AI@Goizueta

Teaching AI

How to Master AI for Business

AI Literacy for Business School Education

AI literacy is defined as the understanding and ability to interact with artificial intelligence technologies efficiently and ethically. In business education, AI literacy prepares students to apply AI in solving business challenges, enhancing decision-making processes, and fostering innovation, while considering the societal and ethical ramifications of AI applications. Promoting AI literacy within business schools equips students for future careers in an AI-driven world, ensuring they can utilize AI technologies effectively and ethically. These strategies are designed to weave AI literacy throughout the business curriculum, arming students with necessary skills to drive innovation and make informed decisions in their careers.

Core AI Concepts: Understand the definitions, historical development, and various forms of AI, including machine learning, deep learning, and neural networks, essential for comprehending AI's role in contemporary business contexts.

Machine Learning and Deep Learning: Comprehend methods like supervised learning (e.g., linear regression, decision trees), unsupervised learning (e.g., clustering), and deep learning approaches (e.g., convolutional and recurrent neural networks), crucial for applications such as customer segmentation and predictive analytics.

AI in Marketing: Use AI for advanced customer segmentation, personalization, and predictive analytics to improve marketing effectiveness and customer interactions.

AI in Finance/Accounting: Employ AI for enhancing fraud detection, credit scoring, algorithmic trading, and risk management.

AI in Operations: Apply AI to improve supply chain management, inventory optimization, and predictive maintenance.

AI in Human Resources: Utilize AI for optimizing talent acquisition, performance management, and employee engagement through predictive analytics and automation.

AI Tools and Technologies: Gain proficiency in essential programming languages (Python, R) and AI frameworks (TensorFlow, Keras, PyTorch) to build and deploy AI solutions.

Data Analysis and Visualization: Develop skills in manipulating data and visualizing results using tools like Pandas and Matplotlib to extract actionable insights from AI models.

AI Literacy Implementation Strategies

Integrated Curriculum

Core Courses embed AI concepts within core business disciplines like marketing, finance, and operations, highlighting AI's relevance across various sectors.
Specialized Courses

Dedicated AI Courses provide specialized courses on AI for business, encompassing both theoretical and practical aspects to deepen understanding of AI technologies and applications.
Experiential Learning

Projects and case studies integrate practical projects, case studies, and labs, enabling students to apply AI tools to real business scenarios, thereby enhancing their practical skills and critical thinking.
Industry Collaboration

Guest Lectures and Partnerships facilitate interaction with AI experts through guest lectures and industry collaborations, offering insights into current AI trends and practices.

Virtual TA

Goizueta faculty have designed an AI-driven virtual teaching assistants. Faculty can create a Virtual TA for their courses and students have 24-hour access to the Virtual TA to use as a learning resource.

Virtual TA