GenAI-powered Agentic Platforms promise to foster productivity in Universities. Being used for academic (e.g. Tutors) or administrative (e.g. Chatbots) support, the potential is huge. Some Latin American universities have started the creation of these platforms using available platforms and models. In this panel, we will present the experience of three universities, members of the AI Global Education Network (AIGEN), in creating these agents.
Panelists
Moderator: José Escamilla / Héctor G. Ceballos, Tecnológico de Monterrey, México
One of the key problems in Higher Education is Student Dropout. The reasons for dropout are complex and diverse, and it has resulted in the need of specific interventions for each situation. For these interventions to be successful, institutions have put in place academic support programs based on proper criteria of in-risk of dropout students. Although AI-based predictive models could help to identify those students at risk of dropout, the staff that will use these tools may face limitations. This panel showcases the strategies followed by three Latin American Universities to support in-risk students and how they plan to incorporate AI to this aim.
Panelists
Moderator: Juan A. Talamás, Tecnológico de Monterrey, México
The purpose of this panel is to share strategies developed by higher education institutions to guide, regulate, and support the educational use of Generative AI (GenAI). Through policies, guidelines, training activities, and systematization processes, the panel seeks to promote a critical, ethical, and pedagogical appropriation of this emerging technology in the university setting. Institutional experiences that have specific criteria and recommendations for integrating GenAI in four key dimensions of academic practice will be analyzed: teaching, learning, assessment, and educational management.
Panelists
Moderator: Héctor G. Ceballos, Tecnológico de Monterrey, México
This meta-analysis aims to synthesize the empirical evidence on the effectiveness of AI chatbots in educational settings, focusing on their impact on cognitive and affective outcomes. By systematically comparing findings across diverse learning environments and learner populations, this study seeks to identify key factors that moderate the effectiveness of AI-powered chatbots.
Panelists:
Moderator: Rob Moore
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The use of Large Language Models (LLMs) has demonstrated clear performance gains for students. Yet performance is only part of the story. Scholars caution that polished outputs may come at the expense of genuine learning, as students risk offloading critical thinking and problem-solving to AI. This panel explores how we can move beyond productivity to design AI learning companions that prioritise learning gains over performance gains, nurturing curiosity, understanding, and deeper engagement.
Moderator: Hassan Khosravi
Feedback is one of the most powerful drivers of student learning, yet it is often delayed, inconsistent, or inaccessible at scale. While AI can generate instant responses, concerns remain about accuracy, trust, and the loss of the human dimension. This panel explores how human–AI teams can combine complementary expertise—AI’s scalability and speed with human empathy and contextual judgement—to reinvent the feedback loop and provide guidance that is timely, personalised, and transformative.
Moderator: Hassan Khosravi
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