What’s Included?
High-Quality Video, E-book & Audiobook
Modules Quizzes
AI Mentor
Access for Tablet & Phone
Online Proctored Exam with One Free Retake
Prerequisites
- A foundational understanding of AI concepts, no technical skills are required.
- Openness to exploring unconventional approaches to problem-solving within the context of AI and research.
- Enthusiastic about uncovering new insights and tools that arise from combining AI technologies with research principles.
- Willingness to engage critically with ethical dilemmas and considerations related to AI technology in research practices
Self Study Materials Included
Videos
Engaging visual content to enhance understanding and learning experience.
Podcasts
Insightful audio sessions featuring expert discussions and real-world cases.
Audiobooks
Listen and learn anytime with convenient audio-based knowledge sharing.
E-Books
Comprehensive digital guides offering in-depth knowledge and learning support.
Module Wise Quizzes
Interactive assessments to reinforce learning and test conceptual clarity.
Additional Resources
Supplementary references and list of tools to deepen knowledge and practical application.
Tools You’ll Master
TensorFlow
Scikit-learn
AI Fairness 360
Zotero
What You’ll Learn
Data Preprocessing and Management
Develop skills in cleaning, organizing, and augmenting datasets to improve the quality and reliability of AI-driven research.
Machine Learning Model Development
Gain expertise in designing, training, and evaluating machine learning models tailored to specific research problems.
Advanced Statistical Analysis
Apply advanced statistical techniques to interpret AI-generated data, ensuring robust and valid research conclusions.
AI-Enhanced Scholarly Publishing
Proficiency in using AI tools to improve the scholarly publishing process.
Course Modules
Course Overview
- Course Introduction Preview
Module 1: Introduction to Artificial Intelligence (AI) for Researchers
- 1.1 Understanding AI, Machine Learning, and Deep Learning
- 1.2 Overview of AI Tools and Technologies
- 1.3 AI’s Impact on Research
Module 2: AI in Market Research
- 2.1 Introduction to AI in Market Research
- 2.2 Audience Analysis and Persona Creation Using AI
- 2.3 Using AI for Branding and Marketing Insights
Module 3: Leveraging AI for Scientific Discovery
- 3.1 AI in Data Science and Analysis
- 3.2 Machine Learning Models in Scientific Research
- 3.3 AI for Drug Discovery and Advanced Research
Module 4: AI for Academic and Scholarly Research
- 4.1 Integrating AI into Academic Workflows
- 4.2 Ethical Considerations in Academic AI Use
- 4.3 AI Tools for Enhancing Academic Research and Writing
Module 5: Enhancing Research with AI Tools
- 5.1 AI for Qualitative and Quantitative Research
- 5.2 AI Tools for Data Visualization and Analysis
- 5.3 Case Studies of AI in Research
Module 6: AI for Research Design and Methodology
- 6.1 Innovating Research Design with AI
- 6.2 AI in Survey Design and Implementation
- 6.3 Operational Efficiency and AI
Module 7: Ethical and Responsible Use of AI in Research
- 7.1 Ethical Considerations in AI Research
- 7.2 Data Privacy and AI
- 7.3 Developing and Implementing Ethical AI Guidelines
Module 8: Future of AI in Research
- 8.1 Emerging Trends in AI Research
- 8.2 Preparing for the AI-Driven Research Future
Optional Module: AI Agents for Researcher
- 1. What Are AI Agents
- 2. Key Capabilities of AI Agents in Research
- 3. Applications and Trends for AI Agents in Research
- 4. Benefits of AI Agents in Research
- 5. How Does an AI Agent Work
- 6. Core Characteristics of AI Agents
- 7. Types of AI Agents
Frequently Asked Questions
The AI+ Researcher™ certification is a one-day comprehensive program designed to equip scholars and researchers with the tools and knowledge to effectively leverage artificial intelligence (AI) in their research fields. The course covers fundamental AI concepts, tools, and applications specific to research.
This course is ideal for scholars, researchers, and academics who want to integrate AI into their research processes. It is suitable for individuals with a foundational understanding of AI concepts, though no technical skills are required.
The course introduces various AI tools and technologies, including ChatGPT, AI in data collection and analysis, and other AI tools like Bard, data analysis software, and machine learning platforms.
Upon completion, participants will possess a solid understanding of AI fundamentals and their application in research, enabling them to leverage AI tools to enhance research methodologies, productivity, and outcomes.
The course explores how AI can be used in data collection and analysis, literature review, hypothesis generation, pattern recognition, predictive modeling, and enhancing research methodologies.