Eye Tracking and Galvanic Skin Response (GSR) in the Study of Journalism with AI
Abstract
This article presents the initial results of a research study that combines neuromarketing techniques and qualitative methods to analyze how journalism students perceive the integration of artificial intelligence (AI) into the profession. The study was conducted with fifty participants from the Polytechnic Institute of Guarda (Portugal), using Eye Tracking and Galvanic Skin Response (GSR) techniques, complemented by in-depth interviews and focus groups. The results show that audiovisual stimuli featuring institutional texts with strong contrast and human presence generate higher levels of attention and emotional activation. Likewise, visual pieces that combine informative clarity and technological innovation elicit more stable and positive responses. These findings demonstrate the relevance of integrated methodologies that articulate visual attention, emotion, and narrative perception, providing a holistic understanding of how institutional messages related to AI are received in the field of journalism.
References
Ariely, D., & Berns, G. S. (2010). Neuromarketing: The hope and hype of neuroimaging in business. Nature Reviews Neuroscience, 11(4), 284–292. https://doi.org/10.1038/nrn2795
Boucsein, W. (2012). Electrodermal activity (2nd ed.). Springer Science & Business Media. https://doi.org/10.1007/978-1-4614-1126-0
Creswell, J. W., & Plano Clark, V. L. (2017). Designing and conducting mixed methods research (3rd ed.). SAGE Publications. https://doi.org/10.4135/9781483348858
Duchowski, A. T. (2017). Eye tracking methodology: Theory and practice (3rd ed.). Springer. https://doi.org/10.1007/978-3-319-57883-5
GazePoint. (s.f.). Biometrics | Hardware only. https://acortar.link/2gwpj7
GazePoint. (s.f.). Eye tracking questions and answers. https://acortar.link/QxO89j
Holmqvist, K., Nyström, M., Andersson, R., Dewhurst, R., Jarodzka, H., & Van de Weijer, J. (2011). Eye tracking: A comprehensive guide to methods and measures. Oxford University Press. https://doi.org/10.1093/acprof:oso/9780199697083.001.0001
Lang, A. (2006). Using the limited capacity model of motivated mediated message processing to design effective cancer communication messages. Journal of Communication, 56(suppl_1), S57–S80. https://doi.org/10.1111/j.1460-2466.2006.00283.x
Nummenmaa, L., Glerean, E., Viinikainen, M., Jääskeläinen, I. P., Hari, R., & Sams, M. (2014). Emotions promote social interaction by synchronizing brain activity across individuals. Proceedings of the National Academy of Sciences, 109(24), 9599–9604. https://doi.org/10.1073/pnas.1206095111
Radsch, C. C. (2023). Inteligencia artificial en el periodismo: Un manual para editores. UNESCO. https://acortar.link/PrI2xv
Tashakkori, A., & Teddlie, C. (Eds.). (2021). SAGE handbook of mixed methods in social & behavioral research (2nd ed.). SAGE Publications. https://doi.org/10.4135/9781526485090
Wedel, M., & Pieters, R. (2008). A review of eye-tracking research in marketing. Review of Marketing Research, 4, 123–147. https://doi.org/10.2139/ssrn.1981933
Zheng, Y., & Pan, Z. (2021). The impact of artificial intelligence on journalism: A systematic review of the literature. Digital Journalism, 9(10), 1489–1508. https://doi.org/10.1080/21670811.2021.1991820
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