PLAiTO AI is a powerful AI built by extensive research in Learning Science, Artificial Intelligence and Educational Data Mining. It is powered by learning experiences and system interactions generated by more than hundred thousand students having spent time for more than 5.5 Million hours. PLAiTO AI optimizes learning outcomes and contributes to learning science research owing to its several hundreds of experimental conditions.
PLAiTO AI is an amalgamation of different AI paradigms and algorithms like Deep Learning, Reinforcement Learning, Planning Algorithms, Machine Learning, Psychometrics (Item Response Theory) and much more. PLAiTO AI traces the knowledge of the student in each minute learning unit with each and every interaction in real time with the help of its best in class Knowledge Tracing models like Bayesian Knowledge Tracing and Deep Knowledge Tracing.
Heavily backed by Learning Science research, PLAiTO AI ensures that students gain deep conceptual understanding of the skills, rather than the shallow functional knowledge to solve the problems. The goal of PLAiTO AI is to impart robust learning to the student as defined by education scientist Kenneth Keodinger and his colleagues in the COGNITIVE SCIENCE - A Multidisciplinary Journal. Learning is said to be robust if persists for long term, can be transferred to the different and novel situations, and can prepare for and accelerate future learning.
Personalized Learning Path
Granular Knowledge Graph – PLAiTO Pedagogy Model deconstructs every concept into granular units of learning. For example, the world of math is classified as per common standards into learning concepts and sub-concepts. At the lowest level are atomic learning units which address a set of learning gaps. This granular decomposition enables PLAiTO to monitor learning gaps as a child explores each concept.
Intelligent Lesson (Subtopic) Recommendations
What should I learn next? Based on their past performance in several concepts, PLAiTO AI recommends most appropriate concepts for the learner. This way, the learner does not get lost in the plethora of learning activities and can work on a guided learning path designed according to their capabilities and needs.
To help students master the sub-topics by solving the problems effectively, PLAiTO AI provides interventions like hints, micro hints, attempts and solutions at the right moment. At times, the system chooses to not provide any hints or micro hints which challenges the students to push their cognitive faculties and help them think deeper. PLAiTO AI uses psychologically proven theories like Incubation Effect to force students to break and disengage from a task to help overcome frustration, thus refocusing them on the task for enhanced learning.
PLAiTO AI focuses on the step-by-step approach used by the student while solving the problem, and not only on the final answer. This enables the system to diagnose granular learning gaps and to provide better help to the student accordingly.
CCLP (Cross-Concept Learning Path)
To master any concept, PLAiTO AI designs the learning path considering the learning gaps from other concepts which might be useful for the current concept. This individualized cross-concept learning path helps the student understand the concept in a deep and efficient manner.
Learning content is organized and deconstructed into learning units ranging from very general to very specific and granular in nature. This Knowledge Graph has thousands of interconnections between these granular learning units which makes it really powerful. Initially created by domain experts, KG experiences constant evolution with the help of PLAiTO AI.