Towards Artificial Intelligence for Enhancing Quality of Education

What is Artificial Intelligence?

Artificial Intelligence is a vast body of knowledge. It has been studied for decades and is still one of the most elusive subjects in Computer Science. This is partly due to how large and nebulous the subject is. AI ranges from machines truly capable of thinking to search algorithms used to play board games. It has applications in nearly every way we use computers in society [1]. Even though AI is broad and deep concept the meaning it`s given from different experts is almost related and similar. To mention some definitions of AI:

According to Daniel Castro and Joshua new AI is a field of computer science devoted to creating computing machines and systems that perform operations analogous to human learning and decision-making [2]. The Association for the Advancement of Artificial Intelligence describes AI as the scientific understanding of the mechanisms underlying thought and intelligent behavior and their embodiment in machines. The level of intelligence in any particular implementation of AI can vary greatly, and the term does not imply human-level intelligence [3].

According to AI and Human development research, AI is an area of computer science devoted to developing systems that can be taught or learn to make decisions and predictions within specific contexts. AI applications can perform a wide range of intelligent behaviors: optimization (e.g., supply chains); pattern recognition and detection (e.g., facial recognition); prediction and hypothesis testing (e.g., predicting disease outbreaks); natural language processing; and machine translation. AI technologies are poised to have a significant impact on society because they leverage existing infrastructure (the internet, large data sets) to dramatically reduce the costs of activities (both new and old, good and bad) on a large scale [4]. Although AI is not new, there has been a recent explosion of activity and interest in the field, which has largely been driven by advances in ‘machine learning’, and the related field of ‘deep learning’. These are computer programs that learn and improve with experience. Progress in machine learning has allowed more versatile AI systems to be developed that can perform well at a range of tasks, particularly those that involve sorting data, finding patterns, and making predictions (see Figure 1). The training process can involve in practice adjusting for millions of parameters relative to computational efforts of the machine being used with “astronomically more possible outcomes than any algorithm could ever hope to try. Figure 1 describes the concepts of Artificial intelligence, machine learning and deep learning and how these concepts relate to each other

Figure 1: Artificial intelligence, machine learning and deep learning [5]

This article discusses the impact Artificial intelligence in enhancing the quality of education.


Education is widely accepted to be a fundamental resource, both for individuals and societies. Indeed, in most countries basic education is nowadays perceived not only as a right, but also as a duty – governments are typically expected to ensure access to basic education, while citizens are often required by law to attain education up to a certain basic level. Regarding the consequences of education, a growing body of empirical research suggests that better education yields higher individual income and contributes towards the construction of social capital and long-term economic growth [6]. To ensure quality of education effective incorporation of ICT in teaching practices and improvement of ICT equipment needs to be sustained [7].

1. Personalized learning

The term personalized learning refers to variety of educational programs in which the pace of learning and the instructional approach are optimized for the needs of each learner. The experience is tailored to learning preferences and the specific interests of different learners. AI can adapt to the individual pace of learning and can consistently offer more complex tasks to accelerate learning. Thus, both fast and slow students can continue to study at their own pace [8]. Perhaps the key move away from a teacher-centered learning process is the development of personalized learning. One way to provide this is through intelligent tutoring systems (ITS), also known as cognitive tutors. An ITS is typically an expert system that attempts to recreate one-on-one instruction by adapting and personalizing the learning experience to the individual learner [4]. An ITS assesses each learner’s actions within these interactive environments and develops a model of their knowledge, skills, and expertise. Based on the learner model, it can tailor instructional strategies, in terms of both the content and style, and provides relevant explanations, hints, examples, demonstrations, and practice problems to individual learners As an expert system, an ITS incorporates three types of knowledge models: the expert (representing the domain expertise of the teacher), the learner (models of how the individual learns), and the instructional (for making decisions about instructional tactics). See Figure 2 below.

Figure 2: Structure of an intelligent tutor [9]

ITS are also beginning to incorporate other AI techniques to enhance instruction. Some ITS known as affective tutoring systems also incorporate emotional recognition as a means to enhance the tutoring adaptation to the student [10]. ITS can also apply natural language processing and speech recognition to help identify language errors or interact with students in novel ways.

Learning analytics is “the measurement, collection, analysis and reporting of data about learners and their contexts, for purposes of understanding and optimizing learning and the environments in which it occurs” [11]. To do so, learning analytics take advantage of large amounts of educational data and ML techniques. For example, applying learning analytics to large amounts of learner data can help to improve the knowledge models in ITS, such as by detecting learning tasks that offer the most effective gains [12]. Learning analytics can also employ predictive analytics to identify students who are at risk of failing a course. Let`s mention examples where we can use AI for personalized learning.

