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Standards Mapping

for Arkansas Artificial Intelligence

66

Standards in this Framework

27

Standards Mapped

40%

Mapped to Course

Standard Lessons
1.1.1
Research career paths in AI (e.g., data scientist, machine learning engineer, and AI ethicist).
1.1.2
Communicate effectively in team-based, AI-related projects.
1.2.1
Identify key trends in AI and AI-related professional certifications (e.g., AWS AI/ML, Google TensorFlow Developer).
1.2.2
Discuss the importance of ethical responsibilities in AI professions.
  1. 1.4 The Ethics of Artificial Intelligence
2.1.1
Define artificial intelligence and its subfields (e.g., machine learning, natural language processing, computer vision, and robotics).
  1. 1.2 Artificial Intelligence and Machine Learning
  2. 1.3 Machine Learning and Neural Networks
2.1.2
Differentiate between AI, machine learning, and deep learning.
  1. 1.2 Artificial Intelligence and Machine Learning
  2. 1.3 Machine Learning and Neural Networks
2.1.3
Describe real-world applications of AI in various industries (e.g., healthcare, finance, education, and entertainment).
  1. 1.1 Introduction to Artificial Intelligence
  2. 2.2 Creating an Image Prediction Model
  3. 3.1 Creating a Sentiment Model
  4. 3.2 Generating New Text
  5. 4.1 Final Project
2.1.4
Discuss symbolic AI and its relevance to early AI research.
2.1.5
Compare rule-based systems to learning-based systems.
2.2.1
Summarize milestones in the history of AI development.
2.2.2
Discuss the societal impact of AI technologies, including potential benefits and risks.
  1. 1.4 The Ethics of Artificial Intelligence
2.2.3
Discuss the concept of explainable AI (XAI) and its importance.
3.1.1
Describe core AI concepts such as decision-making, uninformed and informed search algorithms, and planning.
3.1.2
Demonstrate the use of heuristic algorithms (e.g., A*, greedy algorithms) for search problems.
3.1.3
Implement basic search algorithms (e.g., breadth-first search, depth-first search).
3.1.4
Investigate optimization techniques (e.g., gradient descent).
3.2.1
Describe supervised, unsupervised, and reinforcement learning.
  1. 1.2 Artificial Intelligence and Machine Learning
3.2.2
Implement a basic machine learning model (e.g., linear regression, decision tree) using a programming library.
  1. 2.1 Introduction to TensorFlow
  2. 2.2 Creating an Image Prediction Model
  3. 3.1 Creating a Sentiment Model
  4. 3.2 Generating New Text
  5. 4.1 Final Project
3.2.3
Evaluate model performance using metrics such as accuracy, precision, recall, and F1 score.
3.2.4
Develop algorithms to preprocess data (e.g., splitting into training and test datasets).
  1. 2.1 Introduction to TensorFlow
  2. 2.2 Creating an Image Prediction Model
  3. 3.1 Creating a Sentiment Model
  4. 3.2 Generating New Text
  5. 4.1 Final Project
3.2.5
Implement clustering algorithms (e.g., K-Means, DBSCAN) using a programming library.
3.2.6
Apply regularization techniques (e.g., L1, L2) to address and reduce overfitting in machine learning models.
4.1.1
Collect and clean data from multiple sources (e.g., CSV, APIs, and web scraping).
4.1.2
Perform feature engineering (e.g., scaling, encoding, and handling missing values).
4.1.3
Investigate data augmentation techniques for AI models (e.g., image transformation, text tokenization).
  1. 2.2 Creating an Image Prediction Model
  2. 3.1 Creating a Sentiment Model
  3. 3.2 Generating New Text
4.1.4
Create visualizations to understand data distribution and relationships (e.g., histograms, scatter plots).
  1. 2.1 Introduction to TensorFlow
  2. 2.2 Creating an Image Prediction Model
  3. 3.1 Creating a Sentiment Model
  4. 3.2 Generating New Text
4.2.1
Describe the importance of data quality and quantity in AI applications.
  1. 2.3 Selecting Valid Datasets
4.2.2
Describe the potential for bias in training data and its impact on AI systems.
  1. 1.4 The Ethics of Artificial Intelligence
4.2.3
Investigate strategies for addressing data imbalance (e.g., oversampling, undersampling).
5.1.1
Define computer vision and its applications (e.g., image recognition, object detection, image segmentation).
  1. 2.2 Creating an Image Prediction Model
5.1.2
Examine the steps involved in computer vision tasks (e.g., image acquisition, preprocessing, feature extraction, classification).
