WSQ - Core Principles and Ethical Challenges in Generative AI

LU1: Generative AI Fundamentals

• T1: Underlying principles, core concepts and theories governing generative AI
• T2: Difference between generative and discriminative models
• T3: Demonstrate the use of generation AI in diverse applications (e.g., summarisation, inference, reasoning, transformation of content, augmentation of content)

LU2: Prompt Engineering

• T1: Importance of data quality, preprocessing, model pipeline and model training (e.g., impact of data bias from training data)
• T2: Impact of prompt engineering on the model outputs of generative AI
• T3: Apply understanding of generative AI principles to use cases
• T4: Analyse generative AI models' performance metrics and evaluate the influence of prompt variations

LU3: Ethical Considerations

• T1: Generative AI model workings, including training data, algorithms, and outputs
• T2: Identify the ethical implications and societal impact of AI-generated content
• T3: Analyse limitations and potential biases in AI-generated content

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Minimum Entry Requirement

Knowledge and Skills

• Able to operate using computer functions
• Minimum 3 GCE ‘O’ Levels Passes including English or WPL Level 5 (Average of Reading, Listening, Speaking & Writing Scores)

Attitude

• Positive Learning Attitude
• Enthusiastic Learner

Experience

• Minimum of 1 year of working experience.

Target Age Group: 18-65 years old

Minimum Software/Hardware Requirement

Software:

TBD

Hardware: Window or Mac Laptops

• Customer Experience Manager
• Digital Transformation Executive
• AI Support Specialist
• Service Quality Analyst
• Marketing Executive
• Hospitality Manager
• Contact Centre Supervisor
• Customer Insights Analyst
• Business Process Analyst
• Operations Coordinator
• Digital Innovation Officer
• Prompt Engineer
• Data Ethics Consultant
• AI Trainer
• Learning & Development Specialist
• Frontline Team Lead
• Customer Support Executive
• User Experience Designer
• Corporate Trainer (AI Tools)
• AI Adoption Consultant

Dwight Nuwan Fonseka: Dwight Nuwan Fonseka is the Head of Data Science at Plano Pte. Ltd., where he leads advanced projects in machine learning, deep learning, big data, and AI-driven predictive analytics for healthcare. His work spans the development of RShiny applications, large-scale data pipelines, and applied AI solutions in domains where ethical considerations such as bias, transparency, and accountability are paramount. With expertise in R, Keras, h2oAI, Spark, and Tableau, Dwight has overseen initiatives in healthcare data analysis, social media sentiment mining, and AI-powered vision prediction models—giving him deep insight into both the transformative potential and the ethical complexities of generative AI. He holds a Master’s in Education from NTU, a Bachelor’s degree in Applied Science from NUS, and is an ACLP-certified trainer
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Alongside his industry leadership, Dwight serves as a lecturer at the London School of Business and Finance (LSBF) and as an associate trainer with Tertiary Courses, where he designs and delivers programs on data analytics, machine learning, and responsible AI adoption. He has taught extensively on R, deep learning frameworks, and AI deployment, with a focus on bridging technical proficiency and ethical responsibility. His dual background in applied data science and training makes him well-positioned to guide learners through the core principles of generative AI, addressing critical challenges such as bias, misinformation, environmental impact, and responsible deployment. With a strong record of both real-world application and teaching, Dwight equips professionals with the knowledge to navigate the opportunities and ethical dilemmas posed by generative AI