Kūlia's Artificial Intelligence and Data Science Curriculum
Mapped to CSTA standards, this course takes a wide lens on computer science by covering topics such as problem solving, programming, physical computing, user centered design, and data, while inspiring students as they build their own websites, apps, animations, games, and physical computing systems. This course includes a unit on data science as one of 7 topics covered and another one on Artificial Intelligence (AI) and Machine Learning (ML). The AI unit focuses on AI ethics, examines issues of bias, and explores and explains fundamental concepts through a number of online and unplugged activities and full-group discussions.
● Our curriculum aligns with Common Core and CSTA K-12.
● Students are provided with the details of authentic science research projects, and then get to work through an activity that gives them practice looking for patterns and developing explanations about natural phenomena using the scientific data from the study.
● We will use National Oceanic and Atmospheric Administration's data. Explore NOAA provides data collected around the globe in formats designed just for educators. These resources take information from our atmosphere and ocean and package it in easily accessible, classroom-friendly lesson plans, activities, and curricula.
1st Semester
● AI and Ethics: An in-depth curriculum with activities and worksheets. These activities were developed at the MIT Media Lab to meet a growing need for children to understand artificial intelligence, its impact on society, and how they might shape the future of AI. Through a series of lessons and activities, students learn technical concepts — such as how to train a simple classifier — and the ethical implications those technical concepts entail, such as algorithmic bias. It provides a set of activities, teacher guides, assessments and materials to assist educators in teaching about the ethics of artificial intelligence. This curriculum was designed and tested for middle school students.
● This introduction is completed with an “out of the box ready” and complete program to teach AI by a prominent nationwide program.
2nd Semester
● Python: - This course teaches students the Python coding language through a series of mini-courses that integrate Common Core State Standards aligned middle school math curriculum in Statistics, Probability and Geometry. Students master fundamental skills and tools to enhance their understanding of coding. Students then use these abilities to complete standards-aligned assignments relating to curriculum-based math lessons. Students create unique simulations for probability, geometry, and statistics.
● Introduction to Data Science by University of California at Los Angeles funded by the National Science Foundation. The standards used for the IDS curriculum are based on the High School Probability and Statistics of Mathematics Common Core State Standards (CCSS-M), and include the Standards for Mathematical Practice (SMP). The Computer Science Teachers Association (CSTA) K-12 Computer Science Standards were also consulted and incorporated.
● Applied Computational Thinking Standards (ACT) delineate the application of Data Science concepts using technology.
● Students will learn and practice statistical computing with R programming language.
● Topics include Interpreting Categorical and Quantitative Data, Making Inferences and Justifying Conclusions, Conditional Probability and the Rules of Probability, Using Probability to Make Decisions.
● The Concord Consortium’s Dynamic Data Science Activities - This set of dynamic data science activities designed for grades 9-14- will be used for classroom activities. By working with data frequently and repeatedly, learners develop experience and competence, gaining fluency with the data moves necessary for structuring, examining, and diving into data, and ultimately building excitement for their ability to work with data.
● All dynamic data science activities are embedded in their Common Online Data Analysis Platform (CODAP) software, and follow a similar design: students make context-specific actions, store their data, organize it, analyze it, and visualize it in the surrounding CODAP environment. These activities were made possible through our National Science Foundation-funded Data Science Games project, and are aligned to Next Generation Science Standards (NGSS) and Common Core Standards.
1st Semester:
Fundamentals of Artificial Intelligence: This course covers the four modern AI models: Thompson Sampling, Q-Learning, Deep Q-Learning, and Deep Convolutional Q-Learning. It starts with a brief introduction of those models followed by the list of AI-applicable industries.
2nd Semester
Hands-on practice in machine learning, reinforcement learning, deep learning, and artificial intelligence.
Python: Students will architect AI models and practice with Python.
AP Computer Science A
Introduces students to computer science through programming.
Fundamental topics in this course include the design of solutions to problems, the use of data structures to organize large sets of data, the development and implementation of algorithms to process data and discover new information, the analysis of potential solutions, and the ethical and social implications of computing systems. The course emphasizes object-oriented programming and design using the Java programming language.
AP Computer Science Principles
An introductory college-level computing course that introduces students to the breadth of the field of computer science. Students learn to design and evaluate solutions and to apply computer science to solve problems through the development of algorithms and programs. They incorporate abstraction into programs and use data to discover new knowledge. Students also explain how computing innovations and computing systems—including the internet—work, explore their potential impacts, and contribute to a computing culture that is collaborative and ethical.
