Inquire Biology is a prototype of a new kind of intelligent textbook -- one that answers students' questions, engages their interest, and improves their understanding. Inquire Biology provides unique capabilities via a knowledge representation that captures conceptual knowledge from the textbook and uses inference procedures to answer students' questions. In an initial controlled experiment, community college students using the Inquire Biology prototype outperformed students using either a hardcopy or conventional E-book version of the same biology textbook. While additional research is needed to fully develop Inquire Biology, the initial prototype clearly demonstrates the promise of applying knowledge representation and question-answering technology to electronic textbooks.

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.

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.

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.

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Inquire Biology is a prototype of a new kind of intelligent textbook — one that answers students’ questions, engages their interest, and improves their understanding. Inquire Biology provides unique capabilities via a knowledge representation that captures conceptual knowledge from the textbook and uses inference procedures to answer students’ questions. Students ask questions by typing free-form natural language queries or by selecting passages of text. The system then attempts to answer the question and also generates suggested questions related to the query or selection. The questions supported by the system were chosen to be educationally useful, for example: what is the structure of X? compare X and Y? how does X relate to Y? In user studies, students found this question-answering capability to be extremely useful while reading and while doing problem solving. In an initial controlled experiment, community college students using the Inquire Biology prototype outperformed students using either a hardcopy or conventional E-book version of the same biology textbook. While additional research is needed to fully develop Inquire Biology, the initial prototype clearly demonstrates the promise of applying knowledge representation and question-answering technology to electronic textbooks.