Abstract:

This project addresses a prevalent pedagogical challenge within Artificial Intelligence Systems Engineering: the widespread tendency to teach fundamental subjects, such as Search Algorithms, focusing solely on technical and computational aspects. This narrow focus often results in passive learning and the neglect of the essential humanistic, ethical, and social dimensions required for responsible innovation.

To effectively bridge this gap, the project advocates for the integration of active learning methodologies, specifically gamification and cooperative learning, aligned with the university's core pedagogical model. This model seeks to integrate scientific, philosophical, and theological perspectives to cultivate a holistic understanding of reality and human purpose. This approach is designed to foster deeper student motivation, significant learning, and the development of a critical consciousness regarding the societal impact of AI technologies.

The intervention is situated within the third-year curriculum of the Degree in Artificial Intelligence Systems Engineering at Francisco de Vitoria University (UFV), focusing on the Search Algorithms course. The methodology is implemented with a cohort of approximately 20 students. The central methodological component is an evaluated gamified Scavenger Hunt. Students are organized into cooperative teams with assigned roles (e.g., Planner, Spokesperson) and tasked with implementing various search algorithms (such as A*, Breadth-First Search, Greedy, Hill Climbing, etc.) to solve real-world optimization problems on a graph modeled after the university's campus map.

The process includes practical challenges and ethical mini-challenges. The activity culminates in a technical-reflective report that integrates performance analysis with an exploration of the four fundamental questions proposed by the pedagogical model at UFV mentioned before: What, Who, Why, and What For.

Anticipated outcomes include the achievement of a deeper technical and critical understanding of the algorithms, the development of self-regulation and teamwork skills, and the practical integration of the pedagogical model within a core STEM discipline. The results are expected to demonstrate the effectiveness of this methodological blend in forming AI professionals who are both technically competent and humanely conscious.

Keywords:

Active learning, Motivation, Gamification, Scavenger Hunt, Search Algorithms, Artificial intelligence.