Artificial Intelligence and Classroom Teaching

Running head: ATIFICIAL INTELLIGENCE AND EFFECTIVE TEACHING

Artificial Intelligence and Effective Teaching Janie Dam ELM 8845 July 10, 2016 Professor Rick Ansoff Alliant International University, San Diego

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ABSTRACT Behind recent headlines such as “Promising New Roles for AI in the Classroom” or “10 Ways Artificial Intelligence Can Reinvent Education” lies the uncomfortable question: “Is artificial intelligence a friendly tool to teachers, helping them to become significantly more efficient and effective at delivering individualized instruction to all children; or is it an insidious Trojan horse, silently and ubiquitously invading the teaching profession, threatening to eventually render all human teachers obsolete?” As both classroom teacher and school data systems coordinator, the author of this paper seeks to shed light on this question through a discussion of the art and science of teaching, the qualities of effective teachers, and artificial intelligence’s potential for effective teaching.

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Artificial Intelligence and Effective Teaching The Fourth Industrial Revolution Advancement in artificial intelligence (AI), robotics, and biotechnology, referred to as “the Fourth Industrial Revolution,” has proved to be just as disruptive and transformational to the business world as the previous three industrial revolutions (World Economic Forum). With technological development come some alarming news. Results from the Forum’s January 2016 survey of senior executives from over 350 of the largest companies in 15 major national economies representing 65% of the global workforce suggest that as many as 5 million of the world’s jobs could be eliminated over the next 5 years through industrial and business automation.

Artificial intelligence is also developing rapidly in the educational arena. The journal of the International Artificial Intelligence in Education Society (IAIED) offers an impressive collection of scholarly articles; a display of the flourishing growth of AI that has taken place in the world of teaching and learning. Given parallel technology trends in business and industry and in education, does it follow, then, that vast numbers of teaching jobs will likewise be lost in the near future through educational automation? In other words, does intelligent technology have the capacity to and will it replace humans as effective teachers? These questions are worth asking as their implications may impact the way in which school districts, principals and assistant principals, instructional technology coordinators will need to implement their technology programs, including teacher professional development and gaining buy-in among instructional staff for an administrative vision of AI-enhanced classrooms. These questions cannot be adequately answered without an understanding of the following:  Teaching as art and science.

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 The qualities of effective teachers.  What artificial intelligence is able to do.  What artificial intelligence is not able to do.

The Art and Science of Teaching When asked, most educators today would probably describe teaching as both art and science. To deliver that message, Robert Marzano, a leading advocate of schooling and the teaching profession, had “carefully” selected “the Art and Science of Teaching” as the title for his seminal book (Marzano, 2009, p. 1). Teaching is often considered an art because effective teaching styles can be as varied and unique as the teachers themselves. However, teaching has more recently been recognized as having a scientific side: research over time has produced evidence that certain teaching strategies are highly correlated to student academic success (Marzano, 2003). As such, the desirable outcomes of these strategies are predictable and therefore potentially replicable, regardless of who the teacher is.

One could also argue that the art of teaching is actually a higher form of the science of teaching, or vice versa, or perhaps more of a craft. That is, master teachers are great artists because they have the ability to utilize a wide range or whole repertoire of media to effectively connect and communicate both emotionally and intellectually to their audience of students on a selected topic. This ability allows them to be highly adaptive to their audience and hence very fluid in carrying out the strategies that have now been empirically “proven” to have a high probability of success at imparting knowledge to students. If they continue to focus on these strategies in developing their art, they become more of a master craftsman. Another way this paper looks at teaching, in order to evaluate the potential for artificial intelligence as an effective

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teacher, is through a distinction between the art of teaching as a function of the teacher’s personality and freedom of action, and the science of teaching as the set of scriptable actions that any teacher can be trained (or “programmed”) to perform in the classroom. In an environment of school funding being tied to high-stakes standardized testing, aggravated by shrinking tax dollars alongside a demand for teacher pay increase, it is only understandable that school districts are tempted to purchase and roll out scripted curriculum and instruction offered to them by the major publishers as the solution to school accountability on a budget. However, the failure of scripted teaching such as the Open Court reading program instated by the Los Angeles Unified School District several years ago, accompanied by a series of protests by frustrated parents and emotionally charged teachers, is motivation to study more deeply why the science of teaching can fail so miserably, and whether or under what conditions the involvement of artificial intelligence might have been more appropriate or made it more feasible. More fundamentally, the question to ask is whether, or where, does AI fit in, if teaching is an art, a science?

The Qualities of Effective Teachers There are multiple schools of thought as to what constitutes or can produce effective teaching, and what makes a teacher an effective teacher. None of them clearly delineates between art and science; neither do or can they refer to themselves as either purely art or purely science.

