ARTIFICIAL INTELLIGENCE TO SUPPORT THE ASSESSMENT OF EMOTIONAL INTELLIGENCE

Chiara Scuotto, Emanuele Marsico, Stefano Triberti

Abstract


Initially, Emotional Intelligence (EI) was defined as an ability to process emotional information measurable through performance tasks. Later, other authors conceptualized EI as a set of aspects related to the recognition and regulation of emotions both in oneself and in others, that could be assessed through self-report instruments. Both performance tasks and self-report instruments present several problems. AI could support the assessment of EI by developing an algorithm that detects emotional states associated with facial expressions in response to viewing videos validated to induce specific emotions.  The project proposal aims to present a protocol that involves the use of an algorithm capable of comparing each subject's responses at the level of emotional states experienced. The project also includes the proposal of a comparative analysis of the quality and intensity of emotional states during the video, by monitoring some physiological parameters (HRV, GSR and temperature) through a biofeedback instrumentation. Based on the level of consistency among these data, the algorithm will provide a percentage related to the ability to recognize one's own emotions.


Keywords


Emotional Intelligence, Biofeedback, Emotions, Artificial Intelligence, Emotion Recognition

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References


Albraikan, A. A., Hafidh, B., & El Saddik, A. (2018). iAware: A Real-Time Emotional Biofeedback System Based on Physiological Signals. IEEE Access, 6, 78780–78789

Arriaga, O., Valdenegro-Toro, M., & Plöger, P.-G. (2017). Real-time Convolutional Neural Networks for emotion and gender classification. ArXiv, abs/1710.07557

Bailen, N. H., Wu, H., & Thompson, R. J. (2019). Meta-emotions in daily life: Associations with emotional awareness and depression. Emotion, 19(5), 776.

Bar-On, R. (1997). BarOn emotional quotient inventory (Vol. 40). Multi-health systems.

Bar-On, R., Maree, J. G., & Elias, M. J. (Eds.). (2007). Educating people to be emotionally intelligent. Bloomsbury Publishing USA.

Bartsch, A., Appel, M., & Storch, D. (2010). Predicting emotions and meta-emotions at the movies: The role of the need for affect in audiences’ experience of horror and drama. Communication research, 37(2), 167-190.

Bastian, V. A., Burns, N. R., & Nettelbeck, T. (2005). Emotional intelligence predicts life skills, but not as well as personality and cognitive abilities. Personality and Individual Differences, 39, 1135–1145

Boucsein, W., Koglbauer, I. V., Braunstingl, R., & Kallus, K. W. (2010). The Use of Psychophysiological Measures During Complex Flight Manoeuvres – An Expert Pilot Study.

Brackett, M. A., Rivers, S. E., Shiffman, S., Lerner, N., & Salovey, P. (2006). Relating emotional abilities to social functioning: a comparison of self-report and performance measures of emotional intelligence. Journal of personality and social psychology, 91(4), 780.

Bradley, M. M., & Lang, P. J. (1994). Measuring emotion: The SelfAssessment Manikin and the Semantic Differential. Journal of behavior therapy and experimental psychiatry, 25 1, 49–59.

Brown, K. W., & Ryan, R. M. (2009). The mindfulness attention awareness scale (MAAS). Acceptance and commitment therapy. Measures Package, 82, 82-84.

Bru-Luna, L. M., Martí-Vilar, M., Merino-Soto, C., & Cervera-Santiago, J. L. (2021, December). Emotional intelligence measures: A systematic review. In Healthcare (Vol. 9, No. 12, p. 1696). MDPI.

Davies, K. A., Lane, A. M., Devonport, T. J., & Scott, J. A. (2010). Validity and reliability of a brief emotional intelligence scale (BEIS-10). Journal of Individual Differences.

De Raad, B. (2005). The trait-coverage of emotional intelligence. Personality and Individual Differences, 38, 673–687

Dominguez-Catena, I., Paternain, D., & Galar, M. (2023). Metrics for Dataset Demographic Bias: A Case Study on Facial Expression Recognition. ArXiv, abs/2303.15889.

