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).

The Eighth International Workshop on Planning and Scheduling for Space (IWPSS 2013) was held on March 25–26 2013 at the NASA Ames Research Center, Moffett Field, California. This was the eighth in a regular series that started in 1997.

Springs, NY. ICCBR is the annual meeting of the CBR community and the ICCBR also featured a workshop program consisting of three workshops. The main conference track featured 16 research paper presentations, nine posters, and two invited speakers. The papers and posters reflected the state of the art of case-based reasoning, dealing both with open problems at the core of CBR (especially in similarity assessment, case adaptation, and case-based maintenance), as well as trending applications of CBR (especially recommender systems and computer games) and the intersections of CBR with other areas such as multiagent systems. The first invited speaker, Igor Jurisica from the Ontario Cancer Institute and the University of Toronto, spoke about how to scale up case-based reasoning for "big data" applications. The Case-Based Reasoning in Health Sciences workshop, organized by Isabelle Bichindaritz, Cindy Marling, and Stefania Montani, and the EXPPORT workshop (Experience Reuse: Provenance, Process-Orientation and Traces), organized by David Leake, Béatrice Fuchs, Juan A. Recio Garcia, and Stefania Montani, were held jointly and dealt with how to deal with data represented CDPHP, was the local chair; William E. University, and Jonathan Rubin, from Registration information is available at www.aaai.org/Symposia/ the Palo Alto Research Center, were the Spring/ sss14.php.

Under the auspices of the Humaine Association (now called the Association for the Advancement of Affective Computing, AAAC), the ACII conference series has become an important international forum for research on affective human-machine interaction and intelligent affective systems. Affect is a phenomenon of substantial importance in most if not all of human activities. This ACII conference therefore strived to emphasize the humanistic side of affective computing by promoting research at the crossroads between engineering and human sciences, including biological, social, and cultural aspects of human life. This has been exemplified by conference topics as varied as computerized psychological emotional modeling; art and cinema studies; gaming; learning; depression, stress, and anxiety management; robots, avatars, and virtual worlds; social media analysis; pattern recognition, classification, and data mining; real-time and embedded affective systems; and others. All have in common affect and emotions, with an emphasis on a computational view of emotion.

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 two invited talks illustrated used NASA's Deep Space Habitat both the diverse applications of planning (DSH), an analog spacecraft habitat, for and scheduling technologies for the simulation, and a number of scenarios space, as well as the degree to which covering a range of activities these technologies have been successfully were applied. Scheduling for Space (IWPSS) focuses infused into space systems. In addition to the two full days of he Workshop on Planning and on the technical challenges Chien from JPL presented a talk titled technical talks, there were demonstrations and opportunities facing the AI planning "Using Space, Air, Marine, and Ground of six planning and scheduling and scheduling community when Assets for Disaster Response and Environmental systems in various stages of deployment. He described Copies of papers and slides are space-based applications, from mission how space, air, Inin-situ, and marine available at robotics.estec.esa.int/IWoperations to autonomy in space exploration assets have been integrated into sensor PSS. There have been webs to enable detection, tracking, The next IWPSS workshop will be eight workshops in the series. At this and response to a wide range of terrestrial held in 2015 at a location to be determined.

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

Benjamin Grosof (Coherent Knowledge from episodic memory to great progress is being made on methods Systems) on representing activity create semantic memory, using a combination to solve problems related to structure context through semantic rule methods, of semantic memory and prediction, motion simulation, deriving from experience in the episodic memory to guide users?

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. Personalized learning is described using computational tools that enhance student and group experience, reflection, and analysis, and supply data for development of novel theory development.