AI pioneer Marvin Minsky and his colleagues highlighted key aspects of vision for Artificial Intelligence in Education (AI in Ed):
‘…we could try to build a personalized teaching machine that would adapt itself to someone’s particular circumstances, difﬁculties, and needs. The system would carry out a conversation with you, to help you understand a problem or achieve some goal. You could discuss with it such subjects as how to choose a house or car, how to learn to play a game or get better at some subject, how to decide whether to go to the doctor, and so forth. It would help you by telling you what to read, stepping you through solutions, and teaching you about the subject in other ways it found to be effective for you. Textbooks then could be replaced by systems that know how to explain ideas to you in particular, because they would know your background, your skills, and how you best learn.” (Minsky et al., 2004, p. 122)
Similar visions can be found in Pressey’s teaching machines (1924), the “Illustrated Primer” of Neal Stephenson’s science fiction novel The Diamond Age (1995), and many other contemporary sources. Although, the vision of a conversational tutor which can help anyone to learn any topic more quickly or easily has not yet been achieved, researchers have made many advances on aspects of this vision.
For example, researchers have developed applications of AI in Ed that can:
- track the “mental steps” of the learner and underlying goal structure of problem solving tasks (Anderson et al., 1995)
- diagnose misconceptions and estimate the learner’s understanding of the domain (VanLehn, 1988)
- provide timely guidance, feedback and explanations (Shute, 2008)
- promote productive learning behaviors, such as self-regulation, self-monitoring, and self-explanation (Azevedo & Hadwin, 2005)
- prescribe learning activities at the right level of difficulty and with the most appropriate content (VanLehn, 2006)
Indeed, over the course of different investigations, nearly every AI technique – natural language processing, uncertain reasoning, planning, cognitive modeling, case-based reasoning, machine learning and more – has been applied to challenges of learning (Woolf, 2009).
In particular, AI techniques can enable educational technologies to better track, adapt to, and support individual learners. For example, AI technologies can track learners by building a model of what a students knows based on their successes and errors in solving problems. AI technologies can adapt to students by changing the sequence of problems presented to students and by changing the interactions of the computer with the student on specific steps of a problem. AI technologies can give students help within well-defined and carefully limited domains in the style of a conversational agent, sometimes even with a virtual avatar.
Many AI in Ed systems have aimed to mimic the benefits of one-to-one tutoring shown in Bloom (1984); some of these systems now outperform untrained tutors in specific topics and can approach the effectiveness of expert tutors (VanLehn, 2011). Close analyses have found that much of the benefit of both human and AI tutors arises from intervening at the specific step where a student makes a mistake, rather than giving feedback only on the correctness of the entire problem solution (Corbett & Anderson, 2001; Shute, 2008). Implementing step-based feedback on a computer requires representing how problems are solved step-by-step; AI technologies are a good choice for specifying these step-by-step processes. In general, however, building an expert model that is suitable for use in education is a difficult and slow undertaking, requiring highly skilled labor and access to expertise. Therefore, a key difference between the Minsky’s vision and practical accomplishments of AI in Ed is the careful selection of a constrained and well-specified domain of knowledge.
One particularly well-known and fruitful line of work began with development of John Anderson’s ACT-R theory (Anderson, 1983, 1993). This theory models how people learn to solve problems in terms of if-then rules, called “productions.” ACT-R implements the theory in a kind of programming language, which can be programmed to solve information processing tasks in the way people do. This line of work gave birth to a type of Intelligent Tutor System (ITS) called the Cognitive Tutor. The cognitive tutor can simulate how an expert would solve the same problem that a student is currently working on. This simulation is used to trace differences between student and expert problem solving, and when the steps diverge, to provide feedback. This approach has led to commercial products from Carnegie Learning such as the Algebra Tutor, and these products have been used with hundreds of thousands of students. Some careful analyses of these products have found positive results, although results in other cases have been mixed (What Works Clearinghouse [WWC], 2010). A meta-analysis that compared outcomes of learners using an ITS to outcomes of learners using other instructional methods found that over a wide array of conditions, learning from ITS was associated with higher outcome scores (Ma et al, 2014).
AI in Ed today is not limited to simulating expert human tutors and teachers. For example, the use of physiological monitoring technologies (e.g., skin conductance, posture) is helping researchers understand the role of emotions in learning and develop new models of pedagogical intervention. Further, work on teachable agents leverages the idea of reciprocal teaching by having the (human) student take on the role of teacher. Here, AI techniques are used in a variety of ways, such as simulating human communication, learning, and emotions. A final example is the use of narrative learning environments and educational games that can be designed to provide customized experiential learning opportunities and better maintain learner motivation and engagement.
AI is poised to play a pivotal role in growing the field of learning analytics and personalized learning. There is also potential for the use of AI to create unique learning pathways in MOOCs and adaptive systems for use blended and online learning, however this vision is yet to be fully realized. In a recent special double issue of AI Magazine (Chaudhry, et al., 2013), these topics were addressed in some detail.