Dunning, D., Heath, C., & Suls, J. M. (2004). Flawed self-assessment: Implications for health, education, and the workplace. Psychological Science in the Public Interest, 5, 69–106.

Durosini, I., & Triberti, S. (2022). Le emozioni tra cura e malattia. Sant’Arcangelo di Romagna: Maggioli

Durosini, I., Triberti, S., Ongaro, G., & Pravettoni, G. (2021). Validation of the Italian version of the brief emotional intelligence scale (BEIS-10). Psychological Reports, 124(5), 2356-2376.

Durosini, I., Triberti, S., Savioni, L., Sebri, V., & Pravettoni, G. (2022). The role of emotion-related abilities in the quality of life of breast cancer survivors: a systematic review. International Journal of Environmental Research and Public Health, 19(19), 12704.

Extremera Pacheco, N., Rey Peña, L., & Sánchez Álvarez, N. (2019). Validation of the Spanish version of the Wong Law emotional intelligence scale (WLEIS-S). Psicothema.

Fiori, M., & Antonakis, J. (2011). The ability model of emotional intelligence: Searching for valid measures. Personality and individual differences, 50(3), 329-334.

Fiori, M., Antonietti, J. P., Mikolajczak, M., Luminet, O., Hansenne, M., & Rossier, J. (2014). What is the ability emotional intelligence test (MSCEIT) good for? An evaluation using item response theory. PloS one, 9(6), e98827.

Fiori, M., & Vesely-Maillefer, A. K. (2018). Emotional intelligence as an ability: Theory, challenges, and new directions. Emotional intelligence in education: Integrating research with practice, 23-47.

Goleman, D. (1995). Emotional intelligence: Why it can matter more than IQ New.

Goleman, D. (2001). Emotional intelligence: Issues in paradigm building. The emotionally intelligent workplace, 13, 26.

Grubb, W. L., & McDaniel, M. A. (2007). The fakability of Bar-On’s Emotional Quotient Inventory short form: Catch me if you can? Human Performance, 20, 43–59.

Halimi, F., AlShammari, I., & Navarro, C. (2021). Emotional intelligence and academic achievement in higher education. Journal of Applied Research in Higher Education, 13(2), 485-503.

Humphrey, N., Curran, A., Morris, E., Farrell, P., & Woods, K. (2007). Emotional intelligence and education: A critical review. Educational Psychology, 27(2), 235-254.

Koopman, E. M. E. (2015). Why do we read sad books? Eudaimonic motives and meta-emotions. Poetics, 52, 18-31.

Leutner, D. (2014). Motivation and emotion as mediators in multimedia learning. Learning and Instruction, 29, 174–175.

MacCann, C., Jiang, Y., Brown, L. E., Double, K. S., Bucich, M., & Minbashian, A. (2020). Emotional intelligence predicts academic performance: A meta-analysis. Psychological bulletin, 146(2), 150.

Matsumoto, D., Yoo, S. H., & Nakagawa, S. (2008). Culture, emotion regulation, and adjustment. Journal of personality and social psychology, 94(6), 925.

Mayer, J. D., Caruso, D. R., & Salovey, P. (1999). Emotional intelligence meets traditional standards for an intelligence. Intelligence, 27, 267–298.

Mayer, J. D., Roberts, R. D., & Barsade, S. G. (2008). Human abilities: Emotional intelligence. Annu. Rev. Psychol., 59, 507-536.

Mayer, J. D., Salovey, P., Caruso, D. R., & Sitarenios, G. (2003). Measuring emotional intelligence with the MSCEIT V2. 0. Emotion, 3(1), 97.

Mellouk, W., & Handouzi, W. (2020). Facial emotion recognition using deep learning: Review and insights. FNC/MobiSPC

Mendes, W. B., Major, B., Mccoy, S. K., & Blascovich, J. (2008). How attributional ambiguity shapes physiological and emotional responses to social rejection and acceptance. Journal of personality and social psychology, 94 2, 278–291.

