After completing the course, participants will be able to:

• describe how AI systems and large language models work at a practical level without treating them as conscious or infallible;
• select an appropriate rule-based, analytical, or generative tool for a workplace task and frame the task with relevant context;
• identify hallucination, bias, deepfake, and overreliance risks and verify high-impact claims using independent evidence;
• classify AI uses through the green-yellow-red framework and determine when human review or an expert decision is required; and
• use approved tools, handle sensitive information appropriately, and apply the ten-point safe-use checklist in everyday work.

Module 1: What AI is and is not (AI as a family of systems, common misconceptions, analytical versus generative AI, how large language models produce responses, and matching tasks to appropriate tools)[

Module 2: Using generative AI effectively (instruction, context and model, strong and weak use cases, adoption patterns, how task framing changes results, and the differences between consumer and enterprise tools)

Module 3: Trust but verify (hallucination, real-world professional failures, bias, deepfake fraud, organisational risk concerns, five verification habits, and the traffic-light framework)

Module 4: Data security and practical workplace scenarios (the golden rule, information that must not be entered into public or unapproved tools, shadow AI, effective everyday uses, an end-to-end briefing scenario, and the ten-point safe-use checklist. Closing, next steps for achieving better results, three key messages, BSC AI Factory support, references and conclusion

Total duration: approximately 72 minutes

Dr. Duygu ÇAKIR

Dr. Duygu Çakır is an academic in the Department of Software Engineering at Bahçeşehir University. She has extensive experience in academic research, software engineering education, and research and development, focusing on artificial intelligence, computer vision, automated machine learning, AI governance, and bias mitigation. She has contributed to and managed government-supported and industry-collaborative projects and has taught programming, data structures, computer graphics, data science, and responsible AI at undergraduate and graduate levels. Dr. Çakır holds a national patent on mobile eye tracking and received an IEEE Best Paper Award in 2021. Her current work focuses on the responsible and value-oriented adoption of AI in organisations. Alongside her academic work, she actively contributes to advancing AI governance practices for SMEs.

Artificial intelligence tools are becoming part of everyday work, but effective use requires more than knowing which button to press. This non-technical course equips professionals with a practical foundation for using AI confidently without trusting it blindly. Participants build a clear mental model of analytical and generative AI; learn how large language models produce responses; and match deterministic, predictive, and generative tasks to appropriate tools. The course then examines effective task framing, context, tool selection, and the differences between consumer and enterprise AI services. Real cases illustrate hallucination, bias, deepfake fraud, and the consequences of using unverified outputs. Participants apply five verification habits and a traffic-light framework to distinguish low-, medium-, and high-risk uses. The final module addresses data protection, shadow AI, approved tools, sensitive information, and practical workplace scenarios such as drafting, summarising, translating, and preparing briefings. The course concludes with a ten-point safe-use checklist for responsible everyday practice.

No prior knowledge of AI, machine learning, or programming is required. Familiarity with common workplace tasks and organisational policies on data protection and approved digital tools is helpful.

None required.

An internet-connected computer or tablet, a current web browser, and audio-playback capability. No specialised software or programming environment is required.