Mixed Methods Research: AI-Augmented Design, Analysis, and Publishing

Mixed Methods Research: AI-Augmented Design, Analysis, and Publishing is a practical and scholarly guide for doctoral students, faculty members, institutional researchers, and interdisciplinary scholars seeking to conduct rigorous mixed-methods studies. While grounded in education and the social sciences, it is relevant to researchers in health, policy, business, and STEM fields. The book provides a roadmap from research design to publication. It covers foundational mixed methods paradigms, core designs, integration strategies, and meta-inference development. It also demonstrates how AI tools such as ChatGPT, Google Gemini, and Microsoft Copilot can be used responsibly to support literature reviews, instrument design, data analysis preparation, writing, and revision without compromising rigor or ethics. Each chapter blends conceptual clarity, applied examples, and practical workflows suitable for doctoral courses, faculty development, and research teams.

Key Features

1. Rigorous Mixed Methods Framework
   Guidance on design selection, integration, joint displays, and defensible interpretation.
2. Responsible AI Integration
   Structured use of AI tools across research phases with attention to ethics, bias, and transparency.
3. From Research to Publication
   Strategies for manuscript development, journal targeting, and navigating peer review.

CONTENTS

CHAPTER 1  What Is Mixed Methods Research?

Defining Mixed Methods Research
Why Mixed Methods?
Key Advantages of Conducting Mixed Methods Research
Types of Mixed Methods Research
Key Elements of Mixed Methods Research

CHAPTER 2  AI and Philosophical Foundations of Mixed Methods Research

Pragmatism as Methodological Foundation
Critical Realism and Ontological Depth
Transformative Paradigms and Justice-Centered Inquiry
Integration as the Core of Mixed Methods
Rethinking Mixed Methods in the Age of AI

CHAPTER 3  Convergent Mixed Methods Design

Definition and Purpose
Key Procedures of the Convergent Mixed Methods Design
Examples of Convergent Mixed Methods Design
AI Applications in the Convergent Mixed Methods Design

CHAPTER 4  Explanatory Sequential Mixed Methods Design

Definition and Purpose
Key Procedures of the Explanatory Sequential Design
Examples of Explanatory Sequential Design
AI Applications in the Explanatory Sequential Design

CHAPTER 5  Exploratory Sequential Mixed Methods Design

Definition and Purpose
Key Procedures of the Exploratory Sequential Design
Examples of Exploratory Sequential Design
AI Applications in the Exploratory Sequential Design

CHAPTER 6  AI-Augmented Mixed Methods Methodology

Conceptual Foundations of AI-Augmented Mixed Methods
AI in Instrument Development
AI for Qualitative Coding Validation
AI for Statistical Assumption Checking
Bias Detection and Ethical Safeguards
Prompt Engineering for Research
Empirical Mini-Case Studies
Reporting AI in Mixed Methods Publications

CHAPTER 7  Advanced Integration, Joint Displays, and Meta-Inference

What Is Integration in Mixed Methods?
Timing, Weighting, and Connecting Data Strands
Building Joint Displays
Types of Joint Displays
Handling Convergence and Divergence
Constructing Defensible Meta-Inferences
Weak vs. Strong Meta-Inference
AI-Assisted Integration with Human Validation

CHAPTER 8  Writing a Mixed Methods Dissertation Proposal

Structuring a Mixed Methods Proposal
Introduction
Purpose Statement
Research Questions
Methodology
Anticipated Results
Table of Contents for a Mixed Methods Dissertation Proposal
AI Applications in Crafting a Mixed Methods Proposal
Sample Assignment: Writing a Mixed Methods Research Proposal

BIBLIOGRAPHY AND RESOURCES 

Mixed Methods Books
Mixed Methods Journals
Prompts for AI Tools
Research and Literature Review Tools
Writing Tools

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