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Innovation and Digital Transformation for a Sustainable World
       the CNN with sparse cross-entropy underscores the commitment  tactile nature of touchscreens provides a natural and user-friendly
       to achieving high accuracy in recognizing foundational elements  way for young children to explore and manipulate visual content,
       like alphabets and numbers, aligning with the core objectives of  surpassing the limitations associated with mousebased or indirect
       the Alpha-Bit research initiative.                     command interfaces (Geist, 2014).
       D. Integrating TensorFlow Lite Model into Android Studio for
       Alpha-bit Application
         Integrating a TensorFlow Lite model file into Android Studio
       for the Alpha-Bit application, developed with Kotlin and Jetpack
       Compose, involves a systematic process. First, the TensorFlow
       Lite model is prepared by training and optimizing it for character
       recognition, and then it is converted to the TensorFlow Lite
       format (‘.tflite‘) to ensure compatibility with mobile devices. In
       Android Studio, the project setup includes adding the necessary
       dependencies for TensorFlow Lite in the ‘build.gradle‘ file. The
       TensorFlow Lite model file is placed in the ‘assets‘ folder within
       the ‘main‘ directory of the Alpha-Bit project.
         Within the Kotlin codebase of Alpha-Bit, the TensorFlow Lite
       interpreter is used to load the model. This involves creating
       an interpreter instance and implementing a function (‘loadMod-
       elFile()‘) to load the TensorFlow Lite model file from the assets
       folder. The TensorFlow Lite model is then invoked for inference
       on input data, which, in Alpha-Bit’s case, would typically be
       images of characters. The TensorFlow Lite interpreter runs the
       model on the input data, producing output predictions.
         To seamlessly integrate TensorFlow Lite with the Jetpack Com-
       pose UI, the TensorFlow Lite inference is incorporated within the
       Kotlin code that defines the UI components. This ensures real-
       time character recognition within the Jetpack Compose interface.
       For example, the recognized character can be displayed within a
       Composable function using the TensorFlow Lite inference results.
         Throughout the integration process, thorough testing on various
       devices is conducted to ensure compatibility and optimal perfor-
       mance. Additionally, the TensorFlow Lite model and inference
       process are optimized to enhance efficiency on mobile devices.
       This comprehensive integration aligns with the overarching goal
       of Alpha-Bit, leveraging TensorFlow Lite to augment character         Fig. 4. Homepage of Alpha-Bit
       recognition capabilities within an Android application developed
       using Kotlin and Jetpack Compose. The seamless integration of  Thirdly, smartphones, with their built-in AI capabilities, present
       machine learning models into the mobile application contributes  an opportunity to leverage advanced technologies like Optical
       to the vision of democratizing education through innovative OCR  Character Recognition (OCR) for enhanced educational cus-
       technologies.                                          tomization. OCR, utilizing deep learning convolutional neural
                                                              networks, can recognize text in images and documents (Reddy
                    VI. RESULTS AND DISCUSSION
                                                              & Suruliandi, 2020). Integrating OCR into educational apps on
         This research focuses on the introduction of AI and computer  smartphones holds the potential for personalized and adaptive
       science concepts to young children through educational games,  learning experiences. This technology could automatically detect
       activities, and technologies. While existing studies demonstrate  children’s worksheets or drawings, providing valuable insights
       promise in enhancing foundational AI/CS skills and knowledge,  into their progress.
       a notable research gap lies in the absence of comparative eval-
       uations across various technological mediums. The inference  A. System Requirements
       drawn here suggests that utilizing smartphones as the primary
                                                                1) Hardware:
       technology interface may prove to be a more effective approach
       than some of the tools currently explored, for several compelling  • Processor: Intel Core i3 or equivalent
       reasons:                                                 • RAM: 4GB or higher
         Firstly, smartphones, being ubiquitous and mobile, transcend  • Storage: 50GB available disk space
       the confines of the classroom, enabling learning experiences  • Display: Minimum resolution of 1280x720 pixels
       to extend seamlessly into homes and flexible environments.  2) Operating System:
       The portability and widespread usage of smartphones empower
                                                                • Windows 10 or later
       children to engage in interactive AI/CS learning activities beyond
                                                                • macOS 10.12 (Sierra) or later
       traditional classroom settings. Research supports the advantages
                                                                • Linux distributions with kernel version 4.4 or later
       of mobile learning, indicating increased student engagement and
       personalized learning compared to exclusive reliance on conven-  3) Web Browser:
       tional classroom instruction (Sung et al., 2016).        • Google Chrome (latest version recommended)
         Secondly, smartphones offer an intuitive touch interface, allow-  • Mozilla Firefox (latest version recommended)
       ing for direct interaction that aligns seamlessly with the hands-  • Microsoft Edge (latest version recommended)
       on, interactive nature of effective early childhood pedagogy. The  • Safari (latest version recommended)
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