Abstract:

Generative artificial intelligence (AI) is transforming professional roles in education and industry, shifting the emphasis from manual problem-solving to solution design. This evolution has significant implications for computing and data science curricula, requiring educators to prepare students for roles as architects of AI-driven solutions. Rather than focusing solely on technical implementation, future professionals must learn to analyze organizational needs, articulate complex problems, and design modular solutions that leverage generative AI for efficiency and scalability.

This presentation introduces a pedagogical framework for integrating generative AI into educational contexts, particularly within data analytics courses. The approach begins with problem analysis and decomposition, followed by identifying datasets and applying generative AI to create solutions for subproblems using techniques such as clustering, association mining, regression, classification, and optimization. Students also learn to critically evaluate AI-generated outputs for accuracy, reliability, and computational feasibility, ensuring solutions meet ethical and scalability standards.

Through practical examples and classroom strategies, this session demonstrates how educators can embed these concepts into project-based learning experiences. By positioning generative AI as a collaborative partner rather than a replacement for human expertise, this approach fosters higher-order thinking and prepares graduates for leadership roles in an AI-driven world.

Keywords:

Generative AI, Educational Technology, Data Analytics, Curriculum Design, Problem Decomposition, AI in Education, Clustering, Regression, Classification, Optimization, Project-Based Learning, Ethical AI, Scalable Solutions.