විෂය නිර්දේශය · Syllabus
Full 24-week curriculum
This 24-week, 100% online curriculum is built for absolute beginners who want to move beyond merely chatting with AI to actively directing, integrating, and building with it. The syllabus is completely devoid of psychological theory, focusing 100% on the technology, mechanics, and practical application of Artificial Intelligence systems. By completing this practical, project-based diploma, students will acquire the technical capability and portfolio necessary to transition directly into a Bachelor of Science (BSc) program in Artificial Intelligence or Computer Science. There are absolutely no written exams. All assessments are evaluated through digital documents, system blueprints, and generated assets submitted online.
Module 1 · Weeks 1-4
Fundamentals of Artificial Intelligence
Understanding the core mechanics and hardware behind AI systems.
- Week 1AI Architecture & Mechanics: A beginner's breakdown of Machine Learning, Deep Learning, and Neural Networks without the complex math. Understanding how AI processes data.
- Week 2The Hardware of AI (Compute): Understanding why AI requires massive data centers. An introduction to GPUs, cloud compute, and the physical infrastructure required to run Large Language Models.
- Week 3The Generative AI Landscape: Analyzing the major platforms (OpenAI, Anthropic, Google) and understanding the critical differences between closed-source and open-source models (like Llama).
- Week 4Data Processing & Training Basics: How models learn from datasets. Understanding data cleaning, bias, and why "hallucinations" occur on a technical level.
Module 2 · Weeks 5-8
Advanced LLMs & Prompt Engineering
Commanding AI models to execute highly complex, specific logical tasks.
- Week 5Structured Prompt Engineering: Moving beyond conversational AI. Mastering zero-shot, few-shot, and chain-of-thought prompting to force models to follow strict logical rules.
- Week 6Model Specialization: Testing the technical limits and strengths of different models (e.g., using Claude for heavy code generation versus Gemini for live data retrieval).
- Week 7Building Custom AI Assistants: Creating specialized Custom GPTs. Teaching an AI specific operational parameters and feeding it localized knowledge bases (RAG - Retrieval-Augmented Generation basics).
- Week 8Introduction to AI APIs: What an API is and how it works. Learning how external software communicates directly with AI models behind the scenes.
Module 3 · Weeks 9-12
Generative Media & Visual AI
Creating high-fidelity, consistent media using diffusion models.
- Week 9Diffusion Models & Image Generation: Mastering Midjourney, DALL-E 3, and Stable Diffusion. Learning to prompt for specific camera lenses, lighting setups, and aspect ratios.
- Week 10Advanced Image Control & Consistency: Using AI tools (like ControlNet concepts) to modify existing images, expand backgrounds, and maintain character consistency across multiple generations.
- Week 11AI Audio & Voice Synthesis: Generating hyper-realistic text-to-speech, exploring voice cloning technology safely, and generating AI soundscapes.
- Week 12AI Video Production: Using text-to-video engines (Runway, Sora) and AI avatars (HeyGen) to generate seamless video sequences entirely from text prompts.
Module 4 · Weeks 13-16
No-Code AI Automation & Systems
Integrating AI into everyday software to build autonomous workflows.
- Week 13AI Coding Assistants: Using AI to write, debug, and explain basic computer code (Python/HTML) using natural English instructions.
- Week 14Automated Document Processing: Using AI models to instantly ingest, analyze, translate, and extract specific data points from large volumes of PDFs and spreadsheets.
- Week 15The No-Code AI Workflow (Zapier/Make): Understanding how to build automated pipelines. Connecting an AI API to standard software (like email or databases) without writing code.
- Week 16Building Autonomous AI Agents: Introduction to agentic workflows. Setting up AI that doesn't just answer questions, but performs multi-step tasks independently based on a single trigger.
Module 5 · Weeks 17-20
Applied AI, Data Analysis, & The Future
Using AI for high-level data processing and understanding future tech trajectories.
- Week 17AI for Data Analysis: Using AI environments (like Advanced Data Analysis) to clean messy datasets, generate Python charts, and find mathematical trends automatically.
- Week 18Computer Vision Fundamentals: How AI interprets visual data. A beginner’s look at object detection, facial recognition, and how self-driving cars "see."
- Week 19AI Security & Deepfake Detection: The technical methods used to spot AI manipulation and an overview of prompt injection attacks.
- Week 20The Future of AI (AGI & Robotics): Understanding the trajectory toward Artificial General Intelligence, embodied AI (robotics), and edge computing.
Module 6 · Weeks 21-24
Capstone Project & Portfolio Assembly
Bringing all skills together into a professional, degree-ready final project.
- Week 21Capstone Ideation & Architecture: Receiving the scenario from the Examination Council and planning the technical system architecture.
- Week 22Capstone Prototyping: Building the initial version of the AI workflow, custom model, or media suite.
- Week 23Capstone Execution & Testing: Dedicated 40-hour week to refine, troubleshoot, and finalize the AI project.
- Week 24Final Documentation Assembly: Formatting the final project into a professional technical manual and submitting the digital package.
Final assessment
Capstone assignment
A comprehensive digital document (System Architecture Blueprint & Generated Outputs) submitted directly to the Examination Council. No live presentation or written exam is required.
To graduate and prove readiness for a BSc degree, students will build a functional AI-powered solution. The specific industry context or business scenario will be determined by the Trident Campus Examination Council and lecturers prior to Month 6. Students must apply that assigned scenario to one of the following practical tracks:
Track A
The Custom AI Assistant & Knowledge Base
Submit a comprehensive digital manual documenting the creation of a highly specialized AI Assistant tailored to the assigned scenario. The submission must include the exact system instructions, the structure of the data uploaded to its knowledge base, and exported chat logs proving the AI can successfully perform the required industry-specific tasks.
Track B
The Automated AI System Integration
Submit a technical blueprint using a no-code platform (like Make or Zapier) that builds a fully automated system for the assigned scenario. The student must document a workflow that connects an AI model to at least two other applications (e.g., receiving data, processing it with AI, and storing the output) and provide the technical flowcharts.
Track C
The AI Generative Media Suite
Submit a complete digital portfolio of AI-generated media designed specifically for the assigned scenario. This must include high-quality, consistent images, an AI-generated script, cloned or AI-generated voiceovers, and a final assembled video presentation, utilizing at least three different generative AI platforms with full prompt documentation.
24-week accelerated pathway · 60 credits · assessed online without written examinations
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