## 4 Discussion
This method leverages the strengths of multiple models, reducing misclassification during gameplay and ensuring reliable predictions by compensating for individual model weaknesses. However, its application in real-time scenarios presents challenges, including increased computational overhead from running multiple base models and a meta-model, which can lead to higher inference times. This latency sensitivity may impact the performance of real-time applications, particularly on devices with limited computational resources, such as mobile or embedded systems. User testing for the Mineral Quest quiz game was conducted at the Geo-ORAMA Mineralogy Museum, where secondary school students (aged 9-18) took part in interactive activities, including quizzes and classification games. These activities were facilitated by both a human instructor and the humanoid robot [PERSON]. Analysis of pre- and post-visit questionnaires revealed a significant increase in participants' willingness to engage in future educational programs, with post-visit responses frequently scoring 4 or 5 on a 5-point scale. Furthermore, 67% of participants agreed that the activities enhanced their understanding of minerals, and 77% reported that their interaction with [PERSON] was enjoyable and engaging. Demographic analyses highlighted that girls and younger participants (aged 9-12) experienced the largest positive shifts in both engagement and their willingness to participate in future programs. These findings demonstrate how educational programs based on advanced technology, such as the Mineral Quest quiz game, can effectively enhance learning outcomes in education. More generally, AI and ML are revolutionizing education by providing innovative solutions to improve teaching-learning processes at all levels. Technological developments in recent years are able to empower teachers and students by automating complex tasks, enhancing digital literacy, and supporting personalized learning experiences. The proposed Mineral Quest quiz game serves as a compelling example of how AI/ML can bridge the gap between abstract scientific concepts and practical learning, encouraging deeper engagement and understanding of real-world applications. Such initiatives align with ongoing efforts to promote digital transformation in education, paving the way for more inclusive and effective teaching practices. The diversity of the dataset plays a crucial role in determining the model's ability to generalize effectively across real-world scenarios. The current dataset includes seven distinct minerals (Pyrite, Aragonite, Quartz, Obsidian, Gypsum, Azurite, and Hematite), it is limited in scope and may not fully represent the wide range of mineral types and appearances encountered in practical applications. Variations in texture, luster, color, and crystalline structure among untested minerals could pose challenges to the model's classification accuracy. Furthermore, environmental factors such as inconsistent lighting, weathered surfaces, and overlapping mineral features introduce additional complexity that remains unaccounted for in the current study. False positives and false negatives remain a concern, as the Haar Cascade may misidentify non-mineral objects or fail to detect less distinct mineral features. HCC may struggle to detect subtle or less distinct mineral features, or it may mistakenly identify non-mineral objects, especially under complex environmental conditions such as variable lighting, occlusions, or noisy backgrounds. These issues can undermine the system's overall reliability and accuracy in real-time applications. Addressing these challenges could involve optimizing HCC parameters, adopting advanced feature extraction methods, and integrating complementary detection algorithms to improve robustness. Furthermore, refining preprocessing steps and incorporating adaptive feedback mechanisms into the workflow could help enhance the precision of ROI detection, enabling more consistent and accurate classification in a wider range of real-world scenarios.