Skip to content Skip to navigation

Intelligent Tutoring Systems and Online Learning

ITS have been developed from research laboratory projects such as Why-2 Atlas,[79] which supported human-machine dialogue to solve physics problems early in the era. The rapid migration of ITS from laboratory experimental stages to real use is surprising and welcome. Downloadable software and online systems such as Carnegie Speech or Duolingo provide foreign language training using Automatic Speech Recognition (ASR) and NLP techniques to recognize language errors and help users correct them.[80] Tutoring systems such as the Carnegie Cognitive Tutor[81] have been used in US high schools to help students learn mathematics. Other ITS have been developed for training in geography, circuits, medical diagnosis, computer literacy and programming, genetics, and chemistry. Cognitive tutors use software to mimic the role of a good human tutor by, for example, providing hints when a student gets stuck on a math problem. Based on the hint requested and the answer provided, the tutor offers context specific feedback.

Applications are growing in higher education. An ITS called SHERLOCK[82] is beginning to be used to teach Air Force technicians to diagnose electrical systems problems in aircraft. And the University of Southern California’s Information Sciences Institute has developed more advanced avatar-based training modules to train military personnel being sent to international posts in appropriate behavior when dealing with people from different cultural backgrounds. New algorithms for personalized tutoring, such as Bayesian Knowledge Tracing, enable individualized mastery learning and problem sequencing.[83]

Most surprising has been the explosion of the Massive Open Online Courses (MOOCs) and other models of online education at all levels—including the use of tools like Wikipedia and Khan Academy as well as sophisticated learning management systems that build in synchronous as well as asynchronous education and adaptive learning tools. Since the late 1990s, companies such as the Educational Testing Service and Pearson have been developing automatic NLP assessment tools to co-grade essays in standardized testing.[84] Many of the MOOCs which have become so popular, including those created by EdX, Coursera, and Udacity, are making use of NLP, machine learning, and crowdsourcing techniques for grading short-answer and essay questions as well as programming assignments.[85] Online education systems that support graduate-level professional education and lifelong learning are also expanding rapidly. These systems have great promise because the need for face-to-face interaction is less important for working professionals and career changers. While not the leaders in AI-supported systems and applications, they will become early adopters as the technologies are tested and validated.

It can be argued that AI is the secret sauce that has enabled instructors, particularly in higher education, to multiply the size of their classrooms by a few orders of magnitude—class sizes of a few tens of thousands are not uncommon. In order to continually test large classes of students, automated generation of the questions is also possible, such as those designed to assess vocabulary,[86] wh (who/what/when/where/why) questions,[87] and multiple choice questions,[88] using electronic resources such as WordNet, Wikipedia, and online ontologies. With the explosion of online courses, these techniques are sure to be eagerly adopted for use in online education. Although the long term impact of these systems will have on the educational system remains unclear, the AI community has learned a great deal in a very short time.



[79] Kurt VanLehn, Pamela W. Jordan, Carolyn P. Rosé, Dumisizwe Bhembe, Michael Böttner, Andy Gaydos, Maxim Makatchev, Umarani Pappuswamy, Michael Ringenberg, Antonio Roque, Stephanie Siler, and Ramesh Srivastava, “The Architecture of Why2-Atlas: A Coach for Qualitative Physics Essay Writing,” Intelligent Tutoring Systems: Proceedings of the 6th International Conference, (Springer Berlin Heidelberg, 2002), 158-167.

[80] VanLehn et al, “The Architecture of Why2-Atlas.”

[81] “Resources and Support,” Carnegie Learning, accessed August 1, 2016, https://www.carnegielearning.com/resources-support/.

[82] Alan Lesgold, Suzanne Lajoie, Marilyn Bunzo, and Gary Eggan, “SHERLOCK: A Coached Practice Environment for an Electronics Troubleshooting Job,” in J. H. Larkin and R. W. Chabay, eds., Computer-Assisted Instruction and Intelligent Tutoring Systems: Shared Goals and Complementary Approaches (Hillsdale, New Jersey: Lawrence Erlbaum Associates, 1988).

[83] Michael V. Yudelson, Kenneth R. Koedinger, and Geoffrey J. Gordon, (2013). " Individualized Bayesian Knowledge Tracing Models," Artificial Intelligence in Education, (Springer Berlin Heidelberg, 2013), 171-180.

[84] Jill Burstein, Karen Kukich, Susanne Wolff, Chi Lu, Martin Chodorow, Lisa Braden-Harder, and Mary Dee Harris, “Automated Scoring Using a Hybrid Feature Identification Technique” in Proceedings of the Annual Meeting of the Association of Computational Linguistics, Montreal, Canada, August 1998, accessed August 1, 2016, https://www.ets.org/Media/Research/pdf/erater_acl98.pdf.

[85] EdX, https://www.edx.org/, Coursera, https://www.coursera.org/, Udacity, https://www.udacity.com/, all accessed August 1, 2016.

[86] Jonathan C. Brown, Gwen A. Frishkoff , and Maxine Eskenazi, “Automatic Question Generation for Vocabulary Assessment,” Proceedings of Human Language Technology Conference and Conference on Empirical Methods in Natural Language Processing (HLT/EMNLP), Vancouver, October 2005, (Association for Computational Linguistics, 2005), 819–826.

[87] Michael Heilman, “Automatic Factual Question Generation from Text,” PhD thesis CMU-LTI-11-004, (Carnegie Mellon University, 2011), accessed August 1, 2016, http://www.cs.cmu.edu/~ark/mheilman/questions/papers/heilman-question-generation-dissertation.pdf.

[88] Tahani Alsubait, Bijan Parsia, and Uli Sattler, “Generating Multiple Choice Questions from Ontologies: How Far Can We Go?,” in eds. P. Lambrix, E. Hyvönen. E. Blomqvist, V. Presutti, G. Qi, U. Sattler, Y. Ding, and C. Ghidini, Knowledge Engineering and Knowledge Management: EKAW 2014 Satellite Events, VISUAL, EKM1, and ARCOE-Logic Linköping, Sweden, November 24–28, 2014 Revised Selected Papers, (Switzerland: Springer International Publishing, 2015), 66-79.

 

Cite This Report

Peter Stone, Rodney Brooks, Erik Brynjolfsson, Ryan Calo, Oren Etzioni, Greg Hager, Julia Hirschberg, Shivaram Kalyanakrishnan, Ece Kamar, Sarit Kraus, Kevin Leyton-Brown, David Parkes, William Press, AnnaLee Saxenian, Julie Shah, Milind Tambe, and Astro Teller.  "Artificial Intelligence and Life in 2030." One Hundred Year Study on Artificial Intelligence: Report of the 2015-2016 Study Panel, Stanford University, Stanford, CA,  September 2016. Doc: http://ai100.stanford.edu/2016-report. Accessed:  September 6, 2016.