The role of artificial intelligence in the classroom can only be evaluated properly in recognizing the complexity of this current learning theory landscape. Marzano points to time-tested research in stating that “Among elements such as a well-articulated curriculum and a safe and orderly environment, the one factor that surfaced as the single most influential component of an effective school is the individual teachers within that school”(Marzano, 2007, p.1). While acknowledging the art of teaching, this educationalist and his team emphasize the usefulness of treating teaching

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as a science, with its own system for measuring teacher quality. Adopting student academic growth as the key measurable indicator of teacher effectiveness, Marzano and Toth claim confidently in their 2013 publication that teachers can be trained to become effective by analyzing scores from their students’ standardized tests, end-of-course benchmarks and selfreported surveys, and then formulating the appropriate professional development plan based on this analysis and on the reinforcement of particular teacher behaviors that have been demonstrated by empirical study to yield desired learning outcomes. Also in 2013 Mark Barnes published a book on Results Only Learning Environment (ROLE), suggesting that schools and teachers can gain more learning from students if they abandon the tradition of grading and scoring students and instead, only provide appropriate narrative work product feedback to students. Barnes bases his theory on Dylan William and colleagues’ previous findings over 25 years of studying assessments that “Students given marks are likely to see it as a way to compare themselves with others; those given only comments see it as helping them to improve. The latter group outperforms the former” (Barnes, p.72). Feedback is defined as “information with which a learner can confirm, add to, overwrite, tune, or restructure information in memory, whether that information is domain knowledge, meta-cognitive knowledge, beliefs about self and tasks, or cognitive tactics and strategies” (Winne & Butler, 1994 in Barnes, 2013). Unlike Marzano and Barnes who believe in student-centered teaching and learning, Charles Morrison recommends in his 2014 article that education move even beyond the now cliché shift from ‘sage on the stage’ to ‘guide by the side’ teacher role. He advocates for learning-centered or subject-centered classrooms, where both teacher and student are active participants in the teaching-learning nexus to more dynamically and effectively transform information into knowledge of a discipline. In order to carry this out, students must be held more accountable for their own learning and be

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responsible for assessing and tracking their own progress. Morrison’s theory is based on Parker Palmer’s earlier notion of placing the subject being learned, not the teacher nor the student, at the “center of the pedagogical circle.” Meanwhile, James Stronge is more interested in the affective characteristics, or emotional and social behaviors, of effective teachers, which he found to have frequently made an appearance in students’, school administrators’ and teachers’ responses to surveys on what makes a good teacher (Stronge, 2007). In reviewing and synthesizing the body of research, Stronge came up with the following list of very human, almost maternal, affective behaviors that over the years have been reported by school stakeholder groups to be conducive to student learning: 

Caring: Bringing about the best in students through affirmation and encouragement, as well as patience, kindness, trust, honesty, courage, listening, understanding, nurturing, warmth, gentleness, knowledge of the student as a person and as an individual.

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Listening: Focused and sympathetic listening to initiate and support two-way communication between student and teacher.

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Understanding: Respectfully listening to individual students’ concerns, questions and arguments; appropriately sharing the teacher’s own personal experiences and showing empathy; making an effort to assist the student to work out problems.

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Knowing students: Be informed of how the student is doing personally, not just as a student; be aware of his/her particular likes and dislikes and life situation.

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Fairness and respect: Emphasize, model and practice fairness and respect by investigating and understanding all the facts before disciplining the student, responding to misbehavior at an individual level rather holding the whole class responsible for the actions of one or a small group of students in the class; be able to tell students specifically what they did wrong as well as what they need to do right; treat all students equitably regardless of race, ethnicity, cultural background and gender, and do not practice favoritism.

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Social interactions with students: Interact in a friendly and positive manner that demonstrate interest in students’ lives beyond the classroom; able to individually, realistically, and successfully challenge each and every student to succeed; attend sports events, concerts, and other extracurricular activities to socialize with students outside of the regular classroom; exhibit a sense of humor and comfortable personal style.

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Promoting enthusiasm and motivating learning: Exhibit own enthusiasm for teaching, learning, subject matter being taught; serve as a motivational leader to foster student academic responsibility and achievement; maintain an organized classroom environment; set high standards for students; provide positive reinforcement for completing tasks; involve all students in class activities.

