Research

Computer Sciences and Information Technology

Title :

Exploring Human and Machine Cognition Building a Learning Analytics Portal of AI Assisted Problem Solving Models for Visuo-Spatial Tasks

Area of Research :

Computer Sciences and Information Technology

Focus Area :

Cognitive Science

Principal Investigator :

Dr. Rajlakshmi Guha, IIT Kharagpur, Kolkata, West Bengal (700045)

Co-PI:

Dr. Aritra Hazra, Indian Institute of Technology (IIT) Kharagpur, West Bengal (721302), Prof. Partha Pratim Chakrabarti, Indian Institute Of Technology (IIT) Kharagpur, West Bengal (721302)

Contact info :

Total Budget (INR):

61,25,930

Details

Executive Summary :

Human reasoning has been a subject of interest for centuries, with studies focusing on visual behavior during problem-solving. However, there is a lack of literature on how humans tackle cognitive problems and how AI engines solve them. Learning is a common form of problem-solving, with human information processing being influenced by limited-capacity short-term memory systems and unlimited long-term memory systems. There is a conceptual gap between human problem-solving and machine models, often attributed to human information processing and long-short-term memory features. To understand and improve human problem-solving abilities, it is crucial to develop an equivalent computational model that can aid the problem-solving process. This project aims to discover and analyze deeper patterns in human problem-solving processes based on features obtained from humans solving cognitive problems in different platforms. A cognitive-AI-based learning analytics and assistive tool will be developed to visualize improvements in human problem-solving processes. This tool will provide a platform to incorporate AI-driven methods to understand problem-solving behaviors in visuo-spatial problems with the assistance of multimodal sensors. The tool framework will surface automated cues as feedback to the human problem-solver, guiding the repair and improvement of the conceptual learning process. Eye-tracking movements and suitable metrics will be used to draw conclusions about the improvement in the learning process.

Equipments :

Organizations involved