https://ror.org/03vek6s52Future education requires fostering high-level competencies to enhance student talent, and artificial intelligence (AI) can help in profile analysis. The aim was to determine the variables that predict the GPA of students in the ‘Leaders of Tomorrow’ program through an integrated methodology of data analytics, machine learning modeling, and feature engineering in order to generate knowledge about the application of AI in social impact programs. This research focused on 466 graduates of a ‘Leaders of Tomorrow’. A regression analysis was performed to model the relationship between the dependent variable and multiple independent variables. The findings revealed: (a) Analysis of variance (ANOVA) demonstrated exceptional model fit for predicting ‘student.term_Grade Academic Performance (GPA)_program’ with an R-squared of 0.999; (b) Visual analysis showed that significant variables like age and origin-school Grade-Point Average (GPA) affect term GPA; (c) Kendall tau correlation revealed a positive correlation of origin-school GPA with term GPA and a slightly negative one with age; (d) Support Vector Machine (SVM) regression aligned actual and predicted GPAs closely, indicating high accuracy; and (e) Recursive Feature Elimination (RFE) identified ‘student_originSchool.gpa’ as the most predictive feature. This study is intended to be of value to academic communities interested in enhancing the academic profiles of students with complex competencies, as well as communities interested in applying AI in education for predictions that contribute to trajectories for training.
- AI-Based platform design for complex thinking assessment: a case study of an ideathon using the transition design approach(Taylor @ Francis Online, 2023-11-28) Sanabria Zepeda, Jorge Carlos; Alfaro Ponce, Berenice; Argüelles Cruz, Amadeo; Ramírez Montoya, María Soledad; https://ror.org/03ayjn504; https://ror.org/059sp8j34Emerging Artificial Intelligence-enhanced technology platforms in education warrant attention to exploring new learning strategies and dynamics. Keeping up with the accelerating momentum to bring classic traditional learning activities to Artificial Intelligence-supported platforms may unbalance the interest in developing the participants’ higher-order thinking. This article presents case study research of an Artificial Intelligence-based technological platform to measure complex thinking traits of higher education participants in an Ideathon learning scenario. The didactical strategy was grounded in the Transition Design approach, with Sharing Economy as the challenge. An overview of the process for developing Artificial Intelligence-supported activities, the challenges and risks identified in the development, and a classification model and enhancements for future implementation in a subsequent pilot are presented. The findings set a guideline for balancing Artificial Intelligence-powered educational activities and the development of the participants’ complex thinking.
- Use of ChatGPT at university as a tool for complex thinking:students' perceived usefulness(Universidad de Alicante, 2023-07-15) Romero Rodríguez, José María; Ramírez Montoya, María Soledad; Buenestado Fernández, Mariana; Lara Lara, Fernando; https://ror.org/04njjy449; https://ror.org/03ayjn504; https://ror.org/046ffzj20Artificial intelligence (AI) and AI-based chatbots, such as ChatGPT, are transforming the approach to education. In particular, ChatGPT's potential to process large amounts of data and learn from user interactions makes it a beneficial resource for students, albeit with some reluctance from some teachers. This study aimed to explore the acceptance of ChatGPT by university students. The researchers administered an online survey to 400 Spanish university students aged 18-64 (M = 21.80; SD = 6.40). The results of the methodological approach based on the UTAUT2 model for technology adoption showed that: 1) gender was not a determining variable in any construct while the experience of use was a factor conditioning a higher score on all constructs; 2) experience, performance expectancy, hedonic motivation, price value, and habit were influential in behavioral intention to use ChatGPT; 3) facilitating conditions, habit, and behavioral intention were conditioning factors in user behavior. Finally, this report discusses the findings and practical implications of the work and recommends some good uses for ChatGPT.
- Complex thinking through a Transition Design-guided Ideathon: testing an AI platform on the topic of sharing economy(2023-05-30) Sanabria Zepeda, Jorge Carlos; Castillo Martínez, Isolda Margarita; González Pérez, Laura Icela; Ramírez Montoya, María Soledad; https://ror.org/03ayjn504This proof-of-concept study of an AI-based platform aimed to integrate a sequence of activities into the design of an online platform to assess the development of complex thinking competency in higher education students.