About the Artificial Intelligence (AI) Training Course
The Artificial Intelligence (AI) — AI Tools and Applications for Beginners course is designed to help students, researchers, faculty members, teachers, and professionals understand and use modern Artificial Intelligence tools for academic, research, educational, and professional activities. Artificial Intelligence is rapidly transforming how people search for information, prepare documents, analyze data, develop educational materials, and solve everyday problems. Tools powered by generative AI can assist with writing, summarization, brainstorming, information extraction, content creation, programming, and many other tasks. However, using these tools effectively requires more than simply entering questions into a chatbot. Users need to understand the capabilities and limitations of AI systems, formulate clear instructions, evaluate generated responses, and verify information before relying on the results. This training introduces the fundamental concepts of Artificial Intelligence, Machine Learning, Deep Learning, and Generative AI in an accessible, beginner-friendly manner. Participants will learn how these technologies differ, where they are used, and how AI assistants generate responses to user instructions. The course also introduces large language models (LLMs), AI chatbots, image generation tools, and multimodal AI applications that can work with different forms of information, including text, images, and supported documents. Through guided demonstrations and practical exercises, participants will explore commonly used AI tools and learn how to select appropriate tools for different tasks. No previous programming or technical background is required for the introductory and practical AI modules, making the course suitable for beginners who want to understand and apply AI in their daily academic or professional work.
A major component of the course focuses on practical AI applications and prompt engineering. Participants will learn how to communicate effectively with AI systems by developing clear, specific, and well-structured prompts. Topics include defining a task, providing context, specifying the desired output, assigning an appropriate role, giving examples, and refining instructions through follow-up interactions. Participants will practice using AI tools to summarize lengthy documents, extract key information, organize content into tables, improve written communication, generate outlines, and explain complex concepts in simpler language. The course also explores AI applications in academic research, including identifying and refining research topics, developing research questions, structuring literature reviews, summarizing supplied research papers, comparing findings, and preparing research proposals and presentations. For teaching and training, participants will learn how AI can assist in preparing lesson plans, lecture outlines, quizzes, multiple-choice questions, learning activities, and training materials. Professional applications include drafting emails, reports, official correspondence, meeting agendas, project plans, and presentations. The course further introduces AI-assisted data analysis, including interpreting spreadsheet information, generating Excel formulas, developing basic analysis plans, creating visualizations, and obtaining assistance with Python or R code. Participants will also explore AI-based image generation and content creation, including writing effective image prompts and developing ideas for posters, illustrations, educational materials, and social media content. Practical demonstrations will emphasize that AI-generated information, calculations, citations, and interpretations must be checked against reliable sources or independently verified. Participants will learn to distinguish between using AI as an assistant and treating its outputs as authoritative results, particularly when working with scientific, statistical, or academic information.
The course also emphasizes responsible AI use, privacy, and the fundamentals of machine learning. Participants will examine common limitations of generative AI, including hallucinations, fabricated references, outdated information, bias, inconsistent answers, and difficulties with complex reasoning. The training discusses privacy considerations when uploading documents, research data, or organizational information to online AI platforms, as well as the importance of protecting confidential information and following applicable institutional policies. Other topics include academic integrity, plagiarism concerns, copyright, misinformation, deepfakes, and the importance of human oversight when using AI-generated content. An introductory module on Machine Learning explains the basic differences between supervised, unsupervised, and reinforcement learning, with examples of classification, regression, clustering, and predictive applications. Participants will gain a basic understanding of training data, testing data, features, predictions, model evaluation, and overfitting without being expected to develop advanced AI systems. The training follows a hands-on approach in which participants apply AI tools to practical tasks, evaluate the resulting outputs, refine their instructions, and identify situations in which AI assistance is useful or inappropriate. By the end of the course, participants should be able to use AI tools more confidently for research, teaching, writing, information processing, content creation, and workplace productivity while recognizing the need for verification and responsible use. The course is particularly suitable for university students, postgraduate and PhD scholars, researchers, educators, administrators, and professionals interested in incorporating AI into their work. It is delivered online through live, instructor-led sessions and can be arranged for individual participants or groups. Supporting materials, including presentation slides, notes, exercises, and relevant examples, are provided to reinforce learning, along with an e-certificate upon completion. The detailed syllabus below outlines the topics covered and provides a clear overview of the practical skills and foundational knowledge participants can expect to develop during the training.
