The field of intelligent tutoring systems has successfully delivered techniques and applications to provide personalized coaching and feedback for problem solving in a variety of domains. The core of this personalized instruction is a student model; the ITS component in charge of assessing student traits and states relevant to tailor the tutorial interaction to specific student needs during problem solving. There are however, other educational activities that can help learners acquire the target skills and abilities at different stages of learning including, among others, exploring interactive simulations and playing educational games. This article describes research on creating student models that support personalization for these novel types of interactions, their unique challenges, and how AI and machine learning can help.

We report recent advances in intelligent tutoring systems with conversational dialogue. Macroadaptivity refers to a system's capability to select appropriate instructional tasks for the learner to work on. Microadaptivity refers to a system's capability to adapt its scaffolding while the learner is working on a particular task. Learning progressions and deeper dialogue and natural language processing techniques are key features of DeepTutor, the first intelligent tutoring system based on learning progressions.

Increasing widespread use of educational technologies is producing vast amounts of data. Such data can be used to help advance our understanding of student learning and enable more intelligent, interactive, engaging, and effective education. In this article, we discuss the status and prospects of this new and powerful opportunity for data-driven development and optimization of educational technologies, focusing on intelligent tutoring systems We provide examples of use of a variety of techniques to develop or optimize the select, evaluate, suggest, and update functions of intelligent tutors, including probabilistic grammar learning, rule induction, Markov decision process, classification, and integrations of symbolic search and statistical inference.

This special issue of AI Magazine presents articles on some of the most interesting projects at the intersection of AI and Education. Included are articles on integrated systems such as virtual humans, an intellgent textbook a game-based learning environment as well as technology focused components such as student models and data mining. The issue concludes with an article summarizing the contemporary and emerging challenges at the intersection of AI and education.

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The Association for the Advancement of Artificial Intelligence was pleased to present the AAAI 2013 Spring Symposium Series, held Monday through Wednesday, March 25-27, 2013. The titles of the eight symposia were Analyzing Microtext, Creativity and (Early) Cognitive Development, Data Driven Wellness: From Self-Tracking to Behavior Change, Designing Intelligent Robots: Reintegrating AI II, Lifelong Machine Learning, Shikakeology: Designing Triggers for Behavior Change, Trust and Autonomous Systems, and Weakly Supervised Learning from Multimedia. This report contains summaries of the symposia, written, in most cases, by the cochairs of the symposium.

We report recent advances in intelligent tutoring systems with conversational dialogue. We highlight progress in terms of macro and microadaptivity. Macroadaptivity refers to a system’s capability to select appropriate instructional tasks for the learner to work on. Microadaptivity refers to a system’s capability to adapt its scaffolding while the learner is working on a particular task. The advances in macro and microadaptivity that are presented here were made possible by the use of learning progressions, deeper dialogue and natural language processing techniques, and by the use of affect-enabled components. Learning progressions and deeper dialogue and natural language processing techniques are key features of DeepTutor, the first intelligent tutoring system based on learning progressions. These improvements extend the bandwidth of possibilities for tailoring instruction to each individual student which is needed for maximizing engagement and ultimately learning.

This special issue of AI Magazine presents articles on some of the most interesting projects at the intersection of AI and Education. Included are articles on integrated systems such as virtual humans, an intellgent textbook a game-based learning environment as well as technology focused components such as student models and data mining. The issue concludes with an article summarizing the contemporary and emerging challenges at the intersection of AI and education.

Increasing widespread use of educational technologies is producing vast amounts of data. Such data can be used to help advance our understanding of student learning and enable more intelligent, interactive, engaging, and effective education. In this article, we discuss the status and prospects of this new and powerful opportunity for data-driven development and optimization of educational technologies, focusing on intelligent tutoring systems We provide examples of use of a variety of techniques to develop or optimize the select, evaluate, suggest, and update functions of intelligent tutors, including probabilistic grammar learning, rule induction, Markov decision process, classification, and integrations of symbolic search and statistical inference.

Learner assessment is nontrivial even in its most basic incarnation, namely evaluating a learner's understanding of a set of domain-dependent skills from ad hoc test items (for example, Desmarais [2011]). The assessment challenges increase with the complexity of the learner's traits to be captured, because how a student behaves during an instructional activity generally provides partial and ambiguous information on the student's underlying states, and the gap between what can be observed and what a learner actually thinks and feels increases as these states go from cognitive to metacognitive and affective. In ITSs, the research field concerned with addressing these challenges is known as student modeling, and a student model is the ITS component in charge of assessing student traits and states relevant to tailor the tutorial interaction to specific student needs. Student modeling research has made the problem solution from the tutor et al. [2010]), given extensive evidence substantial progress in providing reliable (for instance by repeatedly asking for in education research showing that learner assessment during problem help) without trying to solve the problem affective factors play an important role solving or question-answering on their own (Baker et al. 2008), in learning. Educational technology At the cognitive level, knowledge can foster understanding at different however, continues to produce novel assessment, that is, evaluating the student's stages of the learning process or for environments often consisting of knowledge of relevant concepts learners with different preferences and activities not as structured and well and skills at specific points of the interaction abilities.