A practical corporate training program designed to help employees use AI responsibly, safely and ethically in their everyday work.

Rather than focusing on the technical development of AI, the program focuses on the decisions employees make when using generative AI tools for communication, research, content creation, summarization, analysis and productivity.

Participants will learn how to recognize AI-related risks, protect information, evaluate AI-generated content, identify bias, apply human judgment and make responsible decisions about when and how AI should be used.

The training covers responsible use of generative AI, AI assistants, writing tools, research tools, summarization tools, content-generation tools and AI-supported workplace tasks. It focuses on six practical areas: privacy, accuracy, fairness, transparency, human oversight and accountability.

The program runs for 10 training hours over two days, on 28 – 29 October 2026, from 4:00 PM to 9:00 PM, at Symphony Style Hotel. It is delivered bilingually (Arabic and English) by Dr. Jana Mohammad Saab.

Group offer: in case you register 2 candidates, the third candidate will be free of charge.

This program is suitable for:

• Employees from different departments.
• Employees who are new to AI ethics.
• Employees who already use tools such as ChatGPT, Microsoft Copilot, Gemini or similar AI assistants.

Prerequisite: No technical or programming background required.

Level: Basic to Intermediate.

Day 1: Understand the Risks (4:00 PM – 9:00 PM)

1. AI at Work: Where Are the Risks?

• Where employees commonly use AI.
• Generative AI vs. traditional AI.
• AI-assisted work vs. AI-generated work.
• Benefits of AI at work and common employee-level risks.
• Why responsible AI matters.
• Practical focus: participants examine everyday workplace AI situations and identify whether they are low, medium or high risk.

2. What Makes AI Responsible?

• What is AI ethics? What is responsible AI?
• Privacy and data protection.
• Fairness and non-discrimination.
• Transparency.
• Safety and security.
• Human oversight and accountability.

3. Privacy & Confidentiality: What should—and should not—go into AI tools?

• Personal, employee and customer information.
• Financial information and internal company information.
• Confidential documents and intellectual property.
• Public vs. internal vs. confidential information.
• Risks of uploading workplace documents; data minimization and anonymization.
• Using approved vs. unapproved AI tools.
• Practical focus: “Can I paste this into AI?” – participants classify workplace information as Safe, Check First or Do Not Share.

4. Privacy Challenge (scenario-based group exercise)

• Examples from different departments: an HR employee record, a customer complaint, a public company announcement and internal meeting notes.
• Teams determine: Can AI be used? What information is sensitive? What should be removed? Can the information be anonymized? What additional approval or safeguard may be required?

5. When AI Sounds Right but Is Wrong

• What is an AI hallucination? Why AI can produce incorrect information.
• Fabricated sources and outdated information.
• Unsupported claims and overconfident answers.
• Why professional-looking content can still be wrong.
• Verification approach: Claim – Source – Cross-check – Context – Approve.
• Practical focus: “Catch the AI” – identify what is wrong with an AI-generated workplace response.

6. AI Bias & Fairness

• What is bias? Where can bias come from?
• Biased training data; language and cultural bias.
• Stereotypes and representation.
• Recruitment and performance evaluation examples.
• Customer-facing content and AI-assisted decision-making.

7. Ethical AI Decision-Making

• Teams assess workplace scenarios, classify them and explain their decision:
• Use: AI can reasonably assist.
• Use with safeguards: AI can assist, but additional controls or human review are required.
• Do not use: the risk is inappropriate or too high for the proposed use.
• Day 1 reflection: participants identify one AI risk they had not considered, one type of information they should protect and one AI behavior they will change.

Day 2: Use AI Responsibly (4:00 PM – 9:00 PM)

1. Day 1 Recap & AI Ethics Check

• Key principles: privacy, accuracy, bias, human oversight and accountability.

2. Responsible Prompt Engineering

• Writing better instructions for AI: role, task, context, instructions, constraints, desired output, tone and audience, and quality checks.
• Simple prompt structure: Role – Task – Context – Instructions – Output.
• Comparing a weak prompt with an improved prompt.

3. Human Oversight & Accountability: AI can assist. Humans remain responsible.

• AI recommendation vs. AI decision.
• When human review is necessary; high-impact workplace decisions.
• Professional responsibility and checking before acting.
• Documenting important decisions.
• Scenario for discussion: an AI system recommends that an employee should not be promoted – should the manager accept the recommendation? What evidence should be checked? Could bias be involved? Who makes the final decision? What happens if the AI is wrong?

4. Practical Prompt Exercise

• Participants receive poorly written workplace prompts and improve them, focusing on clarity, context, specificity, output and review.
• Examples include prompts for email writing, meeting summaries, reports, research, brainstorming and internal communication.

5. Responsible AI Across Departments

• Participants examine how responsible AI principles apply to different functions.
• For each scenario: benefit, risk, data, safeguard and human decision.

6. Role Play: The AI Decision

• A workplace AI decision is presented and participants take different roles: manager, HR representative, employee, AI specialist and ethics/compliance representative.

7. Final Team Challenge: The Responsible AI Room

• Cross-functional teams receive a realistic workplace proposal for implementing an AI solution.

8. Personal Responsible AI Commitment

• Each participant sets a personal commitment to responsible AI use.

Practical Learning Methods

• Realistic workplace scenarios.
• Individual exercises and cross-functional group work.
• Role plays and case analysis.
• AI output verification and bias detection.
• Prompt improvement and decision-making exercises.
• Final team challenge.

By the end of this training course, participants will be able to:

• Identify common AI risks in workplace situations.
• Decide whether information is appropriate to enter into an AI tool.
• Recognize potential hallucinations, unsupported claims and fabricated information.
• Apply basic methods for checking AI-generated outputs.
• Identify potential bias in AI-generated recommendations or content.
• Write clearer and more responsible prompts.
• Recognize situations requiring human review.
• Distinguish between an AI recommendation and a human decision.
• Apply a simple framework for evaluating AI use cases.
• Make more informed and responsible AI decisions in their daily work.

Participants’ takeaways: each participant will receive a concise Responsible AI Practical Toolkit, including:

• Responsible AI Quick Guide.
• AI Privacy & Confidentiality Checklist.
• AI Output Verification Checklist.