1. Introduction
Drones, also referred to as unmanned aerial vehicles, or UAVs, have become a promising tool in the fields of education sciences, logistics, and industry [1]. Their integration into educational settings has gradually increased over the last ten years at all tuition levels, from elementary and secondary schools to universities and career training programs [2]. In the context of a broader shift towards STEM (Science, Technology, Engineering, and Mathematics) pedagogy practices, which encourage experiential, project-based learning methods, drone usage can be of paramount importance [3]. Thus, drones are frequently used in elementary and secondary education to provide students with interactive demonstrations of basic physics, coding, and 3D geometrical comprehension [4]. In addition, their use tends to be more focused on higher education, assisting with tuition procedures in domains like artificial intelligence (AI), robotics, aerospace engineering, and mechatronics [5,6,7].
Recent educational research has underscored the potential of drones not only to support technical learning objectives but also to develop transversal skills such as creativity, critical thinking, and digital competence. Their visual, hands-on, and open-ended nature fosters an inclusive learning environment that appeals to a wide range of learner profiles, including those under-represented in traditional STEM fields [7,8,9,10].
The use of drones in educational settings offers a plethora of pedagogical benefits. Initially, they support inquiry-based and active learning; promote teamwork; and offer concrete, instantaneous feedback. Additionally, their innate allure and adaptability boost student motivation and engagement; this quality is frequently tapped into through the use of gamification techniques that mimic real-world difficulties [11]. For instance, students can take part in sensor-based activities that mimic industrial applications, obstacle navigation, or drone flight missions [12].
In these contexts, drones function not merely as tools but as platforms for authentic learning, enabling students to engage in iterative design, testing, and refinement cycles [13]. Moreover, by providing a context-rich and immersive experience, drone-based education aligns well with constructivist and constructionist learning theories, wherein learners build meaningful knowledge structures through making and doing. However, there are also drawbacks to the use of drones in such environments. Broader implementation is frequently hampered by high costs, restricted access to technical resources, and safety concerns [14,15,16]. Additionally, a lot of educational drone kits that are sold commercially are either closed-source or incompatible with flexible, modular design; customization; and experimentation [17]. These challenges are especially pronounced in institutions with limited funding or without technical support staff, where accessible and open technologies are crucial for sustainable integration into teaching and laboratory activities.
In this study, a low-cost, open-source, 3D-printed drone is designed and built for use in university-level courses on emerging technologies. Developing a useful and expandable platform to teach advanced subjects in a STEM framework, such as robotics, control theory, mechatronics, and artificial intelligence, is the final target. In this case, in full contrast to commercially pre-assembled systems, the drone was designed as a practical tuition tool that enables undergraduate students to interact directly with the entire development lifecycle, from electronics integration and coding to 3D modeling and additive fabrication. This method fully aligns with constructivist learning principles, which hold that students actively create and manipulate knowledge objects to develop a deeper understanding. It also encourages students to take ownership of their learning, collaborate with peers, and solve authentic engineering problems—skills that are increasingly prioritized in engineering education accreditation frameworks (e.g., ABET and EUR-ACE) [18,19,20,21].
The current work distinguishes itself from other published literature works due to its direct emphasis on accessibility, openness, and adaptability, even though Section 2 (Background) provides a summary of earlier scholarly initiatives that have suggested specially designed educational drones. Current educational drone platforms frequently have limited capacity for expansion and modification, proprietary hardware/software limitations, or high procurement costs [22]. On the other hand, our proposed platform is completely fabricated using desktop Fused Filament Fabrication (FFF) 3D printing technology; utilizes widely accessible materials and components; and integrates with the Arduino ecosystem, an open-source microcontroller platform that is supported worldwide by the maker community [23]. Together, these distinct elements lead to a highly replicable and flexible system that is both economical and pedagogically effective at the same time.
Apart from its technical and economic advantages, our suggested workflow also acts as a platform for additional student research. Due to the fact that all design files, schematics, and control scripts are publicly accessible, an open-access culture is promoted, and students are able to replicate the system, as well as modify it to suit their own needs and coursework. For a variety of educational scenarios, from basic line-of-sight piloting to sophisticated autonomous navigation or environmental data collection, the platform is purposefully made to support and utilize a wide range of sensors and actuators. For academic institutions looking to update their STEM curricula with approachable, practical, and future-ready technology, it distinguishes itself from a mere teaching tool, also functioning as a versatile research and development tool.
2. Background
A growing corpus of scholarly work devoted to the design and development of specialized UAV platforms for teaching purposes has resulted from the increased attention that drone integration into educational contexts has received in recent years [24,25,26]. Drone-based educational kits and frameworks have been suggested in a number of studies, with the goals of improving STEM engagement, assisting with programming instruction, and facilitating practical learning in robotics and control systems. The complexity, cost, accessibility, and transparency of these initiatives vary greatly. Some strategies use commercial kits with proprietary firmware and closed hardware, while others try to create open frameworks for more pedagogical flexibility. A concise literature review of some of these educational drone implementations is given in this section, emphasizing both their main advantages and disadvantages. In order to depict the value of creating a low-cost, open-source, 3D-printed drone platform specifically designed to meet the demands of higher education, we compare these approaches in terms of cost, modularity, openness, 3D printability, and adaptability, while fully respecting the scientific merit and effort made by other researchers.
A multidisciplinary 3D-printed multirotor UAV platform for mechatronics education that is integrated with the PX4 flight stack and MATLAB/Simulink 2023a was presented by Kotarski et al. [27]. The system comes with extensive modeling and practical experiments and is publicly available. Because of the necessary autopilot board and finishing materials, it is still rather expensive, despite its high educational value. Furthermore, the platform’s modularity features some restrictions; non-PX4 tools cannot access its software ecosystem, and there are no simple options for Arduino expansion. By using a microcontroller core that is completely compatible with Arduino and focusing on low-cost extensibility across various sensor and actuator configurations, our platform tries to tackle the aforementioned gaps.
In another instance, a UAV education module was created by Bolick et al. [28], with an emphasis on teaching remote sensing and data processing through the use of simulations and pre-gathered datasets. Their method, which emphasizes drone data workflows over actual drone assembly, is highly approachable. Although useful for mass deployment and theoretical training, experiential learning in control theory, hardware integration, and structural design is somewhat hampered by the absence of practical fabrication and real-world flight. Students physically construct and program a flying device as part of our drone framework, which incorporates these experiential elements and strengthens their practical engineering skills.
Also, in 2019, Eller et al. [29] proposed “PiDrone”, a completely self-sufficient quadrotor intended for use in robotics classes. The platform uses a Raspberry Pi, utilizing Python-based SLAM and state estimation to provide onboard autonomy. Despite its impressive software capabilities, the project’s reliance on off-the-shelf Raspberry Pi hardware and lack of an inexpensive, 3D-printed frame raises costs and decreases student-led fabrication involvement. The autonomy idea is expanded upon in our work, which focuses on a self-designed printable structure and an Arduino core to provide an affordable, practical learning environment.
Based on interviews with American educators, Slater investigated workable methods for incorporating drones into STEM and Career and Technical Education (CTE) curricula [3]. In order to effectively introduce UAV concepts in primary and secondary classrooms, the paper identified accessible entry points, such as timed drone racing, precision flight challenges, and simulation-driven activities. Although this module promotes interdisciplinary collaboration and improves theoretical understanding, it usually uses inexpensive commercial drones with limited hardware literacy and customization options. Slater highlights a lack of open, adaptable drone platforms that facilitate practical experimentation and design. By providing a fully open-source, 3D-printed drone design—complete with electronics and firmware—our project directly meets this need by enabling students to investigate fabrication, sensor integration, and control systems as part of a university-level STEM curriculum.
In addition, a “Quadcopters Testing Platform for Educational Environments”, created especially for use in university robotics and control labs, was proposed by Veyna et al. [30]. Students can test control algorithms, including PID, LQR, and sliding-mode controllers, on their platform’s three-degrees-of-freedom (DoFs) experimental rig before implementing them on actual quadrotors. By fusing theoretical modeling, simulation, and physical experimentation, the system prioritizes experiential learning. Despite being sturdy and reasonably priced, the setup does not allow for complete drone assembly or aerostructural design. Rather than building the UAV themselves, students engage with pre-built rigs. On the other hand, our platform reinforces the mechanical, electrical, and computational aspects of STEM education by providing the complete experiential journey, from desktop 3D printing of the airframe to soldering of electronics and programming.
A notable implementation is the UAV-based Smart Educational Mechatronics System proposed by Luque-Vega et al. [31], which integrates a DJI Phantom 4 drone with a motion capture (MoCap) system and a hardware-in-the-loop (HIL) simulation framework. This system is structured within the Educational Mechatronics Conceptual Framework (EMCF), aiming to develop UAV-related knowledge and skills across three learning levels: concrete, graphic, and abstract. The instructional design combines real drone flight, virtual visualization via MoCap, and simulated control design using MATLAB/Simulink. Although the setup provides an advanced educational experience in topics like waypoint navigation and control system validation, it relies on high-cost commercial equipment (e.g., a DJI drone and MoCap cameras) and does not involve student-led fabrication or 3D printing. Furthermore, its use of PX4-based hardware over Arduino restricts accessibility and modifiability for broader educational applications. Nonetheless, the approach demonstrates the effectiveness of structured, simulation-enhanced drone education at the university level.
Wang et al. [32] introduced RflySim, an open-source multicopter development platform designed for education and research on UAVs, with a strong emphasis on rapid development and hardware-in-the-loop (HIL) testing. The system is built around the Pixhawk/PX4 autopilot and integrates seamlessly with MATLAB/Simulink, enabling users to design both low-level controllers (e.g., attitude and position control) and high-level decision-making logic without writing embedded C/C++ code. It follows a structured development flow—Software-in-the-Loop (SIL), HIL, and real flight tests—allowing for a smooth transition from simulation to real-world validation. The platform does not utilize 3D-printed parts or student-built airframes, instead focusing on software development, controller design, and algorithm testing on existing Pixhawk-based drones. It targets higher education audiences, particularly in control systems and robotics curricula. Although it excels in simulation and control customization, its focus is more on advanced control software than on full hardware construction or mechanical design by students.
González -Morgado et al. [33] introduced a one-degree-of-freedom UAV testbench designed specifically for classroom and laboratory education. This low-cost, open-source platform employs an Arduino-based control loop and MATLAB/Simulink interface, enabling students to actively design and test roll-angle controllers before working with full multirotor systems. Though the setup uses only a single axis and mock arm, it serves as an effective bridge between simulation and real-world flight control, highlighting fundamental UAV dynamics and control design in an accessible educational format [34].
Table 1 provides a comparative overview across important dimensions like cost, openness, fabrication method, hardware extensibility, and educational scope in order to summarize the salient characteristics and constraints of the evaluated educational drone platforms. This comparative study demonstrates the distinct benefits of our suggested drone framework and explains why, in spite of their many advantages, current solutions fall short of meeting all the requirements for an open-source, low-cost, hands-on platform designed for STEM higher education.
