Personalized learning through artificial intelligence

by Sudarshan Srinivas, Krishna Moorthy, Aarthi Thiru, and Sukant Khurana

(Credits: Sudarshan Srinivas, Krishna Moorthy, and Aarthi Thiru wrote the article under the guidance of Dr. Sukant Khurana)

Photo by Štefan Štefančík on Unsplash

Personalized learning refers to instruction in which the pace of learning and the instructional approach are optimized for the needs of each learner. Learning objectives, instructional approaches, and instructional content (and its sequencing) may all vary based on learner needs. In addition, in personalized learning, learning activities are made available that are meaningful and relevant to learners, driven by their interests and often self-initiated.

There are few key approaches toward personalized learning:

· Adaptive learning: technology used to assign human or digital resources to learners based on their unique needs

· Individualized learning: the pace of learning is adjusted to meet the needs of individual students

· Differentiated learning: the approach to learning is adjusted to meet the needs of individual students

· Competence-based learning: learners advance through a learning pathway based on their ability to demonstrate competency, including the application and creation of knowledge along with skills and dispositions

Personalized learning capitalizes on students’ almost instinctual ability to use technology but it is so much more than technology and algorithms. It is the purposeful design of blended instruction to combine face-to-face teaching, technology-assisted instruction and student-to-student collaboration to leverage each student’s learning style and interests for deeper learning.

Why personalized learning?

Every student is different — including the ways in which they learn. Just like students should focus on fit when selecting colleges to which to apply, fit is also important when analyzing the best way to help students achieve their academic goals in the college prep process.

The premise for Personalized Learning is to create custom curriculum and learning objectives, based on the learning needs of each individual student.

Implementation of Personalized Learning

The first component of implementing personalized learning is getting to know the student and their educational background, interests, and academic goals. Having an understanding of where a student is coming from and where they want to go helps create a custom experience. Why? Distinguishing the student’s background and goals from past students allows our team to recognize that there is no ‘one size fits all’ for academic material retention and comprehension.

There can be different systems for personalized education and even within Khurana group there are different teams working with different approaches, let us present one approach as a thought experiment. When it comes to test preparation, a core component needed to understand the background and current academic level of a student are their diagnostic test scores. Prior to beginning tutoring for a standardized test, we require the students to take a practice test in replicated test-like conditions. From the analysis, we then recommend test prep or academic materials that are relevant to our student’s needs and send them to our student and their tutor to begin tutoring. Based on a student’s diagnostic score, tutors will customize their strategy and approach to match the starting point of the student.

If we choose to think of personalized learning as a practice rather than a product, we can start by taking a hard look at course designs and identifying those areas that fail to make meaningful individual contact with students. These gaps will be different from course to course, subject to subject, student population to student population, and teacher to teacher. Although there is no generic answer to the question of where students are most likely to fall through the cracks in a course, there are some patterns to look for.

BLENDING DATA SCIENCE AND EDUCATION

In recent times, the adaptive learning technology has reshaped education making it more streamlined. The mapping of personalized learning system along with Artificial intelligence and data science can enable a transparent growth of individuals.

Just the availability of data alone does not ensure successful data-driven decision-making, consequently, there is an urgent need for further research on the use of an appropriate data-analytic thinking framework for education. The purpose of this proposal is first to identify research goals to incorporate an appropriate data-analytic thinking framework for pursuing such goals, and second to present a case study of social-emotional learning in which we use data science.

Here are some of the possible benefits of AI in our educational systems.

Personalization. It can be overwhelmingly difficult for one teacher to figure out how to meet the needs of every student in their classroom. AI systems easily adapt to each student’s individual learning needs and can target instruction based on their strengths and weaknesses, meaningless work for teachers and a more meaningful learning experience for students.

Tutoring. Yes, it’s already happening: thanks to AI, machines are taking on the role of humans in many capacities, including tutors. As with human tutors, “Intelligent Tutoring Systems” can gauge a student’s learning style and pre-existing knowledge to deliver customized support and instruction.

Grading. This is arguably one of the most tedious teaching tasks and takes time away from more meaningful and purposeful pursuits, like lesson planning and professional development. Machines are now so far advanced that they can do much more than simply grade an exam with an answer key; they can compile data about how students performed and even grade more abstract assessments such as essays.

