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Name URL Cost Address Year Created Type Audiences Technologies User count Creators Description Evolution Tech Description Uses Reasons Managers Strategies Pros Limitations Difficulties Publications
iTalk2Learn https://www.italk2learn.com/ Free University College London, Gower Street Wc1e 6bt London, UK 2012 Academic - School Primary (6-11 years old) Natural language processing, Speech Recognition and Analytics 0 Projet de recherche, Franctions Lab , financé par l'Union Européenne iTalk2Learn is a learning platform that supports maths learning for students aged 5 to 11. It facilitates interaction in different modalities including speech, as well as multiple representations which can be manipulated and reasoned with when learning fractions. The system takes into account all historical performance across an entire student base as well as the behavioural patterns of each individual student in order to adapt more intelligently to their needs. It is able to interact with and respond to a student’s speech throughout a tutoring session to detect patterns of behaviour, attitude to the learning situation and affective states. Machine learning, Natural language processing, Bayesian networks (an AI technique) to detect and improve the emotional states of the student, Speech recognition to interact with and respond to a student's speech. Mathematics learning N/A Learn in a more natural way, Individualized learning, Detect and improve the emotional states of the student, Provide context specific feedback N/A https://www.italk2learn.com/deliverables-and-publications/publications/
Betty’s Brain https://wp0.vanderbilt.edu/oele/bettys-brain/ Free Vanderbilt University, Nashville, Tennessee 37240, USA Academic - School Secondary (12-17 years old) Natural language processing and Analytics 0 The Teachable Agents Group (à Vanderbilt University) Betty’s Brain is a computer-based learning environment that utilizes the learning-by-teaching paradigm to engage students in learning about science topics. Students are encouraged to teach a fellow student, in fact a virtual agent, called Betty. students are supported to teach Betty, then to query Betty to see how much she has understood, and finally to quiz Betty to see how well she does on questions generated automatically by the system, many of which the student may not have considered. Additionally, the system monitors the students’ actions, guides and mentors them as they go about their learning and teaching tasks. Science learning N/A Development of metacognitive skills, strengthen knowledge of river ecosystems N/A
Crystal Island http://projects.intellimedia.ncsu.edu/crystalisland/ Free North carolina state university, Raleigh, NC 27695, USA Academic - School Post-secondary (18 years and over) and Teachers Analytics 4,000 North carolina state university Crystal Island is a game-based learning environment for middle grade science and literacy. In the game, students play the role of a medical field detective investigating a mysterious infectious disease outbreak affecting a team of scientists on a remote island. While exploring the island research camp from an immersive first-person viewpoint, students utilize critical thinking skills to solve problems about the source and impact of the outbreak, test hypotheses using virtual laboratory equipment, and complete a diagnosis worksheet to record their findings. AI techniques to create an intelligent game-based learning environment, to provide automated supportive feedback, to build AI-driven autonomous non-player characters (companion agents) Learning science and language arts N/A Motivating and engaging context for students to solve a scientific problem, Develop the ability to interpret complex information texts. complete classroom instruction, test bench for researching intelligent tutoring systems, Free. N/A http://projects.intellimedia.ncsu.edu/crystalisland/publications
Ada http://www.aftabhussain.com/ada.html Free Deane Road Campus, Deane Road, Bolton, BL3 5BG, USA Academic - School Post-secondary (18 years and over), Teachers, Researchers and Other education actors Natural language processing 0 Bolton College Ada is able to respond to a spectrum of student enquiries, delivering personalised and contextualised responses that draw on data such as the student’s courses, their progress, their goals and their individual targets. Ada is able to respond to questions about the library, student services, finance, accommodation, transport, careers, and examinations – and it learns more with every interaction. Natural language processing to process requests (texts) Digital assistant for students, teachers and support teams N/A Immediate responses at any time, Respond to students' questions about a wide variety of College services, contextualized responses to questions asked by students, enable teachers and support teams to trigger actions and interventions N/A
Smart Learning Partner https://aic-fe.bnu.edu.cn/en/news/95078.html Free The 4th and 5th Floor, Building 3, Beishahe West 3rd Road and Manjing Road Intersection, Beijing Normal University (Changping Campus, G Section), Changping District, Beijing 102206, China Academic - School Post-secondary (18 years and over) Analytics 0 Advanced Innovation Center for Future Education de Beijing Normal University et Tongzhou (district de Beijing) The Smart Learning Partner uses AI technologies to put students more in control of their own learning. The platform uses AI to match students and tutors according to student queries and tutor areas of expertise, together with the tutor’s availability and ratings given to them by other students whom they have already tutored. The student uses the app to search for a tutor, to ask what they want to know about any school topic, and they then receive twenty minutes of one-to-one online tuition (sharing audio and screens only). AI to match students and tutors, recognition of emotions. N/A Find the tutor best suited to the subject Chinese site Lu, Y., Chen, C., Chen, P., Chen, X., & Zhuang, Z. (2018, June). Smart learning partner: an interactive robot for education. In International Conference on Artificial Intelligence in Education (pp. 447-451). Springer, Cham.
