ISCAP Proceedings: Abstract Presentation
Aye Aye, Captain, what does AI say?
Li-Jen Lester
Sam Houston State University
Abstract
Incorporating artificial intelligence (AI) teaching modules into a Science, Technology, Engineering, and Mathematics (STEM) curriculum can provide students with valuable skills and knowledge for their future careers. This project will use a quantitative method to investigate STEM faculty members' levels of agreement and willingness to include AI teaching modules in their curriculum. The author will host a STEM Educators' Workshop as a Fulbright Specialist to motivate educators to apply various teaching strategies to inspire more students to major in STEM fields. This research will collect samples from two universities: Sam Houston State University, USA, and Universitas Negeri Jakarta, Indonesia. The goal of this research is to prepare students for hands-on projects and labs for their future capstone ideas or competitions by using AI applications from scratch or by collaborating with industry partners on real-world problems. The AI teaching modules are expected to inspire students to learn the following AI tools and technologies, but not limited to: Programming Languages for AI (such as Python, R), Libraries and frameworks (such as TensorFlow, PyTorch, Keras, Scikit-learn), Data Science and Analytics, Data preprocessing and visualization, Big Data technologies (Hadoop, Spark), and Statistical analysis and modeling.
Two research questions for this project are designed to study:
1. To what extent do STEM faculty members agree on the benefits of AI concepts for students’ learning processes?
2. What teaching modules are STEM faculty members familiar with when applying AI?
The AI modules included in this STEM curriculum research will focus on three areas to understand participants' levels: Core AI Concepts, Practical AI Applications, and AI Ethics.
A. Core AI Concepts: An introduction to basic AI concepts and terminology, understanding the fundamentals of machine learning, including types of machine learning (supervised, unsupervised, reinforcement), and training and testing datasets.
B. Practical AI Applications: An introduction to Natural Language Processing (NLP) with text processing and sentiment analysis, speech recognition and generation, chatbots and virtual assistants. Examples of computer vision, such as image recognition and classification, object detection and tracking, and image generation and enhancement. Introduction to AI in robotics, autonomous systems and vehicles, industrial automation, and smart manufacturing.
C. AI Ethics: Understanding AI ethics and policy, including bias and fairness in AI, privacy and security concerns, and AI regulation and governance. Familiarity with AI research and development in cutting-edge areas such as healthcare, finance, and other industries, and future trends in AI.