Deep symbolic processing of human-performed musical sequences

Artificial music tutors are needed for assisting a performer during his/her practice time whenever a human tutor is not available. But for these artificial tutors to be intelligent and fulfill the role of a music tutor, they have to be able to identify errors made by the performer while playing a musical sequence. This task is not a trivial one, since all musical activities are considered as open-ended domains. Therefore, not only there is no unique correct way of performing a musical sequence, but also the analysis made by the tutor has to consider the development level of the performer, the difficulty level of the performed musical sequence, and many other variables. This paper describes an ongoing research that uses cascading connected layers of symbolic processing as the core of a human-performed error identification and characterization module able to overcome the complexity of the studied open-ended domain.

Social Responses to Virtual Humans: The Effect of Human-Like Characteristics

As a virtual human is provided with more human-like characteristics, will it elicit stronger social responses from people? Two experiments were conducted to address these questions. The first experiment investigated whether virtual humans can evoke a social facilitation response and how strong that response is when people are given different cognitive tasks that vary in difficulty. The second experiment investigated whether people apply politeness norms to virtual humans. Participants were tutored either by a human tutor or a virtual human tutor that varied in features and then evaluated the tutor’s performance. Results indicate that virtual humans can produce social facilitation not only with facial appearance but also with voice. In addition, performance in the presence of voice synced facial appearance seems to elicit stronger social facilitation than in the presence of voice only or face only. Similar findings were observed with the politeness norm experiment. Participants who evaluated their tutor directly reported the tutor’s performance more favorably than participants who evaluated their tutor indirectly. This valence toward the voice synced facial appearance had no statistical difference compared to the valence toward the human tutor condition. The results suggest that designers of virtual humans should be mindful about the social nature of virtual humans.

L2 Vocabulary Teaching by Social Robots: The Role of Gestures and On-Screen Cues as Scaffolds

Social robots are receiving an ever-increasing interest in popular media and scientific literature. Yet, empirical evaluation of the educational use of social robots remains limited. In the current paper, we focus on how different scaffolds (co-speech hand gestures vs. visual cues presented on the screen) influence the effectiveness of a robot second language (L2) tutor. In two studies, Turkish-speaking 5-year-olds (n = 72) learned English measurement terms (e.g., big, wide) either from a robot or a human tutor. We asked whether (1) the robot tutor can be as effective as the human tutor when they follow the same protocol, (2) the scaffolds differ in how they support L2 vocabulary learning, and (3) the types of hand gestures affect the effectiveness of teaching. In all conditions, children learned new L2 words equally successfully from the robot tutor and the human tutor. However, the tutors were more effective when teaching was supported by the on-screen cues that directed children's attention to the referents of target words, compared to when the tutor performed co-speech hand gestures representing the target words (i.e., iconic gestures) or pointing at the referents (i.e., deictic gestures). The types of gestures did not significantly influence learning. These findings support the potential of social robots as a supplementary tool to help young children learn language but suggest that the specifics of implementation need to be carefully considered to maximize learning gains. Broader theoretical and practical issues regarding the use of educational robots are also discussed.

COLLABORATIVE TUTORING: A MULTI-TUTOR APPROACH

An Intelligent Tutorial System (ITS) is a learning computer environment. Many ITSs do not integrate human tutor since they are designed to use in autonomy by the learner. One of the reasons to increase the rate of desertion in a distance training framework compared to that of a face-to-face course is the absence of the human killer. Besides, the existing ITSs are dedicated to a single learning object based on domain-dependent modelling. Our contribution consists in proposing an ITS, independent of the learning domain, capable of initiating learning, of managing an articulation between machine tutoring and human tutoring (teaching and peers) to offer an individualized and personalized follow-up, and ensure certification of the learner’s assessment.

Bug Model Based Intelligent Recommender System with Exclusive Curriculum Sequencing for Learner-Centric Tutoring

Face to face human tutoring in classroom environments amply facilitates human tutor-learner interactions wherein the tutor gets opportunity to exercise his cognitive intelligence to understand learner's pre-knowledge level, learning pattern, specific learning difficulties, and be able to offer course content well-aligned to the learner's requirements and tutor in a manner that best suits the learner. Reaching this level in an intelligent tutoring system is a challenge even today given the advanced developments in the field. This article focuses on ITS, mimicking a human tutor in terms of providing a curriculum sequence exclusive for the learner. Unsuitable courseware disorients the learner and thus degrades the overall performance. A bug model approach has been used for curriculum design and its re-alignment as per requirements and is demonstrated through a prototype tutoring recommender system, SeisTutor, developed for this purpose. The experimental results indicate an enhanced learning gain through a curriculum recommender approach of SeisTutor as opposed to its absence.

Suggestive GAN for supporting Dysgraphic drawing skills

<span lang="EN-US">The squat competence of dysgraphia affected students in drawing graphics on paper may deter the normal pace of learning skills of children. Convolutional neural network may tend to extract and stabilize the action-motion disorder by reconstructing features and inferences on natural drawings. The work in this context is to devise a scalable Generative Adversarial Network system that allows training and compilation of image generation using real time generated images and Google QuickDraw dataset to use quick and accurate modalities to provide feedback to empower the guiding software as an apt substitute for human tutor. The training loss accuracy of both discriminator and generator networks is also compared for the SGAN optimizer.</span>

Action learning and grounding in simulated human–robot interactions

In order to enable robots to interact with humans in a natural way, they need to be able to autonomously learn new tasks. The most natural way for humans to tell another agent, which can be a human or robot, to perform a task is via natural language. Thus, natural human–robot interactions also require robots to understand natural language, i.e. extract the meaning of words and phrases. To do this, words and phrases need to be linked to their corresponding percepts through grounding. Afterward, agents can learn the optimal micro-action patterns to reach the goal states of the desired tasks. Most previous studies investigated only learning of actions or grounding of words, but not both. Additionally, they often used only a small set of tasks as well as very short and unnaturally simplified utterances. In this paper, we introduce a framework that uses reinforcement learning to learn actions for several tasks and cross-situational learning to ground actions, object shapes and colors, and prepositions. The proposed framework is evaluated through a simulated interaction experiment between a human tutor and a robot. The results show that the employed framework can be used for both action learning and grounding.

Incorporation of Artificial Intelligence Tutoring Techniques in Mathematics

Intelligent Tutoring Systems incorporate Artificial Intelligence techniques, in order to imitate a human tutor. These expert systems are able to assess student’s proficiency, to provide solved examples and exercises for practice in each topic, as well as to provide immediate and personalized feedback to learners. The present study is a systematic review that evaluates the contribution of the Intelligent Tutoring Systems developed so far, to Mathematics Education, representing some of the most representative studies of the last decade.