An Investigation into using Artificial Intelligence in Education

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Over the past decade, educational institutions have increasingly offered online, web-based courses. While there has been a great deal of research comparing the effectiveness of online and traditional courses (Young, 2006), there has been less research on how to use instructional design strategies to increase student engagement, student satisfaction, and achievement in online courses (Gunter, 2007). Research has shown that instructional immediacy can increase cognition and student success (LaRose & Whitten, 2000).

Educators teaching online have turned to various technologies to improve student-to-instructor interactions. Personal response systems, teleconferencing tools, and computer-supported collaborative learning (CSCL) environments have been used (Soh, Khandaker, & Jiang, 2008). Educators have used chat sessions to facilitate communication, as well. However, students often are required to meet in a chat room or teleconference during predetermined times. While having synchronous meetings does improve student-to-instructor interaction, these systems are largely passive (Soh et al, 2008). Artificial intelligence is a technology that can provide immediate responses to user questions and it can adapt to individual users needs. This paper will discuss what artificial intelligence is and how artificial intelligence has been used. It is hypothesized that the use of artificial intelligence in online courses will increase student success and engagement.

Artificial intelligence can be defined as the science and engineering of creating intelligent machines, computer programs in particular (McCarthy, 2007). There are multiple branches of artificial intelligence or AI, as it will be referred to for the remainder of this paper. Logical AI refers to what a program knows about the world in general and the facts of a particular situation in which it must act. Goals are represented by mathematical logical language and the AI acts by deducing which actions are appropriate for achieving its goals (McCarthy). Search AI programs study large numbers of possibilities. A chess playing computer is an example of a search AI program. There are pattern recognition AI programs. These types of AI programs are programmed to compare what it sees with a pattern. There are AI programs that can plan or learn from experience (McCarthy). These examples of various AI program types are not exhaustive.

AI programs have been designed for multiple educational purposes. I-MINDS is an AI program that has been created to help instructors with classroom management and to increase student collaboration. The theoretical framework of the I-MINDS intelligent computer-supported collaborative learning (CSCL) environment was based on three fundamental principles. In the first principle, the authors proposed building a CSCL system that was "responsive, flexible, distributed, and adaptive to individual student behaviors" (Khandaker et al., 2008, p. 3). In the second principle, the authors desired to build a CSCL "that is able to evolve over time in terms of its pedagogical knowledge, student and even group modeling, and performance in decision support" (Khandaker et al., 2008, p. 3). In the third principle, the authors proposed building a CSCL system "is able to form effective student learning groups on its own" (Khandaker et al., 2008, p. 3).

The authors studied the impact of I-MINDS on structured cooperative learning. A two-semester study was launched at the University of Nebraska during the Spring and Fall semesters of 2005. I-MINDS was deployed and evaluated in an introductory computer science course. The study utilized a control section where a group of students did not use I-MINDS. The authors' results show "that I-MINDS can support cooperative learning effectively in the place of face-to-face collaboration among students in weekly laboratory sessions" (Khandaker et al., 2008, p. 28). The results also show that modular extension to the system is supported. Finally, I-MINDS collected data that provided vital information on student group activities. This showed that I-MINDS can be used effectively as a test-bed for educational research.

AI programs can be developed to provide individualized and adaptive language learning and vocabulary tutoring. In Personalization of Reading Passages Improves Vocabulary Acquisition by Heilman, Collins-Thompson, Callan, & Eskenazi, the REAP tutoring system, which provides English as a Second Language vocabulary practice, was examined. According to the authors, "REAP can automatically personalize instruction by providing practice readings about topics that match interests as well as domain-based, cognitive objectives" (Heilman, Collins-Thompson, Callan, & Eskenazi, 2010). The authors pointed out that most previous research on motivation in intelligent tutoring environments has focused on increasing extrinsic motivation. The authors focused their study on increasing personal interest. The students in the study were randomly split into control and treatment groups. The control condition tutor selected texts to maximize domain-based goals. The treatment-condition tutor also preferred texts that matched personal interests. The results show positive effects of personalization. In addition, the importance of negotiating between motivational and domain-based goals was demonstrated (Heilman et al., 2010).

Gunter, G. (2007). The effects of the impact of instructional. International Journal of Human and Social Sciences , 2 (3), 195-201.

Heilman, M., Collins-Thompson, K., Callan, J., & Eskenazi, M. (2010). Personalization of reading passages improves vocabulary. International Journal of Artificial Intelligence in Education , 20, 73-98.

LaRose, R., & Whitten, P. (2000). Re-thinking instructional immediacy for web courses: A social cognitive exploration. Communication Education , 49 (4), 320-338.

McCarthy, J. (2007, November). What is Artificial Intelligence? Retrieved February 14, 2011, from Basic Questions:

Soh, L., Khandaker, N., & Jiang, H. (2008). I-MINDS: a multiagent system for intelligent computer-supported collaborative learning and classroom management. International Journal of Artificial Intelligence in Education , 18 (2).

Young, S. (2006). Student views of effective online teaching in higher. The American Journal of Distance Education , 20 (2).