RI: Small: CompCog: Pique: A Cognitive Model of Curiosity for Personalizing Sequences of Learning Resources

教育中最重要的挑战之一是同时提供个性化和规模化。 如何为数百或数千名在线课程中的每个学习者提供适合他们个人的知识和挑战？ 该项目将开发一个用于个性化学习的人工智能系统，该系统受到好奇心，创造力和内在动机的认知模型的启发。Pique（“个性化好奇引擎”的缩写）基于理解是什么让个体学习者好奇，然后推荐能够激发他们好奇心的资源。 Pique的学习者认知模型使用自然语言处理技术来找出哪些资源序列足够熟悉，可以访问，但又足够新，不会无聊。 Pique是一种新颖的认知系统，它借鉴了智能辅导系统、计算创造力和自然语言处理的技术。 它的主要贡献是将好奇心的认知模型与教育推荐系统相结合。我们将首先通过模拟评估皮克的有效性，然后与一所大型综合性公立大学的学生进行评估。 评估将采取完整的皮克系统和修改后的版本与好奇心的认知模型禁用之间的比较的形式。 这将使我们能够确定推荐既能激发好奇心又适合任务的资源是否比推荐只适合任务的资源更有效。鉴于这项研究的跨学科性质，我们将广泛传播我们的成果，包括教育技术，认知系统，信息检索和计算创造力社区。

One of the most significant challenges in education is to simultaneously provide personalization and scale. How can each learner in an online class of hundreds or thousands be provided with knowledge and challenges that suit them personally? This project will develop an AI system for personalized learning that is inspired by cognitive models of curiosity, creativity and intrinsic motivation. Pique (short for the "Personalized Curiosity Engine") is based on understanding what makes an individual learner curious, and then recommending resources that will stimulate their curiosity. Pique's cognitive model of its learner uses natural language processing techniques to figure out what sequences of resources will be familiar enough to be accessible, but sufficiently new to not be boring. Pique is a novel cognitive system drawing on technologies from intelligent tutoring systems, computational creativity, and natural language processing. Its key contribution is combining a cognitive model of curiosity with educational recommender systems. We will evaluate the effectiveness of Pique first with simulations and then with students at a large comprehensive public university. Evaluation will take the form of a comparison between the full Pique system and a modified version with its cognitive model of curiosity disabled. This will enable us to determine whether recommending resources that are simultaneously curiosity-stimulating and fit to the task is more effective than recommending resources that are just fit to the task. Given the interdisciplinary nature of this research we will disseminate our results broadly, including to the educational technology, cognitive systems, information retrieval, and computational creativity communities.