Breakout Box: AI Mission

• Collaborate in teams to solve multi-part puzzles that require communication, logical reasoning, and shared decision-making.
• Apply critical thinking to real-world cybersecurity and AI challenges, analyzing scenarios for patterns and vulnerabilities.
• Identify sources of bias in training data and explain how biased datasets can lead to unfair or inaccurate AI outcomes.
• Evaluate the appropriateness of datasets for training specific AI models

10.NI.C.01: Illustrate how sensitive data and critical infrastructure can be affected by malware and other attacks and recommend security measures to address various scenarios based on factors such as efficiency, feasibility, and ethical impacts

12.AP.A.01: Describe how artificial intelligence drives many software and physical systems (e.g., autonomous robots, computer vision, pattern recognition, test analysis).

127.M.TAFCS.4.B: Communicate an understanding of binary representation of data in computer systems, perform conversions between decimal and binary number systems, and count in binary number systems

127.M.TAFCS.4.I: Communicate an understanding of and use conditional statements within a programmed story, game, or animation

127.M.TACYBCAP.9.B: Describe the integration of artificial intelligence and machine learning in cybersecurity

127.M.TAGMPD.9.C: Demonstrate an understanding of artificial intelligence and develop and implement artificial intelligence;

CSF.AP.2.D: Analyze the outcomes of programs to identify logic and syntax errors.

CSF.AP.3.A: Read and interpret algorithms and programs expressed using plain language, pseudocode, and text-based programming languages.

CSF.CYB.1.A: Describe ways data and computing systems can be threatened by malware, ransomware, social engineering, phishing, and other cyberattacks.

CSF.CYB.1.B: Compare strategies to protect data and computing systems from malware, ransomware, social engineering, phishing, and other cyberattacks.

CSF.DA.3.A: Evaluate the quality of training data: completeness, accuracy, consistency, and relevance.

CSF.DA.4.A: Explain the use of training data and the role it has in the development of machine learning models.