Furthermore, there are many projects on the Internet with open-source drones based on Arduino, such as the Arduino Nano Quadcopter (
In addition, there are several commercial educational micro drones on the market, such as the DJI RoboMaster TT (
3. Drone Development Framework for Academic Use
The primary concern of the researchers during the design phase of the drone was to integrate it into university education and to use it in the maximum number university courses. To this end, special attention was first given to the development of the drone’s learning objectives and concepts [35].
3.1. Drone’s Learning Objectives and Concepts
The structure of a multicopter drone (Figure 1) such as the proposed one is identified as a hybrid system incorporating multiple technologies, each of which is a field of research in its own right [36,37]: As a mechanical structure, it consists of a frame that supports the engines, the flight controller, the flight sensors and actuators, the batteries, and other parts [38]. As an electronic device, it consists of the basic circuits of the flight controller, the Electronic Speed Controllers (ESCs), the Inertial Measurement Unit (IMU), and some supportive circuits for the drone’s power supply and remote control [39]. As an automatic system, it incorporates control theory, for example, PID controllers [40]. As an autonomous system, it incorporates intelligent system theories, for example, machine learning, fuzzy systems, and neural networks, among others [41]. As an information system, it contains software—code in high-level and/or low-level languages, depending on its configuration. Last but not least, many other technologies are integrated inside the drone: data acquisition for the sensors, manipulation and filtering of sensor data (e.g., Kalman filters), radio communications for drone’s remote control, video transmission for First-Person View (FPV) operation, IoT technologies for telemetry, etc.
Anatomy of a drone and its fundamental subsystems (adapted from [39]).
[Figure omitted. See PDF]
Consequently, the design and construction of a drone requires a strong background in mechanical engineering, electronics, control theory, intelligent systems, telecommunications, informatics, and other scientific fields [42]. For example, as a natural synergistic system of multiple technologies, a drone is a mechatronic system and can be analyzed in the context of mechatronics; however, as an automatic system that moves autonomously in space, a drone is a also robotic system, which requires knowledge of robotics in order to move.
In this context, this drone was designed to fulfill all the above and below learning objectives and to help students, through a playful STEM educational experience [43], gain a deep understanding of difficult theoretical concepts and apply them in practice.
As a starting point for further evaluation of the drone, the course “Study and Development of Unmanned Systems” from the postgraduate “Unmanned Autonomous and Remote-Controlled Systems” curriculum was chosen to apply the above educational concepts. The course outline includes the following modules [44]: Drone design process: Determination of purpose and mission; drone cost analysis; definition of drone specifications and initial dimensioning. Drone architecture: Industrial design; 3D design and 3D construction of prototypes; prototype evaluation; selection of dimensions and basic parameters; selection of drone systems (payload and sensors); weight estimation; structural analysis; propulsion power supply; stability and aerodynamics for drones [45]. Drone flight controller: Design and development of an Arduino-based flight controller; program it with advanced controls: self-level, attitude stabilization, altitude hold, hover/position hold, headless mode, etc. [46].
3.2. Drone’s Use in Teaching University Courses
Thus, the proposed drone can be used by students to achieve several learning objectives: Mechatronics: Understanding and conceptualizing the mechatronics principles [47], meaning to make a mental connection between the design of the drone and the mechatronic system, and understanding all the drone’s required components, which leads to the engineering design process (Figure 2) [48].
2.. Mechanical Engineering: Deepening their knowledge of mechanical design and product manufacturing, meaning to gain experience in drones’ 3D design and 3D prototype production.
3.. Electronics: Dive into electronics design and embedded systems development, which involves designing, building, and evaluating a drone’s flight controller, IMU, power management systems, etc.
4.. Telecommunications: Handling the drone’s remote control, telemetry, data, and video (camera) transmission.
5.. Control Theory: Taking care of the drone’s stabilization involves the motors’ PID controllers [49].
6.. Robotics: Dealing with the drone’s path planning [50].
7.. Informatics: Diving into the drone’s flight controller programming and operational control, which involves analyzing the drone’s motions, controls, navigation, and operation and programming it in a high-level or low-level computer language.
8.. Intelligent Systems: All the extreme, fancy autonomous operations of the drone.
9.. Evaluation and testing of the drone prototype as a whole or its components individually. If necessary, redesign and reconstruction of the entire drone or improvement of the components that need improvement.
3.3. Drone Design and Development
The first step before designing a drone is to define its purpose and mission as it depicted in Figure 2. This, in combination with a cost analysis, results in the definition of the drone specifications and initial dimensioning (Figure 3) [45,51].
The proposed educational drone was designed (i) to be used as an educational tool to support university courses; (ii) to be an open-source, low-cost drone fabrication that can be built by students themselves using 3D printer; (iii) to be modular, extensible, and adaptable; (iv) to be accompanied by lesson plans for different courses; (v) to be easily simulated by free and/or open-source software adapted to higher education needs; and (vi) to be safe.
To support the above requirements, the researchers proceeded with the following design options for the drone. The authors designed a Quad-X quadcopter—meaning a multicopter drone with four motors—with propellers arranged in an X shape [46]: The drone’s frame (Figure 4) was designed in Tinkercad, a free cloud-based software for 3D design and electronic circuit simulation by Autodesk [52]. Tinkercad is a very handy and helpful tool, ideal for educators, as it supports classes, allows for project sharing among students, and is easy to use, with a short learning curve. In addition, it allows for the import of 3D designs from other 3D software such as Solid-Works, Inventor, Fusion, etc. In this way, students have a basic frame for the drone as a starting point to freely experiment, at no cost, and further customize it for subsequent 3D printing. The drone’s safety issues were initially addressed with a drone base (Figure 5) that limits the drone’s maneuverability, designed and manufactured in 3D, exclusively for the safe use of the drone. Note the four mounting holes on the drone’s frame, which serve a dual purpose: (i) securing the drone via an elastic thread or (ii) securing the drone via a wooden guide. In addition, appropriate safety parts were selected for the drone, such as propellers with low mass and a short length, so that, in the event of contact with a student, they would not cause serious injury. Low-mass, low-voltage, small-size brushless (BLDC) motors were selected for the same reason. The selected battery (Model: 18650, 3.7 V, 2600 mAh), although a lithium-ion type, has built-in PCB protection that protects against overcharging, short circuits, and deep discharge. The drone’s operating voltage (3.3 V) was chosen to be as low as possible for safety reasons. Students can safely experiment with the drone resting on its base. Initially, all the drone’s circuitry (flight controller, sensors, IMU, ESC, and battery) is located outside the drone, on a breadboard wired to its motors. Through this approach, students better understand the aerodynamics of the drone and learn its basic movements by experimenting with the speed of its motors. As they gain experience with potential flight and stabilization problems, circuitry is gradually transferred to the drone with the ultimate goal of releasing it from its base. The technical details of the drone are illustrated in the block diagram presented in Figure 6. Its microcontroller, an Arduino Nano BLE Sense [53], can be seen, as well as the IMU unit, with its built-in acceleration sensors, barometer, gyroscope, sensors for measuring light, proximity, gestures, temperature, humidity, and sound. The microcontroller drives the drone’s four BLDC motors through Electronic Speed Control (ESC). The drone’s telecommunications (remote control and telemetry) are covered by the Bluetooth protocol. Special provision was made for the future expansion of the drone with new hardware. For this purpose, there is an expansion port, where additional sensors (distance, LIDAR, etc.) and actuators, e.g., servos, can be connected.
Diving into more hardware details, the drone is equipped with a flight controller implemented by an Arduino Nano BLE Sense [53], a very powerful 64 MHz Arm® Cortex®-M4F microcontroller with artificial intelligence (AI) capabilities that incorporates many useful features, such as (i) a six-axis low-power IMU (BMI270 chip); (ii) a three-axis geomagnetic sensor (BMM150 chip); (iii) a barometer sensor (LPS22HB chip); (iv) a digital proximity, ambient light, RGB, and gesture sensor (APDS-9960 chip); (v) a humidity and temperature sensor (HS3003 chip); (vi) an omnidirectional digital microphone audio sensor (MP34DT06J chip); and (vii) native USB support. But the main thing is its support for wireless communication through Bluetooth® 5, 802.15.4 radio support, and Zigbee. This feature gives the advantage of easy interconnection of the drone to support the drone’s radio control and telemetry, without external circuitry.
Moreover, the drone’s flight controller must ensure compatibility with alternative microcontrollers [54]. For example, an Arduino Uno can be simulated using Tinkercad (Figure 7 and Figure 8), providing a low-cost option. Alternatively, an Arduino Nano ESP32 offers a built-in Wi-Fi interface, enabling Internet-based drone control, while a Raspberry Pi® RP2040 supports MicroPython programming, expanding the platform’s flexibility.
The choice of the appropriate microcontroller depends on the intended learning objectives. For introductory courses aimed at beginners, the Arduino Uno is more suitable due to its simplicity and lower cost. In contrast, if the objective is to teach artificial intelligence algorithms, the Arduino Nano BLE Sense is a more appropriate choice, given its built-in sensor suite and enhanced processing capabilities.
The total cost of the drone’s hardware depends on its final configuration (microcontroller, sensors, actuators, batteries, etc., as depicted in Table 2), but in any case, it is less than EUR 100, and if you use an Arduino Uno or ESP32 microcontroller, it is less than EUR 50 (Table 2). One of the many drone configurations based on an Arduino BLE Sense Rev2 is depicted in Figure 9.
The technical characteristics and performance of the drone are directly linked to the drone’s configuration (choice of microcontroller, sensors, etc.), which is selected each time. Therefore, drone performance indicators such as flight stability, flight time, and precision are linked to the respective configuration. For example, if you choose to use an Arduino Nani that integrates the sensors, the flight duration time reaches 7 min. The drone’s flight stability is closely related to its software, meaning that it is the students’ job to write a reliable flight controller (code) for the drone, since this is an educational drone built for educational purposes, e.g., learning about PID control. The drone’s software is presented in the paragraph below, but the researchers have already tested the drone with their software, and they find it quite reliable.
To further contextualize the economic benefits of the proposed platform, a brief comparison with commercially available and DIY educational drones is warranted. Most entry-level commercial STEM drones (e.g., the DJI RoboMaster TT, Ryze Tello EDU, and Robolink CoDrone EDU) are priced between EUR 200 and EUR 350 and are often tied to proprietary ecosystems that limit hardware modification. Open-source DIY builds based on Arduino or ESP32 can theoretically achieve lower costs (EUR80–120) but typically lack safety provisions, supporting documentation, and structured educational material, thereby reducing their suitability for supervised academic use. In contrast, the proposed platform maintains a reproducible total cost below EUR 100 (or even <EUR 50, depending on the microcontroller option, as depicted in Table 2) while additionally offering 3D-printable components, structured lesson plans, and built-in safety features such as tethering mounts and low-voltage power systems. Therefore, it equally balances affordability, openness, and pedagogical readiness, positioning it as a cost-efficient yet academically robust alternative to existing solutions.
