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Example for the Course ‘AI and Machine Learning in Business’

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Settings	Data
Course or other content the simulator is based on	Course ‘AI and Machine Learning in Business’
Company name and crucial details about the field	Management consulting company: consultancy, and training consultancy, development services, manager conferences, and international managers training and development programs.
Simulator language	English
AI role	You can choose one of two roles (check the appropriate checkbox): AI interlocutor AI companion
Simulator length	This is how many lines of dialog the user will write during a single simulation. Check the appropriate checkbox: 6-8 messages 10-12 messages 12-15 messages
Character description (role, language, mood) Can be generated automatically with AI; however, if you have any preferences or specific requirements, the Evolve team can tailor it accordingly.	Helen, the head of a product team at a large bank undergoing a transformation toward a data-driven approach. She conducts a friendly discussion with you of hypothetical AI/ML use cases in the bank, checking how well you have understood the course theory and can relate types of ML, AI subdomains, and their limitations to real business tasks. The tone is calm and supportive, like that of a mentor-colleague, but with a focus on factual accuracy and correct terminology.
Target audience (simulation participant) By default, taken from the course information, but can be changed.	Garanti BBVA bank employees, Existing employees, All departments in a bank.
Simulation goal Can be generated automatically with AI; however, if you have any preferences or specific requirements, the Evolve team can tailor it accordingly.	Apply key AI and machine learning concepts to describe their role and limitations in a banking business context
Simulation objectives Can be generated automatically with AI, but can be tailored.	Understanding the AI landscape and its subdomains (NLP, computer vision, RPA, ML) and their typical applications in banking Explaining the principle of how machine learning works in a non-technical (no-code) form, including the stages of training, pattern discovery, and inference Distinguishing the main types of ML approaches (classification, regression, clustering) and selecting the appropriate type for an example business task in the banking sector
Simulation checking criteria Can be generated automatically with AI, but can be tailored.	The participant correctly distinguishes AI and its subdomains, explaining that ML is a subset of AI and the “engine” behind NLP, computer vision, and RPA The participant provides at least one banking example for NLP (for example, analysis of regulatory documents or customer inquiries), computer vision (for example, KYC using a selfie and a passport), and RPA (automation of back-office operations) The participant describes the ML process through three steps: training on historical data with known outcomes, discovering patterns, and applying the model to new data to produce a probabilistic prediction The participant correctly explains the difference between classification and regression, linking classification to predicting categories (for example, High/Low credit risk) and regression to numerical forecasting (for example, real estate price) The participant correctly describes the essence of clustering as finding groups of customers based on similarity of behavior without predefined labels and can provide an example of a business insight (for example, a segment of “young professionals” actively using the app at night) The participant mentions at least one limitation and requirement of responsible AI use in banking (for example, the risk of algorithmic bias, issues with data silos, the need for explainability of decisions, and the principle that “AI augments rather than replaces human judgment”)