EER-Tutor https://ictg.cosc.canterbury.ac.nz:8005/eer-tutor/login Free Christchurch, New Zealand Academic - School Post-secondary (18 years and over) Natural language processing and Analytics 0 Intelligent Computer Tutoring Group, University of Canterbury EER-Tutor is a constraint-based tutor that teaches conceptual database design using the Entity Relationship model. Students are provided a problem solving environment to design a data model for a real world scenario. Adaptive tutorial dialogues facilitate discussion of mistakes in a student solution. Natural language processing, Machine learning to discover commonly occurring learning behaviours, Adaptive tutorial dialogues. Teaching conceptual database design using the Entity Relationship model N/A Adaptive tutorial dialogues, higher learning rate N/A
FUN MOOC https://www.fun-mooc.fr/ Free 12 VLA DE LOURCINE 75014 PARIS 2013 Academic - School General public and Post-secondary (18 years and over) Analytics 1,700,000 Le ministère de l'enseignement supérieur et de la recherche français, INRIA, CINES et RENATER FUN MOOC is a platform designed to promote the use of massive open online courses (MOOCs). More than 130 MOOC and SPOC production partners in France and around the world share their trainings via this platform. Machine learning, Adaptive learning and assessment Teaching massive open online courses (MOOCs) N/A Online learning, Reduces cost, location and access barriers, Many partner schools and universities, Open courses. N/A Mongenet, C. (2016). FUN, une plate-forme de MOOCs au service des établissements d’enseignement supérieur. Annales des Mines - Réalités industrielles, mai 2016(2), 42-47. doi:10.3917/rindu1.162.0042.
TeachLivE http://teachlive.org/about/about-teachlive/ Free 12494 University Blvd., Orlando, FL 32816, USA 2013 Academic - School Post-secondary (18 years and over) and Teachers Natural language processing, Speech Recognition and Analytics 0 UCF Center for Research in Education Simulation Technology TLE TeachLivE™ is a mixed-reality classroom with simulated students that provides teachers the opportunity to develop their pedagogical practice in a safe environment that doesn’t place real students at risk. Experiential learning, avatar-based characters, mimic human nuances, voice, vocabulary, gestures Teacher training N/A Teachers can learn the instruction and management skills, Practicing teachers can hone and refine their skills, Examining how participants’ respond to a changing classroom environment, Experimenting with a new teaching idea poses no danger to the learning of any real student N/A Dieker, L. A., Rodriguez, J. A., Lignugaris/Kraft, B., Hynes, M. C., et Hughes, C. E. (2014). The potential of simulated environments in teacher education: Current and future possibilities. Teacher Education and Special Education, 37(1), 21-33.
SimStudent http://www.simstudent.org/project Free 5000 Forbes Ave, Pittsburgh, PA 15213, USA Academic - School Post-secondary (18 years and over), Teachers and Researchers Natural language processing 0 Carnegie Mellon University SimStudent is an artificial intelligent agent that is able to learn procedural skills by observing user’s input examples. There are three main uses of SimStudent: (1) As it is well known that students learn by teaching others, we can build an on-line learning environment where SimStudent acts as a peer tutoree and have human students tutor SimStudent; (2) SimStudents can be used as simulated students with which researchers can conduct various controlled studies to explore theories of learning; (3) Use of SimStudent within the Cognitive Tutor Authoring Tools (CTAT) allows authors to build a Cognitive Tutor simply by teaching SimStudent how to solve problems. SimStudent is an artificial intelligent agent Computational model of human learning N/A Teachable by learner, Computational model of Learning, Building Intelligent tutors. N/A Matsuda, N., Yarzebinski, E., Keiser, V., Raizada, R., Stylianides, G. J., Cohen, W. W., & Koedinger, K. R. (2011, June). Learning by teaching SimStudent–An initial classroom baseline study comparing with Cognitive Tutor. In International Conference on Artificial Intelligence in Education (pp. 213-221). Springer, Berlin, Heidelberg.
