From AI to Straight A’s: Artificial Intelligence Within Education

Zachery Omer


As U.S. students’ test scores in math, reading, and science continue to fall in the middle of pack worldwide, it’s become apparent that a change to our education system is necessary. I believe that the system could be bolstered by recent advancements in artificial intelligence technology, such as automation and adaptive learning, including gamification and knowledge monitoring. I also think that the implementation of these technologies could lead to significant changes in the profession of teaching, such as the methods, curriculum, environment, and materials needed to do the job effectively. This essay will pursue the research question: How can these AI technologies best be implemented and integrated into our education system without putting too much pressure on teachers, students, or the technologies themselves? I will explore the writings and musings of professionals and thought leaders across the fields of technology and education, as well as anecdotal evidence and several case studies from the past few years. 


When I think back on my own educational experience, I can distinctly remember the impact of technology as the years passed. In early elementary school, when a television would be rolled into the classroom on a cart and we realized that we’d get to watch a VHS episode of Bill Nye the Science Guy or Reading Rainbow with Levar Burton, the excitement was palpable.

By 5th grade, we had all survived Y2K, and the digital revolution had officially begun. Our class had a small set of Alphasmart 2000 keyboard devices so we could begin learning how to type. In 6th grade, our classroom was the site of the school’s first SMART Board, and there was a bulky desktop computer for every 2-3 students. For a small old school building in rural Missouri that didn’t even have air conditioning in every room, this felt very cutting edge, futuristic, and exciting.

Throughout middle and high school, there were still plenty of dry erase markers and overhead projectors, but SMART Boards became more and more common, and course materials became increasingly digital. First it was correspondence: emails, syllabi, grades, etc., but eventually class notes, presentations, modules, and assignments moved online as well. I got my first smartphone for my 18th birthday, and my first laptop as a high school graduation gift.

In college I became familiar with a learning management system (LMS) for the first time, using Blackboard as a central location for assignments, blog posts, course materials, grades, and more for almost all of my classes. This LMS experience had mixed results, largely depending on the professor using the system and their relationship with technology. Nearly all assignments, quizzes, and essays were submitted electronically, and I even took several courses that were entirely online, where I never physically met my professor or classmates.

Upon graduation, I began working at a public high school, and found that the technological environment had continued it drastic development. Every classroom had a SMART Board, most classes had their own set of Google Chromebook laptops, and students were allowed– although not encouraged– to keep their personal smartphone devices out in plain view on their desk. As I observed students listening to music, texting, playing games, taking photos, and browsing social media throughout the school day, I couldn’t help but think of my own high school experience (only 5 years prior), where if you were caught with your phone out during class, it was taken for the remainder of the day.  Nearly all of my coworkers at the school agreed that these devices were a distraction, and many had different methods of trying to govern or regulate their use during class, but very few were willing to endure the inevitable revolt that would accompany the outright banning of phones in class. Simply put, most students expected perpetual connectivity through their smartphones, and depriving them of that, even for a few hours, led to feelings of isolation and irritability.

In only 20 years, the education system underwent a colossal change, on a scale that has likely never been seen before. In parallel with society, it was a shift from primarily analog activity to almost exclusively digital. Along with that shift came massive changes in pedagogy and even epistemology. As we look forward from our current point in time, the possibilities appear endless for technology within education. Artificial intelligence, automation, and machine learning have become quite the buzzwords across all industries, and education is no exception. Can artificial intelligence help to produce real intelligence in the classroom? Can deep-learning algorithms produce deep-learning students? How can these technologies best be implemented and integrated into our education system without putting too much pressure on teachers, students, or the technologies themselves?  

According to Pew Research data, students in the United States rank near the middle of the pack in math, science, and reading, and are below many other industrialized nations in those categories. According to the 2015 study, among 71 participating countries, the US ranked 38th in math and 24th in science (Desilver, 2017).

