DCS: MCS 703 – AI-Driven Optimization Techniques
Categories: Doctorate of Computer Science
About Course
- This core course explores optimization techniques driven by artificial intelligence, such as genetic algorithms, swarm intelligence, and reinforcement learning.
- Students will apply these methods to solve complex real-world problems in various domains, including logistics, resource management, and engineering.
- The course emphasizes the development of practical skills in AI-driven optimization and prepares students to engage in advanced research and applications in this rapidly evolving field.
Course Objectives
- Develop a deep understanding of AI-driven optimization techniques, including genetic algorithms, swarm intelligence, and reinforcement learning.
- Learn to design and implement AI-based optimization solutions for complex real-world problems.
- Explore the application of these techniques across various domains such as logistics, resource management, and engineering.
- Engage in advanced research and development of AI-driven optimization methods.
- Prepare for leadership roles in the application of AI techniques to optimize processes and solve critical challenges.
Course Content
Week 1: Introduction to AI-Driven Optimization
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Introduction to AI-Driven Optimization
00:00 -
LO1: Define the concept of Optimization in Artificial Intelligence
00:00 -
LO2: Explain the role of AI-Driven Optimization techniques in solving Computational problems
00:00 -
LO3: Summarize the historical development of Optimization in Artificial Intelligence applications
00:00 -
Multiple Choice Questions
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True/False Questions
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Scenario-Based Multiple Choice Questions
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Key Terms and Concepts Questions
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Short Answer Questions
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Written Assignment
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Presentation Task
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Role-Playing Activity
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Peer Review Task
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Exercises and Activities Adaptation
Week 2: Fundamentals of Genetic Algorithms
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Fundamentals of Genetic Algorithms
00:00 -
LO1: Describe the principles of Genetic Algorithms and their Evolutionary basis.
00:00 -
LO2: Apply operators such as Selection, Crossover, and Mutation in Genetic Algorithms.
00:00 -
LO3: Evaluate the role of Fitness Functions in guiding Optimal solutions.
00:00 -
Multiple Choice Questions
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True/False Questions
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Scenario-Based Multiple Choice Questions
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Key Terms and Concepts Questions
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Short Answer Questions
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Written Assignment
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Presentation Task
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Role-Playing Activity
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Peer Review Task
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Exercises and Activities Adaptation
Week 3: Advanced Applications of Genetic Algorithms
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Advanced Applications of Genetic Algorithms
00:00 -
LO1: Analyze applications of Genetic Algorithms in Engineering, Logistics, and Resource Management.
00:00 -
LO2: Compare Genetic Algorithms with traditional Optimization methods.
00:00 -
LO3: Design a Genetic Algorithm solution for a selected real-world problem.
00:00 -
Multiple Choice Questions
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True/False Questions
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Scenario-Based Multiple Choice Questions
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Key Terms and Concepts Questions
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Short Answer Questions
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Written Assignment
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Presentation Task
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Role-Playing Activity
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Peer Review Task
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Exercises and Activities Adaptation
Week 4: Swarm Intelligence: Concepts and Applications
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Swarm Intelligence: Concepts and Applications
00:00 -
LO1: Define the concept of Swarm Intelligence and its Biological inspirations.
00:00 -
LO2: Demonstrate the functioning of Ant Colony Optimization and Particle Swarm Optimization.
00:00 -
LO3: Differentiate between Swarm Intelligence methods and Evolutionary Computation techniques.
00:00 -
Multiple Choice Questions
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True/False Questions
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Scenario-Based Multiple Choice Questions
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Key Terms and Concepts Questions
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Short Answer Questions
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Written Assignment
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Presentation Task
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Role-Playing Activity
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Peer Review Task
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Exercises and Activities Adaptation
Week 5: Swarm Intelligence in Real-World Problems
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Swarm Intelligence in Real-World Problems
00:00 -
LO1: Apply Swarm Intelligence methods to problems such as Scheduling and Network Optimization.
00:00 -
LO2: Evaluate the effectiveness of Swarm Intelligence in Robotics and distributed problem-solving.
00:00 -
LO3: Interpret case studies showcasing Swarm Intelligence Applications.
00:00 -
Multiple Choice Questions
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True/False Questions
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Scenario-Based Multiple Choice Questions
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Key Terms and Concepts Questions
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Short Answer Questions
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Written Assignment
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Presentation Task
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Role-Playing Activity
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Peer Review Task
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Exercises and Activities Adaptation
Week 6: Reinforcement Learning: Fundamentals
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Reinforcement Learning: Fundamentals
00:00 -
LO1: Explain the core concepts of Reinforcement Learning, including Agents, States, and Rewards.
00:00 -
LO2: Illustrate the role of Markov Decision Processes and Q-Learning in Reinforcement Learning.
00:00 -
LO3: Apply Reinforcement Learning algorithms to simple decision-making problems.
00:00 -
Multiple Choice Questions
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True/False Questions
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Scenario-Based Multiple Choice Questions
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Key Terms and Concepts Questions
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Short Answer Questions
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Written Assignment
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Presentation Task
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Role-Playing Activity
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Peer Review Task
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Exercises and Activities Adaptation
Week 7: Advanced Reinforcement Learning Techniques
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Advanced Reinforcement Learning Techniques
00:00 -
LO1: Describe advanced Reinforcement Learning methods such as Deep Reinforcement Learning.
00:00 -
LO2: Compare Policy Gradient and Actor-Critic methods with traditional Reinforcement Learning.
00:00 -
LO3: Evaluate the performance of advanced Reinforcement Learning in dynamic environments.
