Artificial Intelligence for the development of qualitative studies

Authors

DOI:

https://doi.org/10.62486/latia20234

Keywords:

Artificial Intelligence, Qualitative Research, Data Analysis, Virtual Methods, Biometrics

Abstract

The integration of Artificial Intelligence (AI) is revolutionizing qualitative research by optimizing data collection and analysis. Tools such as machine learning and natural language processing enable the analysis of large volumes of information with precision and speed, facilitating the identification of patterns and trends. The adoption of virtual research methods, such as online focus groups and video interviews, has overcome geographical barriers, enabling the participation of diverse and representative samples, in addition to being more cost-effective and allowing real-time data acquisition. The incorporation of advanced biometric techniques, such as eye tracking, facial expression analysis, and neuroimaging, provides a more holistic and accurate understanding of consumers' emotional and subconscious responses. These innovations allow companies to adapt their marketing strategies and product designs more effectively, enhancing personalization and emotional resonance of the experiences offered. 

References

Zawacki-Richter, O., Marín, V., Bond, M., & Gouverneur, F. Systematic review of research on artificial intelligence applications in higher education – where are the educators? International Journal of Educational Technology in Higher Education. 2019;16(1):1-27. https://doi.org/10.1186/s41239-019-0171-0 DOI: https://doi.org/10.1186/s41239-019-0171-0

Cardeño-Portela N, Cardeño-Portela EJ, Bonilla-Blanchar E. Las TIC y la transformación académica en las universidades. Región Científica. 2023;2(2):202370. https://doi.org/10.58763/rc202370 DOI: https://doi.org/10.58763/rc202370

Secinaro S, Calandra D, Secinaro A, Muthurangu V, Biancone P. The role of artificial intelligence in healthcare: a structured literature review. BMC Medical Informatics and Decision Making. 2021;21(125). https://doi.org/10.1186/s12911-021-01488-9

Kammerer-David MI, Murgas-Téllez B. La innovación tecnológica desde un enfoque de dinámica de sistemas. Región Científica. 2024;3(1):2024217. https://doi.org/10.58763/rc2024217 DOI: https://doi.org/10.58763/rc2024217

Vindrola-Padros C, Johnson G. Rapid Techniques in Qualitative Research: A Critical Review of the Literature. Qualitative Health Research. 2020;30(10):1596-1604. https://doi.org/10.1177/1049732320921835 DOI: https://doi.org/10.1177/1049732320921835

Velásquez Castro LA, Paredes-Águila JA. Revisión sistemática sobre los desafíos que enfrenta el desarrollo e integración de las tecnologías digitales en el contexto escolar chileno, desde la docencia. Región Científica. 2024;3(1):2024226. https://doi.org/10.58763/rc2024226 DOI: https://doi.org/10.58763/rc2024226

Goldenberg S, Nir G, Salcudean SE. A new era: artificial intelligence and machine learning in prostate cancer. Nature Reviews Urology. 2019;16:391-403. https://doi.org/10.1038/s41585-019-0193-3 DOI: https://doi.org/10.1038/s41585-019-0193-3

López-González YY. Competencia digital del profesorado para las habilidades TIC en el siglo XXI: una evaluación de su desarrollo. Región Científica. 2023;2(2):2023119. https://doi.org/10.58763/rc2023119 DOI: https://doi.org/10.58763/rc2023119

Roman-Acosta D, Rodríguez-Torres E, Baquedano-Montoya MB, López-Zavala L, Pérez-Gamboa AJ. ChatGPT y su uso para perfeccionar la escritura académica en educandos de posgrado. Praxis Pedagógica. 2024;24(36):53-75. https://revistas.uniminuto.edu/index.php/praxis/article/view/3536 DOI: https://doi.org/10.26620/uniminuto.praxis.24.36.2024.53-75

Hamilton L, Elliott D, Quick A, Smith S, Choplin V. Exploring the Use of AI in Qualitative Analysis: A Comparative Study of Guaranteed Income Data. International Journal of Qualitative Methods. enero de 2023;22:16094069231201504. https://doi.org/10.1177/16094069231201504 DOI: https://doi.org/10.1177/16094069231201504

Feuston, J., & Brubaker, J. Putting Tools in Their Place: The Role of Time and Perspective in Human-AI Collaboration for Qualitative Analysis. Proceedings of the ACM on Human-Computer Interaction. 2021;5:1-25. https://doi.org/10.1145/3479856 DOI: https://doi.org/10.1145/3479856

Salas-Pilco, S. The impact of AI and robotics on physical, social-emotional and intellectual learning outcomes: An integrated analytical framework. British Journal of Education Technology. 2020;51(5):1808-1825. https://doi.org/10.1111/bjet.12984 DOI: https://doi.org/10.1111/bjet.12984