MathTutor https://mathtutor.web.cmu.edu/ Free 5000 Forbes Ave, Pittsburgh, PA 15213, USA Academic - School Secondary (12-17 years old) Analytics 0 Carnegie Mellon University MathTutor is a platform that helps middle school students learn math. It enables a teacher to assign individual tutors to a single student, a group of students, or an entire class. Intelligent software tutors give step-by-step help and feedback, as needed. As students work, detailed records are stored that allow a teacher to see which students need more attention. Students can view their reports as well. AI to give step-by-step help and feedback Mathematics learning N/A Adaptive learning, Error detection, available free N/A http://www.cs.cmu.edu/~bmclaren/projects/AdaptErrEx/publications.html
Martha https://it.gwu.edu/learn-more-about-martha-gws-virtual-agent-pilot-project Free 801 22nd Street, NW B101, Washington, DC 20052, USA Academic - School Post-secondary (18 years and over) and Teachers Natural language processing and Analytics 0 George Washington University Martha is a virtual agent used by students at The George Washington University to search knowledge, create service requests and check the status of your outstanding requests, anytime you want, through an intuitive conversational interface. AI to learn from and process students requests. N/A Access the information you need, anytime. Reduce the burden of service center agents. N/A
Dytective https://www.changedyslexia.org/en Free Carrer del Dr. Trueta, 183, 08005 Barcelona, Espagne Academic - School Preschool (0-5 years), Primary (6-11 years old) and Learners with special needs Analytics 270,000 Change Dyslexia Dytective is a tool that can detect dyslexia in about 15 minutes. It also provides exercises in the form of games for dyslexic learners to help them overcome their writing and reading challenges. The app includes a mix of online testing and applications for associated predictive machine learning. Machine learning models to predict reading and writing difficulties by watching how people interact with a linguistic web-based game. Detecting dyslexia and helping dyslexic learners overcome their challenges N/A Detects dyslexia, Helps learners overcome their writing and reading challenges. N/A Rello, L., Ballesteros, M., Ali, A., Serra, M., Alarcón, D., & Bigham, J. P. (2016). Dytective: Diagnosing risk of dyslexia with a game. Proc. Pervasive Health, 16.
iSTART https://www.adaptiveliteracy.com/istart Free Payne Hall 108, 1000 S. Forest Mall, Tempe, AZ 85287-2111, USA Academic - School Secondary (12-17 years old), Teachers and Researchers Natural language processing and Analytics 0 SoLET Lab, Arizona State University iSTART is an intelligent tutoring system that teaches self-explanation (ability to explain information from texts to yourself) strategies through a series of videos and game-based practice exercises. After submitting a self-explanation, students receive formative feedback that rates their self-explanation. Natural Language Processing (NLP) to enable students to use their own thoughts and ideas to communicate with the system, and to drive feedback and adaptive interactions during practice. Teaching self-explanation strategies N/A Improve understanding of scientific texts, increase motivation to learn and maintain commitment, Free N/A Jackson, T., Boonthum-Denecke, C., & McNamara, D. (2015). Natural language processing and game-based practice in iSTART. Journal of Interactive Learning Research, 26(2), 189-208.
Mathia https://www.mathia.education/ Free France 2019 Academic - School Primary (6-11 years old) and Learners with special needs Natural language processing, Speech Recognition and Analytics 0 Prof en poche, Tralalere, LumenAI et Cabrilog Mathia is a smart 3D voice assistant. It lets teachers monitor their students’ progress and offer personalized lessons. Data analysis, creation of personalized paths, voice assistant and visualization tools Smart mathematics educational assistant N/A Personalized paths, monitors student progress N/A
Navi https://navi.education/ Free France 2020 Academic - School Primary (6-11 years old) and Learners with special needs Analytics 0 DOMOSCIO, BENEYLu, HACHETTE, DXC, AIDODYS, Laboratoire CHArt, Laboratoire KDIS, MOBIDYS, STORYPLAYR An ingenious assistant for teaching reading and writing. Navi recommends reading and writing activities tailored to the needs of your students in Cycle 2 [of the French school system] based on the learning traces you give them. Organization and characterization of educational content, personalization of student learning and memorization experiences, data partitioning (clustering) Learning to read and write N/A Assists teachers and gives them more time for remedial instruction. Personalized learning N/A
