When faced with the problem of learning a model of a high-dimensional environment, a common approach is to limit the model to make only a restricted set of predictions, thereby simplifying the learning problem. These partial models may be directly useful for making decisions or may be combined together to form a more complete, structured model. However, in partially observable (non-Markov) environments, standard model-learning methods learn generative models, i.e. models that provide a probability distribution over all possible futures (such as POMDPs). It is not straightforward to restrict such models to make only certain predictions, and doing so does not always simplify the learning problem. In this paper we present prediction profile models: non-generative partial models for partially observable systems that make only a given set of predictions, and are therefore far simpler than generative models in some cases. We formalize the problem of learning a prediction profile model as a transformation of the original model-learning problem, and show empirically that one can learn prediction profile models that make a small set of important predictions even in systems that are too complex for standard generative models.

The CBA algorithm consists of two components which take advantage of the complimentary abilities of humans and computer agents. The first component, Confident Execution, enables the agent to identify states in which demonstration is required, to request a demonstration from the human teacher and to learn a policy based on the acquired data. The algorithm selects demonstrations based on a measure of action selection confidence, and our results show that using Confident Execution the agent requires fewer demonstrations to learn the policy than when demonstrations are selected by a human teacher. The second algorithmic component, Corrective Demonstration, enables the teacher to correct any mistakes made by the agent through additional demonstrations in order to improve the policy and future task performance. CBA and its individual components are compared and evaluated in a complex simulated driving domain.

This article focuses on contributions that AI can make to address long-term educational goals. It describes five challenges that would support: (1) mentors for every learner; (2) learning twenty-first century skills; (3) interaction data to support learning; (4) universal access to global classrooms; and (5) lifelong and life-wide learning. A vision and brief research agenda are described for each challenge along with goals that lead to access to global educational resources and the reuse and sharing of digital educational resources. Instructional systems with AI technology are described that currently support richer experiences for learners and supply researchers with new opportunities to analyze vast data sets of instructional behavior from big databases, containing elements of learning, affect, motivation, and social interaction.

Articulating thought in computer-based media is a powerful means for humans to develop their understanding of phenomena. We have created DynaLearn, an Intelligent Learning Environment that allows learners to acquire conceptual knowledge by constructing and simulating qualitative models of how systems behave. DynaLearn uses diagrammatic representations for learners to express their ideas. This article presents an overview of the DynaLearn system.

Part Two of the 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.

Intelligent game-based learning environments integrate commercial game technologies with AI methods from intelligent tutoring systems and intelligent narrative technologies. This article introduces the CRYSTAL ISLAND intelligent game-based learning environment, which has been under development in the authors' laboratory for the past seven years. After presenting CRYSTAL ISLAND, the principal technical problems of intelligent game-based learning environments are discussed: narrative-centered tutorial planning, student affect recognition, student knowledge modeling, and student goal recognition. Solutions to these problems are illustrated with research conducted with the CRYSTAL ISLAND learning environment.

The emergence of massive open online courses has initiated a broad national-wide discussion on higher education practices, models, and pedagogy. Artificial intelligence and machine learning courses were at the forefront of this trend and are also being used to serve personalized, managed content in the back-end systems. Massive open online courses are just one example of the sorts of pedagogical innovations being developed to better teach AI. This column will discuss and share innovative educational approaches that teach or leverage AI and its many subfields, including robotics, machine learning, natural language processing, computer vision, and others at all levels of education (K-12, undergraduate, and graduate levels).

Articulating thought in computer-based media is a powerful means for humans to develop their understanding of phenomena. We have created DynaLearn, an Intelligent Learning Environment that allows learners to acquire conceptual knowledge by constructing and simulating qualitative models of how systems behave. DynaLearn uses diagrammatic representations for learners to express their ideas. The environment is equipped with semantic technology components capable of generating knowledge-based feedback, and virtual characters enhancing the interaction with learners. Teachers have created course material, and successful evaluation studies have been performed. This article presents an overview of the DynaLearn system.

The Twenty-Sixth Annual Conference on Innovative Applications of Artificial Intelligence (IAAI-14) will focus on successful applications of AI technology.

The emergence of massive open online courses has initiated a broad national-wide discussion on higher education practices, models, and pedagogy.  Artificial intelligence and machine learning courses were at the forefront of this trend and are also being used to serve personalized, managed content in the back-end systems. Massive open online courses are just one example of the sorts of pedagogical innovations being developed to better teach AI. This column will discuss and share innovative educational approaches that teach or leverage AI and its many subfields, including robotics, machine learning, natural language processing, computer vision, and others at all levels of education (K-12, undergraduate, and graduate levels).  In particular, this column will serve the community as a venue to learn about the Symposium on Educational Advances in Artificial Intelligence (EAAI) (colocated with AAAI for the past four years); introductions to innovative pedagogy and best practices for AI and across the computer science curricula; resources for teaching AI, including model AI assignments, software packages, online videos and lectures that can be used in your classroom; topic tutorials introducing a subject to students and researchers with links to articles, presentations, and online materials; and discussion of the use of AI methods in education shaping personalized tutorials, learning analytics, and data mining