Li, W., & Hung, Y., & Guttman, R., & Zhang, S., & Yu, N. (2024, October), Designing an AI-Enhanced Module for Robotics Education in Mechanical Engineering Technology  Paper presented at 2024 Fall ASEE Mid-Atlantic Section Conference, Farmingdale State College, NY, New York. 10.18260/1-2--49433

\bibitem{asee_peer_49433}
  Li, W., \& Hung, Y., \& Guttman, R., \& Zhang, S., \& Yu, N. (2024, October), \emph{Designing an AI-Enhanced Module for Robotics Education in Mechanical Engineering Technology}
  Paper presented at 2024 Fall ASEE Mid-Atlantic Section Conference, Farmingdale State College, NY, New York.
  10.18260/1-2--49433

Wenhai Li, Yue Hung, Reiss Guttman, Sen Zhang, and Ning Yu.  "Designing an AI-Enhanced Module for Robotics Education in Mechanical Engineering Technology".  2024 Fall ASEE Mid-Atlantic Section Conference, Farmingdale State College, NY, New York, 2024, October.  ASEE Conferences, 2024.  https://peer.asee.org/49433 Internet.  02 Jul, 2025

\bibitem{asee_peer_49433}
  Wenhai Li, Yue Hung, Reiss Guttman, Sen Zhang, and Ning Yu.
  "Designing an AI-Enhanced Module for Robotics Education in Mechanical Engineering Technology".
  \emph{2024 Fall ASEE Mid-Atlantic Section Conference, Farmingdale State College, NY, New York, 2024, October}.
  ASEE Conferences, 2024.
  https://peer.asee.org/49433 Internet.  02 Jul, 2025

@INPROCEEDINGS{asee_peer_49433
author = "Wenhai Li, Yue Hung, Reiss Guttman, Sen Zhang, and Ning Yu"
title = "Designing an AI-Enhanced Module for Robotics Education in Mechanical Engineering Technology"
booktitle = "2024 Fall ASEE Mid-Atlantic Section Conference"
year = "2024"
month = "October"
address = "Farmingdale State College, NY, New York"
publisher = "ASEE Conferences"
note = {https://peer.asee.org/49433}
number = {10.18260/1-2--49433}
}

TY  - CPAPER
AB  - Introduction:

Artificial Intelligence (AI) is rapidly transforming various industries. At SUNY, we recognize the critical importance of introducing AI to our students and equipping them with the knowledge and skills necessary for future success. In a collaborative effort across four SUNY campuses, we are developing AI applications tailored to different disciplines. At the Farmingdale campus, the demand among students to learn how to integrate AI into mechanical systems is accelerating. However, there is a significant gap in AI education specifically designed for Mechanical Engineering Technology (MET) students, particularly those with limited backgrounds in mathematics and programming. Our proposed five-week module integrates foundational AI concepts with hands-on robotics applications, ensuring that MET graduates are prepared to lead and innovate in their field. This work is the result of the 2024 SUNY IITG grant.

Educational Approach and Objectives:

This module will be integrated into the final five weeks of the robotics class, with each week comprising a lecture and a corresponding lab session:
Lecture Topics:
•	Week 1: Introduction to Machine Learning (ML) - Covers the basics of ML, emphasizing applications in robotics and automation.
•	Week 2: Classification & Regression - Explores AI algorithms used in robotics for tasks like object detection and sensor data analysis.
•	Week 3: Neural Networks - Introduces neural networks and deep learning, focusing on applications in robotics.
•	Week 4: Computer Vision & Natural Language Processing - Discusses CV and NLP fundamentals, highlighting their roles in robotics.
•	Week 5: Reinforcement Learning - Covers core concepts and applications of reinforcement learning in robotics.
Lab Projects:
•	Week 1: Remote Control Robot Arm with Python - Students will set up and control a robot arm using Python.
•	Week 2: Automation using Computer Vision - Students will use Pixy2 sensors to automate a robotic pick-and-place operation.
•	Week 3: Automation using Self-Trained Models - Students will train a model using Google Teachable Machine to automate a sorting operation.
•	Week 4: Voice Controlled Robotic Arm - Students will develop a project to control the robot arm’s pose using natural language.
•	Week 5: Reinforcement Learning for Robotic Arms - Students will set up a simulation where a robot arm learns to improve its performance.

Significance and Goals:

This module provides MET students with a foundational understanding of AI and ML, focusing on practical applications in robotics. It is designed to integrate seamlessly into the existing curriculum, offering students the opportunity to become proficient AI practitioners. By aligning with industry trends, this module ensures that students are equipped with skills highly valued in the workforce, enhancing their job prospects in a rapidly evolving market.

Acknowledgments:

This project is supported by the SUNY Innovative Instruction Technology Grant (IITG). 

AU  - Wenhai Li
AU  - Yue Hung
AU  - Reiss Guttman
AU  - Sen Zhang
AU  - Ning Yu
CY  - Farmingdale State College, NY, New York
DA  - 2024/10/25
PB  - ASEE Conferences
TI  - Designing an AI-Enhanced Module for Robotics Education in Mechanical Engineering Technology
UR  - https://peer.asee.org/49433
DO  - 10.18260/1-2--49433
ER  -