4.. The drone’s software depends on its final hardware configuration (particularly, the choice of microcontroller), as well as the specific learning objectives. It is implemented using mainly high-level programming languages, e.g., C/C++ for programming Arduino and ESP microcontrollers or Python for programming Raspberry Pi Pico and for writing AI algorithms. In any case, the software is provided free to anyone, as open source, or when ready-made software is used, open-source software is selected. The developed drone software includes, among other components, the flight controller firmware, systems for collecting and transmitting flight and sensor data, and the implementation of PID controllers for the drone’s motors. These elements are directly aligned with the learning objectives of the courses in which the drone is used.
5.. Apart from the drone’s hardware and software design, the researchers provide a series of learning activities and lesson plans to support the related courses. For example, in the outline of the “Study and Development of Unmanned Systems” course, the lesson “Drone flight controller development” is included. In this lesson, the students come across microcontroller programming to control their drone. Thus, they write code to read the drone’s IMU, remotely control it (starting with its basic movements: thrust, yaw, roll, and pitch), and drive the drone’s motors with accuracy using PID and PWM techniques. In the outline of the “Mechatronics” course, the students deal with the drone as a mechatronic system and program it with advanced controls: attitude stabilization, self-level, altitude hold, hover hold, etc. In this way, students are gradually trained in more demanding tasks that require a stronger theoretical background in science, technology, engineering, and mathematics, but this is done playfully following a STEM educational approach [43]. Despite the difficulty, there is a high degree of student engagement, increased interest, and positive learning outcomes, as our research below shows.
It should be noted that this drone is a work in progress [18]. Although the initial, previous version was based on the same design principles: open hardware and software, 3D-printed, Arduino-based, low-cost, etc., the current version was modified in the following areas, which arose from the evaluation of the previous version: Redesign of the drone’s frame with improved safety that integrates four mounting holes for securing (instead of two as in the previous model); Use of BLDC motors (instead of brushed motors as used by the previous model) for better performance, reliability, and durability over time; Lower voltage operation of 3.7 V (instead of 7.4 V), meaning increased safety and lower cost; Use of one battery 18650 (instead of two as in the previous model), meaning increased safety and lower cost; Use of a better microcontroller, and Arduino Nano BLE Sense; Use of a better and lower power IMU (implemented by the BMI270, LPS22HB, and BMM150 chips) and use of more sensors (three-axis geomagnetic, barometric, digital proximity, ambient light, RGB, gesture, humidity, and temperature sensors, as well as microphones); Use of Bluetooth for the drone’s control and communication (instead of the wired connection used by the previous model); Simpler and less demanding electronic schematic and hardware with respect to the previous model; Possibility of modifying the drone parameters, such as the use of alternative microcontrollers, sensors, etc.
4. Methodology and Research Design
During its development, the drone has been used sporadically in courses for the purpose of rapid evaluation/feedback from students. However, the first recorded case study—others will follow in the future—was conducted at the University of West Attica (UNIWA) as part of the course “Study and Development of Unmanned Aerial Systems” to evaluate the usability of the drone as a practical tuition tool that enables students to interact directly with the entire development lifecycle of an Unmanned Aerial System (UAS)—a superset of Unmanned Aerial Vehicles (UAVs or drones)—from electronics integration and coding to 3D modeling and additive fabrication.
4.1. Research Questions
This research addressed three main Research Questions (RQs): RQ1.. Does this drone, along with the accompanying educational materials and activities, enhance the overall learning experience for students? RQ2.. Does this drone—as an educational tool—have practical value in students’ education? RQ3.. Do students intend to use this drone?
4.2. Research Context and Participants
The research was conducted at UNIWA during the spring semester (13 weeks, March to July) in 2024 and 2025. Participants were students attending the course “Study and Development of Unmanned Systems”: 37 males and 2 females (94.9% male, 5.1% female) aged 22–64 years old (Table 3). The gender distribution in our sample reflects the current participation trends in this course, where the subject matter tends to attract predominantly male students due to its specific academic and professional relevance.
The vast majority of the participants were working (97.4% employed, 2.6% unemployed), and a significant percentage of them had a Master’s/PhD degree (71.8% university degree, 28.2% Master’s/PhD). The vast majority of the participants had taken technology and engineering courses (76.9% Yes, 23.1% No), and about half of them had been previously involved in projects (56.4% Yes, 43.6% No), but most of them had not participated in student competitions in the past (76.9% No, 23.1% Yes). This information was crucial to understanding the participants’ background related to their experience in engineering project solving and is related to research items Q5–Q7 of the instrument development, as described below in Section 4.3.
In all activities, the proposed drone and its Tinkercad simulation model were used. The study utilized a STEM teaching approach, which is based on the content of the science, technology, engineering, and mathematics disciplines, all situated in a real-world context to interconnect these subjects and enhance student learning [55,56]. Based on this approach, the course’s methodology followed a structured integration of the above STEM disciplines within the context of the development of Unmanned Aerial Systems (UASs, i.e., UAVs or drones).
For the science discipline, the researchers covered several fundamental theoretical topics useful for the students to build a solid theoretical framework for understanding how to build a UAV. For the technology discipline, the researchers provided students with hands-on use of Arduino’s C/C++ programming environment and the Tinkercad simulation software based on the course content delivered through E-class, a learning management system, YouTube, and on-site lectures. In this way, the educational material was widely accessible and more interactive, and the students had the ability to explore, experiment, and adjust their self-paced learning experience according to their needs. The mathematics discipline was seamlessly integrated into the science and technology disciplines, as students worked on a series of equations that they had learned in theory (science discipline) and that are necessary for the development of UAVs. Finally, the engineering discipline was implemented by applying the above disciplines’ knowledge to the proposed physical drone and to a drone simulator developed in Tinkercad. The final step was the most important: students were allowed to apply their knowledge by assembling the drone and its electronic circuit and writing the code to control it. Tinkercad’s virtual environment was more suitable for drone’s quick experiments and fast code testing, but the hands-on physical drone is the realistic environment that reinforced the practical application of the engineering principles within the simulations. Together, all these STEM disciplines form an interdisciplinary approach to STEM education, fostering a comprehensive and experiential learning process in UAS and UAV development, as depicted in Figure 10 [56].
In addition, STEM supportive teaching techniques such as “Scenario”, “Role Play”, and “Project-Based Learning” were used [39]. The students formed teams of 2 people and worked together with district roles, tasks, and deliverables. The scenario technique describes the students’ collaboration and the team’s operation. The roleplay technique applies two roles (maker and programmer) to the students within the team. The maker was responsible for the hardware implementation and the assembly of the drone, and the programmer was responsible for writing the software (computer language code) for the drone to operate. During the course, the two roles (maker and programmer) were rotated cyclically after the end of each week’s activity, so that all students could know and become familiar with all the roles [55]. Through this STEM teaching approach and these supportive techniques, students gain theoretical knowledge, problem-solving abilities, and hands-on skills while they are working together as teams, preparing themselves for practical challenges in engineering [56].
During the case study, the researchers’ role was to guide, coordinate, motivate, and support the learning process for the students to enrich their knowledge and acquire important skills, resulting in a very practical pedagogical approach.
In summary, the course “Study and Development of Unmanned Systems” was delivered as follows (Table 4): Familiarization of the students with the operation of multicopter drones in general and quadcopters in particular: A short lecture is presented by the researchers to understand the components necessary for the drone and their importance. Familiarization of the students with the components of the proposed drone: The students receive the components of the drone to assemble and program it from scratch. Introduction to motors, propellers, and thrust: Students test the operation of a single motor and measure the operating voltage, current, and thrust produced for different propellers to understand how they affect the flying ability of the drone. Assembling the drone: Students place the drone on the base and verify its safe operation by providing the appropriate voltage/current. At this point, the motors are not controlled by a circuit—they are only powered. The students experiment with the clockwise and counterclockwise rotations of the 4 motors and their effects on the flight of the drone. Assembly of the flight controller of the drone: Micro-controllers (Arduino) and motor control using Pulse Width Modulation (PWM) are introduced. Students assemble the Arduino flight controller circuit and the motor drivers and start their experiments by writing the drone’s software (C/C++ code). To this end, they implement the drone model in the Tinkercad simulation software. First, they test their experimental code inside Tinkercad, and if this works, they apply it to the physical drone. In summary, the following learning activities are implemented: (i) Drone takeoff and landing; (ii) drone’s movement pitch, roll, and yaw; (iii) drone’s height stabilization using a distance sensor; (iv) reading the drone’s IMU sensors; (v) proportional integral derivative (PID) drone motor control; (vi) drone power management (battery), etc. In almost all experiments, the drone was sitting on its base for safety reasons and was powered by wire to maximize the experiment’s testing time. At the end of the course, students were allowed to test the drone without the base, powered by its battery.
4.3. Instrument Development
To answer both the research questions and the drone design functionality, quantitative (questionnaire) and qualitative (notes, students’ observations, and interviews) analyses were used. A questionnaire with 30 items was used for the quantitative research, which included both closed-ended Likert scale questions and open-ended descriptive questions for further qualitative analysis. Throughout the course, the researcher observed the students and their interaction with the drone, taking useful notes. In addition, during the hands-on experiments, he received useful feedback from the students about the problems and challenges they encountered with the drone. The research instrument—questionnaire (Table 5)—consisted of a demographic information part (gender, age, professional status, educational status, etc.) and a research part (questions related to RQs). To measure both the practical value of the drone and the intention to use it by the students, the TAM model was used, appropriately adapted to the present study. The Technology Acceptance Model (TAM) is a framework developed by Davis [57], designed for information systems, to measure four key constructs: Perceived Usefulness (PU), Perceived Ease Of Use (PEOU), Attitude Towards Use (ATU), and Behavioral Intention to Use (BIU). Previous studies have used the TAM to measure similar engineering education projects [58]. For this purpose, the Davis’s original TAM items were adapted to the research needs, translated into Greek, and returned to English to ensure translation equivalence [59]. All these items (Q26–Q30, Table 5) were measured with a 5-point Likert scale from 1 to 5, where 1—strongly disagree, 2—somewhat disagree, 3—neither agree nor disagree, 4—somewhat agree, and 5—strongly agree. The students were asked to fill out the questionnaire at the end of the course, after completing all the lessons with the hands-on drone teaching activities over the 13 weeks.
4.4. Limitations
As with any pilot study, certain limitations were inherent in the present research design. The sample (39 participants), and gender distribution (94.9% male, 5.1% female) reflect the actual enrollment in the selected postgraduate course. While these factors may limit the breadth of generalization, they do not diminish the validity of the findings within this context. The results offer meaningful insights into the platform’s usability and acceptance among the target student group.
