The notion of artificial intelligence (AI) conjures an image of a supercomputer with enormous processing capacity along with adaptive behavior, like the incorporation of sensors, which empower it to possess the functional and cognition abilities of humans and enhance the supercomputer's contact with individuals. A variety of research works and films have been produced to demonstrate AI capabilities, such as in smart buildings, which can manage temperatures and air quality, or play music based on the mood of occupants. Now, embedded computer systems have surpassed the traditional comprehension of AI as a supercomputer to become more prevalent in the education sector.
For instance, AI, embedded robots or computers and supporting apparatus allow the development of robots that enhance the learning experience of students, beginning with early childhood education. Robots, or cobots, can be deployed to teach children routine work, such as spelling and pronunciation while adapting to the students' skills. According to various studies, web-based and online education has evolved from merely providing materials for students to study, download and complete assignments to pass, to incorporating intelligent and adaptive web-based systems that study learner and instructor behavior and make adjustments to benefit the learning environment. In education, AI has also been implemented in instruction or teaching, administration and learning.
AI-aided education scenarios
AI-assisted education components involve advanced virtual learning, intelligent education, data prediction and analysis. Intelligent education systems offer instructors and students timely, individualized instruction and feedback. Various computing techniques, particularly machine learning-related innovations which are directly connected to the cognitive learning theory and statistics model, are aimed to enhance learning value and efficiency. Some examples of AI-related techniques that are being applied in different scenarios of education are as follows:
• Personalized learning methods, adaptive learning approach and academic analytics are being used for the assessment of schools and students.
• Speech recognition, face recognition, augmented reality, virtual reality, virtual labs, and sensing and hearing technologies are being used in smart schools.
• Prediction systems, computer vision and image recognition are being applied in the evaluation and grading of exams and papers.
• Bayesian knowledge interference or data mining, learning analytics and intelligent teaching systems bolster personalized intelligent teaching.
• Real-time analysis, virtual personalized assistants and edge computing increasingly serve in mobile and online remote education.
By automating computer programs or web-based platforms, AI has increased the efficacy of administrative work such as grading, evaluating students' work and giving feedback on assignments. Curriculum and content development, as well as instruction utilizing web-based platforms, virtual reality, video conferencing, robotics and 3D technology, have also benefited from the implementation of AI in the learning sector.
How can AI develop an education model?
The learner model is essential for enhancing the independent process of learning in an AI education system. It is determined according to learner behavior data produced by the education process. To evaluate an individual's learning capacity, their reasoning process and abilities are evaluated, after which knowledge analysis is transformed to acquire the mastery of learners' data. Learner modeling creates links between learning outcomes and multiple features, such as instructional materials, assets and teaching practices.
The knowledge model defines a knowledge framework map with specified learning contents, typically comprising expert knowledge, rules for frequently committed errors by learners and misinterpretation. Merging the learner and the knowledge field model, the teaching model establishes the policies for entry into the knowledge field, allowing teachers to customize their teaching policies and tactics. As education proceeds, students are more likely to exhibit positive behavior, act accordingly or seek assistance. The AI system is always ready to offer assistance based on the tutoring model's inbuilt teaching analytical frameworks. The sophisticated human-machine interface includes AI-assisted features such as natural language interaction, emotion detection in learners and speech recognition.
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Going beyond the borders
As learning materials are now located on the internet and the World Wide Web, the implementation of AI in education also provides a chance to eliminate the physical barriers outlined by national and international borders. Online learning or the use of web-based educational technologies means that the educational content is available from all over the world, and utilizing other facets of AI, like language translation toolkits, enables learners to study optimally in the sense of their individual talents.
Conclusion
AI-supported learning is presently viewed as an education assistant in its infancy, but AI will offer a greater role as educational requirements evolve. AI systems will start generating more and more data to have a clearer understanding of the teaching and learning process, enabling more precise information recommendations as educational interactions become more frequent. It has made and will continue to make teachers more efficient and productive, and students' learning or educational experiences more personalized and comprehensive.
