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 Rigorous Mixed Methods Framework Guidance on design selection, integration, joint displays, and defensible interpretation. Responsible AI Integration Structured use of AI tools across research phases with attention to ethics, bias, and transparency. 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