1.1 Personalizing Math Class

The IBM Foundation and the American Federation of Teachers announced Teachers will have access to a new, first-of-its-kind, free tool using IBM’s innovative Watson cognitive technology that has been trained by teachers and designed to strengthen teachers’ instruction and improve student achievement. Hundreds of elementary school teachers across the United States are piloting Teacher Advisor with Watson – an innovative tool by the IBM Foundation that provides teachers with a complete, personalized online resource. Teacher Advisor enables teachers to deepen their knowledge of key math concepts, access high-quality vetted math lessons and acclaimed teaching strategies and gives teachers the unique ability to tailor those lessons to meet their individual classroom needs [13].

 1.2. Learning at scale

Providing quality learning at scale with low cost is a challenge of the education system. Addressing this challenge requires increasing access to improved connectivity, coupled with online digital learning approaches such as massive open online courses (MOOCs).This has improved the capacity to provide a large number of people with quality educational content and experiences. While early evidence on MOOCs shows that people With higher levels of education and socio-economic status tend to benefit disproportionately [14] [15]. Some research illustrates that certain types of MOOCs – such as those offering job related training can benefit users from low-and middle-income populations in the Global South [16].AI techniques have the potential to build-on online learning to achieve the lofty goal of delivering high-quality learning at scale, particularly to marginalized populations. This overcomes the bottlenecks that arise when engaging with large numbers of students chiefly the lack of human resources to provide individualized feedback, guidance, and assessment of student performance. A combination of AI techniques can help handle high student loads. Intelligent tutors and learning analytics, discussed above, can help provide high-quality personalized learning experiences at a very low cost per additional student [17]. Furthermore, developments in automated scoring using natural language processing and other techniques enable the mass grading of quizzes, exams, and essays.

2. Automating Teacher Assistant

An automated teaching assistant (teacher bot) named Jill, powered by IBM’s Watson cognitive computing platform was implemented by Georgia Institute of Technology to help respond to student inquiries for an online course that receives an average of 10,000 messages from students every semester.  Jill can analyze and answer student questions, such as where to find course materials, and it was effective enough that the course plans to use Jill to field 40 percent of all student inquiries by the end of 2016 [2]. Virtual teaching assistant systems like Jill could help improve retention rates for online courses, which are generally low because students have trouble getting the information they need from professors.

3. Giving Students Feedback in Real Time

Education software company Turnitin has developed a tool called Revision Assistant that uses machine learning to evaluate students’ writing while they draft essays to provide feedback. Revision Assistant evaluates four traits in student writing language, focus, organization, evidence and can detect the use of imprecise language or poor organizational structure to provide both positive feedback and recommendations for improvement. According to an English teacher from George Whittell High School in Zephyr Cove, NV, in a prepared statement. “They write more, and they revise more. Having that instant feedback of the signal bars challenges them to improve. With Revision Assistant, students submitted the best essays I got out of that class all year.  The software is targeted toward grades 6 through 12 but is also designed to support developmental writing in post-secondary settings [18]

4. Making It Easier to Learn New Languages

AI empowered Language-learning application named Duolingo uses Natural language processing (NLP) and Automatic Speech Recognition (ASP) to recognize learning errors and enable users correct them. It also uses machine learning to analyze users’ activity and progression to develop personalized lesson plans, as well as regularly test new strategies for instruction to evaluate their effectiveness. Duolingo contains three chatbots that a user can talk with and ask questions to in order to learn how to say something in French, Spanish, or German. The bots use AI technology that allows the software to react differently to thousands of possible answers [19]

5.Customizing/Localizing Content

Learning content can be customized and improved at low cost  by providing automated translation of existing works into new languages or leveraging AI to create new content. For example, the Pratham Books StoryWeaver platform is working with to leverage Google’s AI-powered translation tool to translate children’s e-books into as many as 60 different languages. It uses Machine learning (ML) techniques to create custom textbooks. Instructors feed their syllabi and material into the AI engine and the system creates textbooks and classroom material based on the core concepts it extracts. Another company, Content Technologies, Inc., One can also envision the automated creation of personalized study guides, quizzes, and tests, which would be particularly helpful in massive online learning environments.

6. Predicting Student Drop Out

The Tacoma, Washington, school district worked with Microsoft to develop a machine learning model that can analyze student data, such as demographics and academic performance, and historical data to predict which students were at risk of dropping out and prompt early intervention. After a multiyear pilot of the system, the Tacoma school district was able to boost its graduation rates from 55 percent in 2010 to 78 percent by 2014 [20].