Feedback on course quality. AI can identify instruction gaps in the course content based on student performance on assessments. For example, if a significant percentage of students answer a question incorrectly, AI can zero in on the specific information or concepts that students are missing, so that educators can deliver targeted improvements in materials and methods.

Meaningful and immediate feedback to students. In an age when most communication occurs online or via text message, students are increasingly hesitant about taking risks in front of teachers and peers. They shrink from receiving critical feedback in such a public forum. With AI, students can feel comfortable to make the mistakes necessary for learning and receive the feedback they need for improvement.

Computer-supported collaborative learning systems

Have you ever participated in an e-learning instance which took place via social interaction? Then you are already familiar with the concept of computer supported collaborative learning systems, abbreviated CSCLS. A CSCLS is a tool that employs social interaction for education. One of the earliest evidences of CSCLS is the design and implementation of Intelligent Collaborative Learning System (ICLS), which then formed the pathway for more recent intersections of artificial intelligence and education.

Grading systems

Artificial Intelligence has found its way to grading assessment sheets of students. It is now possible for teachers to automate grading for multiple-choice questions and fill-in-the-blank questions. Grading student writing by artificial intelligence is in hot pursuit. Essay grading is still in its infancy and might take some time to rise to its full glory. This would take the pressure off the teachers’ shoulders, enabling them to concentrate more on student interaction and class activities.

Educational data mining

The National Education Technology plan of the US Department of Education laid a path for 21st century learning powered by technology, which involves ways of using data from online learning systems to improve instruction. This process is known as educational data mining. At the simplest level, the analytics can determine when a student is going astray during the course of an online training and can nudge the student to concentrate on the course. And at the most complex level, it can detect boredom from the mouse or key clicks of the student and redirect his/her attention.

Detecting room for improvement

Artificial Intelligence can help detect and fill in the gaps in explanation. The massive online open course provider, Coursera implements this technology. What happens here is, when a large number of students submit the wrong answer to a question, the system alerts the teacher about it and sends a customized feedback message to the students offering hints to the correct answer. This way, the students do not need to wait for the teacher to know whether the answers they have submitted are correct or not.

Even though implementing artificial intelligence has gained momentum recently, it does not in any way diminish the role of a teacher in the classroom. Teachers play the significant role of the facilitator aiding in the perfect blend of artificial intelligence in education. However, with newer implementations being put into action, the future of this new technology seems bright in the education sector.

Personalized learning

Personalized learning refers to instruction in which the pace of learning and the instructional approach are optimized for the needs of each learner. Learning objectives, instructional approaches, and instructional content (and its sequencing) may all vary based on learner needs. In addition, learning activities are made available that are meaningful and relevant to learners, driven by their interests and often self-initiated.

These are the definition overlaps in at least one key area with personalized learning.

· Adaptive learning: technology used to assign human or digital resources to learners based on their unique needs

· Individualized learning: the pace of learning is adjusted to meet the needs of individual students¹

· Differentiated learning: the approach to learning is adjusted to meet the needs of individual students

· Competency-based learning: learners advance through a learning pathway based on their ability to demonstrate competency, including the application and creation of knowledge along with skills and dispositions

Personalized learning capitalizes on students’ almost instinctual ability to use technology, but it is so much more than technology and algorithms. It is the purposeful design of blended instruction to combine face-to-face teaching, technology-assisted instruction and student-to-student collaboration to leverage each student’s learning style and interests for deeper learning.

Why personalised learning?

Every student is different — including the ways in which they learn. Just like students should focus on fit when selecting colleges to which to apply, fit is also important when analyzing the best way to help students achieve their academic goals in the college prep process.

The premise for Personalized Learning is to create custom curriculum and learning objectives, based on the learning needs of each individual student.

Implementation of a Personalized Learning

The first component of implementing personalized learning is getting to know the student and his educational background, interests, and academic/test prep goals. Having an understanding of where a student is coming from and where they want to go helps create a custom experience. Why? Distinguishing the student’s background and goals from past students allows our team to recognize that there is no ‘one size fits all’ for academic material retention and comprehension.