Miceli, M., & Castelfranchi, C. (2019). Meta-emotions and the complexity of human emotional experience. New Ideas in Psychology, 55, 42-49.

Moreno, R. (2006). Does the modality principle hold for different media? A test of the method-affectslearning hypothesis. Journal of Computer Assisted Learning, 22, 149–158.

Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., … Chintala, S. (2019). PyTorch: An Imperative Style, High-Performance Deep Learning Library. ArXiv, abs/1912.01703.

Pei, W., Lo, T. T. S., & Guo, X. (2020). A Biofeedback Process: Detecting Architectural Space with the Integration of Emotion Recognition and Eyetracking Technology. CAADRIA proceedings.

Petrides, K. V., & Furnham, A. (2001). Trait emotional intelligence: Psychometric investigation with reference to established trait taxonomies. European journal of personality, 15(6), 425-448.

Petrides, K. V., & Furnham, A. (2009). Trait emotional intelligence questionnaire (TEIQue). Technical Manual. London: London Psychometric Laboratory.

Picard, R. W. (1997b). Affective computing. Cambridge, MA: The MIT Press.

Quílez-Robres, A., Usán, P., Lozano-Blasco, R., & Salavera, C. (2023). Emotional intelligence and academic performance: A systematic review and meta-analysis. Thinking Skills and Creativity, 101355.

Rimm-Kaufman, S. E., & Kagan, J. (1996). The psychological significance of changes in skin temperature. Motivation and Emotion, 20, 63–78.

Roberts, R. D., MacCann, C., Matthews, G., & Zeidner, M. (2010). Emotional intelligence: Toward a consensus of models and measures. Social and Personality Psychology Compass, 4(10), 821-840.

Roberts, R. D., Schulze, R., & MacCann, C. (2008). The measurement of emotional intelligence: A decade of progress? In G. Boyle, G. Matthews & D. Saklofske (Eds.), The Sage Handbook of Personality Theory and Assessment (pp. 461–482). New York: Sage.

Roberts, R. D., Schulze, R., O'Brien, K., MacCann, C., Reid, J., & Maul, A. (2006). Exploring the validity of the Mayer-Salovey-Caruso Emotional Intelligence Test (MSCEIT) with established emotions measures. Emotion, 6(4), 663.

Salovey, P., & Grewal, D. (2005). The science of emotional intelligence. Current directions in psychological science, 14(6), 281-285.

Salovey, P., & Mayer, J. D. (1990). Emotional intelligence. Imagination, cognition and personality, 9(3), 185-211.

Salovey, P., Caruso, D., & Mayer, J. D. (2004). Emotional intelligence in practice. Positive psychology in practice, 447-463.

Semerci, Y. C., Akgün, G., Toprak, E., & Barkana, D. E. (2022). A Comparative Analysis of Deep Learning Methods for Emotion Recognition using Physiological Signals for Robot-Based Intervention Studies. 2022 Medical Technologies Congress (TIPTEKNO), 1–4.

Stough, C., Saklofske, D. H., & Parker, J. D. (2009). Assessing emotional intelligence. Theory, research, and applications.

Wu, C. H., Huang, Y. M., & Hwang, J. P. (2016). Review of affective computing in education/learning: Trends and challenges. British Journal of Educational Technology, 47(6), 1304-1323.

Zeidner, M., Matthews, G., Roberts, R. D., & MacCann, C. (2003). Development of emotional intelligence: Towards a multi-level investment model. Human development, 46(2-3), 69-96.

Zeidner, M., Roberts, R. D., & Matthews, G. (2008). The science of emotional intelligence: Current consensus and controversies. European psychologist, 13(1), 64-78.

Zhoc, K. C., King, R. B., Chung, T. S., & Chen, J. (2020). Emotionally intelligent students are more engaged and successful: examining the role of emotional intelligence in higher education. European Journal of Psychology of Education, 35(4), 839-863.




DOI: https://doi.org/10.32043/gsd.v8i2.1178

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