Additionally, this study was focused on the development of Unmanned Aerial Systems (UASs, UAVs, and drones). However, the use of the proposed drone as an educational tool is not limited to this context; it can also be applied to a wide range of subjects, including mechatronics, control theory, and autonomous systems. This flexibility opens promising avenues for future research across diverse curricula.
The absence of a control group was a deliberate choice, as the primary objective was to conduct an initial assessment of the platform’s usability and the effectiveness of the accompanying educational materials within a STEM framework. This approach aligns with established practices in early-stage educational technology research, where user acceptance and engagement are foundational steps.
To address these limitations, future studies are planned to expand the participant pool, achieve greater diversity, apply the platform in additional engineering subjects, and incorporate control groups. These steps will build upon the current work and further strengthen the evidence base for the platform’s educational impact.
4.5. Research Ethics
This study was conducted following the rules of the Helsinki Declaration and was approved (5112/25 January 2024) by UNIWA’s Research Ethics Committee. The data collected from the participants were handled and stored according to the Data Protection Law.
4.6. Results
To answer the above research questions, the data collected from the questionnaire were examined using descriptive statistics to explore the students’ means. For this reason, the closed-type Likert 5-scale items, ranging from 1 (strongly disagree) to 5 (strongly agree), were duplicated and converted to ordinal variables, i.e., a number from 1 to 5. The “Did not respond” answers were weighted as 0. All the following statistical analyses were conducted with the open-source, free statistical software: Jamovi version 2.6 [60].
First, to answer RQ1, the mean scores of items Q9, Q11, Q12, Q13, Q14, Q15, Q17, and Q18 were calculated. Although items Q10, Q21, Q22, Q23, Q24, and Q25 are related to RQ1, they were excluded from the mean calculation because they are either open-ended questions or use a different scale that is not compatible with averaging. Similarly, to answer RQ2, the means of items Q19, Q26, Q27, Q28, and Q29 were calculated. Items Q16, Q18, and Q20 were not included in the calculations for the same reasons. Finally, to answer RQ3, only item Q30 was used.
The study revealed interesting results. First, we observed that the majority of the students believed, with an average score of 4.49 SD = 0.497 (Table 6), that the proposed drone, with its accompanying educational material and teaching activities, enhanced the overall learning experience. Box plots of the variables for RQ1–RQ3 are depicted in Figure 11.
Looking deeper into the item frequencies (Table 7) that make up RQ1, all the items received positive evaluation, with only a small percentage being moderate. This is also confirmed by the means of each item, as shown in Table 8.
Next, we observed that the majority of the students, with an average score of 4.31 SD = 0.430 (Table 6), consider that the proposed drone, as an educational tool, has a practical value in their education. Last but not least, the majority of the students, with an average score of 4.38 SD = 0.847 (Table 6), intend to use this drone.
In addition, the researchers obtained rich information from specific open-ended and closed-ended responses, as well as from notes taken during the educational activities. Specifically, the answers to question Q20 (“Which part of the course did you like the most?”) reveal that the majority of participants preferred the integration of the proposed drone into the educational process, either as a simulation or as a manual process (Table 9, Figure 12). This is also confirmed by the answers to open-ended question Q23 (“Fill in what you liked about the course in general.”), some of which are presented as follows:
“I liked seeing the design of a drone from scratch and the difficulties involved.”
“I liked that it involved the technical side.”
“The lab exercises”
“Fantastic lab equipment, course structure, and teaching methods at a very high level.”
“The lab exercises (Tinkercad, Arduino) helped me better understand the theory of the course.”
“The direct transition from theory to practice.”
“The hands-on lab exercises.”
“It was practical, unlike the other theoretical courses.”
“In this course, I liked the whole methodology that was followed, as it was not just a reference to theory, but with practical examples, it was possible to understand and implement several points of it in practice with the help and interest of the teaching professors.”
“The connection between theory and practice, testing algorithms directly, problems that arise during vehicle development, and things to watch out for.”
“I liked that we built our own drone.”
“I liked the implementation of the whole process in Tinkercad and Arduino.”
“How the course was delivered.”
“The course material, which is well structured.”
“The overall atmosphere during the lectures and laboratory exercises.”
“Its practical nature.”
Last but not least, the researchers’ notes taken during the training sessions revealed the following: (i) high levels of interest and enthusiasm among participants, partly due to the playful nature of the drone; (ii) teamwork and enthusiasm for work (many even worked during breaks); and (iii) willingness to experiment, critical thinking, and a cooperative environment. Most of these findings are confirmed by relevant research on STEM [17,47,55].
5. Discussion
5.1. Design and Implementation Challenges
The development and deployment of the proposed 3D-printed, open-source drone revealed several technical and pedagogical challenges that align with findings from previous studies on educational robotics and digital fabrication tools. From a design perspective, achieving a balance between structural integrity, low cost, and safety required multiple iterations of the drone frame. Lightweight materials and low-voltage components were selected to ensure safe classroom use, but these choices introduced constraints in terms of flight stability and payload capacity.
One of the most significant challenges was ensuring that the drone remained modular and extensible while being simple enough for students to assemble and program. Integrating multiple sensors and actuators with Arduino-compatible microcontrollers required careful hardware–software coordination. Students initially struggled with embedded programming and sensor calibration, particularly when transitioning from simulation environments like Tinkercad to real-world hardware.
These challenges are consistent with those reported in constructivist STEM education literature, where learners benefit from hands-on engagement but require structured instruction to navigate technical complexity. The iterative design process, supported by role-based learning and guided experimentation, helped address these difficulties and promoted a deeper understanding of system integration and control.
5.2. Integration into University-Level Instruction
The drone was successfully integrated into multiple university courses, including mechatronics, robotics, control theory, and artificial intelligence, serving as a case study for the analysis and design of a mechatronic system. Students engaged with the Engineering Design Process (EDP), progressing from conceptualization and 3D modeling to fabrication, coding, and testing. This end-to-end experience enabled them to apply theoretical knowledge in a practical, interdisciplinary context.
In mechatronics, the drone served as a tangible example of a hybrid system, combining mechanical, electrical, and software subsystems. In robotics and AI, students explored path planning, sensor fusion, and autonomous behavior, while in control theory, they implemented and tuned PID controllers for flight stabilization. These applications align with constructivist and experiential learning models, which emphasize active knowledge construction through real-world problem solving.
Quantitative feedback collected through structured questionnaires indicated high levels of perceived usefulness and ease of use, consistent with the Technology Acceptance Model (TAM). Qualitative observations further revealed increased motivation and problem-solving ability and deeper understanding of complex engineering concepts. These findings support the conclusion that the proposed drone platform is not only technically feasible but also pedagogically effective in enhancing STEM education.
From a broader perspective, the drone platform contributes to the growing body of work promoting open, accessible, and customizable educational technologies. Its low cost and open-source nature make it particularly suitable for institutions with limited resources, addressing equity and inclusion in engineering education.
5.3. Comparison with Existing Research Findings
The findings of this study align with previous research indicating that hands-on engagement with drone-based learning platforms significantly enhances motivation, perceived usefulness, and conceptual understanding among engineering students. Similar to Bolick et al. [28] and Yeung et al. [12], who reported increased learner engagement through UAV-assisted teaching in simulation-based settings, our results confirm high levels of student interest, with 79.5% rating the course as “too much interesting” and a mean perceived usefulness score of 4.31/5. However, whereas most prior studies have emphasized virtual or partially assembled drone kits, our results extend this understanding by demonstrating that full-cycle construction—from 3D printing to flight testing—further deepens comprehension and skill acquisition, as reflected in strong Technology Acceptance Model (TAM) indicators such as behavioral intention to use (4.38/5).
Unlike studies such as that by Luque-Vega et al. [31], which focused on high-cost, MoCap-integrated drone education systems, or that by Wang et al. [32], which prioritized simulation and hardware-in-the-loop testing over physical assembly, our findings suggest that low-cost, open-source fabrication not only offers comparable educational impact but also enhances autonomy, creativity, and collaborative problem solving. This divergence highlights a significant contribution of the current study: while existing literature often separates experiential learning from full system assembly due to safety or complexity constraints, our approach demonstrates that a carefully scaffolded, tethered, and modular drone platform can overcome these barriers while preserving pedagogical richness. Therefore, the present work reinforces earlier conclusions on the effectiveness of UAVs in STEM education while providing empirical evidence that deeper hardware involvement amplifies learning gains beyond what has been previously reported.
6. Conclusions
This study demonstrates the successful development and deployment of a low-cost, open-source, 3D-printed drone platform for university-level STEM education, with a particular focus on mechatronics, robotics, control theory, and artificial intelligence. The platform’s modular architecture and Arduino compatibility enabled students to engage in the full engineering lifecycle, from design and simulation to fabrication, programming, and flight testing. Through its integration in structured lesson plans and role-based learning scenarios, the drone fostered creativity, collaboration, and technical proficiency, supporting both theoretical understanding and practical skill development.
The main contributions of this research are twofold. Theoretically, it advances the field of STEM education by providing an accessible, replicable, and customizable open-source drone platform that supports interdisciplinary, project-based learning. Practically, the study offers a validated educational tool that enhances student engagement, teamwork, and hands-on experience, as evidenced by high levels of satisfaction, motivation, and skill acquisition in electronics prototyping, PID control, and autonomous system design. These findings are supported by a structured questionnaire administered to all participants based on the Technology Acceptance Model (TAM), which captured student perceptions of usefulness, ease of use, and intention to continue using the platform.
The case study employed a single-group design using quantitative survey data, focusing on initial usability and student engagement. However, several limitations should be acknowledged. The pilot case study involved 39 participants within a single postgraduate course over a single semester. The gender distribution in the sample reflects current participation trends in engineering education, where male students are predominant—a pattern observed in many similar programs. The absence of a control group and the low percentage of female participants further restrict the generalizability of the findings. While a control group and pre/post testing were not included at this stage and the evaluation centered on immediate learning outcomes, these choices align with the exploratory nature of this work.
Building on these results, future research should seek to broaden the platform’s application to undergraduate teaching and a wider array of engineering and science disciplines. It will be important to implement longitudinal studies that track long-term learning outcomes and skill retention while also involving larger and more diverse student populations to improve gender balance and include control groups for more robust evaluation. Additionally, further work should explore integration with advanced simulation environments such as CoppeliaSim and MATLAB/Simulink and extend the platform’s use to areas like environmental monitoring, IoT, and autonomous navigation.
By addressing these directions, the drone platform can further establish itself as a scalable, adaptable, and impactful resource for modern STEM curricula, supporting both educational innovation and research.
Conceptualization, A.C., A.K. and P.Z.; methodology, A.C., A.K. and P.Z.; validation, A.C., A.K. and P.Z.; investigation, A.C., A.K. and P.Z.; data curation, A.C., A.K. and P.Z.; writing—original draft preparation, A.C., A.K. and P.Z.; writing—review and editing, T.G. and M.P.; supervision, T.G. and M.P. All authors have read and agreed to the published version of the manuscript.