When it comes to test preparation, a core component needed to understand the background and current academic level of a student is their diagnostic test scores. Prior to beginning tutoring for a standardized test, we require the students to take a practice test in replicated test-like conditions. Encouraging our students to following the proctoring instructions mirroring those of the exam, we can able to acquire close portrayals of how they would perform under pressure.

Once we receive diagnostic scores, Tutoring Manager reviews the scores to create a custom tutoring plan, called a Test Evaluation, evaluating scores, trends, pacing issues, and overall testing themes. From this Test Evaluation, tutors are able to review diagnostic scores and the Test Evaluation to have a strong profile view of what is needed for the student’s test preparation. From the tutor and Tutoring Manager’s analysis, we then recommend test prep or academic materials that are relevant to our student’s needs and send them to our student and their tutor to begin tutoring.

Based on a student’s diagnostic score, tutors will customize their strategy and approach to match the starting point of the student. A student may come in as a novice in a certain section of the exam, perhaps Heart of Algebra on the SAT. Recognizing this, tutor will start with the basics of Algebra and work on mastering easy and medium questions before starting to focus on the advanced levels or hards.

Conclusion

If we choose to think of personalized learning as a practice rather than a product, we can start by taking a hard look at course designs and identifying those areas that fail to make meaningful individual contact with students. These gaps will be different from course to course, subject to subject, student population to student population, and teacher to teacher. Although there is no generic answer to the question of where students are most likely to fall through the cracks in a course, there are some patterns to look for.

BLENDING DATA SCIENCE AND EDUCATION

WHAT IS DATA SCIENCE?

The emergence of data science has largely influenced todays digital world and its functioning. It is a multidisciplinary combination of data inference, algorithm development, and technology in order to solve analytically complex problems. At the core is data. Troves of raw information, streaming in and stored in enterprise data warehouses have to be learnt by mining it. We can even build advanced capabilities with it.

Data science started with statistics, and has evolved to include concepts such as Artificial Intelligence, Machine Learning, the Internet of Things etc. There has been a flood of information with growth of the Internet, Internet of Things(IoT), and the exponential growth of data volumes available to enterprises. Once the doors were opened by businesses seeking to increase benefits and drive better decision making, the use of Big Data started being applied to various fields, such as medicine, engineering, and social sciences.

USE OF DATA SCIENCE IN EDUCATION

Education opens various door for achieving better prospects in life and also promotes career growth. In recent times, the adaptive learning technology has reshaped education making it more streamlined. These externalization activities demand incorporating data analytical techniques for pursuing such goals.

It is mandatory to break huge datasets collected into chunks so as to process it efficiently. In order to mine the data acquired through monitoring student’s performance with the help of machine learning, data analytics plays a predominant role. This approach could make the categorization of students as good, average or weak easier. In turn this could embrace each individual student grow in their own phase with better conceptual understanding. Hence the key role of abstraction to the personalized learning methods, adaptive learning systems is offered by data science.

The mapping of personalized learning system along with Artificial intelligence and data science ,enables transparent growth of individuals to both teachers and parents. Thus directly or indirectly data science has got significant effect on increasing the standard of education by helping the system identifying the strengths and weakness of the each students and accordingly streamline the learning phenomenon.

The literature on education data analytics has many materials on the assessment and prediction of students’ academic performance, as measured by standardized tests. However, research on education data analytics should go beyond explaining student success with the typical three R’s (reading, writing and arithmetic) of literacy. Furthermore, the availability of data alone does not ensure successful data-driven decision-making, Consequently, there is an urgent need for further research on the use of an appropriate data-analytic thinking framework for education. The purpose of this paper is first to identify research goals to incorporate an appropriate data-analytic thinking framework for pursuing such goals, and second to present a case study of social-emotional learning in which we used the data science research methodology.

The collection and analysis of data about learning is a trend that is growing exponentially in all levels of education. Data science is poised to have a substantial influence on the understanding of learning in online and blended learning environments. The mass of data already being collected about student learning provides a source of greater insights into student learning that have not previously been available, and therefore is liable to have a substantial impact on and be impacted by the science of learning in the years ahead.

