Mobile application with Machine Learning and Augmented Reality for historical elements to increase knowledge of the Choquequirao Archaeological Park 2025
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Abstract
The Choquequirao Archaeological Park faces challenges in accessing historical information due to limited connectivity along its route. An offline mobile application called "Qirao" was developed, integrating Augmented Reality, Machine Learning, and Geolocation to enhance knowledge of the park. The methodology employed a quasi-experimental design comparing a control group with an experimental group, using a sample of 30 tourists selected through non-probability convenience sampling. For autonomous visual recognition, independent models based on the YOLO26 Preview architecture were trained and exported to TensorFlow Lite. The results showed a positive and statistically significant impact: the knowledge score increased from 11 to 17 points (+54.55%; t(28)=-5.572, p<.001, d=2.03), the time to locate historical elements was reduced from the 20-25 category to 10-20 minutes (U=216, p<.001, r=0.78), the number of elements located increased from 17 to 22 (+29.41%; t(15.19)=-2.933, p=.010, d=1.07) and the time to obtain information was reduced from the 60-100 range to 20-60 seconds (U=225, p<.001, r=0.85). One limitation was the small sample size (n=30) due to the lack of a stable pattern of daily tourist influx, which suggests caution when generalizing the findings.
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