Citation: Duji´ c Rodi´ c, L.; Stanˇ ci´ c, I.; ˇ Coko, D.; Perkovi´ c, T.; Grani´ c, A. Towards a Machine Learning Smart Toy Design for Early Childhood Geometry Education: Usability and Performance. Electronics 2023, 12, 1951. https://doi.org/10.3390/ electronics12081951 Academic Editors: Mohammad Jafari and Rania Hodhod Received: 23 March 2023 Revised: 11 April 2023 Accepted: 18 April 2023 Published: 21 April 2023 Copyright: © 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/). electronics Article Towards a Machine Learning Smart Toy Design for Early Childhood Geometry Education: Usability and Performance Lea Duji´ c Rodi´ c 1, * , Ivo Stanˇ ci´ c 1 , Duje ˇ Coko 1 , Toni Perkovi´ c 1 and Andrina Grani´ c 2 1 Faculty of Electrical Engineering, Mechanical Engineering and Naval Architecture, University of Split, 32 Ruąera Boškovi´ ca, 21000 Split, Croatia; istancic@fesb.hr (I.S.); dcoko@fesb.hr (D. ˇ C.); toperkov@fesb.hr (T.P.) 2 Faculty of Science, University of Split, Ruąera Boškovi´ ca 33, 21000 Split, Croatia; andrina.granic@pmfst.hr * Correspondence: dujic@fesb.hr Abstract: This study presents the design and evaluation of a plush smart toy prototype for teaching geometry shapes to young children. The hardware design involves the integration of sensors, microcontrollers, an LCD screen, and a machine learning algorithm to enable gesture recognition by the toy. The machine learning algorithm detects whether the child’s gesture outline matches the shape displayed on the LCD screen. A pilot study was conducted with 14 preschool children to assess the usability and performance of the smart toy. The results indicate that the smart toy is easy to use, engages children in learning, and has the potential to be an effective educational tool for preschool children. The findings suggest that smart toys with machine learning algorithms can be used to enhance young children’s learning experiences in a fun and engaging way. This study highlights the importance of designing user-friendly toys that support children’s learning and underscores the potential of machine learning algorithms in developing effective educational toys. Keywords: IoT; smart toy; machine learning; early childhood education; geometry; usability; human–computer interaction 1. Introduction The Internet of Things (IoT) has emerged as a revolutionary technology that connects various devices and systems to a network, allowing them to communicate and exchange data, thus revolutionizing the way we interact with the world around us. The proliferation of the IoT has ushered in a new era of smart and interconnected systems capable of improving efficiency, automating processes, and improving quality of life. This technology has found uses in a variety of industries, including healthcare, agriculture, transportation, smart cities, and energy [1]. In recent years, the integration of IoT in education has been a growing trend, offering innovative solutions for teaching and learning [2]. IoT technology has the potential to create interactive and immersive learning experiences that can improve student engagement, motivation, and learning outcomes due to the low-cost functionalities of smart devices [3]. These devices can collect and analyze data to improve educational quality and help educators make informed decisions [4]. As a consequence, they promote creativity, critical thinking, communication, and collaboration, leading to the development of higher-order thinking skills among learners [5]. Furthermore, the IoT can help bridge the digital divide by providing students with equal access to education regardless of their location or socioeconomic status [6]. Children, in particular, are benefiting from the incorporation of the IoT in education, since their daily activities primarily focus on the manipulation of physical materials such as toys [7]. Various IoT integration methods for child users have been investigated in this regard. For example, a study presented in [8] sought to improve the vocabulary learning of foreign language children by using multimodal cues in a task-based learning system composed of an educational robot and a 3D book powered by the IoT. According Electronics 2023, 12, 1951. https://doi.org/10.3390/electronics12081951 https://www.mdpi.com/journal/electronics