to many educators [1]. However, the scientific community widely recognizes the transformative role of smart technologies in improving teaching and learning processes [2; 3]. [PERSON] et al. [2] identified 33 intelligent techniques that offer substantial benefits to education, including early detection of academic performance challenges, enhancing teachers' professional development, supporting students with autism spectrum disorders (ASDs), predicting and addressing school dropout risks, and improving the quality of educational content. Drawing inspiration from educational tools that teach foundational AI concepts through interactive games and quizzes [4], this paper presents a real-time mineral classification system integrated into an engaging quiz game. Powered by ML, the system allows players to identify minerals using a camera-based interface that recognizes and evaluates responses in real time. This innovative approach combines education with entertainment, enhancing learning experiences while raising awareness about the everyday applications of minerals, ultimately sparking interest in geology and the natural sciences. Accurate identification of rocks and minerals has long been a challenging endeavor, traditionally relying on the analysis of physical and chemical properties. These methods examine mineral assemblages and textures across various sample types, including thin sections and powders, to uncover their defining characteristics [5]. Common methods for this type of analysis include X-ray diffraction (XRD), X-ray fluorescence (XRF), Raman spectroscopy, scanning electron microscopy (SEM), and automated mineralogy systems. These techniques focus on chemical composition and crystal structure, often overlooking fundamental optical properties of minerals. Recent developments in imaging and artificial intelligence offer new potentials on mineral identification [6]. Specifically, machine learning algorithms (ML algorithms), particularly convolutional neural networks (CNNs) and face recognition technology can now be used to classify minerals based on optical characteristics alone. CNNs have overcome many limitations of earlier face recognition methods, such as issues with lighting and posture, by automating feature extraction and enhancing robustness [4; 7; 8; 9; 10; 11]. In geology, ML algorithms are rapidly expanding within artificial intelligence, with a wide range of applications for rock and minerals identification and classification. A key strength of machine learning applications lies in their ability to tackle complex challenges that cannot be resolved through a single, fixed solution [12]. Instead of relying on fixed models, ML algorithms address these challenges by processing large datasets through learning processes that enable them to gain \"expertise\" from experience [13]. Among the earliest machine learning methods for object detection was the Haar Cascade Classifiers (HCC), introduced by [PERSON] and [PERSON] in 2001 [14; 15], which uses Haar features to identify edges and patterns, efficiently computed with integral images. By applying AdaBoost, it combines weak classifiers to create a strong classifier, focusing on challenging positive and negative samples to minimize errors. Renowned for their simplicity and efficiency, they became widely used for detecting objects such as faces and eyes in images; however, with the rise of CNN-based deep learning methods, which excel at feature extraction and recognition, HCC is considered less versatile and less accurate for complex tasks. Notable advancements in the 2010s that applied deep learning algorithms for the identification and classification of minerals and rocks can be mentioned, include the work of [PERSON] et al. [16] who utilized various algorithms to classify rock microscopic images, as well as [PERSON] et al. [17], who presented a semi-automatic algorithm for identifying five pore types in carbonate rocks of a gas field from thin section images.