AI techniques can thus continue to provide deeper insights into human learning, inspire curiosity and interest in the world around us, and ultimately help keep educational technologies caught up with the rest of the world – if they can be made smart enough.
A series of AI Grand Challenges for Education were proposed (Woolf, et al., 2013):
- Virtual mentors for every learner: omnipresent educational support; brings together knowledge representation, user modeling, social simulation, and many more.
- Addressing 21st Century skills: our educational technologies must go beyond conveying knowledge and support learners with self-direction, self-assessment, teamwork, and more.
- Analysis of interaction data: the vast amounts of data about learning, social contexts, learning contexts, personal interests, and more is a key source of information that must be leveraged in future educational technology research.
- Universal access to global classrooms: ensuring that the classrooms of tomorrow are interconnected, accessible, and personalized requires a great deal of computational and automated reasoning power.
- Lifelong and lifewide technologies: understanding, modeling, and guiding learning in the context of a learner’s life outside of school is critical if we are to improve education. Persistent models, interoperable systems, intelligent search, and more all contribute to the vision of technology that stays with you and helps you learn throughout a lifetime.
Examples of NSF Cyberlearning projects that overlap with topics discussed in this primer (see project tag map).
Intelligent tutors and tools
- EXP: Assessing 'Complex Epistemic Performance' in Online Learning Environments
- EXP: Inq-Blotter - A Real Time Alerting Tool to Transform Teachers' Assessment of Science Inquiry Practices
- DIP: Graphical Model Construction by System Decomposition: Increasing the Utility of Algebra Story Problem Solving
- EXP: Exploratory Study on the Adaptive Online Course and its Implication on Synergetic Competency
- EXP: Modeling Perceptual Fluency with Visual Representations in an Intelligent Tutoring System for Undergraduate Chemistry
More posts: intelligent-tutors
AAAI topics page on education (from the Association for the Advancement of Artificial Intelligence)
International Artificial Intelligence in Education Society, which organizes the biennial AIED Conference.
Full text of The Handbook of artificial intelligence, Volume 2
A Roadmap for Learning Technology (from the CCC, CRA, and NSF)
References and key readings documenting the thinking behind the concept, important milestones in the work, foundational examples to build from, and summaries along the way.
Anderson, J. R. (1983). The architecture of cognition. Cambridge, MA: Harvard University Press.
Anderson, J. R. (1993). Rules of the mind. Hillsdale, NJ: Erlbaum.
Anderson, J.R., Corbett, A.T., Koedinger, K.R., & Pelletier, R. (1995). Cognitive tutors: Lessons learned. Journal of the Learning Sciences 4(2): 167-207.
Aleven, V., McLaren, B. M., Sewall, J., & Koedinger, K. R. (2009). A new paradigm for intelligent tutoring systems: Example-tracing tutors. International Journal of Artificial Intelligence in Education, 19(2), 105-154
Azevedo, R. and A. Hadwin (2005). Scaffolding Self-regulated Learning and Metacognition – Implications for the Design of Computer-based Scaffolds. Instructional Science 33(5): 367-379.
Bloom, B. S. (1984). The 2 sigma problem: The search for methods of group instruction as effective as one-to-one tutoring. Educational researcher, 4-16.
Chaudhri, V.K., Gunning, D., Lane, H.C., & Roschelle, J., Eds. (2013) Intelligent Learning Technologies: Applications of Artificial Intelligence to Contemporary and Emerging Educational Challenges, Special Issue of AI Magazine, 34(3-4). Vol 34, No 3: Fall Issue and Vol 34, No 4: Winter Issue
Collins, A. & R. Halverson (2009). Rethinking education in the age of technology: the digital revolution and schooling in America. New York, Teachers College Press.
Corbett, A. T., & Anderson, J. R. (2001). Locus of feedback control in computer-based tutoring: Impact on learning rate, achievement and attitudes. In Proceedings of ACM CHI 2001 Conference on Human Factors in Computing Systems (pp. 245-252). New York: Association for Computing Machinery Press.
Kay, J. (2008). Lifelong learner modeling for lifelong personalized pervasive learning. IEEE Transactions on Learning Technologies 1(4): 215-228.
Koedinger, K. R., Anderson, J. R., Hadley, W. H., & Mark, M. A. (1997). Intelligent tutoring goes to school in the big city. International Journal of Artificial Intelligence in Education (IJAIED), 8, 30-43.
Koedinger, K.R., Brunskill, E., Baker, R.S.J.d., McLaughlin, E.A., Stamper, J. (2013). New potentials for data-driven intelligent tutoring system development and optimization. AI Magazine, 34(3).
Koedinger, K.R., Corbett, A.C., & Perfetti, C. (2012). The Knowledge-Learning-Instruction (KLI) framework: Bridging the science-practice chasm to enhance robust student learning. Cognitive Science, 36 (5), 757-798.
Luckin, R., Holmes, W., Griffiths, M. & Forcier, L. B. (2016). Intelligence Unleashed: An argument for AI in Education. London: Pearson.
Ma, W., Adesope, O. O., Nesbit, J. C., & Liu, Q. (2014). Intelligent tutoring systems and learning outcomes: A meta-analysis. Journal of Educational Psychology, 106(4), 901-918.
