Abstract
In recent years, the intersection of Artificial Intelligence (AI) and education has led to the development of intelligent systems that support personalized, adaptive, and engaging learning experiences. While most research has focused on formal academic settings, this paper explores a novel domain—pilgrimage-based education—as a platform for integrating AI-driven learning. Pilgrimages, whether religious, cultural, or historical, offer a unique blend of physical journey, emotional introspection, and contextual exploration. However, their educational potential remains largely untapped in digital learning paradigms.
This research introduces PathGuideAI, an AI-powered mobile and wearable learning system designed specifically for use during pilgrimage routes. Leveraging a combination of natural language processing (NLP), geospatial tracking, learner modeling, and affective computing, PathGuideAI delivers personalized and contextually relevant educational content to users in real-time. The system provides historical, cultural, and spiritual information tailored to each location, while also promoting self-reflection through emotion-sensitive journaling and interactive storytelling.
A pilot deployment was conducted on the Camino de Santiago in Spain, involving 30 adult participants over a 5-day journey. Pre- and post-assessments, qualitative interviews, and app usage analytics were used to evaluate the system’s impact. The results indicate that AI-enhanced pilgrimage experiences significantly improve knowledge retention, emotional engagement, and reflective learning outcomes compared to traditional guidebooks or static apps.
This paper contributes to the emerging field of AI-supported experiential learning by demonstrating how intelligent systems can enhance spiritual and cultural education through real-time contextualization, personalization, and emotional resonance. The proposed framework also raises important questions regarding digital ethics, data privacy, accessibility, and cultural sensitivity, which are discussed in the context of designing inclusive and respectful AI learning technologies