IMPACTS OF AI IN EDUCATION

“Humans plus machines is not the future it’s the present”

The Science & Technology has become an integral part of our Country’s Education System. Over the past few years we have witnessed the rise and impact of education technology especially through a multitude of adaptive learning platforms. It is positively reported that Artificial Intelligence will transform Education in the coming years.

“We are moving away from simply ‘learning’ a subject or topic to ‘feeling’ the content”

Artificial intelligence (AI) is the perfect example of how something new could be used to change every aspect of our lives when we change the lens. And education is an area that has unlimited potential to utilize innovation. Through AI, tutoring and study programs are growing more advanced, capable of teaching fundamentals to students struggling with basic concepts.

In the future, visual and dynamic learning channels outside the classroom will become not only more prevalent but capable of supporting a range of learning styles. Machines are now so far advanced that they can do much more than simply grade an exam with an answer key; they can compile data about how students performed and even grade more abstract assessments such as essays.

“A year spent in artificial intelligence is enough to make one believe in God.”

Education reformers need to plan for our AI-driven future and its implications for education, both in schools and beyond. The never-ending debate about the sorts of skills needed in the future and the role of schools in teaching and assessing them is becoming a whole lot more urgent and intense. Since education system focuses on standardization to reduce the achievement differences between students.

In fact, AI does not detract from classroom instruction but enhances it in many ways. Here are some of the benefits of AI in our educational systems.

Personalization. It can be overwhelmingly difficult for one teacher to figure out how to meet the needs of every student in his/her classroom: remedial students, advanced students, ESL students and the disabled all need to have the same access to learning. AI systems easily adapt to each student’s individual learning needs and can target instruction based on their strengths and weaknesses, meaningless work for teachers and a more meaningful learning experience for students.

Tutoring. Yes, it’s already happening: thanks to AI, machines are taking on the role of humans in many capacities, including tutors. As with human tutors, “Intelligent Tutoring Systems” can gauge a student’s learning style and pre-existing knowledge to deliver customized support and instruction.

Grading. This is arguably one of the most tedious teaching tasks and takes time away from more meaningful and purposeful pursuits, like lesson planning and professional development. Machines are now so far advanced that they can do much more than simply grade an exam with an answer key; they can compile data about how students performed and even grade more abstract assessments such as essays.

Feedback on course quality. AI can identify instruction gaps in the course content based on student performance on assessments. For example, if a significant percentage of students answer a question incorrectly, AI can zero in on the specific information or concepts that students are missing, so that educators can deliver targeted improvements in materials and methods.

Meaningful and immediate feedback to students. In an age when most communication occurs online or via text message, students are increasingly hesitant about taking risks in front of teachers and peers. They shrink from receiving critical feedback in such a public forum. With AI, students can feel comfortable to make the mistakes necessary for learning and receive the feedback they need for improvement.

Computer-supported collaborative learning systems

Have you ever participated in an e-learning instance which took place via social interaction? Then you are already familiar with the concept of computer supported collaborative learning systems, abbreviated CSCLS. A CSCLS is a tool that employs social interaction for education. One of the earliest evidences of CSCLS is the design and implementation of Intelligent Collaborative Learning System (ICLS), which then formed the pathway for more recent intersections of artificial intelligence and education.

Grading systems

Artificial Intelligence has found its way to grading assessment sheets of students. It is now possible for teachers to automate grading for multiple-choice questions and fill-in-the-blank questions. Grading student writing by artificial intelligence is in hot pursuit. Essay grading is still in its infancy and might take some time to rise to its full glory. This would take the pressure off the teachers’ shoulders, enabling them to concentrate more on student interaction and class activities.

Educational data mining

The National Education Technology plan of the US Department of Education laid a path for 21st century learning powered by technology, which involves ways of using data from online learning systems to improve instruction. This process is known as educational data mining. At the simplest level, the analytics can determine when a student is going astray during the course of an online training and can nudge the student to concentrate on the course. And at the most complex level, it can detect boredom from the mouse or key clicks of the student and redirect his/her attention.

