Achieve More: The AI Path to Enhanced Productivity

This full-day workshop equips participants with practical skills to understand, use, and evaluate Artificial Intelligence (AI) tools responsibly. Through hands-on activities, demonstrations, and applied exercises, participants learn how to integrate AI into professional and creative work.

Purpose

To develop participants’ understanding of Artificial Intelligence (AI) and Machine Learning (ML), build hands-on skills using AI tools and agents, and strengthen their ability to apply AI responsibly and ethically in professional and entrepreneurial contexts.

Objectives

By the end of the workshop, participants will be able to:

• Understand foundational concepts of AI and Machine Learning.
• Apply prompt engineering techniques for professional tasks.
• Use AI agents and research tools to improve productivity and research quality.
• Prototype solutions and automate workflows using AI-supported tools.
• Identify ethical risks and apply responsible AI principles in real-world scenarios.

Course Duration

• 7 hours (including breaks)

Format

• Full-Day Applied Workshop

Session 1: Introduction to AI and Machine Learning

This session provides a foundational understanding of Artificial Intelligence and Machine Learning. Participants explore the core concepts, see practical demonstrations, and experience how ML models learn using accessible tools such as Teachable Machine.

Course Details

Key Learning Outcomes

• Understand core concepts of AI and Machine Learning.
• Distinguish between AI, ML, automation, algorithms, and model training.
• Train a basic ML model through a guided activity.
• Develop awareness of data quality, labeling, and model limitations.

Key Topics and Activities

• Overview of AI: definitions, applications, and cross-disciplinary relevance.
• ML basics: supervised, unsupervised, and reinforcement learning.
• Data quality, bias, generalization, and training datasets.
• Demonstration: building a model using Teachable Machine.
• Hands-on task: create and test a simple image or sound classifier.
• Discussion on model errors, bias, and data influence.

Session 2: Prompt Engineering and Applications of AI

This session strengthens participants’ ability to interact effectively with large language models (LLMs) through structured prompt engineering. They explore essential prompt frameworks and apply them to real and professional tasks.

Key Learning Outcomes

• Write clear, structured, and high-quality prompts.
• Use iterative prompting techniques to refine outputs.
• Apply LLMs in research, planning, analysis, and creative work.
• Evaluate AI-generated responses for accuracy and potential hallucinations.

Key Topics and Activities

• Components of an effective prompt: intent, context, examples, constraints, format.
• Types of prompts: summarizing, analyzing, brainstorming, outlining, rewriting, evaluating.
• Iterative improvement and evaluation strategies.
• Use-case demonstrations: writing, research assistance, data interpretation, planning.
• Activity: Create, test, refine, and present three prompts.
• Critical review of AI outputs for gaps, bias, and errors.

Session 3: AI Agents and Research Tools

This session introduces a range of AI tools designed to enhance writing, research, communication, and content creation. Participants practice using each tool and learn how to choose the right one for different tasks.

Key Learning Outcomes

• Understand the purpose and strengths of various AI agents.
• Use AI tools to improve writing, summarization, research, and presentations.
• Extract insights and structure information using research-focused AI tools.
• Select appropriate tools for different professional needs.

Key Topics and Activities

Grammarly

• Improve clarity, structure, tone, and grammar.

Gamma

• Convert outlines into well-structured presentation slides.
• Activity: build a 3-slide concept deck.

Prezi

• Create dynamic, visually engaging presentations.
• Activity: turn a topic into a visual story map.

HeyZen

• Generate creative ideas, scripts, and content.
• Activity: write a short script or commentary.

Napkin

• Transform complex information into simple visual explanations.
• Activity: convert a dense paragraph into a visual diagram.

NotebookLM

• Summarize, analyze, and answer questions using document-grounded AI.
• Activity: upload a short reading and generate a summary with questions.

Elicit App

• Conduct literature searches and extract evidence from research papers.
• Activity: find relevant papers and create a short evidence table.

Session 4: Advanced Tools for Prototyping, Automation, and Data Analytics

This session shifts participants from AI users to solution designers, introducing tools for prototyping, workflow automation, and AI-assisted data analysis to support problem-solving and innovation.

Key Learning Outcomes

• Build simple wireframes and prototypes with AI-enabled design tools.
• Develop no-code automations to streamline repetitive tasks.
• Use AI to analyze, visualize, and interpret data.
• Combine multiple AI tools to move from ideas to actionable solutions.

Key Topics and Activities

Figma (with AI support)

• Basics of wireframing, layout, and UI design.
• AI-generated layout and content suggestions.
• Activity: create a three-screen prototype.

Opal (Automation Tool)

• Understanding triggers, actions, and conditions.
• Build simple automated workflows.
• Activity: automate form submission notifications and data logging.

Google Sheets with Gemini

• Clean and interpret datasets using AI.
• Generate insights, pivot tables, and visualizations via natural language.
• Activity: analyze a dataset and create a summary with a chart.

Session 5: Responsible Use of AI, Risks, and Ethics

This session covers the ethical, social, and responsibilities associated with using AI. Participants learn how to minimize risks, ensure integrity, and use AI safely and responsibly.

Key Learning Outcomes

• Identify major risks such as bias, misinformation, and privacy issues.
• Apply ethical frameworks to real-world AI use.
• Understand integrity guidelines for AI-assisted work.
• Critically assess, verify, and cross-check AI-generated outputs.

Key Topics and Activities

• AI risks: bias, unfair results, privacy concerns, hallucinations, overreliance, deepfakes.
• Ethical principles: transparency, consent, data minimization, fairness, accountability.
• Integrity: responsible use and proper attribution.
• Activity: analyze a case scenario, identify risks, and recommend solutions.
• Ethical checklist: apply to participants’ prototypes or AI-generated outputs.

Workshop Modality

• Each session is designed to be highly interactive, emphasizing hands-on activities to foster a deep understanding and practical knowledge of each AI tool. This approach ensures that the participants can confidently apply the learnt tools in their work enhancing their work efficiency, effectiveness, and creativity.

Resources

• Participants shall receive all the workbooks and digital handbook.

For Queries:

Contact No: +977-9801898004 (WhatsApp)
Email: services@frontline.com.np
URL: www.frontline.com.np