Minsky, M.L., Singh, P., & Sloman, A. (2004). The St. Thomas Common Sense Symposium: Designing Architectures for Human-Level Intelligence. AI Magazine 25(2): 113-125.
Pane, J.F., Griffin, B., McCaffrey, D.F. & Karam, R. (2014). Effectiveness of Cognitive Tutor Algebra I at Scale. Educational Evaluation and Policy Analysis, 36 (2), 127 – 144.
Pea, R. D. (1985). Beyond amplification: Using the computer to reorganize mental functioning. Educational Psychologist, 20(4), 167-182.
Pressey, S.L. (1927). A machine for automatic teaching of drill material. School and Society, 25(645): 549-552.
Shute, V.J. & Psotka, J. (1996). Intelligent tutoring systems: Past, present, and future. Handbook for research for educational communications and technology. D. H. Jonassen (Ed.). New York, NY, Macmillan: 570-599.
Shute, V. J. (2008). Focus on Formative Feedback. Review of Educational Research 78(1): 153-189.
Stephenson, N. (1995). The Diamond Age: Or, a Young Lady’s Illustrated Primer. Spectra.
VanLehn, K. (1988) Student modeling. In M. Poison & J. Richardson (Eds.), Foundations of Intelligent Tutoring Systems. Hillsdale, NJ: Erlbaum.
VanLehn, K. (2006). The Behavior of Tutoring Systems. International Journal of Artificial Intelligence in Education 16(3): 227-265.
VanLehn, K. (2011). The relative effectiveness of human tutoring, intelligent tutoring systems, and other tutoring systems. Educational Psychologist, 46(4), 197-221.
What Works Clearinghouse. (2010). WWC intervention report high school math: Carnegie Learning Curricula and Cognitive Tutor software.
Woolf, B.P. (2009). Building Intelligent Interactive Tutors: Student-centered Strategies for Revolutionizing E-learning. Amsterdam, Netherlands, Morgan Kaufmann.
Woolf, B., Lane, H.C., Chaudhri, V., & Kolodner, J. (2013). AI Grand Challenges for Education. In V. Chaudhri, D. Gunning, H.C. Lane, & J. Roschelle (Eds) AI Magazine, Special issue on Intelligent Learning Technologies, 34(4), 66-84.
Publications from NSF-funded Cyberlearning Projects
Westlund, J. K., Dickens, L., Jeong, S., Harris, P., DeSteno, D., & Breazeal, C. (2015). Comparison of Children Learning New Words from Robots, Tablets, & People. A. Heerink, M & Jong, M. (Eds.) Conference Proceedings New Friends 2015. (pp. 26-28). Almere, The Netherlands: New Friends.
Cui, J., Popescu, V., Adamo-Villan, N., Cook, S., Duggan, K., Friedman, H. (2014). An Animation Stimuli System for Research on Instructor Gestures in Education. Computer Science Technical Reports. Paper 1771.
Chase, C., Marks, J., Bernett, D., & Aleven, V. (2015). The design of an exploratory learning environment to support Invention. In Proceedings of the 17th International Conference on Artificial Intelligence in Education. Madrid, Spain: Artificial Intelligence in Education.
Marks, J., Bernett, D., & Chase, C. C. (2016). The Invention Coach: Integrating Data and Theory in the Design of an Exploratory Learning Environment. International Journal of Designs for Learning, Vol. 7(2).
Damelin, D., Krajcik, J. (2016). Supporting Secondary Students in Building External Models to Explain Phenomena. Concord Spring 2016.
LaMeres, B. (2015). Deploying Adaptive Learning Environments to Overcome Background Deficiencies and Facilitiate Mastery of Comptuer Engineering Content. In Proceedings of the 122nd ASEE Annual Conference & Exposition. Seattle, WA: American Society for Engineering Education.
Flood, V. J., Schneider, A., & Abrahamson, D. (2015). Moving targets: representing and simulating choreographies of multimodal pedagogical tactics for virtual agent mathematics tutors. Paper presented at the annual meeting of the American Educational Research Association, Chicago, April 16-20.
Flood, V. J., Neff, M. & Abrahamson, D. (2015). Boundary Interactions: Resolving interdisciplinary collaboration challenges using digitized embodied performances. Proceedings of the Computer Supported Collaborative Learning Conference. Gothenberg, Sweden: Computer Supported Collaborative Learning
Arroyo, I., Woolf, B. P., Burelson, W., Muldner, K., Rai, D., & Tai, M. (2014). A multimedia adaptive tutoring system for mathematics that addresses cognition, metacognition and affect. International Journal of Artificial Intelligence in Education, Vol. 24(4), pp. 387-426.
Primers are developed by small teams of volunteers and licensed under a Creative Commons Attribution 4.0 International License.
Lane, C., Grover, S., & Roschelle, J. (2014). CIRCL Primer: AI Applications in Education. In CIRCL Primer Series. Retrieved from http://circlcenter.org/ai-applications-education/
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