Detecting room for improvement

Artificial Intelligence can help detect and fill in the gaps in explanation. The massive online open course provider, Coursera implements this technology. What happens here is, when a large number of students submit the wrong answer to a question, the system alerts the teacher about it and sends a customized feedback message to the students offering hints to the correct answer. This way, the students do not need to wait for the teacher to know whether the answers they have submitted are correct or not.

Even though implementing artificial intelligence has gained momentum recently, it does not in any way diminish the role of a teacher in the classroom. Teachers play the significant role of the facilitator aiding in the perfect blend of artificial intelligence in education. However, with newer implementations being put into action, the future of this new technology seems bright in the education sector.

As educators, we all have fears about instituting large systemic changes, and sometimes those fears are well grounded. However, we cannot afford to ignore the possibilities that AI offers us for dramatically improving the student learning experience. AI tutoring systems that tailor their lessons to different children’s needs would undo this standardization, with some students naturally progressing faster than others. Teachers may not sometime aware of certain gaps in educational material leaving the students confused in certain concepts. AI is capable of solving this problem in an effective manner.

“By far, the greatest danger of Artificial Intelligence is that people conclude too early that they understand it.”

Personalized learning

Personalized learning refers to instruction in which the pace of learning and the instructional approach are optimized for the needs of each learner. Learning objectives, instructional approaches, and instructional content (and its sequencing) may all vary based on learner needs. In addition, learning activities are made available that are meaningful and relevant to learners, driven by their interests and often self-initiated.

These are the definition overlaps in at least one key area with personalized learning.

· Adaptive learning: technology used to assign human or digital resources to learners based on their unique needs

· Individualized learning: the pace of learning is adjusted to meet the needs of individual students¹

· Differentiated learning: the approach to learning is adjusted to meet the needs of individual students

· Competency-based learning: learners advance through a learning pathway based on their ability to demonstrate competency, including the application and creation of knowledge along with skills and dispositions

Personalized learning capitalizes on students’ almost instinctual ability to use technology, but it is so much more than technology and algorithms. It is the purposeful design of blended instruction to combine face-to-face teaching, technology-assisted instruction and student-to-student collaboration to leverage each student’s learning style and interests for deeper learning.

Why personalised learning?

Every student is different — including the ways in which they learn. Just like students should focus on fit when selecting colleges to which to apply, fit is also important when analyzing the best way to help students achieve their academic goals in the college prep process.

The premise for Personalized Learning is to create custom curriculum and learning objectives, based on the learning needs of each individual student.

Implementation of a Personalized Learning

The first component of implementing personalized learning is getting to know the student and his educational background, interests, and academic/test prep goals. Having an understanding of where a student is coming from and where they want to go helps create a custom experience. Why? Distinguishing the student’s background and goals from past students allows our team to recognize that there is no ‘one size fits all’ for academic material retention and comprehension.

When it comes to test preparation, a core component needed to understand the background and current academic level of a student is their diagnostic test scores. Prior to beginning tutoring for a standardized test, we require the students to take a practice test in replicated test-like conditions. Encouraging our students to following the proctoring instructions mirroring those of the exam, we can able to acquire close portrayals of how they would perform under pressure.

Once we receive diagnostic scores, Tutoring Manager reviews the scores to create a custom tutoring plan, called a Test Evaluation, evaluating scores, trends, pacing issues, and overall testing themes. From this Test Evaluation, tutors are able to review diagnostic scores and the Test Evaluation to have a strong profile view of what is needed for the student’s test preparation. From the tutor and Tutoring Manager’s analysis, we then recommend test prep or academic materials that are relevant to our student’s needs and send them to our student and their tutor to begin tutoring.

Based on a student’s diagnostic score, tutors will customize their strategy and approach to match the starting point of the student. A student may come in as a novice in a certain section of the exam, perhaps Heart of Algebra on the SAT. Recognizing this, tutor will start with the basics of Algebra and work on mastering easy and medium questions before starting to focus on the advanced levels or hards.

Conclusion

If we choose to think of personalized learning as a practice rather than a product, we can start by taking a hard look at course designs and identifying those areas that fail to make meaningful individual contact with students. These gaps will be different from course to course, subject to subject, student population to student population, and teacher to teacher. Although there is no generic answer to the question of where students are most likely to fall through the cracks in a course, there are some patterns to look for.

