Too many qualitative and mixed-methods researchers are currently being asked to make an impossible choice: either remain outside the world of advanced data science and artificial intelligence, or enter it by learning programming, relying on expensive proprietary platforms, and uploading sensitive data to external servers. This book begins from a different premise: researchers should not have to choose between rigor, accessibility, privacy, and interpretive depth.
Data Science, Interactive Visualizations, and Generative AI Tools for the Analysis of Qualitative, Mixed-Methods, and Multimodal Evidence presents an integrated methodological ecosystem for ethical and equity-driven data science in qualitative and mixed-methods research. It is designed for scholars working with textual, relational, temporal, affective, spatial, visual, and multimodal evidence who want access to rigorous data science and AI-supported analytic tools without needing to master programming, pay recurring fees, or surrender control of sensitive materials.
The book introduces a fully local, no-code ecosystem of software tools for analyzing complex evidence across multiple layers of inquiry--from language and structure to time, emotion, interaction, and context. Special attention is given to ISARI (Intelligent Systems for Academic Research Integration), a fully offline, open-source, multimodal brainstorming partner designed to support scholarly memoing, comparison, synthesis, and evidence-grounded writing. ISARI is presented not as a substitute for interpretation, but as part of a broader local analytic environment in which computational outputs remain accountable to researchers' judgment and to participants' original evidence.
This is not a book about replacing researchers with AI. It is a book about giving researchers ethical, privacy-conscious, and equity-driven access to advanced analytic tools that have too often remained restricted to those with programming expertise or privileged institutional support. By bringing together interactive visualizations, machine learning, natural language processing, geocontextualization, temporal analysis, relational modeling, and local generative AI, this book offers a practical and forward-looking vision for doing rigorous research without compromising transparency, scholarly control, or data sovereignty. It is intended for researchers, faculty, graduate students, institutional analysts, and interdisciplinary scholars interested in expanding their analytic toolkit while preserving methodological accountability and interpretive authority.
目前，許多質性和混合方法的研究者面臨一個不可能的選擇：要麼保持在高級數據科學和人工智慧的世界之外，要麼通過學習程式設計、依賴昂貴的專有平台，並將敏感數據上傳到外部伺服器來進入這個領域。本書從不同的前提出發：研究者不應該在嚴謹性、可及性、隱私和詮釋深度之間做出選擇。《數據科學、互動視覺化與生成式 AI 工具在質性、混合方法和多模態證據分析中的應用》提供了一個整合的方法生態系統，旨在推動質性和混合方法研究中的倫理和公平數據科學。它專為處理文本、關係、時間、情感、互動、上下文和多模態證據的學者設計，讓他們能夠在不需要精通程式設計、支付重複費用或放棄對敏感材料控制的情況下，獲得嚴謹的數據科學和 AI 支持的分析工具。
本書介紹了一個完全本地化的無程式碼生態系統，提供分析多層次複雜證據的軟體工具——從語言和結構到時間、情感、互動和上下文。特別關注 ISARI（智能系統學術研究整合），這是一個完全離線的開源多模態頭腦風暴夥伴，旨在支持學術備忘錄、比較、綜合和基於證據的寫作。ISARI 並不是詮釋的替代品，而是更廣泛的本地分析環境的一部分，在這個環境中，計算輸出仍然對研究者的判斷和參與者的原始證據負責。
這不是一本關於用 AI 取代研究者的書。這是一本關於為研究者提供倫理、注重隱私和公平的高級分析工具的書，這些工具往往僅限於具備程式設計專業知識或享有特權機構支持的人士。通過整合互動視覺化、機器學習、自然語言處理、地理情境化、時間分析、關係建模和本地生成式 AI，本書提供了一個實用且前瞻性的願景，讓研究者能夠進行嚴謹的研究，而不妥協於透明度、學術控制或數據主權。它旨在幫助研究者、教職員、研究生、機構分析師和跨學科學者擴展他們的分析工具包，同時保持方法論的責任感和詮釋權威。