The authors declare that this study was conducted following the rules of the Helsinki Declaration and was approved (5112/25 January 2024) by UNIWA’s Research Ethics Committee. The data collected from the participants were handled and stored according to the Data Protection Law.
The data presented in this study are available upon request from the corresponding author.
The authors declare no conflicts of interest.
The following abbreviations are used in this manuscript:
| STEM | Science, Technology, Engineering, and Mathematics |
| UAV | Unmanned Aerial Vehicle |
| UAS | Unmanned Aerial System |
| AI | Artificial Intelligence |
| FFF | Fused Filament Fabrication |
| CTE | Career and Technical Education |
| DoF | Degree of Freedom |
| ESC | Electronic Speed Controller |
| IMU | Inertial Measurement Unit |
Footnotes
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.
Figure 2 The engineering design process of a drone from the perspective of the mechatronic system (adapted from [
Figure 3 Key dimensions and related technologies in a drone (adapted from [
Figure 4 The design of the drone in Tinkercad.
Figure 5 The design of the drone’s base in Tinkercad.
Figure 6 The design of the drone’s base in Tinkercad.
Figure 7 A simple version of the drone implemented with an Arduino Uno microcontroller in Tinkercad for simulation purposes.
Figure 8 Electronic schematic of the drone (based on an Arduino Uno) implemented in Tinkercad.
Figure 9 An assembled drone (implemented with an Arduino Nano BLE Sense microcontroller), ready to fly.
Figure 10 The integration of the STEM disciplines and the contribution of each one to the comprehensive educational experience in the development of UASs and UAVs in this research.
Figure 11 Box plots of RQ1, RQ2, and RQ3.
Figure 12 The plot of Q20. Which part of the course did you like the most?.
Comparative overview of selected educational drone platforms.
| Reference | Cost | Open-source | 3D-Printed Frame | Controller Type | Customizability/Expandability | Target Education Level |
|---|---|---|---|---|---|---|
| Kotarski et al. (2025) [ | High | Partially | Yes | PX4 + MATLAB/Simulink | Moderate (limited Arduino support) | Undergraduate/Graduate |
| Bolick et al. (2022) [ | Low | No | No | Simulation only | Low | Secondary/Introductory |
| Eller et al. (2019)—PiDrone [ | Moderate | Yes | No | Raspberry Pi (Python-based) | Limited (non-Arduino, fixed frame) | University (Robotics courses) |
| Slater (2024) [ | Low | No | No | Commercial kits | Very low | Primary/Secondary |
| Veyna et al. (2021) [ | Moderate | No | No | Pre-built 3DoF rig | Low (predefined testbed) | University (Control courses) |
| Luque-Vega et al. (2022) [ | High | No | No | PX4 with Simulink HIL | Moderate (requires MoCap and DJI) | University (Mechatronics) |
| Wang et al. (2021)—RflySim [ | Moderate | Yes | No | Pixhawk/PX4 + Simulink | High (controller logic, HIL support) | University (STEM/Robotics) |
| González-Morgado et al. (2024) [ | Low | Yes | No | Arduino + MATLAB/Simulink | Moderate (control modes, GUI tuning) | University and technical labs |
| Jeong et al. (2019) [ | Low–Moderate | Yes | No | Raspberry Pi Zero (Python-based) | Moderate–High (sensor add-ons, autonomy scripts) | High School/Undergraduate |
| This work (2025) | Low | Yes | Yes | Arduino-compatible MCU | High (modular and extensible) | University (STEM fields) |
Bill of materials (BOM) for two (of the many) different versions of the proposed drone.
| Drone Version Based on an Arduino Nano BLE Sense Rev2 Microcontroller | |||
|---|---|---|---|
| Component | Quantity | Price | Sub Total |
| Arduino Nano 33 BLE Sense Rev2 | 1 | 28.15€ | 28.15€ |
| Set of 1503 2750 KV Brushless Motor with 1503 Propeller 4-blade (plus screws) | 4 | 3.08€ | 12.32€ |
| 3.7 V–5 V Miniature Brushless ESC 1 S Brushless Driver without BEC 6 A | 4 | 0.49€ | 1.96€ |
| Joystick Dual-axis XY | 2 | 0.79€ | 1.58€ |
| 18650 1300 Mah Lithium—3.7 V Battery | 1 | 2.00€ | 2.00€ |
| Case 1 × 18650 Battery Holder With Wire Leads 3.7 V | 1 | 0.10€ | 0.10€ |
| Heat-resistant Cable 24 AWG Ultra Soft Silicone Wire High Temperature Flexible Copper (1 m) | 1 | 0.41€ | 0.41€ |
| Heat-resistant Cable 14 AWG Ultra Soft Silicone Wire High Temperature Flexible Copper (1 m) | 1 | 1.19€ | 1.19€ |
| Heat-resistant Cable 30 AWG Ultra Soft Silicone Wire High Temperature Flexible Copper (1 m) | 10 | 0.31€ | 3.10€ |
| 40 Pin 1 × 40 Single Row Male And Female 2.54 Pin Header Connector | 1 | 0.24€ | 0.24€ |
| Heat Shrinkable Tube Black 1 m 1.5 mm | 1 | 0.07€ | 0.07€ |
| Heat Shrinkable Tube Black 1 m 2.5 mm | 1 | 0.08€ | 0.08€ |
| Heat Shrinkable Tube Black 1 m 4 mm | 1 | 0.09€ | 0.09€ |
| PCB for prototype 6 × 8 | 1 | 0.23€ | 0.23€ |
| 400 Tie Points Solderless PCB Breadboard Mini | 1 | 1.27€ | 1.27€ |
| Hot Glue stick | 1 | 0.03€ | 0.03€ |
| Double sided tape (300 cm Transparent Adhesive Tape Masking 10 mm) | 1 | 0.02€ | 0.02€ |
| Plastic Nylon Cable Ties Wire Zip Tie 2 × 100 mm | 10 | 0.01€ | 0.10€ |
| 0.8 MM Tin Soldering Wire 12 g Silver Solder Wire | 1 | 1.12€ | 1.12€ |
| PLA 37 g, 12.3 m for drone’s frame | 1 | 0.93€ | 0.93€ |
| Total | 54.06€ | ||
| Drone Version Based on an Arduino UNO R3 Microcontroller | |||
| Component | Quantity | Price | Sub Total |
| Arduino Uno R3 Clone (R3 ATMEGA328P Chip CH340G) | 1 | 5.50€ | 5.50€ |
| Set of 1503 2750 KV Brushless Motor with 1503 Propeller 4-blade (plus screws) | 4 | 3.08€ | 12.32€ |
| 3.7 V–7.4 V Miniature Brushless ESC 1 S Brushless Driver without BEC 6 A | 4 | 0.49€ | 1.96€ |
| Joystick Dual-axis XY | 2 | 0.79€ | 1.58€ |
| GY-521 MPU-6050 MPU6050 Module 3 Axis analog gyro sensors+ 3 Axis Accelerometer Module | 1 | 0.96€ | 0.96€ |
| 18650 1300 Mah Lithium—3.7 V Battery | 2 | 2.00€ | 4.00€ |
| Case 1 × 18650 Battery Holder With Wire Leads 3.7 V | 2 | 0.10€ | 0.20€ |
| Heat-resistant Cable 24 AWG Ultra Soft Silicone Wire High Temperature Flexible Copper (1 m) | 1 | 0.41€ | 0.41€ |
| Heat-resistant Cable 14 AWG Ultra Soft Silicone Wire High Temperature Flexible Copper (1 m) | 1 | 1.19€ | 1.19€ |
| Heat-resistant Cable 30 AWG Ultra Soft Silicone Wire High Temperature Flexible Copper (1 m) | 10 | 0.31€ | 3.10€ |
| 40 Pin 1 × 40 Single Row Male And Female 2.54 Pin Header Connector | 1 | 0.24€ | 0.24€ |
| Heat Shrinkable Tube Black 1 m 1.5 mm | 1 | 0.07€ | 0.07€ |
| Heat Shrinkable Tube Black 1 m 2.5 mm | 1 | 0.08€ | 0.08€ |
| Heat Shrinkable Tube Black 1 m 4 mm | 1 | 0.09€ | 0.09€ |
| PCB for prototype 6 × 8 | 1 | 0.23€ | 0.23€ |
| 400 Tie Points Solderless PCB Breadboard Mini | 1 | 1.27€ | 1.27€ |
| Hot Glue stick | 1 | 0.03€ | 0.03€ |
| Double sided tape (300 cm Transparent Adhesive Tape Masking 10 mm) | 1 | 0.02€ | 0.02€ |
| Plastic Nylon Cable Ties Wire Zip Tie 2 × 100 mm | 10 | 0.01€ | 0.10€ |
| 0.8 MM Tin Soldering Wire 12 g Silver Solder Wire | 1 | 1.12€ | 1.12€ |
| PLA 37 g, 12.3 m for drone’s frame | 1 | 0.93€ | 0.93€ |
| Total | 33.23€ | ||
Frequencies of participants’ gender; age; professional status; educational status; and participation in technology courses, competitions, and projects.
| 1. Gender | Count | % of Total | Cumulative % |
|---|---|---|---|
| Female | 2 | 5.1% | 5.1% |
| Male | 37 | 94.9% | 100.0% |
| 2. Age | Count | % of Total | Cumulative % |
| 22–34 | 24 | 61.5% | 61.5% |
| 35–44 | 7 | 17.9% | 79.5% |
| 45–54 | 6 | 15.4% | 94.9% |
| 55–64 | 2 | 5.1% | 100.0% |
| 3. Professional | Count | % of Total | Cumulative % |
| Employed | 38 | 97.4% | 97.4% |
| Unemployed | 1 | 2.6% | 100.0% |
| 4. Educational | Count | % of Total | Cumulative % |
| University degree | 28 | 71.8% | 71.8% |
| Master/PhD | 11 | 28.2% | 100.0% |
| 5. Have you taken technology and engineering courses at school? | Count | % of Total | Cumulative % |
| No | 9 | 23.1% | 23.1% |
| Yes | 30 | 76.9% | 100.0% |
| 6. Have you participated in student competitions? | Count | % of Total | Cumulative % |
| No | 30 | 76.9% | 76.9% |
| Yes | 9 | 23.1% | 100.0% |
| 7. Have you been involved in the design, development, and construction of projects? | Count | % of Total | Cumulative % |
| No | 17 | 43.6% | 43.6% |
| Yes | 22 | 56.4% | 100.0% |
Bill of materials (BOM) for two (of the many) different versions of the proposed drone.
