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 Deep-Tutor, the first intelligent tutoring system based on learning progressions.

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 datadriven 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. AI methods have advanced considerably since those early days, and so have intelligent tutoring systems. Today, intelligent tutoring systems are in widespread use in K-12 schools and colleges and are enhancing the student learning experience (for example, Graesser et al. [2005]; Mitrovic [2003]; VanLehn [2006]).

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 de - scribes 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. Intelligent tutoring systems (ITSs) are the ultimate example of this challenge: their goal is to provide instruction personalized to the specific needs of each learner, as good human tutors do. But understanding these needs can be extremely hard, because it entails modeling and capturing that complex ensemble of processes and states that constitutes human learning.

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. The burgeoning field of game-based learning has made significant advances, including theoretical developments (Gee 2007), as well as the creation of gamebased learning environments for a broad range of K-12 subjects (Habgood and Ainsworth 2011; Ketelhut et al. 2010; Warren, Dondlinger, and Barab 2008) and training objectives (Johnson 2010; Kim et al. 2009). Of particular note are the results of recent empirical studies demonstrating that in addition to game-based learning environments' potential for motivation, they can enable students to achieve learning gains in controlled laboratory settings (Habgood and Ainsworth 2011) as well as classroom settings (Ketelhut et al. 2010).

Useful feedback makes use of models of domain-specific knowledge, especially models that are commonly held by potential students. To empirically determine what these models are, student data can be clustered to reveal common misconceptions or common problem-solving strategies. This article describes how analogical retrieval and generalization can be used to cluster automatically analyzed handdrawn sketches incorporating both spatial and conceptual information. We use this approach to cluster a corpus of hand-drawn student sketches to discover common answers. Common answer clusters can be used for the design of targeted feedback and for assessment.

Column n The Educational Advances in Artificial Intelligence column discusses and shares innovative educational approaches that teach or leverage AI and its many subfields at all levels of education (K-12, undergraduate, and graduate levels). In this column I describe my experience adapting the content and infrastructure from massive, open, online courses (MOOCs) to enhance my courses in the Department of Electrical Engineering and Computer Science at Vanderbilt University. I begin with my informal, early use of MOOC content and then move to two deliberatively designed strategies for adapting MOOCs to campus (that is, wrappers and small private online classes [SPOCs]). I describe student reactions and touch on selected policy and institutional considerations. In the never-ending search for increasing student bang-for-the-buck, I was motivated to increase the bang, rather than reduce the buck, the latter being well above my pay grade.

This article presents new algorithms for inferring users' activities in a class of flexible and open-ended educational software called exploratory learning environments (ELEs). Such settings provide a rich educational environment for students, but challenge teachers to keep track of students' progress and to assess their performance. This article presents techniques for recognizing students' activities in ELEs and visualizing these activities to students. It describes a new plan-recognition algorithm that takes into account repetition and interleaving of activities. This algorithm was evaluated empirically using two ELEs for teaching chemistry and statistics used by thousands of students in several countries.

In this article I argue that achieving human-level AI is equivalent to learning how to create sufficiently smart software social organisms. This implies that no single test will be sufficient to measure progress. Instead, evaluations should be organized around showing increasing abilities to participate in our culture, as apprentices. This provides multiple dimensions within which progress can be measured, including how well different interaction modalities can be used, what range of domains can be tackled, what human-normed levels of knowledge they are able to acquire, as well as others. I begin by motivating the idea of software social organisms, drawing on ideas from other areas of cognitive science, and provide an analysis of the substrate capabilities that are needed in social organisms in terms closer to what is needed for computational modeling.

Social-emotional intelligence is an essential part of being a competent human and is thus required for humanlevel AI. When considering alternatives to the Turing test it is therefore a capacity that is important to test. We characterize this capacity as affective theory of mind and describe some unique challenges associated with its interpretive or generative nature. Mindful of these challenges we describe a five-step method along with preliminary investigations into its application. We also describe certain characteristics of the approach such as its incremental nature, and countermeasures that make it difficult to game or cheat.

Human readers comprehend vastly more, and in vastly different ways, than any existing comprehension test would suggest. An ideal comprehension test for a story should cover the full range of questions and answers that humans would expect other humans to reasonably learn or infer from a given story. ICCG uses structured crowdsourcing to comprehensively generate relevant questions and supported answers for arbitrary stories, whether fiction or nonfiction, presented across a variety of media such as videos, podcasts, and still images. While the AI scientific community had hoped that by 2015 machines would be able to read and comprehend language, current models are typically superficial, capable of understanding sentences in limited domains (such as extracting movie times and restaurant locations from text) but without the sort of widecoverage comprehension that we expect of any teenager. Comprehension itself extends beyond the written word; most adults and children can comprehend a variety of narratives, both fiction and nonfiction, presented in a wide variety of formats, such as movies, television and radio programs, written stories, YouTube videos, still images, and cartoons.