  1. 2.2 Creating an Image Prediction Model
5.1.3
Analyze different image data types and formats commonly used in computer vision applications.
5.2.1
Apply basic image processing techniques (e.g., filtering, edge detection, and color manipulation) using libraries like OpenCV.
5.2.2
Extract relevant features from images (e.g., edges, corners, and textures).
5.2.3
Implement object detection algorithms (e.g., using Haar cascades or pre-trained models) to identify objects in images.
  1. 2.2 Creating an Image Prediction Model
6.1.1
Define natural language processing and its applications (e.g., text summarization, machine translation, sentiment analysis, and chatbots).
  1. 3.1 Creating a Sentiment Model
  2. 3.2 Generating New Text
6.1.2
Describe the challenges of natural language processing (e.g., ambiguity, context, variability).
6.1.3
Explain fundamental concepts (e.g., tokenization, stemming, and lemmatization) of natural language processing.
  1. 3.1 Creating a Sentiment Model
6.2.1
Implement text preprocessing techniques (e.g., removing stop words, handling punctuation).
  1. 3.1 Creating a Sentiment Model
  2. 3.2 Generating New Text
6.2.2
Create numerical representations of text data (e.g., bag-of-words, TF-IDF).
6.3.1
Perform sentiment analysis on text data using pre-trained models or simple techniques.
  1. 3.1 Creating a Sentiment Model
6.3.2
Investigate text generation using recurrent neural network (RNN) or transformers.
  1. 3.2 Generating New Text
7.1.1
Describe the structure of a neural network, including layers, neurons, and activation functions.
  1. 1.3 Machine Learning and Neural Networks
7.1.2
Implement a simple neural network using a programming framework (e.g., TensorFlow, PyTorch).
  1. 2.1 Introduction to TensorFlow
  2. 2.2 Creating an Image Prediction Model
  3. 3.1 Creating a Sentiment Model
  4. 3.2 Generating New Text
7.1.3
Visualize the training process using tools (e.g., TensorBoard or matplotlib).
  1. 2.1 Introduction to TensorFlow
  2. 2.2 Creating an Image Prediction Model
  3. 3.1 Creating a Sentiment Model
  4. 3.2 Generating New Text
7.2.1
Investigate the concepts of convolutional neural networks (CNNs) and recurrent neural networks (RNNs).
  1. 2.1 Introduction to TensorFlow
  2. 2.2 Creating an Image Prediction Model
  3. 3.1 Creating a Sentiment Model
  4. 3.2 Generating New Text
7.2.2
Train a CNN for image classification tasks.
  1. 2.2 Creating an Image Prediction Model
7.2.3
Analyze natural language processing tasks using RNNs or transformers (e.g., sentiment analysis, text generation).
  1. 3.1 Creating a Sentiment Model
7.2.4
Investigate transfer learning and fine-tune pre-trained models for custom tasks.
8.1.1
Investigate the role of knowledge representation in AI.
8.1.2
Analyze representation methods (e.g., semantic networks, frames, and ontologies).
8.1.3
Discuss the importance of reasoning and inference in AI systems.
8.2.1
Investigate propositional logic and first-order logic.
8.2.2
Implement logical reasoning techniques (e.g., forward chaining and backward chaining) in simple scenarios.
8.3.1
Investigate reasoning under uncertainty (e.g., Bayesian networks, probabilistic reasoning).
8.3.2
Implement algorithms for reasoning with uncertainty (e.g., calculating probabilities in a Bayesian network).
8.3.3
Apply decision-making techniques in simple scenarios (e.g., using decision trees or expected value calculations).
8.4.1
Investigate how KRR is used in expert systems, recommendation engines, and natural language understanding.
8.4.2
Investigate real-world applications, such as knowledge graphs and automated reasoning systems (e.g., IBM Watson).
8.4.3
Use tools and libraries for KRR (e.g., Resource Description Framework and Web Ontology Language).
9.1.1
Analyze case studies of AI misuse (e.g., facial recognition, social media algorithms).
  1. 1.4 The Ethics of Artificial Intelligence
9.1.2
Propose strategies for ensuring fairness and reducing bias in AI systems.
9.1.3
Investigate legal and regulatory frameworks around AI in different industries.
9.2.1
Describe the importance of transparency and interpretability in AI.
9.2.2
Identify methods to protect user privacy in AI applications.
9.2.3
Investigate tools for auditing and mitigating bias in AI models (e.g., Fairlearn, SHAP).