In an attempt to help counter this disappointing educational mediocrity, I have researched several different aspects of AI and machine learning to discern how these readily available technologies could be utilized effectively in schools. Over the course of this essay, I will explore the potential role(s) of artificial intelligence in our education system, and discuss the changing role of educators alongside these new AI technologies, to effectively prepare and equip our students (and teachers) for the inevitable advancement of the Digital Age.   


Section 1: The Role of Artificial Intelligence in Education

While artificial intelligence seems like a product of the 21st century, the concept was actually conceived back in 1936 by Alan Turing and the term was coined in 1956 at Dartmouth University (Computer History Museum, 2014). Since its ideation, AI has undergone multiple cycles of hope, hype, and hysteria, where people marvel at its potential, get excited at its release, and become concerned that it will somehow destroy us. The terms “artificial intelligence” and “AI” have been eagerly– and broadly– adopted by companies and media outlets without realizing the full meaning behind them, causing a rift in the public’s understanding of these technologies. According to Margaret Boden (2016) in her book AI: It’s Nature and Future, “Intelligence isn’t a single dimension, but a richly structured space of diverse information-processing capacities. Accordingly, AI uses many different techniques, addressing many different tasks” (p. 12). For the purposes of this essay, I will focus on the areas of automation and adaptive learning within artificial intelligence, and how those concepts may be applied to the field of education.

  • Automation

Schools have been already begun implementing automation in several capacities, such as machine-graded Scantron tests and automated class registration, but further potential applications are vast. Automation can fast-track many of the tedious, repetitive, paper-heavy administrative tasks that are necessary for the system but have burdened educators for ages, such as “creating class schedules, keeping student attendance, processing grades and report cards, as well as helping to admit new students” (Ostdick, 2016). School support staff can also benefit from automation. Librarians, for example, are utilizing specialized search portals, streamlined shelving navigation, and automated self-checkout more commonly; this frees the staff from “repetitive and low-value tasks so they can help students with more educational inquiries, while giving students more autonomy through technology” (Kinson, 2018).

For teachers, with routine tasks like grading, attendance, and scheduling being further outsourced to automation technologies, more time will be available to concentrate on relationship-building with students and pedagogical strategy. Automated grading software can already handle multiple choice assignments and exams, and most fill-in-the-blank exercises, but with advancements in natural language processing the practice of essay grading will also soon be in the hands of artificially intelligent software (TeachThought Staff, 2018). One example of this type of software is Gradescope, an AI-based grading system already used by universities like Stanford and UC-Berkeley (Rdt, 2018). Simon Rdt of Luminovo AI, an affiliate of Medium, writes about the effectiveness of these automated essay scoring (AES) programs:

One approach to AES is finding objective measures such as the word length, the number of spelling mistakes, and the ratio of upper case to lower case letters. However, these obvious and quantifiable measures are not insightful for evaluating crucial aspects of an essay such as the argument strength or conclusiveness. (Rdt, 2018).

Despite this glaring flaw, back in 2012 when these types of technologies were first being introduced, the William and Flora Hewlett Foundation organized a competition to compare the grading of AES programs and real teachers. According to Rdt (2018), “the output of the winning team was in 81% agreement with the teachers’ gradings, an impressive result that marked a turning point in teachers’ perceptions towards education technology.”

With this kind of technological assistance, students will no longer need to wait days or weeks to receive grades and feedback on their work; instead, this will be done within moments of submitting. Advanced progress monitoring will allow for faster identification of gaps in the class material and the need for more focused personal intervention (Ostdick, 2016). This opens the door for a more individualized learning experience for students, and a more reflective and purposeful teaching experience for educators.

Furthermore, the potential of natural language processing (NLP) within education can go far beyond just grading essays. Automated virtual assistants, such as Alexa and Siri, use NLP to receive spoken commands and questions, and to react accordingly, often serving as a convenient and efficient source of knowledge, information and feedback. These types of technology could be extremely useful in orally centered educational environments, such as speech pathology and foreign language courses. Several schools, such as Saint Louis University, have even begun installing specialized Amazon Echo devices equipped with Alexa in campus dormitories and other living spaces (Saint Louis University, 2018).