Adaptiv'Math https://www.adaptivmath.fr/ Free France 2020 Academic - School Primary (6-11 years old) and Learners with special needs Analytics 0 EVIDENCEBAPMEP, BLUE FROG,ROBOTICS,DAESIGN,INRIA,FLOWERS,ISOGRAD, LIP 6,SCHOOLAB, SJER, NATHAN Adaptiv'Math gives school teachers a unique approach, combining new elements of cognitive explanations for foundation learning with an AI engine. The strength of this AI engine is that it assigns a series of exercises to each student in an increasingly personalized manner. Data analysis, group organization, personalized learning Learning mathematics N/A Remediation approach, feedback for students and teachers, evaluation assistance, differentiated teaching, signal detection N/A
Smart enseigno https://www.smartenseigno.fr/ Free Educlever PARIS, 59 rue Benoit Malon, 94250 Gentilly, France 2019 Academic - School Primary (6-11 years old) Analytics 0 EDUCLEVER, CABRILOG, INRIA WIMMICS, LUDOTIC A rich, evolving body of mathematical resources for CP, CE1 and CE2 classes [in the French school system], SmartEnseigno offers students a range of learning activities reflective of their needs (adaptive learning). The teacher has a dedicated digital assistant (smart dashboard). Adaptive learning, smart dashboard, expert system, reasoner, machine learning Learning mathematics N/A Personalized learning, student monitoring N/A
ECHOES https://www.ucl.ac.uk/ioe/research-projects/2018/oct/echoes-project Free University College London, Gower Street, London, WC1E 6BT. UK 2012 Academic - School Primary (6-11 years old) and Learners with special needs Analytics 0 University College London ECHOES is a technology-enhanced learning (TEL) environment for 5 to 7 years old children where they can explore and practise skills needed for successful social interaction, such as sharing of attention with others, turn-taking, initiating and responding to bids for interaction. ECHOES supports typically developing children (TD) and children with autism spectrum disorders (ASCs). Artificial intelligence techniques to record and interpret interaction data between children and the environment. Communicative skills learning N/A A learning tool (communicative skills) for children, A research tool to explore the specific difficulties of each child in relation to social interaction. N/A Bernardini, S., Porayska-Pomsta, K., & Smith, T. J. (2014). ECHOES: An intelligent serious game for fostering social communication in children with autism. Information Sciences, 264, 41-60.
LightSide http://ankara.lti.cs.cmu.edu/side/ Free 5000 Forbes Avenue, Pittsburgh, PA 15213 Academic - School Researchers Analytics 0 Carnegie Mellon University LightSIDE is a free and open text mining toolbench, which is used both for teaching and for research. It provides a convenient GUI environment for novice users of text classification technology easily run text extraction and classification experiments. Machine learning algorightms that can learn to extract features based on training examples entered by humans Text mining toolbench used for teaching and for research N/A Text mining toolbench, freely available for research and education N/A Mayfield, E. et Rosé, C. P. (2013). LightSIDE: Open Source Machine Learning for Text Accessible to Non-Experts, Invited chapter in the Handbook of Automated Essay Grading, Routledge Academic Press.
The MIT Education Arcade https://education.mit.edu/project-type/games/ Free 700 Technology Square, Suite 328, Cambridge, MA 02139, USA 2018 Academic - School General public Analytics 0 The MIT Scheller Teacher Education Program We work with educators to make sure our games tie into their math and science curricula. The games we develop can be played on computers or mobile devices and are used both inside and outside the classroom. Serious game Mathematics ans sciences N/A Engage learners to interact actively for longer periods of time, Maximize motivation N/A Groff, J. S. (2018). The potentials of game‐based environments for integrated, immersive learning data. European Journal of Education, 53(2), 188-201.
Alloprof https://www.alloprof.qc.ca Free Canada 1996 Academic - School Primary (6-11 years old), Secondary (12-17 years old), Teachers and Other education actors Analytics 0 Alloprof Alloprof engages Quebec students and their parents in educational success by offering them free professional and stimulating academic support. Tools that save students time and help them succeed with homework and lessons include videos, pedagogic sheets, texting, exam preparation tips and advice, and interactive exercises. Through the integration of an artificial intelligence of the type chatbot, the immersive game 'Spellers' allows players to improve their writing skills in English. AI to maintain a personalized relationship with learners, Immersive game to help students learn English as a second language. Helping students with homework and fostering learning N/A Helping students with homework , Fostering and gamifying learning, motivating students, Equipping parents. N/A Karsenti, T. (2015). Quel est le rôle d’Allô prof dans la persévérance et la réussite scolaires des élèves? Étude auprès de 6659 acteurs scolaires (élèves, enseignants, directions et parents). Rapport synthèse de recherche. Montréal, QC : CRIFPE. Tanguay, M. A. (2017). Stratégie numérique : Pour mieux engager l’élève et les acteurs de l’éducation. Alloprof. URL: blogue.alloprof.qc.ca/wp-content/uploads/2017/02/AP_memoire_Strategie_numerique_vf-4.pdf