Méthodes d'apprentissage inspirées de l'humain pour un tuteur cognitif artificiel

Les systèmes tuteurs intelligents sont considérés comme un remarquable concentré de technologies qui permettent un processus d'apprentissage. Ces systèmes sont capables de jouer le rôle d'assistants voire même de tuteur humain. Afin d'y arriver, ces systèmes ont besoin de maintenir et d'utiliser une représentation interne de l'environnement. Ainsi, ils peuvent tenir compte des évènements passés et présents ainsi que de certains aspects socioculturels. Parallèlement à l'évolution dynamique de l'environnement, un agent STI doit évoluer en modifiant ses structures et en ajoutant de nouveaux phénomènes. Cette importante capacité d'adaptation est observée dans le cas de tuteurs humains. Les humains sont capables de gérer toutes ces complexités à l'aide de l'attention et du mécanisme de conscience (Baars B. J., 1983, 1988), et (Sloman, A and Chrisley, R., 2003). Toutefois, reconstruire et implémenter des capacités humaines dans un agent artificiel est loin des possibilités actuelles de la connaissance de même que des machines les plus sophistiquées. Pour réaliser un comportement humanoïde dans une machine, ou simplement pour mieux comprendre l'adaptabilité et la souplesse humaine, nous avons à développer un mécanisme d'apprentissage proche de celui de l'homme. Ce présent travail décrit quelques concepts d'apprentissage fondamentaux implémentés dans un agent cognitif autonome, nommé CTS (Conscious Tutoring System) développé dans le GDAC (Dubois, D., 2007). Nous proposons un modèle qui étend un apprentissage conscient et inconscient afin d'accroître l'autonomie de l'agent dans un environnement changeant ainsi que d'améliorer sa finesse. ______________________________________________________________________________ MOTS-CLÉS DE L’AUTEUR : Apprentissage, Conscience, Agent cognitif, Codelet

An open learner model for trainee pilots

This paper investigates the potential for simple open learner models for highly motivated, independent learners, using the example of trainee pilots. In particular we consider whether such users access their learner model to help them identify their current knowledge level, areas of difficulty and specific misconceptions, to help them plan their immediate learning activities; and whether they find a longer‐term planning aid useful. The Flight Club open learner model was deployed in a UK flight school over four weeks. Results suggest that motivated users such as trainee pilots will use a system with a simple open learner model, and are interested in consulting their learner model information both to facilitate planning over time, and to understand their current knowledge state. We discuss the extent to which our findings may be relevant to learners in other contexts

Evaluating the Effectiveness of tutorial dialogue instruction in a Explotary learning context

[Proceedings of] ITS 2006, 8th International Conference on Intelligent Tutoring Systems, 26-30 June 2006, Jhongli, Taoyuan County, TaiwanIn this paper we evaluate the instructional effectiveness of tutorial dialogue agents in an exploratory learning setting. We hypothesize that the creative nature of an exploratory learning environment creates an opportunity for the benefits of tutorial dialogue to be more clearly evidenced than in previously published studies. In a previous study we showed an advantage for tutorial dialogue support in an exploratory learning environment where that support was administered by human tutors [9]. Here, using a similar experimental setup and materials, we evaluate the effectiveness of tutorial dialogue agents modeled after the human tutors from that study. The results from this study provide evidence of a significant learning benefit of the dialogue agentsThis project is supported by ONR Cognitive and Neural Sciences Division, Grant number N000140410107proceedingPublicad

Proceedings of the 1993 Conference on Intelligent Computer-Aided Training and Virtual Environment Technology

The volume 2 proceedings from the 1993 Conference on Intelligent Computer-Aided Training and Virtual Environment Technology are presented. Topics discussed include intelligent computer assisted training (ICAT) systems architectures, ICAT educational and medical applications, virtual environment (VE) training and assessment, human factors engineering and VE, ICAT theory and natural language processing, ICAT military applications, VE engineering applications, ICAT knowledge acquisition processes and applications, and ICAT aerospace applications

Information enforcement in learning with graphics : improving syllogistic reasoning skills

