■人工智能(AI)正在改变我们社会中的多个部门,包括教育。在这种情况下,情绪在教学过程中起着根本性的作用,因为它们会影响学习成绩,动机,信息保留,和学生的福祉。因此,人工智能在教育环境中的情感评估中的整合提供了几个优势,可以改变我们理解和解决学生社会情感发展的方式。然而,仍然缺乏将进步系统化的全面方法,挑战,和这个领域的机会。
■这篇系统的文献综述旨在探讨如何在教育环境中使用人工智能(AI)来评估情绪。我们全面概述了研究的现状,专注于进步,挑战,以及教育环境中人工智能驱动的情感评估领域的机会。
■该评论涉及在以下学术数据库中进行搜索:Pubmed,WebofScience,PsycINFO和Scopus。选择了符合既定纳入标准的41篇文章。对这些文章进行了分析,以提取与教育环境中AI和情感评估的集成相关的关键见解。
■这些发现揭示了各种人工智能驱动的方法,这些方法被开发用于捕捉和分析学生在学习活动中的情绪状态。研究结果总结为四个基本主题:(1)教育中的情感识别,(2)技术整合和学习成果,(3)特殊教育和辅助技术,(4)情感盘算。采用的关键AI技术包括机器学习和面部识别,用来评估情绪。这些方法在增强教学策略和创建满足个人情感需求的适应性学习环境方面显示出很有希望的潜力。审查确定了新出现的因素,虽然重要,需要进一步调查,以充分了解他们的关系和影响。这些元素可以显着增强AI在教育环境中评估情绪的使用。具体来说,我们指的是:(1)联合学习,(2)卷积神经网络(CNN),(3)递归神经网络(RNN),(4)面部表情数据库,(5)智能系统发展中的伦理。
■这篇系统的文献综述展示了人工智能在通过情绪评估彻底改变教育实践中的意义。虽然进步是显而易见的,与准确性相关的挑战,隐私,并确定了跨文化有效性。现有研究的综合强调了需要进一步研究改进用于情感识别的AI模型,并强调了在教育环境中实施AI技术的伦理考虑的重要性。
UNASSIGNED: 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.
UNASSIGNED: 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.
UNASSIGNED: 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.
UNASSIGNED: 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.
UNASSIGNED: 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.