7.Empowering People with Disabilities

AI can empower people with disabilities with tools that support independence and productivity, as technology rapidly changes the way we live, learn, and work. For example Seeing AI is a free mobile application designed by Microsoft Company to support people with visual impairments by narrating the world around them. The app which is an ongoing research project bringing together deep learning and Microsoft Cognitive Services — can read documents, making sense of structural elements such as headings, paragraphs, and lists, as well as identify a product using its bar-code. It can additionally recognize and describe images in other apps, and even pinpoint people’s faces and provide a description of their appearance, though camera quality and lighting might influence its description [21].

8. Adaptive Learning

Different individuals learn in a different way and at a different pace and an adaptive program would be able to adjust a training program to those individual learning styles. Such learning program could transform a portion of written course into visuals which can prove a boon for learners as the impact of visual learning is far greater than any other medium. Based on the latest technologies of artificial intelligence (machine-learning and deep-learning), the adaptive learning engines customize the learning path, increase the engagement and efficiency of traditional, digital and blended learning. They can be integrated in any solution thanks to its API or used in Domoscio’s platform. Domoscio developed adaptive learning and memorization algorithms to create a solution focused on knowledge acquisition. With the idea that the learning process is completed in three phases: 1) learn, (2) assimilate, and (3) apply, Domoscio believes improved learning and performance outcomes can be achieved if the learning is better assimilated and solidified[22]

The Future of Artificial intelligence in Education

  • Elevating connections: Because AI operates on a computer system, it would be possible to connect different classes around the globe, enhancing cooperation among schools and nations in general.
  • Adapting with a changing world: AI helps people capture occurrences around the world as they happen, so students will be able to experience current events in real time. Whatever changes need to be incorporated into the syllabus also will be able to be made efficiently with constant updates.
  • Life long learning: If each student gets to keep their own personal AI-powered robot, they’ll always have a teacher to simplify tasks for them. Google will be a thing of the past as students get knowledge from their robots [23]


AI can transform the education system from the usual teacher centered methodology to personalized learning which gives teachers and students robust accessibility of resources especially for academic schools, colleges and universities having improper ratio of teachers to students. AI has also the potential to bring diversified people in terms of language, culture, learning abilities, people with disabilities to get equitable access of education. Having this in mind and considering education as the main road to growth of one country, Countries need to create Legal frameworks for the   adoption and implementation of AI in education. Researchers and AI experts must work to utilize full potential of AI in enhancing quality of education through their innovations which can produce future talented AI experts and researchers as the process of education is an ending cycle.


[1].Chris Smith, the History of Artificial Intelligence, December 2006

[2]. Daniel Castro and Joshua New, the Promise of Artificial Intelligence, October 2016).

[3].AI Overview: Broad Discussions of Artificial Intelligence,” AI Topics, accessed September 29, 2016

[4].Matthew Smith, Artificial intelligence and human development, toward a research agenda, June 2018

[5].World Wide Web Foundation, Artificial Intelligence: The Road Ahead in Low and Middle-Income Countries, June 2017

[6]. Max and Esteban, Global Rise of Education, 2019

[7]. Ministry of education, Guyana, Eliminating illiteracy, modernizing education and strengthening Tolerance, 2010

[8]. Emily watts, 9 ways to use Artificial Intelligence (AI) in education, 2018

[9]. Phobun and Vicheanpanya, Adaptive intelligent tutoring systems for e-learning systems, 2010

 [10]. Petrovica et al, Emotion Recognition in Affective Tutoring Systems: Collection of Ground-truth Data, 2017

[11]. Long and Siemens, Penetrating the Fog: Analytics in Learning and Education, 2011

[12]. Lim and Tinio, Digital Learning for Developing Asia Countries: Achieving equity, quality, and efficiency in education

[13]. Armonk, n.y. , reflections on IBM foundation-collaborates with aft and education leaders to use Watson to help teachers, September 2016

[14]. Christensen et al., The MOOC Phenomenon: Who Takes Massive Open Online Courses and Why?  , 2013;

[15]. Hansen and Reich, Democratizing education? Examining access and usage patterns in massive open online course, 2015

[16]. Garrido et al ,  Advancing MOOCs for Development Initiative,2016

[17]. Diana Laurelled and Eileen Kennedy, The potential of #MOOC for learning at scale in the Global South, 2017

[18]. David Nagel,Turnitin Launches Service Designed to Improve Student Writing, 2016

[19]. Daisy wadhwa, using artificial intelligence technologies for personalized learning and responsive teaching: a survey, 2017

[20]. Centric, Combat the Fear-Filled Future of Artificial Intelligence, 2017

[21]. Tas Bindi , Microsoft using AI to empower people living with disabilities ,Nov 2017

[22].[online resource], Adaptive Learning platforms and benefits,2018

[23]. Kevin Nelson, the Future of Artificial Intelligence in Education, 2018

Opinions expressed by AI Time Journal contributors are their own.

About Frehiwot Gebrekrstos

Contributor Lecturer Department of Computing Technology, Aksum University

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