Zapata Muriel FA, Montoya Zapata S, Montoya-Zapata D. Dilemas éticos planteados por el auge de la inteligencia artificial: una mirada desde el transhumanismo. Región Científica. 2024;3(1):2024225. https://doi.org/10.58763/rc2024225 DOI: https://doi.org/10.58763/rc2024225

Sun Z, Liu J, Ke Q, Rahmani H. Human Action Recognition From Various Data Modalities: A Review. IEEE Transactions on Pattern Analysis and Machine Intelligence. 2020;45(3):3200-3225. https://doi.org/10.1109/TPAMI.2022.3183112 DOI: https://doi.org/10.1109/TPAMI.2022.3183112

Suomala J, Kauttonen J. Human’s Intuitive Mental Models as a Source of Realistic Artificial Intelligence and Engineering. Frontiers in Psychology. 2022;13. https://doi.org/10.3389/fpsyg.2022.873289 DOI: https://doi.org/10.3389/fpsyg.2022.873289

Creswell JW. Research Design. Qualitative, Quantitative and Mixed Method Approaches. 4 ed. 2019.

Ledesma F, Malave-González BE. Patrones de comunicación científica sobre E-commerce: un estudio bibliométrico en la base de datos Scopus. Región Científica. 2022;1(1):202214. https://doi.org/10.58763/rc202214 DOI: https://doi.org/10.58763/rc202214

Casasempere-Satorres A, Vercher-Ferrándiz ML. Bibliographic documentary analysis. Getting the most out of the literature review in qualitative research. New Trends in Qualitative Research. 2020;4:247-57. https://doi.org/10.36367/ntqr.4.2020.247-257 DOI: https://doi.org/10.36367/ntqr.4.2020.247-257

Pérez-Gamboa AJ, Rodríguez-Torres E, Camejo-Pérez Y. Fundamentos de la atención psicopedagógica para la configuración del proyecto de vida en estudiantes universitarios. Educación y Sociedad. 2023;21(2):67-89. https://doi.org/10.5281/zenodo.7979972

Mwita K. Strengths and weaknesses of qualitative research in social science studies. Related Topics in Social Science. 2022;11(6):618-625. https://doi.org/10.20525/ijrbs.v11i6.1920 DOI: https://doi.org/10.20525/ijrbs.v11i6.1920

Pérez-Gamboa AJ, García Acevedo Y, García Batán J. Proyecto de vida y proceso formativo universitario: un estudio exploratorio en la Universidad de Camagüey. Trasnsformación. 2019;15(3):280-296. http://scielo.sld.cu/scielo.php?script=sci_arttext&pid=S2077-29552019000300280

Batra, R., Song, L., & Ramprasad, R. Emerging materials intelligence ecosystems propelled by machine learning. Nature Reviews Materials. 2020;6:655-678. https://doi.org/10.1038/s41578-020-00255-y DOI: https://doi.org/10.1038/s41578-020-00255-y

Glaz, A., Haralambous, Y., Kim-Dufor, D., Lenca, P., Billot, R., Ryan, T., et al. Machine Learning and Natural Language Processing in Mental Health: Systematic Review. Journal of Medical Internet Research. 2019;23(5):e15708. https://doi.org/10.2196/15708 DOI: https://doi.org/10.2196/15708

Schäffer, B., & Lieder, F. Distributed interpretation – teaching reconstructive methods in the social sciences supported by artificial intelligence. Journal of Research on Technology in Education. 2022;55(1):111-124. https://doi.org/10.1080/15391523.2022.2148786 DOI: https://doi.org/10.1080/15391523.2022.2148786

Mustak, M., Salminen, J., Plé, L., & Wirtz, J. Artificial intelligence in marketing: Topic modeling, scientometric analysis, and research agenda. Journal of Business Research. 2020;124:389-404. https://doi.org/10.1016/j.jbusres.2020.10.044 DOI: https://doi.org/10.1016/j.jbusres.2020.10.044

Secinaro, S., Calandra, D., Secinaro, A., Muthurangu, V., & Biancone, P. The role of artificial intelligence in healthcare: a structured literature review. BMC Medical Informatics and Decision Making. 2021;21(125). https://doi.org/10.1186/s12911-021-01488-9 DOI: https://doi.org/10.1186/s12911-021-01488-9

Wang, D., Weisz, J., Muller, M., Ram, P., Geyer, W., Dugan, C., et al. Human-AI Collaboration in Data Science. Proceedings of the ACM on Human-Computer Interaction. 2019;3:1-24. https://doi.org/10.1145/3359313 DOI: https://doi.org/10.1145/3359313

