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    MoPI.IA: Exploring the usefulness of generative AI for thesis project ideation
    (HISIN (History of Information Systems), 2024-06-28)
    The “Modelo Personal para la Investigación” (MoPI) is a methodological tool created at the Pontificia Universidad Católica del Perú (PUCP) to support students in the elaboration of thesis proposals. In 2023, the MoPI.IA project was launched to integrate generative AI (IAG) into the MoPI methodology, with the aim of improving the ideation of research topics. Methodology: In this first phase of the project, ChatGPT 3.5 was used applying prompts engineering techniques. The responses generated by the chatbot were evaluated for relevance, consistency and practical value. Results: Initial exploration showed that MoPI.IA has significant benefits, especially for students facing difficulties in generating thesis ideas. The incorporation of chatbots showed great potential in this process. Discussion: Although MoPI.IA proved useful in research topic ideation, areas for improvement were identified in prompt engineering techniques and chatbot selection. Conclusions: Future research will evaluate other chatbots and additional techniques to further improve the MoPI methodology, with the goal of further innovation in supporting students in formulating thesis projects.
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    Foresight Study on the Influence of ChatGPT In Peruvian Universities Towards 2033
    (Academic Conferences and Publishing International, 2025-04-29)
    This article investigates how the integration of ChatGPT could transform Peruvian universities by 2033. Twenty drivers were identified, including digital competencies, critical thinking, academic performance, and motivation. Using a foresight approach, possible future scenarios were generated. The methodology consists of four stages: (1) Exploration of the system to identify key drivers; (2) Validation of the information by applying the Delphi method in real time to confirm these drivers; (3) Construction of scenarios using Schwartz axes and structural analysis; and (4) Validation of scenarios using the Probability, Desirability and Governance (PDG) method. The analysis revealed that the most important and uncertain drivers are related to 'training in critical thinking, feedback through intelligent tutoring, and the development of technological infrastructure,' located in Schwartz's Quadrant III. These drivers are crucial to building four scenarios, including the target scenario dubbed "Overcoming Challenges with Innovation and Technology".
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    Enhancing Methodological Integrity with GenAI: A Multi-case Study of Experiential Learning using Sequential Augmented Analysis
    (University of Wollongong, 2026-06-09)
    The rapid expansion of Generative Artificial Intelligence (GenAI) in higher education presents a critical pedagogical challenge for research training: how to integrate these tools without undermining methodological integrity. In qualitative research, unstructured GenAI use may encourage overreliance, superficiality, and unreflexive judgment among novice researchers. Despite growing debate, limited empirical evidence shows how GenAI can be deliberately designed to strengthen rigor in undergraduate qualitative data analysis. This study proposes and analyze the Sequencial Augmented Analysis a structured instructional model that embeds the use of chatbots within Human-Centered AI in Education and Experiential Learning frameworks. Using an exploratory multiple-case design with final-year pre-service teachers, we examined how GenAI-enhanced investigator triangulation and guided reflexivity support methodological integrity. Findings indicate that introducing chatbots after manual analysis stimulated collective reconsideration of decisions, systematic returns to original data, and clearer justification of methodological choices. It also surfaced personal biases, methodological assumptions, and ethical concerns regarding authorship and disclosure. Rather than replacing human judgment, GenAI functioned as a catalyst for dialogue and critique. The study offers a replicable pedagogical design for integrating GenAI into qualitative research courses while reinforcing academic integrity.