Overview of Artificial Intelligence in differential education

     Artificial intelligence has incredible promise for the future of differentiation in education. As differentiation is essentially examining those things that make learning different between people, algorithms can be developed that can uncover some of these differences, and then tailor the educational content accordingly. In fact, Intelligent Tutoring Systems (ITS) have been gaining popularity and have been the subject of much recent research.

Latham, et al. describe one of the current limitations of ITS systems today:

Some ITS adapt tutoring to an individual’s learning style, either determined using a formal questionnaire or by analyzing learner behavior. However, there are no tutor-led CITS that can predict and adapt to learning style during the tutoring session like a human tutor (2012).

     There is good reason to believe that flexible and adaptable systems that closely mimic human tutors are on the horizon. Advances in programming, data storage, and processing speeds have fueled the development of artificially intelligent systems and there is a noticeable influx of this technology into education. Samuelis (2007) stated that “the primary developmental goals of the ITSs community are aligned with advanced distributed learning’s (ADL) long-term vision: ‘To generate, assemble, and sequence content that dynamically adapts to the learner to optimize learning. Specifically, ADL is actively engaging in research and implementation of the digital knowledge environment of the future in the areas of standards and authoring tools that give instructors the ability to create ITS functionality within a virtual training environment.’”  That statement “dynamically adapts to the learner to optimize learning” is what differentiation is all about. We know that learning is not a one-size-fits-all endeavor and educators have struggled with balancing efficiency of delivery with maximum educational effect. ITS and A.I. are the differentiated learning tools of the future.

     Research is showing that flexible learning systems result in higher learning outcomes, as shown by Luckin and du Boulay, Blanchard and Frasson, and Baylor and Kim among others.  When a system is capable of adapting the style, mode, and pace of instruction based on real-time feedback from the learner, the potential for a unique student learning experience is greatly increased.  

Examples of Artificial Intelligent Tools in Education

Why Artificial Intelligence is a great tech solution for differentiation

Intelligent:

• "Considering the fact that students differ in learning preferences, learning approach (such as: language, perspective, typical learning time/-involvement, interactivity type and level, learning resources, semantic density, etc.), amount and kind of prior knowledge, cognitive skills, etc., one and the same instructional content sequence cannot provide optimal knowledge gain for all students” (Pedrazzoli, 2010).
• Example system: OPUS One
• OPUS One’s learning environment “allows a personalized learning approach based on the actual learning curricula of the student, taking in consideration positive or negative progress made during the completion of the learning path” (Pedrazzoli, 2010).

Efficient:

• Branching software and other similar AI-style programs can easily and quickly differentiate instruction based on student performance.
• Tiene and Ingram (2001) suggest that an initial diagnostic test with the student may be able to determine “reading skill level, mathematical aptitude, attitudes about learning, preferred sensory mode, cognitive style, degree of independence, favorite subject areas, topics of interest, etc.” in an artificial intelligence system.
• Computers can also provide learning at all hours of the day and night so learning can happen whenever the learner wants.
• The basic tenet of intelligent tutors is that information about the user (e.g., knowledge, skill level, personality traits, mood level or motivational level) can be used to modify the presentation of information so that learning proceeds more efficiently.” (Johnson & Taatgen, 2005) 

Reliable:

• Besides maintenance problems, Artificial Intelligence systems never need sick days, personal days, or vacation days.
• Artificial intelligence systems “can be very precise, do not make mistakes (barring a malfunction), can keep at the job twenty-four hours a day, demand neither wages nor benefit packages, and never go out on strike” (Tiene and Ingram, 2001).
• AI systems never have colds or get tired.
• Artificial intelligence systems never need to negotiate for a new contract.
• Average personnel costs in a medium-to-small district add up to about $10,000,000 yearly. An artificial intelligence system would surely be less than this!

Research Articles about Artificial Intelligence and Differentiation

Brna P. (2011). First Workshop on Artificial Intelligence in Education to Support the Social Inclusion of Communities. Retrieved from: http://www.aied2011.canterbury.ac.nz/docs/workshops/aiedsic-proceedings.pdf#page=14 

Allen, C., & Wallach, W. (2011). Wise machines?. On The Horizon, 19(4), 251-258. doi:10.1108/10748121111179376 

Anusuya, M. A., & Katti, S. K. (2010). On Human Intelligence. International Journal On Computer Science & Engineering, 1(5), 1674-1678. 

 
Gwo-Dong, C., Jih-Hsien, L., Chin-Yeh, W., Po-Yao, C., Liang-Yi, L., & Tzung- Yi, L. (2012). An Empathic Avatar in a Computer-Aided Learning Program to Encourage and Persuade Learners. Journal Of Educational Technology & Society, 15(2), 62-72.

Johnson, B. (2012, October 3). Engineers hope to download bees' brains into robots. Retrieved from  http://www.cbsnews.com/8301-205_162-57525206/engineers-hope-to-upload-bees-brains-into-robots/ 

Kenny, C., & Pahl, C. (2009). Intelligent and adaptive tutoring for active learning and training environments. Interactive Learning Environments, 17(2), 181-195. doi:10.1080/10494820802090277 

Pedrazzoli, A. (2010). OPUS One: An Intelligent Adaptive Learning Environment Using Artificial Intelligence Support. AIP Conference Proceedings, 1247(1), 215-227. doi:10.1063/1.346023

Wong, L., & Looi, C. (2012). Swarm intelligence: new techniques for adaptive systems to provide learning support. nteractive Learning Environments, 20(1), 19-40. doi:10.1080/10494821003714681

Other Resources on Artificial Intelligence for Differentiating Education 

• International Conference on Artificial Intelligence in Education 
• The International Journal for Artificial Intelligence In Education

References

Baylor, A. L., & Kim, Y. (2005). Simulating instructional roles through pedagogical agents. International Journal of Artificial Intelligence in Education,15(1), 95-115.

Blanchard, E., & Frasson, C. (2005, June). Making Intelligent Tutoring Systems culturally aware: The use of Hofstede’s cultural dimensions. In International Conference. on Artificial Intelligence, Las Vegas (pp. 644-649).

du Boulay, B., Avramides, K., Luckin, R., Martinez-Miron, E., Rebolledo-Mendez, G., & Carr, A. (2010). Towards Systems that Care: A Conceptual Framework Based on Motivation, Metacognition and Affect. International Journal Of Artificial Intelligence In Education, 20(3), 197-229.

Johnson, A. & Taatgen, N. (2005). User modeling. In R. W. Proctor & L.V. Kim-Phuong (Eds.), The handbook of human factors

in Web design (pp. 424-438). Mahwah, NJ: Lawrence Erlbaum Associates, Inc.

Latham, A., Crockett, K., McLean, D., & Edmonds, B. (2012). A Conversational Intelligent Tutoring System to Automatically Predict Learning Styles. Computers & Education, 59(1), 95-109.

Pedrazzoli, A. (2010). OPUS One: An Intelligent Adaptive Learning Environment Using Artificial Intelligence Support. AIP Conference Proceedings, 1247(1), 215-227. doi:10.1063/1.3460231

Samuelis, L. (2007). Notes on the components for intelligent tutoring systems.Acta Polytechnica Hungarica, 4(2), 77-85.

Tiene, D., & Ingram, A. (2001). Exploring current issues in educational technology. New York, NY: The McGraw-Hill Companies.

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