These types of automated technology will ideally help to cut down on educational bureaucracy and free up time for more creative and engaging instruction, and a more autonomous learning experience for students. In doing so, automation will also help to appease the insatiable desire for instant gratification that has been fostered by the immediacy of the Digital Age.

  • Adaptive Learning

In the same way that social media, online shopping sites, and media streaming platforms can observe our behavior and cater to our interests, preferences, and abilities, so too could educational materials. Perhaps the most impactful implementation of artificial intelligence in education will come in the form of adaptive learning, specifically in the areas of gamification and knowledge monitoring. One head of product management at Google expects that AI adaptive learning will lead to personalized instruction for students “by suggesting individual learning objectives, selecting instructional approaches and displaying exercises that are based on the interests and skill level of every student” (Rdt, 2018).

Some of the earliest conceptualizations of adaptive learning stemmed from the notion of cybernetics and the work of Warren McCulloch. According to Boden (2012), McCulloch’s “knowledge of neurology as well as logic made him an inspiring leader in the budding cybernetics movement of the 1940s” (p. 13). Boden goes on to explain the primary themes of the field of cybernetics:

Their central concept was “circular causation,” or feedback. And a key concern was teleology, or purposiveness. These ideas were closely related, for feedback depended on goal differences: the current distance from the goal was used to guide the next step. (Boden, 2012, p. 13)

As evidenced by the above quote, these ideas of cybernetics are imperative to the implementation of adaptive learning in schools. Using continuous feedback to guide the student toward desired learning goals can be achieved fairly easy through artificial intelligence software. As such, a crucial element of this process is identifying the student’s Zone of Proximal Development (ZPD), which is the cognitive area “between a student’s comfort zone and their frustration zone. It’s the area where students are not repeating material they’ve already mastered nor challenging themselves at a level so challenging that they become frustrated, discouraged, and reluctant to keep learning” (Lynch, 2017). Progress often occurs at the edge of our comfort zones, and so by effectively maximizing ZPD, adaptive learning programs will better prepare students to master the course material and develop creative critical problem solving abilities that can benefit them inside and outside of the classroom (Lynch, 2018). This process could also feature an element of “scaffolding,” where the educator (and/or AI program) “gives aid to the student in her/his ZPD as necessary, and tapers off this aid as it becomes unnecessary, much as a scaffold is removed from a building during construction” (Culatta, 2011). Module-based, goal-oriented online education programs often utilize this method.

Gamification has been a popular concept in education for years, and has seen mixed results. Researchers who have studied this method have found that “The use of educational games represents a shift from ‘learning by listening’ to ‘learning by doing’ model of teaching. These educational games offer different ways for representing complex themes” (Peixoto et al., 2018, p. 158). However, with the exception of a few early-learning games that are cartoon-ified and fun (like Schoolhouse Rock, which I loved as a child and still remember playing to this day), many module-based online learning platforms fail to engage students or achieve their desired outcomes because the material is still presented as it might be on a worksheet or set of lecture slides.

Why do we stick to this outdated method of delivery in the classroom, when the games that kids are playing at home (or in their pockets) are infinitely more fun and engaging? For example, the Assassin’s Creed franchise has been around for over a decade, and while the actual objectives can be a bit dark and gory, the entire premise of the game is traveling back to different (historically accurate) time periods and exploring their cities and culture to solve mysteries and track down your targets. Each game covers a different historical era, such as the Ottoman Empire, Ancient Rome, Industrial London, the Italian Renaissance, Revolutionary America, and many more. Simply by playing these games and achieving their objectives, users can gain a deeper understanding for the culture, architecture, clothes, events, and main characters of these important time periods in our world’s history.