BLENDING DATA SCIENCE AND EDUCATION

WHAT IS DATA SCIENCE?

The emergence of data science has largely influenced todays digital world and its functioning. It is a multidisciplinary combination of data inference, algorithm development, and technology in order to solve analytically complex problems. At the core is data. Troves of raw information, streaming in and stored in enterprise data warehouses have to be learnt by mining it. We can even build advanced capabilities with it.

Data science started with statistics, and has evolved to include concepts such as Artificial Intelligence, Machine Learning, the Internet of Things etc. There has been a flood of information with growth of the Internet, Internet of Things(IoT), and the exponential growth of data volumes available to enterprises. Once the doors were opened by businesses seeking to increase benefits and drive better decision making, the use of Big Data started being applied to various fields, such as medicine, engineering, and social sciences.

USE OF DATA SCIENCE IN EDUCATION

Education opens various door for achieving better prospects in life and also promotes career growth. In recent times, the adaptive learning technology has reshaped education making it more streamlined. These externalization activities demand incorporating data analytical techniques for pursuing such goals.

It is mandatory to break huge datasets collected into chunks so as to process it efficiently. In order to mine the data acquired through monitoring student’s performance with the help of machine learning, data analytics plays a predominant role. This approach could make the categorization of students as good, average or weak easier. In turn this could embrace each individual student grow in their own phase with better conceptual understanding. Hence the key role of abstraction to the personalized learning methods, adaptive learning systems is offered by data science.

The mapping of personalized learning system along with Artificial intelligence and data science ,enables transparent growth of individuals to both teachers and parents. Thus directly or indirectly data science has got significant effect on increasing the standard of education by helping the system identifying the strengths and weakness of the each students and accordingly streamline the learning phenomenon.

The literature on education data analytics has many materials on the assessment and prediction of students’ academic performance, as measured by standardized tests. However, research on education data analytics should go beyond explaining student success with the typical three R’s (reading, writing and arithmetic) of literacy. Furthermore, the availability of data alone does not ensure successful data-driven decision-making, Consequently, there is an urgent need for further research on the use of an appropriate data-analytic thinking framework for education. The purpose of this paper is first to identify research goals to incorporate an appropriate data-analytic thinking framework for pursuing such goals, and second to present a case study of social-emotional learning in which we used the data science research methodology.

The collection and analysis of data about learning is a trend that is growing exponentially in all levels of education. Data science is poised to have a substantial influence on the understanding of learning in online and blended learning environments. The mass of data already being collected about student learning provides a source of greater insights into student learning that have not previously been available, and therefore is liable to have a substantial impact on and be impacted by the science of learning in the years ahead.

IMPACTS OF AI IN EDUCATION

“Humans plus machines is not the future it’s the present”

The Science & Technology has become an integral part of our Country’s Education System. Over the past few years we have witnessed the rise and impact of education technology especially through a multitude of adaptive learning platforms. It is positively reported that Artificial Intelligence will transform Education in the coming years.

“We are moving away from simply ‘learning’ a subject or topic to ‘feeling’ the content”

Artificial intelligence (AI) is the perfect example of how something new could be used to change every aspect of our lives when we change the lens. And education is an area that has unlimited potential to utilize innovation. Through AI, tutoring and study programs are growing more advanced, capable of teaching fundamentals to students struggling with basic concepts.

In the future, visual and dynamic learning channels outside the classroom will become not only more prevalent but capable of supporting a range of learning styles. Machines are now so far advanced that they can do much more than simply grade an exam with an answer key; they can compile data about how students performed and even grade more abstract assessments such as essays.

“A year spent in artificial intelligence is enough to make one believe in God.”

Education reformers need to plan for our AI-driven future and its implications for education, both in schools and beyond. The never-ending debate about the sorts of skills needed in the future and the role of schools in teaching and assessing them is becoming a whole lot more urgent and intense. Since education system focuses on standardization to reduce the achievement differences between students.

In fact, AI does not detract from classroom instruction but enhances it in many ways. Here are some of the benefits of AI in our educational systems.

