Visual Metrics for Educational Videogames Linked to Socially Assistive Robots in an Inclusive Education Framework

In gamification, the development of “visual metrics for educational video games linked to social assistance robots in the framework of inclusive education” seeks to provide support, not only to regular children but also to children with specific psychosocial disabilities, such as those diagnosed with autism spectrum disorder (ASD). However, personalizing each child’s experiences represents a limitation, especially for those with atypical behaviors. “LOLY,” a social assistance robot, works together with mobile applications associated with the family of educational video game series called “MIDI-AM,” forming a social robotic platform. This platform offers the user curricular digital content to reinforce the teaching–learning processes and motivate regular children and those with ASD. In the present study, technical, programmatic experiments and focus groups were carried out, using open-source facial recognition algorithms to monitor and evaluate the degree of user attention throughout the interaction. The objective is to evaluate the management of a social robot linked to educational video games through established metrics, which allow monitoring the user’s facial expressions during its use and define a scenario that ensures consistency in the results for its applicability in therapies and reinforcement in the teaching process, mainly adaptable for inclusive early childhood education.在游戏化研究领域，面向全纳教育（Inclusive Education）框架下、与社交辅助机器人（Social Assistance Robots）联动的教育游戏视觉指标开发项目，旨在为普通儿童以及确诊为自闭症谱系障碍（Autism Spectrum Disorder, ASD）等特定心理社会残疾的儿童提供支持。然而，为每名儿童定制个性化的使用体验存在一定局限，尤其针对存在非典型行为的儿童。名为"LOLY"的社交辅助机器人，与配套的"MIDI-AM"系列教育游戏移动应用协同工作，共同构建起一套社交机器人平台。该平台可为用户提供课程化数字内容，以强化教与学的流程，并激励普通儿童与自闭症谱系障碍儿童。本研究开展了技术实验、程序实验及焦点小组（focus groups）讨论，采用开源面部识别算法（open-source facial recognition algorithms），对交互全程中用户的注意力程度进行监测与评估。本研究的目标为：通过既定视觉指标，评估与教育游戏联动的社交机器人的运行管理效果；该指标可在机器人使用过程中监测用户面部表情，并构建可确保结果一致性的应用场景，以支撑其在治疗干预及教学强化流程中的应用，尤其适配全纳学前教育场景。