In theory, similar styles of games could be adapted for classroom use, although research and development for educational implementation has been relatively minimal thus far. These types of games would utilize elements such as quest/saga-based narratives, continuous performance feedback, instant gratification in the form of progress-tracking and/or rewards systems, objective-based progression, and even an adaptive CPU that gets harder or easier based on a student’s performance. All of these criteria could be met while still delivering the stunning graphics, dynamic gameplay, customizable features, and even theatrical cuts that users have come to expect.

With that said, studies have been conducted into the different effects of gamified learning within education, including potential negative effects. Toda et al. (2018) completed one such experiment, and identified four negative effects of gamification: indifference, loss of performance, undesired behavior, and declining effects (p. 150). Most of these effects are closely related; for example, the main difference between loss of performance and declining effects were the factors of motivation and engagement, and they found that declining effects often to led to loss of performance (Toda et al., 2018, p. 151). A few common aspects of gamification they found to be particularly problematic and contributed to these negative effects were leaderboards, badges, points, and levels (p. 152). The researchers noted that most of these negative effects could be remedied with more efficient game design and instruction (p. 153).  

While the notion of gamified learning is often met with resistance or labeled as “edu-tainment,” we must face the fact that we now live– and modern students were raised from birth– in a society that completely revolves around entertainment. Our phones are always buzzing, social media feeds are always scrolling, TVs are always flashing in the background, headphones are always in, sporting events and other ceremonies are always being covered, and the current President of the United States is a former reality television star. That pervasive entertainment-based lifestyle of perpetual stimulation isn’t the healthiest option for anyone’s brain, let alone a developing child’s, but to completely exclude these modern technologies and platforms from the classroom creates a foreign environment of regressive isolation and uncomfortable disconnection for students. Of course a balance must be struck between screen-based learning and interpersonal interaction, but at the moment the screen-based learning being implemented is often inefficient and and disengaging for students. If we could responsibly harness the technology that drives the rest of our daily entertainment wants and needs, we may see those aforementioned mediocre educational rankings for the United States begin to rise.

Knowledge monitoring is another key to adaptive learning technologies. While the AI-powered program will track progress and skills gained, and the educator will track comprehensive retention and practical application, the student must also be aware and responsible for their own knowledge monitoring as well. The rationale is fairly straightforward:

If students fail to differentiate what they know or have learned previously from what they do not know or need to learn (or relearn), they are not expected to engage more advanced metacognitive strategies, such as evaluating their learning in an instructional setting, or employing more efficient learning and studying strategies. (Tobias & Everson, 2009)

According to a study by Kautzmann & Jaques (2018), effective knowledge monitoring provides long-term metacognitive benefits to students throughout their academic careers by helping them become self-regulated learners. These researchers claim that “Self-regulated learners are proactive in setting goals, planning and deploying study strategies, monitoring the effectiveness of their actions and taking control of their learning and more prone to achievement in college” (Kautzmann & Jaques, 2018, p. 124). Therefore, it is vital that knowledge monitoring take place throughout the AI-supported learning process, by the adaptive learning program, by the educator, and– perhaps most importantly– by the students themselves, to ensure that the material being taught is understood, contextualized, and applied in a practical way.

Section 2: The Role of Educators Alongside Artificial Intelligence

If these AI-powered technologies do integrate within the education system and catalyze impactful change on a large scale, then the role of educators will need to adjust accordingly. While certain elements of AI provide the potential to make the lives of teachers easier, these adjustments will spread across nearly every aspect of the teaching profession, including methods, curriculum, class environment, and materials. This will not be a seamless transition; in the current stage of the Digital Age, many teachers struggle to keep up with the necessary trainings for new technology implementation. According to Daniel Stapp, a current high school teacher:

The level of expectation placed on teachers is ridiculous…I teach five classes of 35 kids who are always writing essays, and the expectation of my school is that I’m using But they gave us training on [TurnItIn] on one of our Grading Days, where we have contracted time to grade…it was ‘volunteer training.’ (Stapp, 2018)

As the development of these new types of technologies advances and becomes more accessible, more time and resources will need to be dedicated to training teachers in these new platforms and programs. By doing so, changes to the roles of the profession will be more readily acquiesced.   

