eprintid: 14386 rev_number: 10 eprint_status: archive userid: 2 dir: disk0/00/01/43/86 datestamp: 2026-02-18 23:30:10 lastmod: 2026-02-18 23:30:12 status_changed: 2026-02-18 23:30:10 type: article metadata_visibility: show creators_name: Rojas Vistorte, Angel Olider creators_name: Deroncele-Acosta, Angel creators_name: Martín Ayala, Juan Luis creators_name: Barrasa, Angel creators_name: López-Granero, Caridad creators_name: Martí-González, Mariacarla creators_id: angel.rojas@uneatlantico.es creators_id: creators_id: juan.martin@uneatlantico.es creators_id: creators_id: creators_id: title: Integrating artificial intelligence to assess emotions in learning environments: a systematic literature review ispublished: pub subjects: uneat_ps divisions: uninimx_produccion_cientifica divisions: uneatlantico_produccion_cientifica full_text_status: public keywords: emotions, artificial intelligence, teaching-learning, education, assessment abstract: Introduction: 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. date: 2024-06 publication: Frontiers in Psychology volume: 15 id_number: doi:10.3389/fpsyg.2024.1387089 refereed: TRUE issn: 1664-1078 official_url: http://doi.org/10.3389/fpsyg.2024.1387089 access: open language: en citation: Artículo Materias > Psicología Universidad Internacional Iberoamericana México > Investigación > Producción Científica Universidad Europea del Atlántico > Investigación > Artículos y libros Abierto Inglés Introduction: 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. metadata Rojas Vistorte, Angel Olider; Deroncele-Acosta, Angel; Martín Ayala, Juan Luis; Barrasa, Angel; López-Granero, Caridad y Martí-González, Mariacarla mail angel.rojas@uneatlantico.es, SIN ESPECIFICAR, juan.martin@uneatlantico.es, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR (2024) Integrating artificial intelligence to assess emotions in learning environments: a systematic literature review. Frontiers in Psychology, 15. ISSN 1664-1078 document_url: http://repositorio.uneatlantico.es/id/eprint/14386/1/fpsyg-15-1387089.pdf