Open Access

ARTICLE

Leveraging AI for Advancements in Qualitative Research Methodology

Ilyas Haouam^*

Laboratoire de Physique Mathématique et de Physique Subatomique (LPMPS), Université Frères Mentouri, Constantine, 25000, Algeria

* Corresponding Author: Ilyas Haouam. Email: Array

Journal on Artificial Intelligence 2025, 7, 85-114. https://doi.org/10.32604/jai.2025.064145

Received 06 February 2025; Accepted 27 April 2025; Issue published 27 May 2025

Abstract

This study investigates the integration of Artificial Intelligence (AI) technologies—particularly natural language processing and machine learning—into qualitative research (QR) workflows. Our research demonstrates that AI can streamline data collection, coding, theme identification, and visualization, significantly improving both speed and accuracy compared to traditional manual methods. Notably, our experimental and numerical results provide a comprehensive analysis of AI’s effect on efficiency, accuracy, and usability across various QR tasks. By presenting and discussing studies on some AI & generative AI models, we contribute to the ongoing scholarly discussion on the role of AI in QR exploring its potential benefits, challenges, and limitations. We highlight the growing use of AI-powered qualitative data analysis tools such as ATLAS.ti, Quirkos, and NVivo for automating coding and data interpretation. Our analysis indicates that while AI tools from leading companies (e.g., OpenAI’s GPT-4, Google’s T5, Meta’s RoBERTa) can enhance efficiency and depth in QR, code-focused models and general-purpose proprietary language models often do not align with qualitative needs. Additionally, certain proprietary and open-source models (e.g., DeepSeek, OLMo) are less prevalent in QR due to specialization gaps or adoption lags, whereas task-specific, transparent models, such as BERT for classification, T5 for text generation and summarization, and BLOOM for multilingual analysis, remain preferable for coding and thematic analysis due to their reproducibility and adaptability. We discuss key stages where AI has made a significant impact, including data collection and pre-processing, advanced text and sentiment analysis, simulation and modeling, improved objectivity and consistency. The benefits of integrating AI into QR, along with corresponding adaptations in research methodologies, are also presented. Noteworthy applications and techniques—including The AI Scientist, Carl, AI co-scientist, augmented physics, and explainable AI (XAI)—further illustrate the diverse potential of AI in research and the challenges to academic norms. Despite AI advancements, challenges persist. AI struggles with contextually nuanced data such as sarcasm, tone, and cultural context, and its reliance on training datasets raises ethical concerns regarding privacy, consent, and bias. Ultimately, we advocate for a hybrid approach where AI augments rather than replaces traditional qualitative methods, anticipating that ongoing AI advancements will enable more sophisticated, collaborative research practices that effectively combine machine capabilities with human expertise. This trend is underpinned and exemplified by applications like AI co-scientist, augmented physics.

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