  • Methods

One major change in pedagogy will be a shift from stand and deliver instruction to more of a coaching and facilitation role for educators (Wagner, 2018). As Wagner (2018) writes, “In an information age, with content available with the click of a mouse, teachers must shift from the ‘sage on a stage’ to the ‘guides on the side.’” This will require a stronger focus on the personal needs of students, and further emphasis on contextualized learning, dynamic methods of class engagement, assisted knowledge monitoring, and extra support to students who may be less tech-savvy. This is not to say that teachers will be handing over the reins of their classroom to these technologies and regressing into a supporting role, but they will need to rethink how these technologies can be effectively maximized while still developing positive interpersonal relationships with students, contextualizing knowledge and applying it practically, and providing mentorship along the way. 

  • Curriculum

A teacher’s curriculum will become increasingly dynamic and individualized in the coming years alongside automation and adaptive learning programs. According to Wagner (2018), it will be a transition from developers of content to developers of learning experiences. This would entail multimodal presentation of materials, such as text, audio, and video, in order to connect with students of all different learning styles. This may also include small reflection groups for certain topics, and providing anecdotal examples and supporting evidence for more contextualized learning. Because syllabi are now primarily online and editable, it has become easier to make alterations throughout the course of the semester such as adding or subtracting required readings or assignments based on student (or class) progress.

  • Environment

In the past, the communication of teachers and student’s learning were often confined within the walls of classroom or the textbook provided, but with the advent of the internet and social media, the learning environment has the potential to become more ubiquitous. Wagner (2018) describes it as a shift from siloed classrooms to virtual social networks. Collaborative platforms such as Google Drive are extremely helpful in this regard. Wagner (2018) mentions another platform called Brainly that can connect students with peers to address subject-specific questions. He gives a fun example:

They type in their question on Brainly and are connected to a short narrated video that uses modern day Marvel characters to explain the concept. If they wish to ask follow up questions, they are connected through to the student creator of the video via a chat box. (Wagner, 2018)

  • Materials

Expanding upon the “Curriculum” section, there will be a strong shift among educators from using textbooks and a set curriculum to blended courses and customized class design (Wagner, 2018). Traditional textbooks are often expensive, heavy, and underutilized by the end of the semester. Furthermore, most textbooks lose value exponentially each year when a new edition is released, making their contents outdated and thus difficult to reuse or sell back.  

Blended courses will combine elements of online learning with interpersonal instruction. Several AI-powered platforms have been developed to help teachers with the creation and implementation of these types of courses, such as Content Technologies, Teachable, CourseCraft, and Udemy (Wagner, 2018).



I do not mean to assert with this essay that technology is the be-all and end-all solution for education in the United States. One of the most important aspects of a child’s education is the socialization process that accompanies interpersonal interaction at school. Daniel Stapp (2018) elaborates on this phenomenon, and the impact that technology can have:

I think one of the biggest skills people gain from a brick-and-mortar school is interpersonal communication and relationship-building. And I think adding a layer of tech between people to do that sometimes takes away from the power of that connection. And it can add to it too, it just depends on what kind of tech you’re using and how you’re using it. (Stapp, 2018)

These AI technologies, when utilized appropriately and effectively, are intended to supplement educators and their mission, not supplant them. It should not be a process that is rushed into; these types of technological transitions take time and training to implement effectively. There have already been examples of AI-powered learning platforms that have backfired, such as the Summit Learning– a platform developed by Facebook– rollout near Wichita, Kansas and other cities around the US in 2018 (Bowles, 2019). Teachers and students alike were unprepared for the massive changes in pedagogy and curriculum that accompanied the Summit Learning program, as well as the cognitive and physical effects of spending more time in front of screens. As a result, many students protested, and parents began pulling their children from the schools participating in the program (Bowles, 2019).

