The term "educational robotics" pertains to a field of study focused on enhancing students' learning experiences by utilizing activities, technologies, and artifacts related to robots. In practical terms, these activities encompass the use of physical robots, whether they are modular systems such as Lego Mindstorm robots or robots specifically designed for tasks. Such activities can be tailored to students from elementary to graduate levels and may encompass design, programming, application, or experimentation with robots.
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Educational robotics activities typically involve the utilization of a robotics kit, through which students gain proficiency in building and programming robots for specific tasks. These activities can take various forms, including interventions, extracurricular activities, voluntary classes, or entire course modules centered around robotics.
The theoretical foundations underpinning the application of educational robots are diverse, with the constructionist educational approach being predominant. The current educational landscape is evolving into the artificial intelligence (AI) education era. In this context, students at all levels will require formal training in AI. Robots can serve as practical teaching aids, enabling students to grasp concepts related to perception, actuation, learning, reasoning, and natural interaction. Moreover, educators can incorporate robotic simulators into online learning scenarios and optimize in-person classroom hours.
It is also crucial to consider the specifics of implementing education involving robotics. The increasing prevalence of robots in education, including telepresence robots, will raise awareness of AI and robotic technologies among a larger student population. When students gain hands-on experience with AI and robots, they are likely to become more knowledgeable and discerning about the advantages and disadvantages of these high-tech components, which are poised to become integral aspects of our daily lives.
History of Education Robotics
Initially, educational robotics focused on conceptual ideas, but researchers began emphasizing the practical benefits for children, including engaging with computer science content, honing problem-solving skills, and developing fine motor skills and eye-hand coordination. Over time, educational theories associated with teaching robotics became more structured. Empirical research reinforces the value of hands-on activities in fostering collaboration, communication, and problem-solving skills.
As educational robots became widely accessible, STEM (Science, Technology, Engineering, and Mathematics) education initiatives gained momentum, initially in informal settings such as robotics camps and competitions. Over time, STEM education permeated formal environments, including classroom mathematics and science courses, evolving from short-term activities into project-based learning integrated into state and national curriculums.
Intelligent Tutoring Robots
Tutoring robots represent a multidisciplinary field, merging education, computer science, automatic control, psychology, optics, and more. Early robotics technology drew from industrial robots, but as robotics technology became more widespread, the educational potential of robots garnered attention. The inception of educational robots dates to Professor Papert's AI laboratory at MIT in the 1960s, evolving into intelligent entities. These robots, tailored for education, aim to nurture students' analytical, creative, and practical skills, offering features like adaptability, interactivity, openness, and scalability. Equipped with AI technologies like voice and emotion recognition, they exhibit human-like abilities in listening, seeing, thinking, and communicating.
Intelligent tutoring robots serve various functions, including subject instruction, assisted instruction, management, routine representation, and direct instruction. They depart from traditional passive learning approaches, igniting students' interest by offering engaging teaching methods. For instance, SoftBank's Pepper robot engages students across different educational stages, fostering interest in AI through play. Similarly, the University of Nottingham, Ningbo, China introduced the SoftBank Pepper humanoid robot as an AI ambassador, providing multi-language communication and educational services. Moreover, the Adaptive Systems Research Group at the University of Hertfordshire utilizes tutoring robots to assist children with autism, with positive outcomes from their interactions.
In recent years, with the advancement of AI technology, robots have evolved to become more intelligent and human-like, making them better suited for future educational environments.
AI-Powered Educational Robotics Tools
Several AI-powered educational robotics tools are designed for students.
Cozmo, the first AI-powered robot for students to explore AI and coding, features proximity sensors, a gyroscope, a downward-facing cliff detector, and a camera for environmental sensing. It possesses the vision capabilities to recognize human faces, objects, and emotions. Cozmo offers coding options such as Code Lab, built on Scratch Blocks, and Python for expert programmers. Apps such as Calypso and educational resources from RedyAI further enhance its learning potential.
Zumi, a Raspberry Pi-based robot, is equipped with various sensors for navigation. It supports Blockly and Python for control and can be trained for object recognition and facial identification.
CogLabs, in collaboration with Google and the United Nations Educational, Scientific, and Cultural Organization (UNESCO), developed open-source DIY robotics kits, which include CogBot and CogMini. These kits empower students to design, assemble, and program their robots using sustainable materials such as recycled boxes and 3D-printed parts. They utilize an Arduino ESP32 board and smartphones for control, offering a machine-learning experience through scratch and teachable machine integration. The aim is to make AI and robotics accessible to students with various coding backgrounds.
