AI and Education: Celebrating 30 Years of Marriage

This article describes contributions that artificial intelligence (AI) has made and needs to continue to make towards long-term educational goals. The article articulates two challenges in education that require the use of AI: personalizing teaching and learning 21st century skills. This article first describes AI and some of its history and then suggests why AI is invaluable to development of instructional systems. Instructional systems that use AI technology are described, e.g., computational tools that personalize instruction, enhance student experience and supply data for development of novel education theory development. Additionally, some intelligent tutors supply researchers with new opportunities to analyze vast data sets of instructional behavior and learn how students behave. 1 A Brief History of Artificial Intelligence in Education The field of Artificial Intelligence in Education is focused on research into, development of and evaluation of computer software that improves teaching and learning. Several long term goals have been espoused, such as to interpret complex student responses and learn as they operate; to discern where and why a student’s understanding has gone astray, to offer hints to help students understand the material at hand and ultimately to simulate a human tutor’s behavior and guidance. Personalized tutors have been envisioned that adapt to an individual student’s needs or to teach to groups of students, e.g., classified by gender, achievement level, amount of time for lesson, etc. Another goal is to use Artificial Intelligence (AI) techniques learn about teaching and learning and to contribute to the theory of learning. AI techniques are needed for almost every phrase in the definition of intelligent tutors above, including interpret complex student responses, learn as they operate, discern where and why a student’s understanding has gone astray and offer hints. The central problems (or goals) of AI research include reasoning, knowledge, planning, learning, natural language processing (communication), perception and the ability to move and manipulate objects . AIED has been applied to complex domains, e.g. physics, programming, writing essays, and reading. These tutors learn about the strengths and weaknesses of students in these domains and also about students’ skills, and emotion. How effective are intelligent tutors? Several tutors have been shown to be very effective in the classroom. Researchers looking at student skills at end of experiments and also at the end of course and large scale standardized testing evaluations found dramatic improvement understanding and learning . Intelligent online tutors are an AI success story , though researchers seek to move beyond domain dependence and to support learning of multiple tasks and domains. AIED 2015  To mentor effectively and support individuals or groups, intelligent tutors will assess learning activities and model changes that occur in learners. Estimates of a learner’s competence or emotional state, stored in user models, represent what learners know, feel, and can do. When and how was knowledge learned? What pedagogy worked best for this individual student? Machine learning and data mining methods, both derived from the field of AI, are needed to explore the unique types of data that derive from educational settings and use those methods to better understand students and the settings in which they learn (see [2, 4]). Technology cannot impact education in isolation, rather it operates as one element in a complex adaptive system that considers domain knowledge, pedagogy and environments that students, instructors and technology co-create . AI and Education researchers need to be driven by the problems of education practice as they exist in school settings. The emerging forms of technology described here will challenge, if not threaten, existing educational practices by suggesting new ways to learn . Policy issues that involve social and political considerations, need to be addressed, but are beyond the scope of this document. 2 AI called by a different name: AI behind the scenes Many components of intelligent instructional systems have their roots in artificial instructional research, e.g., adaptive curriculum, modeling (student, teacher, domain), educational data mining, speech recognition and dialogue systems. All began by using artificial intelligence (AI) techniques. Yet once these algorithms and techniques begin to appear as parts of larger tutors, the tutors are no longer considered AI and AI receives little or no credit for their successes. Many of AI's greatest innovations have been reduced to the status of just another item in the tool chest of instructional designers or computer science. Nick Bostrom explains “A lot of cutting edge AI has filtered into general applications, often without being called AI because once something becomes useful enough and common enough it's not labeled AI anymore.” [7] “After all, all smart technologies currently in use (in the classrooms or homes), from tablet computers to smart phones, from Internet search engines to social networking sites, have a growing reliance on techniques derived from AI.”  The AI effect began in the larger AI field and “occurs when onlookers discount the behavior of an artificial intelligence program by arguing that it is not real intelligence.”  Pamela McCorduck writes: “It's part of the history of the field of artificial intelligence that every time somebody figured out how to make a computer do something—play good checkers, solve simple but relatively informal problems—there was chorus of critics to say, 'that's not thinking'.”  Rodney Brooks complains “Every time we figure out a piece of it, it stops being magical; we say, ‘Oh, that's just a computation.’” . Intelligent personal assistants in classrooms or in smartphones use algorithms that emerged from lengthy AI research. IBM's question answering system, Watson, which defeated the two great Jeopardy champions by a significant margin, was derived from basic AI research in natural language processing, information retrieval, knowledge representation, automated reasoning, and machine learning technologies to the field of AIED 2015  domain question answering [10]. In addition, the Kinect, which provides a 3D body–motion interface for the Xbox 360 and the Xbox One was derived from basic AI research [7]. AI is whatever hasn't been done yet. Software and algorithms developed by AI researchers are now integrated into many applications, without really being called AI, e.g., speech understanding as part of online travel reservations, expert systems that save companies millions of dollars (US). Michael Swaine reports “AI advances are not trumpeted as artificial intelligence so much these days, but are often seen as advances in some other field.”[11] “AI has become more important as it has become less conspicuous,” Patrick Winston says. “These days, it is hard to find a big system that does not work, in part, because of ideas developed or matured in the AI world.” . 3 Impact on Education A related question about AIED relates to the impact of AI on education and focuses on the extent to which the results of AIED research are meaningful to real educational practice [13]. Does the education community even care? Similar to many fields aspiring to scientific rigor, the AIED community can showcase dozens of studies demonstrating the statistical significance of this or that approach or system or their individual components through rigorously designed studies, but it is not always clear how the results of many of those studies actually translate into real educational teaching and learning practices raising a question as to whether all this rigor may not be happening in a vacuum. For example, schools in the USA are not thriving. Too many schools teach in traditional ways and aren’t preparing the next generation to meet new challenges. When today’s students graduate, they’ll be asked to fill the jobs of tomorrow—ones we can’t even imagine . And they’ll be asked to tackle global problems like climate change, endemic hunger, and refugee problems. Additionally, the current use of digital resources in K12 and higher education can be described as dysfunctional: many school stakeholders can’t find sufficient effective digital resources, while large collections of resources exist and sit online, waiting to be discovered. Some solutions have been proposed to migrate successful evidence-based digital resources into classrooms. One solution is to define a roadmap that moves well-tested resources towards publishers and software companies and ultimately into classrooms. More than 4 million USA students at the K12 level took an online course in 2011, up significantly from just 1 million three years earlier. During the coming decade education should shift from print to digital and from batch processing to personalized learning . In addition to virtual schools, online learning is increasingly being incorporated into traditional settings that blend the best of online and faceto-face learning. A shift to online learning is happening in K12 in the USA due in part to the need to implement collegeand careerready standards, the shift to next-generation assessments, and the prevalence of affordable devices. Online learning may move AIED 2015  standardized teaching towards more personalized instruction without increasing the number of teachers. The field of AIED, now nearly thirty years old, has finally achieved some of its oldest goals. Thirty years is calculated from the first Intelligent Tutoring Systems Conference, 1988, organized by Claude Frasson in Montreal, Canada. Some longterm goals are currently being worked on, including understanding and responding to student knowledge, meta-knowledge (thinking