While all four of these educationalists do ground their theories on empirical research, in practice Marzano leans the furthest toward teaching as a science. To Marzano, effective teaching is a set of explicitly defined skills that are measurable and that can be replicated by any teacher through appropriate indoctrination. But even Marzano cannot deny that teachers must have some

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decision- making power, be afforded some level of discretion in how their classroom is run and how their students are taught. Students are human beings whose behaviors, including learning behaviors, are not always rational or predictable; a teacher must be able to have the freedom of action, and the cognitive, emotional, psychological flexibility, to appropriately respond to each individual student. Even research-based teacher training such as Marzano’s cannot instill the wisdom and judgment that it takes to be a truly effective teacher. That comes with experience gained over time thanks to the plasticity of the human brain. Until AI can be programmed to gain wisdom and judgment over time, it is highly unlikely that it can adequately take the place of an experienced human teacher to effectively teach human students. At the other end of the spectrum, Stronge gravitates toward the art of effective teaching that can naturally flow from a teacher’s empathetic, nurturing, sociable, humorous personality. Clearly, until computer programmers are able to come up with algorithms for the human personality traits of empathy, nurturingness, sociableness, and sense of humor, intelligent technologies cannot achieve the affective aspects of teaching that students, school administrators and teachers mention time after time when asked to describe effective teaching or an effective teacher. Barnes’s giving of narrative feedback to students is a specific teacher behavior within the science of effective teaching. However, there is also an art to it, as the word choice or method of delivery selected by the teacher to provide such feedback can be as varied as human language and human modes of communication themselves.

For example, a teacher who chooses to give feedback in a simple but highly encouraging paragraph, one who decides to deliver the feedback in engaging flowery poetry form, and another who finds it more effective to come up with an entertaining rap performance instead can all be equally effective in motivating and supporting student learning. This is the art and the master craftsmanship that effective human teachers offer. Programming software to display

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congratulatory messages on the screen each time that a student responds correctly to a question on a quiz is not the same as providing individualized narrative feedback, and does not carry the same level of effectiveness that Barnes is aiming for. What Morrison proposes is a major paradigm shift. Within the subject-centered effective teaching paradigm, teacher and student are equal participants and equal partners in the construction or expansion of the knowledge base on a selected topic or subject matter. This takes learning and teaching on the part of both teacher and student. This takes communication, i.e. use of human language to exchange ideas. In order for AI to truly participate in what Morrison refers to as the teaching-learning nexus, AI must first be truly able to learn and to communicate as human teachers and students could.

What Artificial Intelligence Is and Is Not Able To Do In recent years companies offering teacher professional development services such as Teach Thought have begun to avidly promote instructional artificial intelligence. Cross posting an article originally penned by the staff of onlineuniversities.com in 2014, Teach Thought speaks of AI as being able to help teachers with grading, identifying gaps in the delivery of curriculum and instruction, adapting to individual student needs, tutoring, providing feedback, conducting adaptive assessments, and making trial and error learning less intimidating (Teach Thought, 2016). These AI abilities sound ideal, as they address teaching tasks that can prove to be quite tedious and time-consuming for a human teacher to manage, especially in the higher grade levels, where there may be as many as 40 students assigned per classroom. However, not all assessments are multiple choice tests; many require constructed responses or essay writing, which still would require a human teacher to read and score. There is no grading and scoring in Barnes’s model, but, again, narrative feedback customized to the student’s work in progress would require the involvement of a human teacher. In terms of identifying gaps in the teacher’s instruction and

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curriculum, the open online provider Coursera offers an alert system whereby the teacher is notified when large number of students have answered a question incorrectly (Teach Thought, 2016). Students found to submit a wrong response are automatically assigned related homework.

Future students are sent hints to the correct answer based on the wrong answers given by current students. If the same system allows students the opportunity to enter their input as well, then this software application could be useful to Morrison. But in such case, the software would still serve only as a tool and not a participant: the source of the content knowledge being accumulated is still human, i.e., the human teacher and student. With regard to adapting to students’ individual needs, online curriculum delivery does open up wonderful opportunities for 24/7 self-paced learning, However, there have been thus far no findings in the body of research that human students are able to receive a rich learning experience from an online course without any involvement of a human teacher or advisor. Stronge’s exhaustive literary review would support the opposite conclusion.

In a recent promotional pamphlet titled “Intelligence Unleashed: An Argument for AI in Education,” Pearson, the world's largest education company, boldly defines Artificial Intelligence as “computer systems that have been designed to interact with the world through capabilities… and intelligent behaviours… that we would think of as essentially human” (Luckin, Holmes, Griffiths & Forcier, 2016, p. 14). The pamphlet mentions a gamut of applications already currently in use on K-12 and university campuses - educational data mining to collect data and track student behaviors at school, physiological sensors for speech and gesture recognition, etc.but focuses on the three categories of software of personal tutors for every learner, intelligent support for collaborative learning, and intelligent virtual reality. While insisting that AI will not negatively impact the future employment of human teachers, the Pearson authors boast that their

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company’s Intelligent Tutoring Systems (ITS) “use AI techniques to simulate one-to-one human tutoring, delivering learning activities best matched to a learner’s cognitive needs and providing targeted and timely feedback, all without an individual teacher having to be present” (Luckin, et al, p. 24). The same removal of the need for a human teacher is implied in Pearson’s pitch for their collaborative classroom “expert facilitation,” intelligent virtual agency,” and “intelligent moderation” products, all done by AI on behalf of the human teacher (and apparently in lieu of the collaborating students themselves). Likewise, engaging Pearson computer-generated virtual reality, presumably unlike a human teacher, can ensure that “the learner engages properly with the intended learning objectives without being confused or overwhelmed” (Luckin, et al, p. 29).