Lennon, R., Fraleigh, R., Scoy, L., Keshaviah, A., Hu, X., Snyder, B., et al. Developing and testing an automated qualitative assistant (AQUA) to support qualitative analysis. Family Medicine and Community Health. 2021;9(1):e001287. https://doi.org/10.1136/fmch-2021-001287 DOI: https://doi.org/10.1136/fmch-2021-001287

Sorkun, M., Astruc, S., & Koelman, V. An artificial intelligence-aided virtual screening recipe for two-dimensional materials discovery. npj Computational Materials. 2020;6(106):1-10. https://doi.org/10.1038/s41524-020-00375-7 DOI: https://doi.org/10.1038/s41524-020-00375-7

Bhatt, P., & Muduli, A. Artificial intelligence in learning and development: a systematic literature review. European Journal of Training and Development. 2022;47(7/8):677-694. https://doi.org/10.1108/ejtd-09-2021-0143 DOI: https://doi.org/10.1108/EJTD-09-2021-0143

Rodgers, W., Yeung, F., Odindo, C., & Degbey, W. Artificial intelligence-driven music biometrics influencing customers’ retail buying behavior. Journal of Business Research. 2021;126:401-414. https://doi.org/10.1016/J.JBUSRES.2020.12.039 DOI: https://doi.org/10.1016/j.jbusres.2020.12.039

Han H, Liu W. The coming era of artificial intelligence in biological data science. BMC Bioinformatics. 2019;20(22):712. https://doi.org/10.1186/s12859-019-3225-3 DOI: https://doi.org/10.1186/s12859-019-3225-3

Yu R, Schubert G, Gu N. Biometric Analysis in Design Cognition Studies: A Systematic Literature Review. Buildings. 2023;13(3):630. https://doi.org/10.3390/buildings13030630 DOI: https://doi.org/10.3390/buildings13030630

Stoumpos A, Kitsios F, Talias M. Digital Transformation in Healthcare: Technology Acceptance and Its Applications. International Journal of Environmental Research and Public Health. 2023;20(4):3407. https://doi.org/10.3390/ijerph20043407 DOI: https://doi.org/10.3390/ijerph20043407

Kraus S, Schiavone F, Pluzhnikova A, Invernizzi A. Digital transformation in healthcare: Analyzing the current state-of-research. Journal of Business Research. 2021;123:557-567. https://doi.org/10.1016/j.jbusres.2020.10.030 DOI: https://doi.org/10.1016/j.jbusres.2020.10.030

Richard B, Sivo S, Orlowski M, Ford R, Murphy J, Boote D, et al. Qualitative Research via Focus Groups: Will Going Online Affect the Diversity of Your Findings? Cornell Hospitality Quarterly. 2020;62(1):32-45. https://doi.org/10.1177/1938965520967769 DOI: https://doi.org/10.1177/1938965520967769

Halliday M, Mill D, Johnson J, Lee K. Let's talk virtual! Online focus group facilitation for the modern researcher. Research in social & administrative pharmacy: RSAP. 2021;17(12):2145-2150. https://doi.org/10.1016/j.sapharm.2021.02.003 DOI: https://doi.org/10.1016/j.sapharm.2021.02.003

Bhattamisra S, Banerjee P, Gupta P, Mayuren J, Patra S, Candasamy M. Artificial Intelligence in Pharmaceutical and Healthcare Research. Big Data and Cognitive Computing. 2023;7(1):10. https://doi.org/10.3390/bdcc7010010 DOI: https://doi.org/10.3390/bdcc7010010

Borges A, Laurindo F, Spínola M, Gonçalves R, Mattos C. The strategic use of artificial intelligence in the digital era: Systematic literature review and future research directions. International Journal of Information Management. 2020;57:102225. https://doi.org/10.1016/J.IJINFOMGT.2020.102225 DOI: https://doi.org/10.1016/j.ijinfomgt.2020.102225

Bonnardel N, Pichot N. Enhancing collaborative creativity with virtual dynamic personas. Applied ergonomics. 2019;82:102949. https://doi.org/10.1016/j.apergo.2019.102949 DOI: https://doi.org/10.1016/j.apergo.2019.102949

Tran B, Rafinejad-Farahani B, Moodie S, O’Hagan R, Glista D. A Scoping Review of Virtual Focus Group Methods Used in Rehabilitation Sciences. International Journal of Qualitative Methods. 2021;20. https://doi.org/10.1177/16094069211042227 DOI: https://doi.org/10.1177/16094069211042227