| Lesson—Topic | Week No. | Hours | Link |
|---|---|---|---|
| 1.1 Familiarization of the students with the operation of multicopter drones in general and quadcopters in particular: A short lecture is presented by the researchers to understand the components necessary for the drone and their importance. | 1 | 2 | |
| 1.2 Familiarization of the students with the components of the proposed drone: The students receive the components of the drone to assemble and program it from scratch. They are familiarized with the parts and the components (frame, base, protective gear, electronic parts, microcontrollers, etc.) of the drone and with the 3D printing process of its components (frame, base, accessories, etc.). | 1 | 2 | Drone frame 3D: |
| 2.1 Introduction to motors, propellers, and thrust: Students test the operation of a single motor and measure the operating voltage, current, and thrust produced for different propellers to understand how they affect the flying ability of the drone. | 2 | 1 | |
| 2.2 Introduction to Tinkercad simulation: Students are introduced to Tinkercad’s circuit design software. They are familiarized with electronic circuits. They build and simulate a circuit to control motor operation by supplying different voltages/amperages. | 2 | 1 | Quadcopter Lab No1—Test & Measure Motor’s Thrust V, I, RPM— |
| 2.3. Testing and measurement of (all) drones’ motor thrust voltage, amperage, and RPM. Students assemble the electronic circuit to control all the drone’s motors at once, taking care to ensure the correct CW/CCW rotation. First, they build and simulate the circuit in Tinkercad, then in breadboard with real parts, before finally implementing it in the drone. They experiment with various voltages/amperages and test the operation of the drone’s motors. | 2 | 2 | Quadcopter Lab No2—Test & Measure All Motors’ Thrust V, I, RPM— |
| 3.1. Assembling the drone: Students place the drone on the base and verify its safe operation by providing the appropriate voltage/current. At this point, the motors are not controlled by a circuit—they are only powered. The students experiment with the clockwise and counterclockwise rotations of the 4 motors and their effects on the flight of the drone. | 3 | 2 | |
| 3.2 Assembly of the flight controller of the drone: Micro-controllers (Arduino) and motor control using Pulse Width Modulation (PWM) are introduced. Students assemble the Arduino flight controller circuit and the motor drivers and start their experiments by writing the drone’s software (C/C++ code). To this end, they implement the drone model in the Tinkercad simulation software. First, they test their experimental code inside Tinkercad, and if this works, they apply it to the physical drone. | 3 | 2 | Quadcopter Lab No3—Control UAV height with Serial Monitor— |
| 4. Manual control of the drone’s takeoff, landing, and height via throttle: Students add a potentiometer to manually control the drone’s takeoff, landing, and height. They are introduced to an Analog-to-Digital Converter (ADC) to read the potentiometer values and transform them to the drone’s throttle. First, they implement the drone’s circuit in the Tinkercad simulation and write the associated software. Then, they test their experimental code inside Tinkercad, and if this works, they apply it to the physical drone. | 4 | 4 | Quadcopter Lab No4—Control UAV height with Potentiometer— |
| 5. Control and stabilization of the drone’s height with sonar: Students add a hypersonic distance module to control the height during flight automatically. They are introduced to hypersonic theory to read the distance sensor. First, they implement the drone’s circuit in the Tinkercad simulation and write the associated software. Then, they test their experimental code inside Tinkercad, and if this works, they apply it to the physical drone. | 5 | 4 | Quadcopter Lab No5—Control and Stabilize UAV height with Sonar— |
| 6. Control the drone’s movements via joysticks: Students add two joysticks to manually control the drone’s takeoff, landing, thrust, pitch, roll, and yaw. They are introduced to the theory of the quadcopter’s flying principles. First, they implement the drone’s circuit in the Tinkercad simulation and write the associated software. Then, they test their experimental code inside Tinkercad, and if this works, they apply it to the physical drone. | 6 | 4 | Quadcopter Lab 6—Control UAV’s movements with Joysticks— |
| 7. Control of the drone’s movements via joysticks and automatic stabilization: Students add two joysticks and a hypersonic distance sensor to manually control the drone’s takeoff, landing, thrust, pitch, roll, and yaw and to automatically stabilize the drone. First, they implement the drone’s circuit in the Tinkercad simulation and write the associated software. Then, they test their experimental code inside Tinkercad, and if this works, they apply it to the physical drone. | 7 | 4 | Quadcopter Lab No7—Control UAV with JoySticks and Sonar Stabilize— |
| 8. Control of the drone’s motors with PID controllers: Students are introduced to control systems. They learn how to use Proportional Integral Derivative (PID) controllers to control the drone’s motors. First, they implement the PID controller in the Tinkercad simulation by writing their code. Then, they test their experimental code inside Tinkercad, and if this works, they apply it to the physical drone. | 8 | 4 | No code/help provided. Students write their own code |
| 9. Control of the drone’s motors with PID controllers: Students calibrate their PID controllers to the physical drone. | 9 | 4 | No code/help provided. |
| 10. Implementation the drone’s Inertial Measurement Unit (IMU): Students are introduced to the concept and meaning of the drone’s IMU. They learn about accelerometers, barometers, gyros, and other sensors and how these are used in the drone’s IMU. Students connect the MPU6050 (accelerometer and gyroscope sensor) to the Arduino. They are familiarized with and experiment with the IMU by reading its data/measurement values. | 10 | 4 | No code/help provided. Students research the Internet and write their own code |
| 11. Stabilization of the drone: Students connect the MPU6050 to the drone’s circuit and implement PID control (via software) to stabilize the drone’s flight. | 11 | 4 | No code/help provided. Students write their own code |
| 12. Introduction to drone communications: Students are familiarized with Bluetooth and Wi-Fi networks. They experiment with ESP32 and Arduino Nano BLE Sense microcontrollers by writing code to send and receive data to and from them. They are free to select to build Web-based applications by writing code in HTML, CSS, and JavaScript, or they can build mobile apps by using MIT’s App Inventor. | 12 | 4 | No code/help provided. Students research the Internet and write their own code |
| 13. Final drone project: Students combine all existing knowledge to build their own drone based on their own decisions. Based on the proposed drone, they select the drone’s microcontroller, the IMU’s sensors, the radio communications, the power management (battery), etc. Finally, they present their project. | 13 | 4 | No code/help provided. Students research the Internet and write their own code |
Instrumental research items.
| Item | Type | Related to |
|---|---|---|
| Q1. Gender | Closed-ended | Demographics |
| Q2. Age | Closed-ended Likert scale | Demographics |
| Q3. Professional status | Closed-ended | Demographics |
| Q4. Educational level | Closed-ended | Demographics |
| Q5. Have you taken technology and engineering courses at school or elsewhere? | Closed-ended | Demographics |
| Q6. Have you participated in student competitions? | Closed-ended | Demographics |
| Q7. Have you been involved in the design, development, and construction of projects? | Closed-ended | Demographics |
| Q8. If you answered “Yes” to the previous question, please briefly describe what you have done in the past. | Open-ended | Demographics |
| Q9. Did you find the course interesting? | Closed-ended Likert scale | RQ1 |
| Q10. How did you participate in the course? | Closed-ended | RQ1 |
| Q11. The course met my expectations and needs? | Closed-ended Likert scale | RQ1 |
| Q12. Were the lectures (theory) of the course useful? | Closed-ended Likert scale | RQ1 |
| Q13. Were the lab exercises (Tinkercad and Arduino projects) useful? | Closed-ended Likert scale | RQ1 |
| Q14. Was it easy to understand the lectures of the course? | Closed-ended Likert scale | RQ1 |
| Q15. Was it easy to understand the laboratory exercises? | Closed-ended Likert scale | RQ1 |
| Q16. Did the lab exercises (Tinkercad, Arduino projects) help you better understand the course theory? | Closed-ended Likert scale | RQ1, RQ2 |
| Q17. I am satisfied with the teaching methodology of this course. | Closed-ended Likert scale | RQ1 |
| Q18. I am satisfied with the educational material of the course. | Closed-ended Likert scale | RQ1, RQ2 |
| Q19. I am able to apply the knowledge acquired during the course. | Closed-ended Likert scale | RQ2 |
| Q20. Which part of the course did you like the most? | Closed-ended | RQ2 |
| Q21. The instructor managed to spark my interest during the educational program. | Closed-ended Likert scale | RQ1 |
| Q22. Write a few words about the instructor. | Open-ended | RQ1 |
| Q23. Fill in what you liked about the course in general. | Open-ended | RQ1 |
| Q24. Fill in what you didn’t like about the course in general. | Open-ended | RQ1 |
| Q25. Suggest any teaching modules that you would like to see added to the course syllabus. | Open-ended | RQ1 |
| Q26. The use of the drone enhanced my learning experience. | Closed-ended Likert scale | RQ2 |
| Q27. Using the drone helped me to better understand the UAVs concepts. | Closed-ended Likert scale | RQ2 |
| Q28. By using the drone I can implement UAV related exercises more quickly and efficiently. | Closed-ended Likert scale | RQ2 |
| Q29. I like the idea of using the drone for my UAV training. | Closed-ended Likert scale | RQ2 |
| Q30. I would recommend the drone to other students who want to learn about UAVs. | Closed-ended Likert scale | RQ3 |
Descriptors for the research questions (RQ1–RQ3).
| RQ1 | RQ2 | RQ3 | |
|---|---|---|---|
| N | 39 | 39 | 39 |
| Missing | 0 | 0 | 0 |
| Mean | 4.49 | 4.31 | 4.38 |
| Median | 4.50 | 4.40 | 5 |
| Standard deviation | 0.497 | 0.430 | 0.847 |
| Minimum | 3.00 | 2.80 | 2 |
RQ1’s research item frequencies.
| Frequencies of Q9. Did you find the course interesting? | |||
| 9. Did you find the course interesting? | Count | % of Total | Cumulative % |
| Moderate | 1 | 2.6% | 2.6% |
| A lot | 7 | 17.9% | 20.5% |
| Too much | 31 | 79.5% | 100.0% |
| Frequencies of Q11. The course met my expectations and needs. | |||
| 11. The course met my expectations and needs. | Count | % of Total | Cumulative % |
| Moderate | 2 | 5.1% | 5.1% |
| A lot | 13 | 33.3% | 38.5% |
| Too much | 24 | 61.5% | 100.0% |
| Frequencies of Q12. Were the lectures (theory) of the course useful? | |||
| 12. Were the lectures (theory) of the course useful? | Count | % of Total | Cumulative % |
| Moderate | 3 | 7.7% | 7.7% |
| A lot | 13 | 33.3% | 41.0% |
| Too much | 23 | 59.0% | 100.0% |
| Frequencies of Q13. Were the laboratory exercises useful? | |||
| 13. Were the laboratory exercises useful? | Count | % of Total | Cumulative % |
| Moderate | 3 | 7.7% | 7.7% |
| A lot | 12 | 30.8% | 38.5% |
| Too much | 24 | 61.5% | 100.0% |
| Frequencies of Q14. Was it easy to understand the lectures of the course? | |||
| 14. Was it easy to understand the lectures of the course? | Count | % of Total | Cumulative % |
| A lot | 20 | 51.3% | 61.5% |
| Too much | 15 | 38.5% | 100.0% |
| Frequencies of Q15. Was it easy to understand the laboratory exercises? | |||
| 15. Was it easy to understand the laboratory exercises? | Count | % of Total | Cumulative % |
| Moderate | 8 | 20.5% | 20.5% |
| A lot | 17 | 43.6% | 64.1% |
| Too much | 14 | 35.9% | 100.0% |
| Frequencies of Q17. I am satisfied with the teaching methodology of this course. | |||
| 17. I am satisfied with the teaching methodology of this course. | Count | % of Total | Cumulative % |
| Moderate | 1 | 2.6% | 2.6% |
| A lot | 13 | 33.3% | 35.9% |
| Too much | 25 | 64.1% | 100.0% |
| Frequencies of Q18. I am satisfied with the educational material of the course. | |||
| 18. I am satisfied with the educational material of the course. | Count | % of Total | Cumulative % |
| Moderate | 4 | 10.3% | 10.3% |
| A lot | 13 | 33.3% | 43.6% |
| Too much | 22 | 56.4% | 100.0% |
Descriptors of RQ1’s research items.
