Intelligent Tutoring Systems for Generation Z's Addiction

As generation Z's big data is flooding the Internet through social nets, neural network based data processing is turning an important cornerstone, showing significant potential for fast extraction of data patterns. Online course delivery and associated tutoring are transforming into customizable, on-demand services driven by the learner. Besides automated grading, strong potential exists for the development and deployment of next generation intelligent tutoring software agents. Self-adaptive, online tutoring agents exhibiting "intelligent-like" behavior, being capable "to learn" from the learner, will become the next educational superstars. Over the past decade, computer-based tutoring agents were deployed in a variety of extended reality environments, from patient rehabilitation to psychological trauma healing. Most of these agents are driven by a set of conditional control statements and a large answers/questions pairs dataset. This article provides a brief introduction on Generation Z's addiction to digital information, highlights important efforts for the development of intelligent dialogue systems, and explains the main components and important design decisions for Intelligent Tutoring System.

[1]  Antonija Mitrovic,et al.  Fifteen years of constraint-based tutors: what we have achieved and where we are going , 2011, User Modeling and User-Adapted Interaction.

[2]  Arthur C. Graesser,et al.  Conversations with AutoTutor Help Students Learn , 2016, International Journal of Artificial Intelligence in Education.

[3]  Carolyn Penstein Rosé,et al.  The Architecture of Why2-Atlas: A Coach for Qualitative Physics Essay Writing , 2002, Intelligent Tutoring Systems.

[4]  Arthur C. Graesser,et al.  AutoTutor and Family: A Review of 17 Years of Natural Language Tutoring , 2014, International Journal of Artificial Intelligence in Education.

[5]  Analía Amandi,et al.  eTeacher: Providing personalized assistance to e-learning students , 2008, Comput. Educ..

[6]  Jaime G. Carbonell,et al.  The XCALIBUR Project: A Natural Language Interface to Expert Systems , 1983, IJCAI.

[7]  Jacqueline Bourdeau,et al.  Advances in Intelligent Tutoring Systems , 2010 .

[8]  Masoud Yazdani,et al.  Intelligent tutoring systems: An overview , 1986 .

[9]  Antonija Mitrovic,et al.  Intelligent tutors for all: Constraint-based modeling methodology, systems and authoring , 2007 .

[10]  Antonija Mitrovic,et al.  Constraint-based knowledge representation for individualized instruction , 2006, Comput. Sci. Inf. Syst..

[11]  Peter Brusilovsky Domain Modeling for Personalized Guidance , 2016 .

[12]  Indira Padayachee,et al.  1 Intelligent Tutoring Systems : Architecture and Characteristics , 2002 .

[13]  Salisu Sani,et al.  Computational Intelligence Approaches for Student/Tutor Modelling: A Review , 2014, 2014 5th International Conference on Intelligent Systems, Modelling and Simulation.

[14]  Arthur C Graesser,et al.  Learning, thinking, and emoting with discourse technologies. , 2011, The American psychologist.

[15]  John R. Anderson,et al.  Student Modeling and Mastery Learning in a Computer-Based Proramming Tutor , 1992, Intelligent Tutoring Systems.

[16]  Siu-Ming Yiu,et al.  SmartTutor: An intelligent tutoring system in web-based adult education , 2003, J. Syst. Softw..

[17]  Hyacinth S. Nwana,et al.  Intelligent tutoring systems: an overview , 1990, Artificial Intelligence Review.

[18]  Valerie J. Shute,et al.  Intelligent Tutoring Systems: Past, Present, and Future. , 1994 .

[19]  James A. Mason,et al.  Surveying Projects on Intelligent Dialogue , 1988, Int. J. Man Mach. Stud..

[20]  Neelu Jyothi Ahuja,et al.  A Critical Review of Development of Intelligent Tutoring Systems: Retrospect, Present and Prospect , 2013 .

[21]  Neil T. Heffernan,et al.  ElectronixTutor: an intelligent tutoring system with multiple learning resources for electronics , 2018, International Journal of STEM Education.