Children with autism spectrum disorder (ASD) usually show little interest in academic activities and may display disruptive behavior when presented with assignments. Research indicates that incorporating motivational variables during interventions results in improvements in behavior and academic performance. However, the impact of such motivational variables varies between children. In this paper, we aim to address the problem of selecting the right motivator for children with ASD using reinforcement learning by adapting to the most influential factors impacting the effectiveness of the contingent motivator used. We model the task of selecting a motivator as a Markov decision process problem. The states, actions and rewards design consider the factors that impact the effectiveness of a motivator based on applied behavior analysis as well as learners’ individual preferences. We use a Q-learning algorithm to solve the modeled problem. Our proposed solution is then implemented as a mobile application developed for special education plans coordination. To evaluate the motivator selection feature, we conduct a study involving a group of teachers and therapists and assess how the added feature aids the participants in their decision-making process of selecting a motivator. Preliminary results indicated that the motivator selection feature improved the usability of the mobile app. Analysis of the algorithm performance showed promising results and indicated improvement of the recommendations over time.

患有自闭症谱系障碍 (ASD) 的儿童通常对学术活动缺乏兴趣，并且在布置作业时可能会表现出破坏性行为。研究表明，在干预过程中纳入动机变量可以改善行为和学业成绩。然而，此类动机变量的影响因儿童而异。在本文中，我们的目标是通过适应影响所使用的偶然激励因素有效性的最有影响力的因素，利用强化学习来解决为自闭症谱系障碍儿童选择正确激励因素的问题。我们将选择激励因素的任务建模为马尔可夫决策过程问题。状态、行动和奖励设计根据应用行为分析以及学习者的个人偏好考虑影响激励器有效性的因素。我们使用 Q 学习算法来解决建模问题。然后，我们提出的解决方案将作为为特殊教育计划协调而开发的移动应用程序来实施。为了评估激励因素选择功能，我们进行了一项涉及一组教师和治疗师的研究，并评估添加的功能如何帮助参与者选择激励因素的决策过程。初步结果表明，激励因素选择功能提高了移动应用程序的可用性。算法性能分析显示出有希望的结果，并表明随着时间的推移，建议会有所改进。