Further research will need to be conducted into the long-term cognitive and physical effects of these types of AI learning programs. Ideally, educators will be able to find a healthy balance in the classroom between traditional, seminar-based instruction and online, self-guided, screen-based learning. To that end, more research will also need to be conducted into the effects of modality switching on the learning process and comprehension abilities of students.

However, the tremendous potential of automation and adaptive learning for education is too tantalizing to resist. This essay painted with broad strokes in an attempt to cover the education system in general, but there will be certain nuances that accompany the integration of technology at the different levels of schooling, from elementary school up to higher education. With the appropriate amount of training, transition time, and consent, these technologies could be utilized to stimulate incredible change in the American school system, and help to re-establish the United States as an educational superpower in the world.

Images Used

“AV Trolley” by mikecogh is licensed under CC BY-SA 2.0

“AS3K and Neo” by nathanww is licensed under CC BY-SA 2.0

“Reflected light Smartboard” by touring_fishman is licensed under CC BY-NC-SA 2.0

DS-UNIVAULT-30 Chromebook Cart sourced from

Zone of Proximal Development” sourced from (Culatta, 2011)

“SchoolHouse Rock!” sourced from

Assassin’s Creed Anthology” sourced from


Boden, M. A. (2016). AI: its nature and future (First edition). Oxford, United Kingdom: Oxford University Press.

Bowles, N. (2019, April 21). Silicon Valley Came to Kansas Schools. That Started a Rebellion. NY Times. Retrieved from

Computer History Museum. (2014). Artificial Intelligence. Retrieved from

Culatta, R. (2011). Zone of proximal development. Retrieved from Innovative Learning website:

Desilver, D. (2017, February 15). U.S. students’ academic achievement still lags that of their peers in many other countries. Pew Research Center. Retrieved from

Kautzmann, T. R., & Jaques, P. A. (2018). Improving the Metacognitive Ability of Knowledge Monitoring in Computer Learning Systems. In Higher Education for All. From Challenges to Novel Technology-Enhanced Solutions (Vol. 832, pp. 124–140).

Kinson, N. (2018, October 24). How automation will impac education. Retrieved from Beta News website:

Levesque, E. M. (2018). The role of AI in education and the changing US workforce. Retrieved from Brookings Institution website:

Lynch, M. (2017, August 7). 5 Things You Should Know About Adaptive Learning. Retrieved from The Tech Advocate website:

Ostdick, N. (2016, December 15). Teach Me: Automation’s Role in Education. Retrieved from UI Path website:

Peixoto, D. C. C., Resende, R. F., & Pádua, C. I. P. S. (2018). An Experience with Software Engineering Education Using a Software Process Improvement Game. In Higher Education for All. From Challenges to Novel Technology-Enhanced Solutions (Vol. 832, pp. 157–173).

Saint Louis University. (2018, August). SLU Installing Amazon Alexa-Enabled Devices in Every Student Living Space on Campus. Retrieved from SLU Alexa Project web page:

Stapp, D. (2018, March). Technology in Secondary Education [Phone].

TeachThought Staff. (2018, September 16). 10 Roles for Artificial Intelligence in Education. Retrieved from TeachThought website:

Tobias, S., & Everson, H. T. (2009). The importance of knowing what you know: A knowledge monitoring framework for studying metacognition in education. In D. J. Hacker, J. Dunlosky, & A. C. Graesser (Eds.), The educational psychology series. Handbook of metacognition in education (pp. 107-127). New York, NY, US: Routledge/Taylor & Francis Group.

Toda, A. M., Valle, P. H. D., & Isotani, S. (2018). The Dark Side of Gamification: An Overview of Negative Effects of Gamification in Education. In Higher Education for All. From Challenges to Novel Technology-Enhanced Solutions (Vol. 832, pp. 143–156).

Wagner, K. (2018, January 15). A blended environment: The future of AI and education. Getting Smart. Retrieved from