| Aspect | Workshop | Hackathon |
| 1. Purpose | Focuses on learning new concepts, tools, and technologies | Focuses on solving real-world problems by building solutions. |
| 2. Learning Approach | Trainer-led sessions with structured content and guidance. | Participant-driven; teams independently develop ideas and prototypes |
| 3. Duration | Usually 1–5 days of structured learning. | Typically 24–72 hours of intensive coding or development. |
| 4. Structure | Includes lectures, demonstrations, and hands-on exercises. | Includes idea pitching, development, and final presentation |
| 5. Outcome | Participants gain knowledge and practical skills | Participants build working prototypes or innovative solutions. |
| 6. Participation Style | Individuals or small groups learning under a trainer | Teams collaborate and compete to develop solutions. |
| 7. Evaluation | Usually no competition; focus is on training and completion. | Solutions are judged and ranked based on innovation and impact. |
| 8. Mentorship | Trainer guides participants step-by-step through topics. | Mentors provide occasional guidance but teams work independently. |
| 9. Deliverables | Labs, exercises, and small practice projects. | Final product, prototype, or working model. |
| 10. Reward | Participants receive certificates of completion. | Winners receive prizes, recognition, and sometimes incubation opportunities. |
Agentic AI – RAG-based Generative AI Applications
Learn to design intelligent AI agents using RAG (Retrieval-Augmented Generation), LLMs, and tool integrations to build real-world applications like chatbots and knowledge systems.
Generative AI and Prompt Engineering
Explore Generative AI models and prompt engineering techniques to create, automate, and optimize tasks using AI tools for productivity and application development.
Self-Driving Car (Application of AI)
Understand the role of AI in autonomous vehicles, including computer vision, sensor fusion, path planning, and real-time decision-making systems.
Artificial Intelligence and Machine Learning
Gain hands-on experience in AI/ML algorithms, model building, training, and evaluation using real-world datasets and industry-standard tools.
Robotics and Collaborative Robots (Cobots)
Learn the fundamentals of robotics and cobot systems, including programming, automation workflows, and human-robot collaboration in industrial environments.
Industrial Internet of Things (IIoT)
Explore industrial IoT architectures, including sensors, data acquisition, cloud integration, and real-time monitoring for smart manufacturing systems
Industrial Cyber Security
Understand cybersecurity practices for industrial systems, including threat detection, network security, vulnerability assessment, and protection of critical infrastructure.
Industrial Automation
Learn about automation technologies, PLC systems, control systems, and industrial processes used in modern manufacturing and production environments.
Digital Twins
Develop knowledge of digital twin technology to create virtual models of physical systems for simulation, monitoring, and performance optimization.
IT-OT Integration
Understand the integration of Information Technology (IT) and Operational Technology (OT) for seamless data flow, system connectivity, and smart industrial operations.
Manufacturing Execution Systems (MES)
Learn how MES platforms enable real-time production monitoring, workflow optimization, and data-driven decision-making in manufacturing environments.
Cloud and Edge Computing
Explore cloud infrastructure and edge computing models for scalable, low-latency data processing in distributed and real-time applications
Dr Juliana R
IT HoD, LICET Chennai