Impact of AI and Robotics in Education
AI's significance in education lies in its ability to foster personalized teaching and learning. It tailors learning plans based on individual needs, provides immersive experiences, and tracks progress, enhancing learning efficiency. Intelligent adaptive learning technology enables one-to-one personalized teaching, though its maturity varies between countries.
Furthermore, AI alleviates the burden on teachers by automating repetitive tasks such as homework grading, freeing them to focus on humanistic care and individualized guidance. Teachers become facilitators of student learning, emphasizing moral and overall development.
Additionally, AI promotes educational equity by bridging geographical and resource gaps through remote, personalized teaching. High-quality remote education, combined with intelligent homework correction and data generation, empowers teachers in remote areas to provide personalized instruction and develop students' abilities effectively. Furthermore, these robotic applications extend to meta-teaching and meta-learning, where robotics are used to assess how teaching and learning occur.
Challenges and Future Directions
As AI continues to reshape education, it encounters unprecedented challenges. These challenges encompass technical issues, personal obstacles, infrastructural limitations, the need for quantitative evaluations, and the need for better-equipped instructors and aligned curricula in higher education institutions. These challenges underscore the complex landscape of integrating robotics into education.
Ensuring Fairness: Introducing AI into educational robotics must address fairness concerns. Developing countries may face educational disparities due to limited access to AI-driven technologies, paralleling the digital divide. Overcoming obstacles such as technology and infrastructure gaps is essential to creating a level playing field.
Ethical and Safety Concerns: Collecting, using, and disseminating data raises ethical and safety issues. AI's personalization capabilities, data privacy, and accountability require increased oversight and public discourse on ethics and safety.
Changing Learning Styles: AI-driven education shifts towards student-centered learning, demanding greater autonomy. Students must independently manage personalized learning plans, select content, and engage in group learning. This places higher demands on self-regulation and self-management skills, requiring teachers to support independent learning.
In advancing AI robot-supported education, the concept of "co-learning" takes center stage. This concept emphasizes the interaction between humans and AI, as well as mutual learning and growth. AI must learn to explain its learning, reasoning, and planning processes to humans, while humans must integrate human intention and values into AI. They also need to explore seamless interaction and teaching methods for AI, enriching it with uniquely human capabilities and perspectives.
To achieve these, several strategies are proposed:
Humans in the Feedback Loop: Humans should be actively involved in the training, testing, and tuning processes of AI model construction. They can validate AI decisions, offering feedback in the event of errors.
Individualized Learning: To enhance individual learning experiences, it's crucial to understand learners' backgrounds, needs, locations, and how they intend to use AI systems.
Testing Learner-AI Interaction: Real-world testing of learner-AI robot interactions is essential for a successful learning experience. Incorporating human-like world understanding and common-sense knowledge into AI is necessary to improve AI robots' perception and interpretation of complex environments and human actions. Social and cultural theories can frame the relationships between AI robotics and learning in various contexts.
Technical Support: Providing training programs that balance content knowledge, pedagogy, and technology can empower instructors with varying skills to effectively integrate AI robots into classrooms. Trained assistants can support instructors in troubleshooting technology issues and tracking student progress, optimizing the blend of human and AI instruction for student learning.
References and Further Readings
Armstrong, L., and Tawfik, A. (2023). The History of Robotics and Implications for K-12 STEM Education. TechTrends 67, 14–16. DOI: https://doi.org/10.1007/s11528-022-00816-8
Eguchi, A. (2022). AI-Powered Educational Robotics as a Learning Tool to Promote Artificial Intelligence and Computer Science Education. In: Merdan, M., Lepuschitz, W., Koppensteiner, G., Balogh, R., Obdržálek, D. (eds) Robotics in Education. RiE 2021. Advances in Intelligent Systems and Computing, vol 1359. Springer, Cham. https://doi.org/10.1007/978-3-030-82544-7_26
Chaka, C. (2023). Fourth industrial revolution—a review of applications, prospects, and challenges for artificial intelligence, robotics and blockchain in higher education. Research and Practice in Technology Enhanced Learning, 18, 002. DOI: https://doi.org/10.58459/rptel.2023.18002
Chen, X., Cheng, G., Zou, D., Zhong, B., and Xie, H. (2023). Artificial Intelligent Robots for Precision Education: A Topic Modeling-Based Bibliometric Analysis. Educational Technology & Society, 26(1), 171-186. https://doi.org/10.30191/ETS.202301_26(1).0013
Huang, J., Saleh, S., and Liu, Y. (2021). A review on artificial intelligence in education. Academic Journal of Interdisciplinary Studies, 10(206).