Conference Agenda

Generative artificial intelligence (AI)–based educational platforms are increasingly used in higher education to support academic organization and self-regulated learning. This study describes user experience, adoption barriers, and self-regulated learning indicators associated with implementing an AI tool that assists students with academic planning and the development of self-regulation skills. The tool combines generative models and learning analytics to deliver personalized recommendations, improve time management, and encourage effective study habits. Fifty students participated; 19 reported using the tool and 31 reported not using it. Among users, perceived usability and integration were high: 84.2% indicated that platform functions were well integrated and 79.0% reported that the tool was easy to use. Confidence and intention to continue were also favorable, with 89.5% reporting that they felt confident using the tool, were interested in frequent continued use, and believed that most people could learn it quickly. Regarding self-regulated learning, 84.2% reported being able to understand and learn both basic and difficult content, and 78.5% felt capable of achieving self-set goals. However, sustained planning behaviors were less consistent: 68.4% reported keeping up with studying through a structured schedule, whereas 26.3% reported studying only before exams. Procrastination-related execution difficulties were common (42.1% delaying non-preferred tasks; 36.8% struggling to start important tasks; 31.6% waiting until the last moment for deadlines). Study strategies suggested room for improvement (42.1% relying on repetition; 15.8% memorizing without understanding). Overall, the tool was positively appraised for usability and integration, while highlighting opportunities to strengthen sustained planning and deeper learning strategies in future deployments.

Game-Based Learning (GBL) has emerged as an effective pedagogical approach for enhancing motivation, engagement, and experiential learning within higher education. Nonetheless, the adoption suffer from stagnant rate in Project Management (PM) education, with limited evidence on how research methodology, game design, and learner context collectively shape educational outcomes. This study conducts a thematic synthesis of existing literature on PM-GBL and categorizes them into three dimensions, including research methodology, serious game design, and target audience. The analysis demonstrates that most studies employ short-term, small-scale explorations, predominantly in construction management, while applications in business and software project management are comparatively underexplored. To address this fragmentation, this paper developed a bubble diagram that demonstrates methodological rigor, design diversity, and audience characteristics interaction to influence learning effectiveness. The framework offers a structured overview of the current research landscape and serves as a foundation for advancing the systematic incorporation of GBL in PM higher education. This study contributes to the evolving discourse on experiential learning by clarifying the thematic relationships shaping PM-GBL integration and identifying key areas of imbalance that require scholarly attention.

This study investigated the effectiveness, challenges, and implementation opportunities of Virtual Reality (VR) and Augmented Reality (AR) technologies in training individuals exposed to emergencies in critical contexts; to this end, a Systematic Literature Review (SLR) was conducted using the PICOC framework and the PRISMA 2020 protocol, searching Scopus, IEEE Xplore, and Web of Science for articles published between January 2020 and April 2025. A total of 665 records were initially retrieved, from which 60 studies were selected after duplicate removal and PICOC-based screening, with references managed in Mendeley and bibliometric analysis performed using the Bibliometrix package in RStudio. Results showed that VR consistently enhances technical skills, knowledge retention, and error reduction compared to traditional methods; in addition, AR optimizes precision in fine motor tasks via real-time contextual overlay, while Extended/Mixed Reality (XR/MR) solutions emerge as promising hybrid alternatives for collaborative decision-making and cognitive load reduction, albeit constrained by technical complexity and a lack of long-term validation; furthermore, significant methodological heterogeneity and the exclusion of users with disabilities were identified. In conclusion, VR and AR deliver clear benefits for emergency training and XR/MR holds potential for collaborative scenarios, prompting recommendations to standardize evaluation protocols with comparable metrics, promote longitudinal studies to assess learning persistence, and design inclusive interfaces that incorporate advanced haptics and physiological monitoring.

The expansion of generative artificial intelligence has led to a significant transformation in formal educational settings, particularly in higher education. This study aimed to critically analyze how recent academic literature conceptualizes the transformation of the teaching role in light of the incorporation of generative models such as ChatGPT. A critical literature review was developed based on Scopus (2023–2026), following PRISMA 2020 guidelines and reflective thematic analysis. The final corpus consisted of 36 indexed articles, complemented by Latin American contributions presented at LACCEI 2023–2025. The results identified four predominant narratives: transformational, instrumental, ethical-regulatory, and identity-based. The evidence indicates that teachers are not being replaced, but rather reconfigured toward functions of pedagogical design with AI, process evaluation, ethical mediation, and critical supervision. Academic evaluation emerges as the main point of tension, requiring redesigns focused on metacognition and learning traceability. Likewise, a geographical asymmetry in scientific production was identified, with limited Latin American representation. Based on the findings, a prospective model of teaching competencies in environments mediated by generative AI is proposed. The study concludes that the transformation of the teaching role constitutes an opportunity for professional strengthening when accompanied by critical training and coherent institutional governance.

In the context of emerging technologies and the increasing demands of the educational field, it is necessary to integrate artificial intelligence (AI) tools to enhance the competencies and skills of university students. This study aimed to analyze the scientific literature on the use of AI tools in higher education and their implications for student learning, considering research published between 2021 and 2025. A literature review methodology was employed to identify, select, and compile the most relevant articles from academic databases, resulting in 48 studies included for analysis. The findings show that the main AI tools are applied in courses in Social Sciences and Humanities, Health Sciences, Foreign Languages, Computer Technology and Programming, and STEM fields. Additionally, the impact of these tools was categorized into four dimensions: cognitive processes and thinking development, student motivation and engagement, communication and language skills, and personalized learning, self-regulation, and feedback.

The integration of Large Language Models (LLMs) into conversational tutoring systems can lower entry barriers in introductory programming courses with heterogeneous student profiles. This study evaluates the impact of such systems on performance and interaction, comparing students from STEM and non-STEM backgrounds. A quasi-experimental design ($N=30$) was implemented, comprising a control group (standard documentation) and an experimental group (intelligent tutor). Performance was assessed through five Python activities validated by unit tests, using a normalized score (0–1). The group utilizing TutorIA demonstrated a significant improvement, with average scores increasing from 0.43 to 0.78. Although STEM students maintained higher overall grades, non-STEM students achieved higher prompt quality scores (6.33/8.0 vs. 5.98/8.0), suggesting superior semantic contextualization. These findings indicate that TutorIA facilitated a leveling of the learning curve; however, both cohorts require distinct support mechanisms: logical reinforcement for non-STEM students and enhanced interaction strategies with AI assistants for STEM students.