Adil Asghar1, Apurba Patra2, Kumar Satish Ravi3. 1. Department of Anatomy, All India Institute of Medical Sciences, Patna, India. 2. Department of Anatomy, All India Institute of Medical Sciences, Bathinda, India. apurba.cnmc03@gmail.com. 3. Department of Anatomy, All India Institute of Medical Sciences, Rishikesh, India.
Abstract
INTRODUCTION: Applications based on artificial intelligence and machine learning are becoming more popular in teaching learning. Advanced technologies have facilitated robots to carry out various human-like functions, which have navigated the interest of educators to discover the role of robots as potential teachers, instructors, or teaching assistants in education. METHODS: An extensive search for articles for humanoid robots and education either in the title or keywords was done utilizing PubMed, Google Scholar and Web of Science data sets. The tracking terms were artificial intelligence, education, medical education, anatomy, robots, humanoid robots, teaching, teaching assistant and tutor. RESULTS: The usage of artificial intelligence in the form of humanoid robots is quite common. However, literature citing its usage in medical education is rare. Humanoid robots as a teacher or teaching assistants are predominantly used in learning foreign languages. Primarily, a humanoid robot can discharge five functions as a potential teacher. CONCLUSION: Humanoid robots can effectively fulfil numerous educational goals in medicine since they can replicate human responses, work relentlessly regardless of students' repeated mistakes, be loaded with innovative teaching methodologies, and be upgraded with more current information. As a subject of medicine, anatomy is highly visual; therefore, constant endeavors have been initiated to develop technology-enhanced learning over the decades. Although artificial intelligence in humanoid robots has been successfully used in primary education and in learning a foreign language, its scope as an anatomy teacher or teaching assistant is a new and unique idea that needs exploration.
INTRODUCTION: Applications based on artificial intelligence and machine learning are becoming more popular in teaching learning. Advanced technologies have facilitated robots to carry out various human-like functions, which have navigated the interest of educators to discover the role of robots as potential teachers, instructors, or teaching assistants in education. METHODS: An extensive search for articles for humanoid robots and education either in the title or keywords was done utilizing PubMed, Google Scholar and Web of Science data sets. The tracking terms were artificial intelligence, education, medical education, anatomy, robots, humanoid robots, teaching, teaching assistant and tutor. RESULTS: The usage of artificial intelligence in the form of humanoid robots is quite common. However, literature citing its usage in medical education is rare. Humanoid robots as a teacher or teaching assistants are predominantly used in learning foreign languages. Primarily, a humanoid robot can discharge five functions as a potential teacher. CONCLUSION: Humanoid robots can effectively fulfil numerous educational goals in medicine since they can replicate human responses, work relentlessly regardless of students' repeated mistakes, be loaded with innovative teaching methodologies, and be upgraded with more current information. As a subject of medicine, anatomy is highly visual; therefore, constant endeavors have been initiated to develop technology-enhanced learning over the decades. Although artificial intelligence in humanoid robots has been successfully used in primary education and in learning a foreign language, its scope as an anatomy teacher or teaching assistant is a new and unique idea that needs exploration.
Authors: Tony Belpaeme; Paul Vogt; Rianne van den Berghe; Kirsten Bergmann; Tilbe Göksun; Mirjam de Haas; Junko Kanero; James Kennedy; Aylin C Küntay; Ora Oudgenoeg-Paz; Fotios Papadopoulos; Thorsten Schodde; Josje Verhagen; Christopher D Wallbridge; Bram Willemsen; Jan de Wit; Vasfiye Geçkin; Laura Hoffmann; Stefan Kopp; Emiel Krahmer; Ezgi Mamus; Jean-Marc Montanier; Cansu Oranç; Amit Kumar Pandey Journal: Int J Soc Robot Date: 2018-01-25 Impact factor: 5.126