The introduction of Artificial Intelligence technology enables the integration of video bots into various aspects of education. Video bots technology has the potential to provide quick and personalized services to everyone in the sector, including institutional employees and students. Recent advancements in cognitive tutors and chatbots allow them to recognize the students' emotions and personalize the teaching-learning process accordingly. Using video bots that identify students' affective states, personalize the learning paths, and express various emotions via an animated agent will improve the teaching-learning processes and make the learning environment more engaging and effective for students. For High school students, mathematics contains more complex learning content, and often the students find it challenging to learn within the academic learning time, and video bots are least explored in this area. Hence, in this project, we propose to develop a web-based learning environment with popular Indian cartoon characters as animated agents (video bots). These video bots will be intelligent, affect-aware, expressive, interactive, encourage active learning, provide prompt feedback, and emphasize time on tasks. We already have a working prototype for the project for a topic and want to make it more robust and generalized. The participants will be Mumbai (India) high school students, and the video bots will use English as a communication language. 

Thesis Title: "Development of Unobtrusive Affective Computing Framework for Students' Engagement Analysis in Classroom Environment"

Description: The goal of my Ph.D. research work was to propose an unobtrusive students’ emotional and behavioral engagement analysis method using their non-verbal cues, which works in both e-learning and classroom environments. Accordingly, the research problem statement was ”To design and develop an unobtrusive affective computing framework for students’ engagement analysis in the classroom environment.”

The research objectives are defined as: (Figure Given Below)

• To develop an effective method for multimodal students’ affective content analysis.

• To develop an effective deep learning architecture for the students’ behavioral engagement analysis.

• To develop a personalized intelligent tutoring system with automatic inquiry intervention based on the students’ affective states in learning environments.

• Creation of a database with students’ affective states and behavioral patterns using their facial expressions, hand gestures, and body postures.

Thesis Link

Guide: Prof. G Ram Mohana Reddy, Dept of IT, NITK Surathkal