In this project, we propose to develop a Generative Artificial Intelligence (GenAI)-enabled teaching ecosystem to enhance student engagement and learning outcomes through innovative pedagogical approaches in core courses taught in the college of IST, such as Basic Programming, Computational Thinking, and Technical Writing. Our study aims to address emerging challenges associated with GenAI in academic environments and introduce new teaching methods centered around its capabilities. 

As global populations age, loneliness has become a growing public health concern among senior citizens, with significant implications for both mental and physical health. Research shows that 33–57% of adults aged 65 and older report experiencing symptoms of loneliness and social isolation. These conditions are closely linked to increased risks of depression, anxiety, dementia, hypertension, heart attacks, and strokes. This project aims to address the loneliness epidemic among seniors through the development of an Artificially Intelligent Digital Service for Wellness (AIDSW). Unlike traditional interventions that rely on occasional human contact, such as phone check-ins or group sessions, AIDSW leverages artificial intelligence and user-centered design to offer consistent, personalized support. This intelligent assistant will monitor wellness in real-time, foster social interaction, and ensure timely communication with caregivers or family members when needed. By integrating accessible technology with AI-driven engagement, this initiative has the potential to enhance quality of life, reduce health risks associated with loneliness, and establish a scalable, proactive model for elder care.

Unmanned Aerial Vehicles (UAVs) are increasingly used across industries such as logistics, surveillance, agriculture, and emergency response. A critical challenge in UAV operations is achieving high-accuracy autonomous landing, especially in constrained, dynamic, or GPS-denied environments.

This project aims to develop a robust precision landing system for UAVs by integrating computer vision, sensor fusion, and advanced control algorithms. The system will leverage onboard cameras, AI/ML Algorithms, and inertial measurement units (IMUs) to detect landing targets, estimate position in real-time, and guide the UAV safely to the designated point with sub-meter accuracy.

Key objectives include:

• Designing a vision-based detection and tracking system for landing markers.

• Implementing sensor fusion techniques for reliable localization in variable conditions.

• Developing adaptive control algorithms for stable descent and touchdown.

• Evaluating system performance under varying environmental and terrain conditions.

The outcomes of this project will enhance the reliability and autonomy of UAV operations, supporting mission-critical deployments where manual control or GPS reliance is not feasible.