00:00 -
Multiple Choice Questions
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True/False Questions
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Scenario-Based Multiple Choice Questions
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Key Terms and Concepts Questions
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Short Answer Questions
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Written Assignment
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Presentation Task
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Role-Playing Activity
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Peer Review Task
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Exercises and Activities Adaptation
Week 8: Mid Term Exam
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Mid Term
00:00 -
Multiple Choice Questions
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True/False Questions
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Scenario-Based Multiple Choice Questions
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Short Answer Questions
Week 9: AI-Driven Optimization in Logistics
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AI-Driven Optimization in Logistics
00:00 -
LO1: Analyze the application of AI-Driven Optimization in Supply Chain and Logistics.
00:00 -
LO2: Apply AI techniques to improve Transportation, Warehousing, and Distribution.
00:00 -
LO3: Evaluate case studies on Logistics Optimization using Artificial Intelligence
00:00 -
Multiple Choice Questions
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True/False Questions
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Scenario-Based Multiple Choice Questions
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Key Terms and Concepts Questions
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Short Answer Questions
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Written Assignment
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Presentation Task
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Role-Playing Activity
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Peer Review Task
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Exercises and Activities Adaptation
Week 10: AI-Driven Optimization in Resource Management
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AI-Driven Optimization in Resource Management
00:00 -
LO1: Explain strategies for Resource Allocation and Utilization using AI Optimization.
00:00 -
LO2: Apply Optimization algorithms to improve efficiency of Energy and Material resources.
00:00 -
LO3: Assess the impact of AI-Driven Optimization on Sustainability and Cost Reduction.
00:00 -
Multiple Choice Questions
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True/False Questions
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Scenario-Based Multiple Choice Questions
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Key Terms and Concepts Questions
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Short Answer Questions
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Written Assignment
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Presentation Task
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Role-Playing Activity
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Peer Review Task
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Exercises and Activities Adaptation
Week 11: Hybrid Optimization Techniques
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Hybrid Optimization Techniques
00:00 -
LO1: Define the concept of Hybrid Optimization and its components.
00:00 -
LO2: Design a Hybrid Optimization model by integrating multiple AI techniques.
00:00 -
LO3: Evaluate the advantages and limitations of Hybrid Optimization approaches.
00:00 -
Multiple Choice Questions
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True/False Questions
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Scenario-Based Multiple Choice Questions
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Key Terms and Concepts Questions
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Short Answer Questions
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Written Assignment
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Presentation Task
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Role-Playing Activity
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Peer Review Task
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Exercises and Activities Adaptation
Week 12: Optimization in Dynamic Environments
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Optimization in Dynamic Environments
00:00 -
LO1: Explain the challenges of Optimization in dynamic and uncertain environments.
00:00 -
LO2: Apply AI-driven methods for real-time Optimization under changing conditions
00:00 -
LO3: Assess the adaptability of Optimization algorithms in evolving systems.
00:00 -
Multiple Choice Questions
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True/False Questions
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Scenario-Based Multiple Choice Questions
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Key Terms and Concepts Questions
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Short Answer Questions
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Written Assignment
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Presentation Task
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Role-Playing Activity
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Peer Review Task
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Exercises and Activities Adaptation
Week 13: Ethical Considerations in AI Optimization
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Ethical Considerations in AI Optimization
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LO1: Identify ethical issues in AI-Driven Optimization decision-making.
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LO2: Analyze the societal impacts of AI-based Optimization techniques.
00:00 -
LO3: Propose strategies for ensuring ethical practices in AI-Driven Optimization.
00:00 -
Multiple Choice Questions
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True/False Questions
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Scenario-Based Multiple Choice Questions
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Key Terms and Concepts Questions
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Short Answer Questions
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Written Assignment
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Presentation Task
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Role-Playing Activity
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Peer Review Task
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Exercises and Activities Adaptation
Week 14: Future Trends in AI-Driven Optimization
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Future Trends in AI-Driven Optimization
00:00 -
LO1: Describe emerging innovations such as Explainable AI and Quantum-Inspired Optimization.
00:00 -
LO2: Evaluate the potential of future AI Optimization techniques in industry.
00:00 -
LO3: Predict future research directions in AI-Driven Optimization.
00:00 -
Multiple Choice Questions
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True/False Questions
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Scenario-Based Multiple Choice Questions
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Key Terms and Concepts Questions
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Short Answer Questions
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Written Assignment
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Presentation Task
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Role-Playing Activity
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Peer Review Task
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Exercises and Activities Adaptation
Week 15: Course Review
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Course Review
00:00 -
LO1: Summarize the key concepts of Genetic Algorithms, Swarm Intelligence, and Reinforcement Learning.
00:00 -
LO2: Integrate knowledge from case studies across multiple application domains.
00:00 -
LO3: Reflect on personal learning to articulate strategies for professional application of AI Optimization.
00:00 -
Multiple Choice Questions
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True/False Questions
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Scenario-Based Multiple Choice Questions
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Key Terms and Concepts Questions
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Short Answer Questions
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Written Assignment
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Presentation Task
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Role-Playing Activity
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Peer Review Task
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Exercises and Activities Adaptation
Week 16: Final Exam
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Final Exam
00:00 -
Multiple Choice Questions
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True/False Questions
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Scenario-Based Multiple Choice Questions
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Key Terms and Concepts Questions
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Short Answer Questions
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Written Assignment
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Presentation Task
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Role-Playing Activity
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Peer Review Task
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Exercises and Activities Adaptation