| Q9 | Q11 | Q12 | Q13 | Q14 | Q15 | Q17 | Q18 | |
| N | 39 | 39 | 39 | 39 | 39 | 39 | 39 | 39 |
| Missing | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| Mean | 4.77 | 4.56 | 4.51 | 4.54 | 4.28 | 4.15 | 4.62 | 4.46 |
| Standard deviation | 0.485 | 0.598 | 0.644 | 0.643 | 0.647 | 0.745 | 0.544 | 0.682 |
| Minimum | 3 | 3 | 3 | 3 | 3 | 3 | 3 | 3 |
| Maximum | 5 | 5 | 5 | 5 | 5 | 5 | 5 | 5 |
Item frequencies for Q20.
| 20. Which Part of the Course Did You Like the Most? | Count | % of Total | Cumulative % |
| Theory lectures | 10 | 25.6% | 25.6% |
| Laboratory exercises (simulation in Tinekrcad) | 16 | 41.0% | 66.7% |
| Laboratory exercises (construction with Arduino) | 13 | 33.3% | 100.0% |
1. Mohsan, S.A.H.; Khan, M.A.; Noor, F.; Ullah, I.; Alsharif, M.H. Towards the Unmanned Aerial Vehicles (UAVs): A Comprehensive Review. Drones; 2022; 6, 147. [DOI: https://dx.doi.org/10.3390/drones6060147]
2. Ryalat, M.; Almtireen, N.; Al-refai, G.; Elmoaqet, H.; Rawashdeh, N. Research and Education in Robotics: A Comprehensive Review, Trends, Challenges, and Future Directions. J. Sens. Actuator Netw.; 2025; 14, 76. [DOI: https://dx.doi.org/10.3390/jsan14040076]
3. Slater, T.F. Identifying Implementation Strategies for Integrating Drones into STEM and Career Technology Education CTE Programs. Educ. Sci.; 2024; 14, 105. [DOI: https://dx.doi.org/10.3390/educsci14010105]
4. Andalib, S.Y.; Monsur, M.; Cook, C.; Lemon, M.; Zawarus, P.; Loon, L. Enhancing Landscape Architecture Construction Learning with Extended Reality (XR): Comparing Interactive Virtual Reality (VR) with Traditional Learning Methods. Educ. Sci.; 2025; 15, 992. [DOI: https://dx.doi.org/10.3390/educsci15080992]
5. Luque Vega, L.F.; Lopez Neri, E.; Santoyo, A.; Ruíz Duarte, J.; Farrera Vázquez, N. Educational Methodology Based on Active Learning for Mechatronics Engineering Students: Towards Educational Mechatronics. Comput. Sist.; 2019; 23, pp. 325-333. [DOI: https://dx.doi.org/10.13053/cys-23-2-3196]
6. Wang, S.I.C.; Liu, E.Z.F.; Huang, Y.Y.; Sang, H.Y. When Drone Meets AI Education: Boosting High School Students’ Computational Thinking and AI Literacy. Proceedings of the 2024 Pacific Neighborhood Consortium Annual Conference and Joint Meetings (PNC); Seoul, Republic of Korea, 29–31 August 2024; pp. 45-52.
7. Ng, D.W.K.; Kwan, B.H.; Phua, Y.N.; Ong, K.J.; Rahman, F.A. The Utilization of Drones as a Catalyst for Igniting Passion in STEM Education in Malaysia. Proceedings of the 2024 Photonics & Electromagnetics Research Symposium (PIERS); Chengdu, China, 21–25 April 2024; Volume 21, pp. 1-5.
8. Sivenas, T.; Koutromanos, G.; Larios, N. Taking a Flight with Drones in STEM Education: Benefits, Affordances, Facilitating Factors and Challenges. Communications in Computer and Information Science; Springer Nature: Cham, Switzerland, 2025; pp. 308-320. ISBN 9783032026743
9. Amenyo, J.-T.; Kpo, W. Leveraging Programmable Educational Drones, Robots and AI for Learning STEM, Computational Thinking and Higher Order Thinking in Schools in Rural Villages. Drones—Various Applications; IntechOpen: London, UK, 2023; ISBN 9780854661640
10. Al-Alawi, A.I.; Alnuwaihedh, S.M. Drones in Innovative Education: A Systematic Literature Review. Proceedings of the 2024 International Conference on IT Innovation and Knowledge Discovery (ITIKD); Manama, Bahrain, 13–15 April 2025; pp. 1-9.
11. Johal, W.; Gatos, D.; Yantac, A.E.; Obaid, M. Envisioning Social Drones in Education. Front. Robot. AI; 2022; 9, 666736. [DOI: https://dx.doi.org/10.3389/frobt.2022.666736]
12. Yeung, R.C.Y.; Yeung, C.H.; Sun, D.; Looi, C.-K. A Systematic Review of Drone Integrated STEM Education at Secondary Schools (2005–2023): Trends, Pedagogies, and Learning Outcomes. Comput. Educ.; 2024; 212, 104999. [DOI: https://dx.doi.org/10.1016/j.compedu.2024.104999]
13. Jiang, M.Y.-C.; Jong, M.S.-Y.; Chai, C.S.; Huang, B.; Chen, G.; Lo, C.-K.; Wong, F.K.-K. They Believe Students Can Fly: A Scoping Review on the Utilization of Drones in Educational Settings. Comput. Educ.; 2024; 220, 105113. [DOI: https://dx.doi.org/10.1016/j.compedu.2024.105113]
14. Ziegler, H.H.; Ryu, J. Designing Drone-Based Solutions for Climate Education in Secondary Schools. Sci. Teach.; 2025; 92, pp. 58-65. [DOI: https://dx.doi.org/10.1080/00368555.2025.2468336]
15. Rezaee, M.R.; Hamid, N.A.W.A.; Hussin, M.; Zukarnain, Z.A. Comprehensive Review of Drones Collision Avoidance Schemes: Challenges and Open Issues. IEEE Trans. Intell. Transp. Syst.; 2024; 25, pp. 6397-6426. [DOI: https://dx.doi.org/10.1109/TITS.2024.3375893]
16. Shuaibu, A.S.; Mahmoud, A.S.; Sheltami, T.R. A Review of Last-Mile Delivery Optimization: Strategies, Technologies, Drone Integration, and Future Trends. Drones; 2025; 9, 158. [DOI: https://dx.doi.org/10.3390/drones9030158]
17. Rahmani, H.; Weckman, G.R. Working under the Shadow of Drones: Investigating Occupational Safety Hazards among Commercial Drone Pilots. IISE Trans. Occup. Ergon. Hum. Factors; 2024; 12, pp. 55-67. [DOI: https://dx.doi.org/10.1080/24725838.2023.2251009]
18. Chatzopoulos, A.; Xenakis, A.; Papoutsidakis, M.; Kalovrektis, K.; Kalogiannakis, M.; Psycharis, S. Proposing and Testing an Open-Source and Low-Cost Drone under the Engineering Design Process for Higher Education: The Mechatronics Course Use Case. Proceedings of the 2024 IEEE Global Engineering Education Conference (EDUCON); Kos Island, Greece, 8–11 May 2024.
19. Kumar, A.; Ahuja, S.; Gupta, G. ABET Accreditation Process for Engineering & Technology Programs: Detailed Process Flow from Criteria 1 to Criteria 8. Proceedings of the 12th International Seminar on New Paradigm and Innovation on Natural Sciences and Its Applications (12th ISNPINSA): Contribution of Science and Technology in the Changing World; Semarang, Indonesia, 19–20 October 2022; AIP Publishing: Melville, NY, USA, 2024.
20. Headley, M.; Benson, L. Literature Review Paper: Exploring Engineering Students Perceptions of Engineering Accreditation. Proceedings of the 2024 World Engineering Education Forum-Global Engineering Deans Council (WEEF-GEDC); Sydney, Australia, 2–5 December 2024; pp. 1-6.
21. Gouia-Zarrad, R.; Gharbi, R.; Amdouni, A. Lessons Learned from a Successful First Time ABET Accreditation of Three Engineering Programs. Proceedings of the 2024 IEEE World Engineering Education Conference (EDUNINE); Guatemala City, Guatemala, 10–13 March 2024; pp. 1-6.
22. Power, J.R.; Buckley, J. The Purpose of Second Level Engineering Education: An Examination of Curricular Consistency and Industry Alignment. Australas. J. Eng. Educ.; 2025; 30, pp. 37-48. [DOI: https://dx.doi.org/10.1080/22054952.2024.2430155]
23. Sattar, F.; Nawaz, M. STEM Learning through Drones: A Pedagogical Approach for Critical Thinking and Problem Solving. Proceedings of the 2024 IEEE Global Engineering Education Conference (EDUCON); Kos Island, Greece, 8–11 May 2024.
24. Shan, J.; Wang, W. Making and Sharing in Asynchronous Discussion: Exploring the Collaboration Process in Online Maker Community. Interact. Learn. Environ.; 2023; 31, pp. 3003-3017. [DOI: https://dx.doi.org/10.1080/10494820.2021.1916764]
25. He, L.; Shi, M.; Dong, Y.; Wang, H.; An, T. Practical Teaching Design of UAV Mission Planning Based on STEM. Proceedings of the 2024 International Symposium on Artificial Intelligence for Education; Xi’an, China, 6–8 September 2024; ACM: New York, NY, USA, 2024; pp. 109-114.
26. Manzoni, M.; Rubinacci, R.; Gozzini, G.; Invernizzi, D. Control-Oriented Modeling and Hierarchical Control of Multirotor UAVs for Research and Teaching Purposes. IFAC-PapersOnLine; 2024; 58, pp. 147-152. [DOI: https://dx.doi.org/10.1016/j.ifacol.2024.08.477]
27. Kotarski, D.; Pranjić, M.; Alharbat, A.; Piljek, P.; Bjažić, T. Multirotor UAV-A Multidisciplinary Platform for Teaching Mechatronics Engineering. Sensors; 2025; 25, 1007. [DOI: https://dx.doi.org/10.3390/s25041007]
28. Bolick, M.M.; Mikhailova, E.A.; Post, C.J. Teaching Innovation in STEM Education Using an Unmanned Aerial Vehicle (UAV). Educ. Sci.; 2022; 12, 224. [DOI: https://dx.doi.org/10.3390/educsci12030224]
29. Eller, L.; Guerin, T.; Huang, B.; Warren, G.; Yang, S.; Roy, J.; Tellex, S. Advanced Autonomy on a Low-Cost Educational Drone Platform. Proceedings of the 2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS); Macau, China, 3–8 November 2019.
