Artificial intelligence tools for education (AIEd) have been used to automate the provision of learning support to mainstream learners. One of the most innovative approaches in this field is the use of data and machine learning for the detection of a student’s affective state, to move them out of negative states that inhibit learning, into positive states such as engagement. In spite of their obvious potential to provide the personalisation that would give extra support for learners with intellectual disabilities, little work on AIEd systems that utilise affect recognition currently addresses this group. Our system used multimodal sensor data and machine learning to first identify three affective states linked to learning (engagement, frustration, boredom) and second determine the presentation of learning content so that the learner is maintained in an optimal affective state and rate of learning is maximised. To evaluate this adaptive learning system, 67 participants aged between 6 and 18 years acting as their own control took part in a series of sessions using the system. Sessions alternated between using the system with both affect detection and learning achievement to drive the selection of learning content (intervention) and using learning achievement alone (control) to drive the selection of learning content. Lack of boredom was the state with the strongest link to achievement, with both frustration and engagement positively related to achievement. There was significantly more engagement and less boredom in intervention than control sessions, but no significant difference in achievement. These results suggest that engagement does increase when activities are tailored to the personal needs and emotional state of the learner and that the system was promoting affective states that in turn promote learning. However, longer exposure is necessary to determine the effect on learning.

中文翻译：

---

基于多模式情感识别的适应性学习系统对智障学习者的评估

人工智能教育工具（AIEd）已被用于自动化向主流学习者的学习支持。该领域最具创新性的方法之一是使用数据和机器学习来检测学生的情感状态，将其从抑制学习的消极状态转变为积极状态，例如参与。尽管他们具有提供个性化服务的明显潜力，这将为智障学习者提供额外的支持，但目前在利用情感识别的AIEd系统上开展的工作很少。我们的系统使用多模式传感器数据和机器学习功能来首先识别与学习相关的三种情感状态（参与，沮丧，第二，确定学习内容的呈现方式，以便使学习者保持最佳的情感状态，并最大程度地提高学习速度。为了评估此自适应学习系统，年龄在6至18岁之间的67位参与者作为自己的控制者参加了使用该系统的一系列会议。会话在使用具有影响检测和学习成果的系统来驱动学习内容的选择（干预）和仅使用学习成果（控制）来驱动学习内容的选择之间交替进行。缺乏无聊是与成就联系最紧密的状态，挫败感和敬业度与成就成正比。与对照组相比，干预的参与度大大提高，而无聊感也减少了，但成就无明显差异。这些结果表明，在根据个人需求和学习者的情感状态量身定制活动时，参与度确实会增加，并且系统正在促进情感状态，进而促进学习。但是，需要更长的时间来确定对学习的影响。