3,358 research outputs found

    A Vision of Teaching and Learning with AI

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    The use of emotions in the implementation of various types of learning in a cognitive agent

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    Les tuteurs professionnels humains sont capables de prendre en considĂ©ration des Ă©vĂ©nements du passĂ© et du prĂ©sent et ont une capacitĂ© d'adaptation en fonction d'Ă©vĂ©nements sociaux. Afin d'ĂȘtre considĂ©rĂ© comme une technologie valable pour l'amĂ©lioration de l'apprentissage humain, un agent cognitif artificiel devrait pouvoir faire de mĂȘme. Puisque les environnements dynamiques sont en constante Ă©volution, un agent cognitif doit pareillement Ă©voluer et s'adapter aux modifications structurales et aux phĂ©nomĂšnes nouveaux. Par consĂ©quent, l'agent cognitif idĂ©al devrait possĂ©der des capacitĂ©s d'apprentissage similaires Ă  celles que l'on retrouve chez l'ĂȘtre humain ; l'apprentissage Ă©motif, l'apprentissage Ă©pisodique, l'apprentissage procĂ©dural, et l'apprentissage causal. Cette thĂšse contribue Ă  l'amĂ©lioration des architectures d'agents cognitifs. Elle propose 1) une mĂ©thode d'intĂ©gration des Ă©motions inspirĂ©e du fonctionnement du cerveau; et 2) un ensemble de mĂ©thodes d'apprentissage (Ă©pisodique, causale, etc.) qui tiennent compte de la dimension Ă©motionnelle. Le modĂšle proposĂ© que nous avons appelĂ© CELTS (Conscious Emotional Learning Tutoring System) est une extension d'un agent cognitif conscient dans le rĂŽle d'un tutoriel intelligent. Il comporte un module de gestion des Ă©motions qui permet d'attribuer des valences Ă©motionnelles positives ou nĂ©gatives Ă  chaque Ă©vĂ©nement perçu par l'agent. Deux voies de traitement sont prĂ©vues : 1) une voie courte qui permet au systĂšme de rĂ©pondre immĂ©diatement Ă  certains Ă©vĂ©nements sans un traitement approfondis, et 2) une voie longue qui intervient lors de tout Ă©vĂ©nement qui exige la volition. Dans cette perspective, la dimension Ă©motionnelle est considĂ©rĂ©e dans les processus cognitifs de l'agent pour la prise de dĂ©cision et l'apprentissage. L'apprentissage Ă©pisodique dans CELTS est basĂ© sur la thĂ©orie du Multiple Trace Memory consolidation qui postule que lorsque l'on perçoit un Ă©vĂ©nement, l'hippocampe fait une premiĂšre interprĂ©tation et un premier apprentissage. Ensuite, l'information acquise est distribuĂ©e aux diffĂ©rents cortex. Selon cette thĂ©orie, la reconsolidation de la mĂ©moire dĂ©pend toujours de l'hippocampe. Pour simuler de tel processus, nous avons utilisĂ© des techniques de fouille de donnĂ©es qui permettent la recherche de motifs sĂ©quentiels frĂ©quents dans les donnĂ©es gĂ©nĂ©rĂ©es durant chaque cycle cognitif. L'apprentissage causal dans CELTS se produit Ă  l'aide de la mĂ©moire Ă©pisodique. Il permet de trouver les causes et les effets possibles entre diffĂ©rents Ă©vĂ©nements. Il est mise en Ɠuvre grĂące Ă  des algorithmes de recherche de rĂšgles d'associations. Les associations Ă©tablies sont utilisĂ©es pour piloter les interventions tutorielles de CELTS et, par le biais des rĂ©ponses de l'apprenant, pour Ă©valuer les rĂšgles causales dĂ©couvertes. \ud ______________________________________________________________________________ \ud MOTS-CLÉS DE L’AUTEUR : agents cognitifs, Ă©motions, apprentissage Ă©pisodique, apprentissage causal

    The development of a rich multimedia training environment for crisis management: using emotional affect to enhance learning

