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AI to Improve the Qualitative Research Approach: A Methodological Contribution to the AOL Method

Yassine Chamsi et al.

International Journal of Market Research2026https://doi.org/10.1177/14707853261427652article
AJG 2ABDC A
Weight
0.50

Abstract

Addressing contemporary debates on the integration of artificial intelligence (AI) in qualitative research, this article proposes a hybrid methodological approach positioned within the “human-in-the-loop” paradigm. This approach preserves interpretive authority while leveraging computational capabilities. When applied to the Album On Line (AOL) method, the use of generative AI (eight different chatbots) and semantic AI, via natural language processing (NLP), improves the interpretation of outcome mapping. Ultimately, the outcomes generated by AI are compared with those derived from researchers’ efforts. This study aims to provide a broader vision of the world of AI and its potential contribution to research. It offers a critical complementarity framework that enhances efficiency, saves time, and improves the quality of results. It enriches the researcher’s toolbox by demonstrating how to leverage AI for research purposes.

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https://doi.org/https://doi.org/10.1177/14707853261427652

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@article{yassine2026,
  title        = {{AI to Improve the Qualitative Research Approach: A Methodological Contribution to the AOL Method}},
  author       = {Yassine Chamsi et al.},
  journal      = {International Journal of Market Research},
  year         = {2026},
  doi          = {https://doi.org/https://doi.org/10.1177/14707853261427652},
}

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Evidence weight

0.50

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.50 × 0.4 = 0.20
M · momentum0.50 × 0.15 = 0.07
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.