Masullo G, Coppola M. Potential and limitations of digital ethnographic research: A case study on a web community. Frontiers in Sociology. 2023;7. https://doi.org/10.3389/fsoc.2022.1092181 DOI: https://doi.org/10.3389/fsoc.2022.1092181

Cottica, A., Hassoun, A., Manca, M., Vallet, J., & Melançon, G. Semantic Social Networks: A Mixed Methods Approach to Digital Ethnography. Field Methods. 2020;32(3):274-290. https://doi.org/10.1177/1525822X20908236 DOI: https://doi.org/10.1177/1525822X20908236

Hernández-Fernández, D., Mora, E., & Hernández, M. When a new technological product launching fails: A multi-method approach of facial recognition and E-WOM sentiment analysis. Physiology & Behavior. 2019;200:130-138. https://doi.org/10.1016/j.physbeh.2018.04.023 DOI: https://doi.org/10.1016/j.physbeh.2018.04.023

Clark, E., Kessinger, J., Duncan, S., Bell, M., Lahne, J., Gallagher, D., & O'keefe, S. The Facial Action Coding System for Characterization of Human Affective Response to Consumer Product-Based Stimuli: A Systematic Review. Frontiers in Psychology. 2020;11. https://doi.org/10.3389/fpsyg.2020.00920 DOI: https://doi.org/10.3389/fpsyg.2020.00920

Casado-Aranda, L., Sánchez-Fernández, J., & Ibáñez-Zapata, J. Evaluating Communication Effectiveness Through Eye Tracking: Benefits, State of the Art, and Unresolved Questions. International Journal of Business Communication. 2020;60(1):24-61. https://doi.org/10.1177/2329488419893746 DOI: https://doi.org/10.1177/2329488419893746

Schröter, I., Grillo, N., Limpak, M., Mestiri, B., Osthold, B., Sebti, F., & Mergenthaler, M. Webcam Eye Tracking for Monitoring Visual Attention in Hypothetical Online Shopping Tasks. Applied Sciences. 2021;11(19):9281. https://doi.org/10.3390/app11199281 DOI: https://doi.org/10.3390/app11199281

Oh, S., & Kim, D. Comparative Analysis of Emotion Classification Based on Facial Expression and Physiological Signals Using Deep Learning. Applied Sciences. 2022;12(3):1286. https://doi.org/10.3390/app12031286 DOI: https://doi.org/10.3390/app12031286

Siam, A., Soliman, N., Algarni, A., El-Samie, F., & Sedik, A. Deploying Machine Learning Techniques for Human Emotion Detection. Computational Intelligence and Neuroscience. 2022;2022(1):8032473. https://doi.org/10.1155/2022/8032673 DOI: https://doi.org/10.1155/2022/8032673

Khurana, V., Gahalawat, M., Kumar, P., Roy, P., Dogra, D., Scheme, E., & Soleymani, M. A Survey on Neuromarketing Using EEG Signals. IEEE Transactions on Cognitive and Developmental Systems. 2021;13(4):732-749. https://doi.org/10.1109/TCDS.2021.3065200 DOI: https://doi.org/10.1109/TCDS.2021.3065200

Liu, K., Yu, Z., Wu, W., Chen, X., Gu, Z., & Guan, C. fMRI-SI-STBF: An fMRI-informed Bayesian electromagnetic spatio-temporal extended source imaging. Neurocomputing. 2021;462:14-30. https://doi.org/10.1016/J.NEUCOM.2021.06.066 DOI: https://doi.org/10.1016/j.neucom.2021.06.066

Hamelin, N., Al-Shihabi, S., Quach, S., & Thaichon, P. Forecasting Advertisement Effectiveness: Neuroscience and Data Envelopment Analysis. Australasian Marketing Journal. 2021;30(4):313-330. https://doi.org/10.1177/18393349211005061 DOI: https://doi.org/10.1177/18393349211005061

Viejo, C., Fuentes, S., Howell, K., Torrico, D., & Dunshea, F. Integration of non-invasive biometrics with sensory analysis techniques to assess acceptability of beer by consumers. Physiology & Behavior. 2019;200:139-147. https://doi.org/10.1016/j.physbeh.2018.02.051 DOI: https://doi.org/10.1016/j.physbeh.2018.02.051

Published

2023-12-12

Issue

Section

Original

How to Cite

1.
Pérez Gamboa AJ, Díaz-Guerra DD. Artificial Intelligence for the development of qualitative studies. LatIA [Internet]. 2023 Dec. 12 [cited 2025 Feb. 19];1:4. Available from: https://latia.ageditor.uy/index.php/latia/article/view/4