Objectives

1. Build robust connections with both national and international companies and scientific institutes, providing students with direct access to resources and real-world AI applications and challenges.

Progress: MOUs in process -} USAID Energy (ESSA), Zonetch, Ajman University

2. Engage experts from Germany’s industry and universities, enabling our members to gain insights from some of the most advanced AI ecosystems globally.

Progress: Proposal DAAD for 15-AI Club students to visit two universities in Germany to study and research in state-of-the-art topics in AI, sustainability

3. Promoting AI awareness in local communities.

Progress: AI-Forum on 23.02 from 10:00-13:00 (announcement attached)
Plan: AI-themed carnivals at Jordanian schools -} April 2025 (In process)

4. Introduce students to essential AI concepts, prompt engineering, and AI-assisted tools.

Time table for planned

0.22	16.02-20.02	Sahar
Python programming for AI	16.02 (segment of the crash course)	Abdullah
Neural Network fundamentals	06.03	Ghassan
Ensemble Methods	20.03	Abdullah Ahmad
Clustering	20.04	Quosai
Prompt engineering	27.05	Sahar

Guest lectures and Forums establishment with AI-activities.
Progress: AI-Forum on 23.02 from 10:00-13:00

5. The club also aims to support activities, internship, and Graduation Projects (GP) by providing mentorship and guidance to students seeking to incorporate AI into their internship and GP work.

Progress: AI-Hackathon participation:

No.	participants	title	description
1	1.Saif Aldeen Kifah Saleh Abdallah. 2.Ahmad Ayed Abu Ajamieh. 3.Mohammad Nihad Issa Abu Ghosh	HeartCare AI	A wearable AI-powered health monitoring device for predicting and preventing heart attacks by analyzing real-time health metrics.
2	1- Yasmin 2- Abdullah Ahmad 3- Andrew Shami 4- Mohammed Ashi 5- Khalid Ghazal	Smart study buddy	An interactive AI model which helps students with their studies, providing information regarding courses and aiding with suggesting and solving test problems.
3	1.Ghassan 2. Zaid 3. Quosai	Adaptive AI for Noise Reduction and Baseline Calibration in Biomedical Multi- Sensor Systems	This project focuses on enhancing the accuracy and reliability of physiological monitoring systems using Adaptive AI for noise reduction and baseline calibration across multiple biosensors. The system integrates a PPG sensor for heart rate detection, an EMG sensor for muscle contraction detection, and a respiratory rate sensor using a flex sensor, all powered by an Arduino-based platform.