This thesis is an investigation into the factors that contribute to good choices among graphical systems used in teaching, and the feasibility of implementing teaching software that uses this knowledge.The thesis describes a mathematical metric derived from a cognitive theory of human diagram processing. The theory characterises differences among representations by their ability to express information. The theory provides the factors and relationships needed to build the metric. It says that good representations are easily processed because they are more vivid, more tractable and less expressive, than poor representations.The metric is applied to abstract systems for teaching and learning syllogistic reasoning, TARSKI'S WORLD, EULER CIRCLES, VENN DIAGRAMS and CARROLL'S GAME OF LOGIC. A rank ordering reflects the value of each system predicted by the theory and the metric. The theory, the metric and the systems are then tested in empirical studies. Five studies involving sixty-eight learners, examined the benefit of software based on these abstract systems.Studies showed the theory correctly predicted learners' success with the circle systems and poorer performance with TARSKI'S WORLD. The metric showed small but clear differences in expressivity between the circle systems. Differences between results of the learners using the circle systems contradicted the predictions of the metric.Learners with mathematical training were better equipped and more successful at learning syllogistic reasoning with the systems. Performance of learners without mathematical training declined after using the software systems. Diagrams drawn by learners together with video footage collected during problem solving, led to a catalogue of errors, misconceptions and some helpful strategies for learning from graphical systems.A cognitive style test investigated the poor performance of non-mathematically trained learners. Learners with mathematics training showed serialist and versatile learning styles while learners without this training showed a holist learning style. This is consistent with the hypothesis that non-mathematically trained learners emphasise the use of semantic cues during learning and problem solving.A card-sorting task investigated learners' preferences for parts of the graphical lexicon used in the diagram systems. Preferences for the EULER lexicon increased difficulty in explaining the system's poor results in earlier studies. Video footage of learners using the systems in the final study illustrated useful learning strategies and improved performance with EULER while individual instruction was available.Further work describes a preliminary design for an adaptive syllogism tutor and other related work

AI in Learning: Designing the Future

AI (Artificial Intelligence) is predicted to radically change teaching and learning in both schools and industry causing radical disruption of work. AI can support well-being initiatives and lifelong learning but educational institutions and companies need to take the changing technology into account. Moving towards AI supported by digital tools requires a dramatic shift in the concept of learning, expertise and the businesses built off of it. Based on the latest research on AI and how it is changing learning and education, this book will focus on the enormous opportunities to expand educational settings with AI for learning in and beyond the traditional classroom. This open access book also introduces ethical challenges related to learning and education, while connecting human learning and machine learning. This book will be of use to a variety of readers, including researchers, AI users, companies and policy makers

AI in Learning: Designing the Future

AI (Artificial Intelligence) is predicted to radically change teaching and learning in both schools and industry causing radical disruption of work. AI can support well-being initiatives and lifelong learning but educational institutions and companies need to take the changing technology into account. Moving towards AI supported by digital tools requires a dramatic shift in the concept of learning, expertise and the businesses built off of it. Based on the latest research on AI and how it is changing learning and education, this book will focus on the enormous opportunities to expand educational settings with AI for learning in and beyond the traditional classroom. This open access book also introduces ethical challenges related to learning and education, while connecting human learning and machine learning. This book will be of use to a variety of readers, including researchers, AI users, companies and policy makers

Aerospace medicine and biology: A continuing bibliography with indexes (supplement 354)

This bibliography lists 225 reports, articles and other documents introduced into the NASA Scientific and Technical Information System during September, 1991. Subject coverage includes aerospace medicine and psychology, life support systems and controlled environments, safety equipment, exobiology and extraterrestrial life, and flight crew behavior and performance

Human Computer Interaction and Emerging Technologies

The INTERACT Conferences are an important platform for researchers and practitioners in the field of human-computer interaction (HCI) to showcase their work. They are organised biennially by the International Federation for Information Processing (IFIP) Technical Committee on Human–Computer Interaction (IFIP TC13), an international committee of 30 member national societies and nine Working Groups. INTERACT is truly international in its spirit and has attracted researchers from several countries and cultures. With an emphasis on inclusiveness, it works to lower the barriers that prevent people in developing countries from participating in conferences. As a multidisciplinary field, HCI requires interaction and discussion among diverse people with different interests and backgrounds. The 17th IFIP TC13 International Conference on Human-Computer Interaction (INTERACT 2019) took place during 2-6 September 2019 in Paphos, Cyprus. The conference was held at the Coral Beach Hotel Resort, and was co-sponsored by the Cyprus University of Technology and Tallinn University, in cooperation with ACM and ACM SIGCHI. This volume contains the Adjunct Proceedings to the 17th INTERACT Conference, comprising a series of selected papers from workshops, the Student Design Consortium and the Doctoral Consortium. The volume follows the INTERACT conference tradition of submitting adjunct papers after the main publication deadline, to be published by a University Press with a connection to the conference itself. In this case, both the Adjunct Proceedings Chair of the conference, Dr Usashi Chatterjee, and the lead Editor of this volume, Dr Fernando Loizides, work at Cardiff University which is the home of Cardiff University Press