A Real-Time Affective Computing Platform Integrated with AI System-on-Chip Design and Multimodal Signal Processing System

Li, Wei-Chih; Yang, Cheng-Jie; Liu, Boting; Fang, Wai-Chi

doi:10.1109/embc46164.2021.9630979

Cited by 8 publications

(4 citation statements)

References 15 publications

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“…In educational settings, when integrated with artificial intelligence, affective computing can personalize teaching by adapting content to align with students’ emotions and individual needs ( Kratzwald et al, 2018 ; Marín-Morales et al, 2018 , 2020 ; Arnau-González et al, 2021 ; Li S. et al, 2021 ; Li W.-C. et al, 2021 ; Wang et al, 2022 ). As such, affective computing is a crucial element in the application of artificial intelligence in emotional management within educational environments.…”

Section: Resultsmentioning

confidence: 99%

“…There is a growing body of research linking intelligent tutoring systems (ITS) to emotion during the learning process. Among the most significant advances is the analysis of facial expressions to estimate the emotional state of a student using ITS ( Sarrafzadeh et al, 2003 ); the relationship between emotion variability, self-regulated learning and task performance in ITS ( Li S. et al, 2021 ; Li W.-C. et al, 2021 ); inducing positive emotional states in ITS ( Chaffar et al, 2009 ); a new approach toward model students’ socio-emotional attributes to predict their performance in ITS ( Assielou et al, 2021 ); the integration of emotion management strategies in ITS ( Malekzadeh et al, 2014 ); emotional pedagogical agents in ITS ( Sun et al, 2013 ); the use of emotional coping strategies in ITS ( Chaffar and Frasson, 2010 ); among many other results that clearly show that ITS have a close link with human emotions.…”

Section: Introductionmentioning

confidence: 99%

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Integrating artificial intelligence to assess emotions in learning environments: a systematic literature review

Vistorte,

Deroncele-Acosta,

Ayala

et al. 2024

Front. Psychol.

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IntroductionArtificial 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.AimThis 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.MethodThe 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.ResultsThe 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.ConclusionThis 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.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Integrating artificial intelligence to assess emotions in learning environments: a systematic literature review

Vistorte,

Deroncele-Acosta,

Ayala

et al. 2024

Front. Psychol.

View full text Add to dashboard Cite

show abstract

“…There is a growing body of research linking intelligent tutoring systems (ITS) to emotion during the learning process. Among the most significant advances is the analysis of facial expressions to estimate the emotional state of a student using ITS (Sarrafzadeh et al, 2003); the relationship between emotion variability, self-regulated learning and task performance in ITS (Li S. et al, 2021;Li W.-C. et al, 2021); inducing positive emotional states in ITS (Chaffar et al, 2009); a new approach toward model students' socio-emotional attributes to predict their performance in ITS (Assielou et al, 2021); the integration of emotion management strategies in ITS (Malekzadeh et al, 2014); emotional pedagogical agents in ITS (Sun et al, 2013); the use of emotional coping strategies in ITS (Chaffar and Frasson, 2010); among many other results that clearly show that ITS have a close link with human emotions.…”

Section: Intelligent Tutoring Systems and Emotionsmentioning

confidence: 99%

“…In educational settings, when integrated with artificial intelligence, affective computing can personalize teaching by adapting content to align with students' emotions and individual needs (Kratzwald et al, 2018;Marín-Morales et al, 2018, 2020Arnau-González et al, 2021;Li S. et al, 2021;Li W.-C. et al, 2021;Wang et al, 2022). As such, affective computing is a crucial element in the application of artificial intelligence in emotional management within educational environments.…”

Section: Advantages Of Affective Computing As An Intelligent Educatio...mentioning

confidence: 99%

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A Novel EEG-Based Real-Time Emotion Recognition Approach Using Deep Neural Networks on Raspberry Pi

Kleybolte

Märtin

2023

Lecture Notes in Computer Science

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A Real-Time Affective Computing Platform Integrated with AI System-on-Chip Design and Multimodal Signal Processing System

Cited by 8 publications

References 15 publications

Integrating artificial intelligence to assess emotions in learning environments: a systematic literature review

Integrating artificial intelligence to assess emotions in learning environments: a systematic literature review

Table_1.DOCX

A Novel EEG-Based Real-Time Emotion Recognition Approach Using Deep Neural Networks on Raspberry Pi

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