30. Veyna, U.; Garcia-Nieto, S.; Simarro, R.; Salcedo, J.V. Quadcopters Testing Platform for Educational Environments. Sensors; 2021; 21, 4134. [DOI: https://dx.doi.org/10.3390/s21124134]
31. Luque-Vega, L.F.; Lopez-Neri, E.; Arellano-Muro, C.A.; González-Jiménez, L.E.; Ghommam, J.; Saad, M.; Carrasco-Navarro, R.; Ruíz-Cruz, R.; Guerrero-Osuna, H.A. UAV-Based Smart Educational Mechatronics System Using a MoCap Laboratory and Hardware-in-the-Loop. Sensors; 2022; 22, 5707. [DOI: https://dx.doi.org/10.3390/s22155707] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/35957267]
32. Wang, S.; Dai, X.; Ke, C.; Quan, Q. RflySim: A Rapid Multicopter Development Platform for Education and Research Based on Pixhawk and MATLAB. Proceedings of the 2021 International Conference on Unmanned Aircraft Systems (ICUAS); Athens, Greece, 15–18 June 2021.
33. González-Morgado, A.; Heredia, G.; Ollero, A. An Open-Source, Low-Cost UAV Testbench for Educational Purposes. Robot 2023: Sixth Iberian Robotics Conference. ROBOT 2023; Marques, L.; Santos, C.; Lima, J.L.; Tardioli, D.; Ferre, M. Lecture Notes in Networks and Systems Springer: Cham, Switzerland, 2024; Volume 978, [DOI: https://dx.doi.org/10.1007/978-3-031-59167-9_44]
34. Jeong, H.; Lin, X.; Shaker, A.; Cardenas, I.; Kim, J.-H.; Robins, J.; Chi, Y.-S. Design of a Low-Cost, Open Source, and Open Hardware Educational Drone. Proceedings of the 36th Florida Conference on Recent Advances in Robotics; Tampa, FL, USA, 9 May 2019.
35. Hell, M.; Bolam, R.C.; Vagapov, Y.; Anuchin, A. Design of a portable drone for educational purposes. Proceedings of the 2018 25th International Workshop on Electric Drives: Optimization in Control of Electric Drives, IWED; Moscow, Russia, 31 January–2 February 2018; pp. 1-5. [DOI: https://dx.doi.org/10.1109/IWED.2018.8321377]
36. Dinelli, C.; Racette, J.; Escarcega, M.; Lotero, S.; Gordon, J.; Montoya, J.; Dunaway, C.; Androulakis, V.; Khaniani, H.; Shao, S.
37. Ugheoke, I.B.; Nurudeen, A.H.; Abiire, O.A.; Edem, A.O.; Towoju, E.A.; Onche, E.O. A Pragmatic Template for Quadcopter Development. Adv. Aerosp. Sci. Technol.; 2024; 9, pp. 61-74. [DOI: https://dx.doi.org/10.4236/aast.2024.92005]
38. Papoutsidakis, M.; Chatzopoulos, A.; Piromalis, D. UAV flight control based on Arduino board implementations. Int. J. Eng. Appl. Sci. Technol.; 2019; 4, pp. 438-443. [DOI: https://dx.doi.org/10.33564/IJEAST.2019.v04i05.064]
39. Tatale, O.; Anekar, N.; Phatak, S.; Sarkale, S. Quadcopter: Design, Construction and Testing. Int. J. Res. Eng. Appl. Manag.; 2019; 4, pp. 1-7. [DOI: https://dx.doi.org/10.18231/2454-9150.2018.1386]
40. Vamsi, D.S.; Tanoj, T.V.S.P.; Krishna, U.M.; Nithya, M. Performance Analysis of PID controller for Path Planning of a Quadcopter. Proceedings of the 2019 2nd International Conference on Power and Embedded Drive Control, ICPEDC; Chennai, India, 21–23 August 2019; pp. 116-121. [DOI: https://dx.doi.org/10.1109/ICPEDC47771.2019.9036558]
41. Chen, W.C.; Lin, C.L.; Chen, Y.Y.; Cheng, H.H. Quadcopter Drone for Vision-Based Autonomous Target Following. Aerospace; 2023; 10, 82. [DOI: https://dx.doi.org/10.3390/aerospace10010082]
42. Corbaci, F.K.; Dogan, Y.E. An Approach to Preliminary Design of a Quadrotor Cargo UAV. Int. J. Aviat. Sci. Technol.; 2023; 4, pp. 63-74. [DOI: https://dx.doi.org/10.23890/IJAST.vm04is02.0202]
43. Sattar, F.; Nawaz, M. Developing Computational Thinking in STEM Education with Drones. Proceedings of the 2023 IEEE Global Engineering Education Conference (EDUCON); Kuwait, Kuwait, 1–4 May 2023; pp. 1-5. [DOI: https://dx.doi.org/10.1109/EDUCON54358.2023.10125117]
44. MSC-Drones. Cources MSc-Drones. Uniwa.gr.; 2021; Available online: https://msc-drones.uniwa.gr/cources_el/ (accessed on 24 July 2025).
45. Tyto Robotics. Drone Building and Optimization: How to Increase Your Flight Time, Payload and Overall Efficiency. 2021; Available online: https://cdn.rcbenchmark.com/landing_pages/eBook_%20Drone%20Building%20and%20Optimization.pdf?mc_cid=98f84accad&mc_eid=13f5a8b5ea (accessed on 15 October 2025).
46. Louros, A.; Papoutsidakis, M.; Chatzopoulos, A.; Drosos, C. Design of an Innovative Flight Controller for Quadcopter Robust Handling. J. Multidiscip. Eng. Sci. Technol.; 2018; 5, pp. 8968-8972.
47. Chatzopoulos, A.; Tzerachoglou, A.; Priniotakis, G.; Papoutsidakis, M.; Drosos, C.; Symeonaki, E. Using STEM to Educate Engineers about Sustainability: A Case Study in Mechatronics Teaching and Building a Mobile Robot Using Upcycled and Recycled Materials. Sustainability; 2023; 15, 15187. [DOI: https://dx.doi.org/10.3390/su152115187]
48. Walden, D.D.; Roedler, G.J.; Forsberg, K.J.; Hamelin, R.D.; Shortell, T.M. Systems Engineering Handbook—A Guide for System Life Cycle Processes and Activities; 4th ed. Wiley: Hoboken, NJ, USA, 2015.
49. Ezhil, V.R.S.; Sriram, B.S.R.; Vijay, R.C.; Yeshwant, S.; Sabareesh, R.K.; Dakkshesh, G.; Raffik, R. Investigation on PID Controller Usage on Unmanned Aerial Vehicle for Stability Control. Mater. Today Proc.; 2022; 66, pp. 1313-1318. [DOI: https://dx.doi.org/10.1016/j.matpr.2022.05.134]
50. Xatzopoulos, A.; Papoutsidakis, M.; Chamilothoris, G. Mobile Robotic Platforms as Educational Tools in Mechatronics Engineering. Proceedings of the 8th International Conference on Energy and Environment Research (eRA–8); Piraeus, Greece, 23–25 September 2013; pp. 41-51.
51. Ramos-Romero, C.; Green, N.; Roberts, S.; Clark, C.; Torija, A.J. Requirements for Drone Operations to Minimise Community Noise Impact. Int. J. Environ. Res. Public Health; 2022; 19, 9299. [DOI: https://dx.doi.org/10.3390/ijerph19159299]
52. Tinkercad. Tinkercad. 2019; Available online: www.tinkercad.com/ (accessed on 18 July 2025).
53. Arduino. Arduino Nano 33 BLE Sense Rev2. Arduino Official Store. 2024; Available online: https://store.arduino.cc/products/nano-33-ble-sense-rev2?_gl=1%2As0wdi4%2A_up%2AMQ.%2A_ga%2ANzIzMjY0NzEwLjE3NTI3NjAzNjk.%2A_ga_NEXN8H46L5%2AczE3NTI3NjAzNjgkbzEkZzEkdDE3NTI3NjAzNzMkajU1JGwwJGgxNjk4NjA1ODMx (accessed on 18 July 2025).
54. Putsep, K.; Rassolkin, A. Methodology for Flight Controllers for Nano, Micro and Mini Drones Classification. Proceedings of the 2021 International Conference on Engineering and Emerging Technologies (ICEET); Istanbul, Turkey, 27–28 October 2021.
55. Chatzopoulos, A.; Papoutsidakis, M.; Kalogiannakis, M.; Psycharis, S.; Papachristos, D. Measuring the impact on student’s Computational Thinking skills through STEM and Educational Robotics projects implementation. Handbook of Research on Tools for Teaching Computational Thinking in P-12 Education; Kalogiannakis, M.; Papadakis, S.J. IGI Global: Hershey, PA, USA, 2020; pp. 234-284. [DOI: https://dx.doi.org/10.4018/978-1-7998-4576-8]
56. Pastrana, M.A.; Santana, M.S.; Mendoza-Peñaloza, J.; Bautista, J.; Lopera, J.E.P.; Muñoz, D.M. A Novel Approach to Teaching Theory Control: Integrating STEM Education, Mobile Robot Simulation, and Bio-inspired Optimization. Oper. Res. Forum; 2025; 6, 85. [DOI: https://dx.doi.org/10.1007/s43069-025-00489-y]
57. Davis, F.D.; Bagozzi, R.P.; Warshaw, P.R. User Acceptance of Computer Technology: A Comparison of Two Theoretical Models. Manag. Sci.; 1989; 35, pp. 982-1003. [DOI: https://dx.doi.org/10.1287/mnsc.35.8.982]
58. Lagogiannis, G.; Chatzopoulos, A. A No-Code Educational Platform for Introducing Internet of Things and Its Application to Agricultural Education. IoT; 2025; 6, 42. [DOI: https://dx.doi.org/10.3390/iot6030042]
59. Zacharis, G.K. Investigating the Factors Influencing Pre-Service Teachers’ Acceptance to Use Mobile Devices for Learning. Mobile Learning Applications in Early Childhood Education; Papadakis, S.; Kalogiannakis, M. IGI Global: Hershey, PA, USA, 2020; pp. 183-208. [DOI: https://dx.doi.org/10.4018/978-1-7998-1486-3.ch010]
60. The jamovi project. jamovi; Version 2.6 2024; Available online: https://www.jamovi.org (accessed on 18 July 2025).
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; Zacharia Paraskevi
; Ganetsos Theodore
; Papoutsidakis Michail