In the Generative AI (GenAI) era, social researchers have explored different tools for different phases of research projects. Despite AI and GenAI being highly relevant topics in current literature, few researchers have addressed the question of how to explore GenAI tools based on human-AI collaboration to improve the quality of methodological literature reviews. Based on a real project as a sample, “a competence framework for social researchers integrating GenAI”, we propose a detailed analytical model that triangulates evidence from both quantitative and qualitative data analyses. A mixed-methods approach is looked at, with a focus on the qualitative dimension (quant-QUAL). The bibliometric analysis is added to the framework analysis. The steps we follow are: (1) bibliometric analysis of Scopus publications (434 with the primary string; 84 with the secondary string from 2021-2024); (2) qualitative analysis of 32 publications using inclusion/exclusion criteria to develop initial categories via Chain-of-Thought prompting with Gemini and ChatGPT; (3) indexing using webQDA; (4) pattern interpretation and categorisation using ChatGPT and webQDA; and (5) mapping and interpretation to identify patterns and connections. These results came from social researchers working together with AI, which helped figure out the pros and cons of using GenAI in different stages of the methodological literature review. During the construction of the model, the researchers identified as a limitation the fact that they did not collect data from all meetings so that their analysis could improve the framework presented.
Keywords
bibliometric analysis, Gemini, ChatGPT, webQDA, MIXAI model
Author Bio(s)
António Pedro Costa (https://orcid.org/0000-0002-4644-5879) is a principal researcher at the Research Centre on Didactics and Technology in the Education of Trainers (CIDTFF), Department of Education and Psychology of the University of Aveiro (Portugal). He holds a Ph.D. in Multimedia in Education and a post-doctorate at the same institution, with the project “Implementation and Evaluation of Instruments for Qualitative Analysis in Research”. He also collaborates with the Artificial Intelligence and Computer Science Laboratory (LIACC) at the Faculty of Engineering, University of Porto (Portugal). He is one of the researchers/authors of the qualitative data analysis software webQDA (www.webqda.net). He is the Coordinator of the Ibero-American Congress on Qualitative Research (www.ciaiq.org) and the World Conference on Qualitative Research (www.wcqr.info), which gathers more than 700 researchers annually. He is the (co)author of more than 300 publications. Coordinated several projects with public and private funding worth more than three million euros. Please direct correspondence to apcosta@ua.pt
Pablo Burneo Arteaga (https://orcid.org/0009-0007-2862-8886) is a lecturer and researcher affiliated with the University of Aveiro (Portugal) and Universidad San Francisco de Quito (Ecuador). With a background in quality engineering and academic innovation, his research explores how Generative Artificial Intelligence can be meaningfully integrated into higher education, with a focus on pedagogical strategies and competence development. He teaches Industrial and Systems Engineering and leads applied research at the CATENA Institute. He is also a member of the Research Centre on Didactics and Technology in the Education of Trainers (CIDTFF).
Judita Kasperiuniene is an Associate Professor in the Faculty of Informatics and a Senior Researcher at the Educational Research Institute, Educational Academy, Vytautas Magnus University (VMU), Lithuania. She leads the “Educational Innovation and Change” research group at the Educational Research Institute and heads the Department of System Analysis at the Faculty of Informatics. Grounded in qualitative and mixed‑methods scholarship, Dr. Kasperiuniene examines how emerging technologies transform teaching, learning, and research practice. Her current work reimagines AI as a co‑researcher, developing AI‑enhanced methodologies for qualitative data analysis and probing human‑AI collaboration in qualitative and mixed‑methods studies. She has coordinated and partnered in several national and EU‑funded research projects, as well as initiatives on AI‑supported learning analytics and EdTech adoption in public‑safety training. Over the past five years (2019 – 2024), Dr. Kasperiuniene has published more than twenty peer‑reviewed articles and multiple book chapters on educational technology, visual literacy, science communication, and computer‑supported qualitative research. Her work informs capacity‑building efforts that foster digitally enriched, inclusive learning environments across higher education and beyond.
Acknowledgements
The work of the first author is financially supported by national funds through FCT - Foundation for Science and Technology, I. P., under the project UIDB/05460/2020.
Costa, A. P., Burneo, P., & Kasperiuniene, J. (2025). Mixed-methods & AI for methodological literature reviews. The Qualitative Report, 30(10), 4515-4540. https://doi.org/10.46743/2160-3715/2025.8434