Throughout their marketing publication the Pearson AI team makes superficial reference and deference to the warnings of Nick Bostrom, the author of Superintelligence: Paths, Dangers and Strategies, to address and calm any fears about AI among potential customers .The credibility of Bostrom’s assertions and their true reflections on educational AI developments such as Pearson’s will be examined in another more extensive and more technical paper that I will write. But for the purpose of the present paper, I will just summarize that everything that Pearson claims AI can do assumes that machine learning is at the same level or will be able to reach the level of human learning. But that assumption can be proved mathematically and meta-mathematically to be false.

Conclusion Aggressive marketing of AI products serves only to fuel the needless fear that advanced artificial intelligence terminates the need for human teachers in the teaching of students. Regardless of which learning theory one sides with, it would be imprudent to consider AI an adequate substitute for human teachers because there is still a very wide gap between machine language

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and machine learning and human language and human learning. Furthermore, extensive survey results support the role of affective contributions by human teachers in the process of effective teaching. No computer at the present time can be programmed to have feelings, to care, to socialize nor to tell a funny joke at just the right moment. Being able to make the right judgment whether, when, where to use artificial intelligence is itself a human task. AI is a gift from science, and is best put to use as another helpful tool in the box of many tools of human teachers; so they may polish and perfect their noble art, or craft of effective teaching.

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REFERENCES Barnes, M. (2013). Role Reversal: Achieving Uncommonly Excellent Results in the StudentCentered Classroom. Alexandria, US: ASCD. Bostrom, N. (2014). Superintelligence:Paths,Dangers, Strategies.Oxford, UK: Oxford University Press.

International Artificial Intelligence in Education Journal (2016). IJAIED papers. Retrieved from http://www.iaied.org/journal/ Luckin, R., Holmes, W., Griffiths, M., & Forcier, L. (2016). Intelligence unleashed: An argument for ai in education. Retrieved from https://www.pearson.com/content/dam/corporate/global/pearson-dotcom/files/innovation/Intelligence-Unleashed-Publication.pdf MagaĂąa, S. (2014). Enhancing the Art & Science of Teaching with Technology. Bloomington, IN: Marzano Research Laboratory.

Marzano, R. J. (2003). What Works in Schools: Translating Research into Action. Alexandria, VA: Association for Supervision and Curriculum Development (ASCD).

Marzano, R.J. (2007). Art and Science of Teaching: A Comprehensive Framework for Effective Instruction. Alexandria, VA: Association for Supervision & Curriculum Development (ASCD).

Marzano, R.J., & Brown, J.L. (2009). A Handbook for the Art and Science of Teaching.

Alexandria, VA: Association for Supervision & Curriculum Development (ASCD).

Marzano, R. J., & Toth, M. D. (2013). Teacher Evaluation That Makes a Difference: A New Model for Teacher Growth and Student Achievement. Alexandria, US: Association for Supervision & Curriculum Development (ASCD).

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Morrison, C.D. (2014). From ‘sage on the stage’ to ‘guide on the side’: A good start.

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Retrieved from http://digitalcommons.georgiasouthern.edu/ij-sotl/vol8/iss1/4 Palmer, P. (2007). The Courage to Teach: Exploring the Inner Landscape of a Teacher’s Life (10th Anniversary ed.). San Francisco, CA: Jossey-Bass.

Stronge, J. H. (2007). Qualities of Effective Teachers (2nd ed.). Alexandria, VA: Association for Supervision & Curriculum Development (ASCD).

Teach Thought (2016). 10 roles for artificial intelligence in education. Retrieved from http://www.teachthought.com/the-future-of-learning/10-roles-for-artificial-intelligence-ineducation/ Wiliam, D. (2011). Embedded Formative Assessment. Bloomington, IN: Solution Tree.

Winne, P. H., & Butler, D. L. (1994). Student cognition in learning from teaching. International Encyclopaedia of Education (2nd ed., pp 5738– 5745). Oxford, UK: Pergamon.

World Economic Forum (2016a). Five million jobs by 2020: The real challenge of the fourth industrial revolution. Retrieved from https://www.weforum.org/press/2016/01/five-millionjobs-by-2020-the-real-challenge-of-the-fourth-industrial-revolution World Economic Forum (2016b). The Global Risks Report 2016. Retrieved from http://www3.weforum.org/docs/Media/TheGlobalRisksReport2016.pdf