000135982 001__ 135982
000135982 005__ 20240704095849.0
000135982 0247_ $$2doi$$a10.3389/fpsyg.2024.1387089
000135982 0248_ $$2sideral$$a138921
000135982 037__ $$aART-2024-138921
000135982 041__ $$aeng
000135982 100__ $$aRojas Vistorte, Angel Olider
000135982 245__ $$aIntegrating artificial intelligence to assess emotions in learning environments: a systematic literature review
000135982 260__ $$c2024
000135982 5060_ $$aAccess copy available to the general public$$fUnrestricted
000135982 5203_ $$aIntroduction: Artificial Intelligence (AI) is transforming multiple sectors within our society, including education. In this context, emotions play a fundamental role in the teaching-learning process given that they influence academic performance, motivation, information retention, and student well-being. Thus, the integration of AI in emotional assessment within educational environments offers several advantages that can transform how we understand and address the socio-emotional development of students. However, there remains a lack of comprehensive approach that systematizes advancements, challenges, and opportunities in this field.
Aim: This systematic literature review aims to explore how artificial intelligence (AI) is used to evaluate emotions within educational settings. We provide a comprehensive overview of the current state of research, focusing on advancements, challenges, and opportunities in the domain of AI-driven emotional assessment within educational settings.
Method: The review involved a search across the following academic databases: Pubmed, Web of Science, PsycINFO and Scopus. Forty-one articles were selected that meet the established inclusion criteria. These articles were analyzed to extract key insights related to the integration of AI and emotional assessment within educational environments.
Results: The findings reveal a variety of AI-driven approaches that were developed to capture and analyze students’ emotional states during learning activities. The findings are summarized in four fundamental topics: (1) emotion recognition in education, (2) technology integration and learning outcomes, (3) special education and assistive technology, (4) affective computing. Among the key AI techniques employed are machine learning and facial recognition, which are used to assess emotions. These approaches demonstrate promising potential in enhancing pedagogical strategies and creating adaptive learning environments that cater to individual emotional needs. The review identified emerging factors that, while important, require further investigation to understand their relationships and implications fully. These elements could significantly enhance the use of AI in assessing emotions within educational settings. Specifically, we are referring to: (1) federated learning, (2) convolutional neural network (CNN), (3) recurrent neural network (RNN), (4) facial expression databases, and (5) ethics in the development of intelligent systems.
Conclusion: This systematic literature review showcases the significance of AI in revolutionizing educational practices through emotion assessment. While advancements are evident, challenges related to accuracy, privacy, and cross-cultural validity were also identified. The synthesis of existing research highlights the need for further research into refining AI models for emotion recognition and emphasizes the importance of ethical considerations in implementing AI technologies within educational contexts.
000135982 540__ $$9info:eu-repo/semantics/openAccess$$aby$$uhttp://creativecommons.org/licenses/by/3.0/es/
000135982 655_4 $$ainfo:eu-repo/semantics/article$$vinfo:eu-repo/semantics/publishedVersion
000135982 700__ $$aDeroncele-Acosta, Ángel
000135982 700__ $$aMartín Ayala, Juan Luis
000135982 700__ $$0(orcid)0000-0001-9320-1888$$aBarrasa, Angel$$uUniversidad de Zaragoza
000135982 700__ $$0(orcid)0000-0001-6767-8252$$aLópez-Granero, Caridad$$uUniversidad de Zaragoza
000135982 700__ $$aMartí-González, Mariacarla
000135982 7102_ $$14009$$2740$$aUniversidad de Zaragoza$$bDpto. Psicología y Sociología$$cÁrea Psicología Social
000135982 7102_ $$14009$$2730$$aUniversidad de Zaragoza$$bDpto. Psicología y Sociología$$cÁrea Psicología Básica
000135982 773__ $$g15 (2024), 1387089 [13 pp.]$$pFront. psychol.$$tFrontiers in Psychology$$x1664-1078
000135982 8564_ $$s752205$$uhttps://zaguan.unizar.es/record/135982/files/texto_completo.pdf$$yVersión publicada
000135982 8564_ $$s2322228$$uhttps://zaguan.unizar.es/record/135982/files/texto_completo.jpg?subformat=icon$$xicon$$yVersión publicada
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000135982 951__ $$a2024-07-04-07:59:32
000135982 980__ $$aARTICLE