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    PANDORA is an EU FP7-funded project developing a novel training and learning environment for Gold Commanders, individuals who carry executive responsibility for the services and facilities identified as strategically critical e.g. Police, Fire, in crisis management strategic planning situations. A key part of the work for this project is considering the emotional and behavioural state of the trainees, and the creation of more realistic, and thereby stressful, representations of multimedia information to impact on the decision-making of those trainees. Existing training models are predominantly paper-based, table-top exercises, which require an exercise of imagination on the part of the trainees to consider not only the various aspects of a crisis situation but also the impacts of interventions, and remediating actions in the event of the failure of an intervention. However, existing computing models and tools are focused on supporting tactical and operational activities in crisis management, not strategic. Therefore, the PANDORA system will provide a rich multimedia information environment, to provide trainees with the detailed information they require to develop strategic plans to deal with a crisis scenario, and will then provide information on the impacts of the implementation of those plans and provide the opportunity for the trainees to revise and remediate those plans. Since this activity is invariably multi-agency, the training environment must support group-based strategic planning activities and trainees will occupy specific roles within the crisis scenario. The system will also provide a range of non-playing characters (NPC) representing domain experts, high-level controllers (e.g. politicians, ministers), low-level controllers (tactical and operational commanders), and missing trainee roles, to ensure a fully populated scenario can be realised in each instantiation. Within the environment, the emotional and behavioural state of the trainees will be monitored, and interventions, in the form of environmental information controls and mechanisms impacting on the stress levels and decisionmaking capabilities of the trainees, will be used to personalise the training environment. This approach enables a richer and more realistic representation of the crisis scenario to be enacted, leading to better strategic plans and providing trainees with structured feedback on their performance under stress

    Cultivating intelligent tutoring cognizing agents in ill-defined domains using hybrid approaches

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    Cognizing agents are those systems that can perceive information from the external environment and can adapt to the changing conditions of that environment. Along the adaptation process a cognizing agent perceives information about the environment and generates reactions. An intelligent tutoring cognizing agent should deal not only with the tutoring system’s world but also with the learner-it should infer and predict new information about the learner and tailor the learning process to fit this specific learner. This paper shows how intelligent tutoring cognizing agents can be cultivated in ill-defined domains using hybrid techniques instantiated in the two example agents AEINS-CA and ALES-CA. These agents offer adaptive learning process and personalized feedback aiming to transfer certain cognitive skills, such as problem solving skills to the learners and develop their reasoning in the two ill-defined domains of ethics and argumentation. The paper focuses on the internal structure of each agent and the reasoning methodology, in which, the cognizing agent administration and construction along with the pedagogical scenarios are described

    Five Lenses on Team Tutor Challenges: A Multidisciplinary Approach

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    This chapter describes five disciplinary domains of research or lenses that contribute to the design of a team tutor. We focus on four significant challenges in developing Intelligent Team Tutoring Systems (ITTSs), and explore how the five lenses can offer guidance for these challenges. The four challenges arise in the design of team member interactions, performance metrics and skill development, feedback, and tutor authoring. The five lenses or research domains that we apply to these four challenges are Tutor Engineering, Learning Sciences, Science of Teams, Data Analyst, and Human–Computer Interaction. This matrix of applications from each perspective offers a framework to guide designers in creating ITTSs

    Overcoming foreign language anxiety in an emotionally intelligent tutoring system

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    Learning a foreign language entails cognitive and emotional obstacles. It involves complicated mental processes that affect learning and emotions. Positive emotions such as motivation, encouragement, and satisfaction increase learning achievement, while negative emotions like anxiety, frustration, and confusion may reduce performance. Foreign Language Anxiety (FLA) is a specific type of anxiety accompanying learning a foreign language. It is considered a main impediment that hinders learning, reduces achievements, and diminishes interest in learning. Detecting FLA is the first step toward reducing and eventually overcoming it. Previously, researchers have been detecting FLA using physical measurements and self-reports. Using physical measures is direct and less regulated by the learner, but it is uncomfortable and requires the learner to be in the lab. Employing self-reports is scalable because it is easy to administer in the lab and online. However, it interrupts the learning flow, and people sometimes respond inaccurately. Using sensor-free human behavioral metrics is a scalable and practical measurement because it is feasible online or in class with minimum adjustments. To overcome FLA, researchers have studied the use of robots, games, or intelligent tutoring systems (ITS). Within these technologies, they applied soothing music, difficulty reduction, or storytelling. These methods lessened FLA but had limitations such as distracting the learner, not improving performance, and producing cognitive overload. Using an animated agent that provides motivational supportive feedback could reduce FLA and increase learning. It is necessary to measure FLA effectively with minimal interruption and then successfully reduce it. In the context of an e-learning system, I investigated ways to detect FLA using sensor-free human behavioral metrics. This scalable and practical method allows us to recognize FLA without being obtrusive. To reduce FLA, I studied applying emotionally adaptive feedback that offers motivational supportive feedback by an animated agent
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