Personalization. It can be overwhelmingly difficult for one teacher to figure out how to meet the needs of every student in his/her classroom: remedial students, advanced students, ESL students and the disabled all need to have the same access to learning. AI systems easily adapt to each student’s individual learning needs and can target instruction based on their strengths and weaknesses, meaningless work for teachers and a more meaningful learning experience for students.

Tutoring. Yes, it’s already happening: thanks to AI, machines are taking on the role of humans in many capacities, including tutors. As with human tutors, “Intelligent Tutoring Systems” can gauge a student’s learning style and pre-existing knowledge to deliver customized support and instruction.

Grading. This is arguably one of the most tedious teaching tasks and takes time away from more meaningful and purposeful pursuits, like lesson planning and professional development. Machines are now so far advanced that they can do much more than simply grade an exam with an answer key; they can compile data about how students performed and even grade more abstract assessments such as essays.

Feedback on course quality. AI can identify instruction gaps in the course content based on student performance on assessments. For example, if a significant percentage of students answer a question incorrectly, AI can zero in on the specific information or concepts that students are missing, so that educators can deliver targeted improvements in materials and methods.

Meaningful and immediate feedback to students. In an age when most communication occurs online or via text message, students are increasingly hesitant about taking risks in front of teachers and peers. They shrink from receiving critical feedback in such a public forum. With AI, students can feel comfortable to make the mistakes necessary for learning and receive the feedback they need for improvement.

Computer-supported collaborative learning systems

Have you ever participated in an e-learning instance which took place via social interaction? Then you are already familiar with the concept of computer supported collaborative learning systems, abbreviated CSCLS. A CSCLS is a tool that employs social interaction for education. One of the earliest evidences of CSCLS is the design and implementation of Intelligent Collaborative Learning System (ICLS), which then formed the pathway for more recent intersections of artificial intelligence and education.

Grading systems

Artificial Intelligence has found its way to grading assessment sheets of students. It is now possible for teachers to automate grading for multiple-choice questions and fill-in-the-blank questions. Grading student writing by artificial intelligence is in hot pursuit. Essay grading is still in its infancy and might take some time to rise to its full glory. This would take the pressure off the teachers’ shoulders, enabling them to concentrate more on student interaction and class activities.

Educational data mining

The National Education Technology plan of the US Department of Education laid a path for 21st century learning powered by technology, which involves ways of using data from online learning systems to improve instruction. This process is known as educational data mining. At the simplest level, the analytics can determine when a student is going astray during the course of an online training and can nudge the student to concentrate on the course. And at the most complex level, it can detect boredom from the mouse or key clicks of the student and redirect his/her attention.

Detecting room for improvement

Artificial Intelligence can help detect and fill in the gaps in explanation. The massive online open course provider, Coursera implements this technology. What happens here is, when a large number of students submit the wrong answer to a question, the system alerts the teacher about it and sends a customized feedback message to the students offering hints to the correct answer. This way, the students do not need to wait for the teacher to know whether the answers they have submitted are correct or not.

Even though implementing artificial intelligence has gained momentum recently, it does not in any way diminish the role of a teacher in the classroom. Teachers play the significant role of the facilitator aiding in the perfect blend of artificial intelligence in education. However, with newer implementations being put into action, the future of this new technology seems bright in the education sector.

As educators, we all have fears about instituting large systemic changes, and sometimes those fears are well grounded. However, we cannot afford to ignore the possibilities that AI offers us for dramatically improving the student learning experience. AI tutoring systems that tailor their lessons to different children’s needs would undo this standardization, with some students naturally progressing faster than others. Teachers may not sometime aware of certain gaps in educational material leaving the students confused in certain concepts. AI is capable of solving this problem in an effective manner.

References

[1] Benyon, D. and Murray, D. (1993). Applying user model-ing to human-computer interaction design. Artificial Intelligence Review, 7(3–4):199–225.

[2] Brusilovsky, P. (1996). Methods and techniques of adap-tive hypermedia. User Modeling and User-Adapted Interaction, 6(2–3):87–129.

[3] Burns, H. L. And Capps, C. G. (1988). Foundations of intel-ligent tutoring systems: An introduction. In Richard-son, M. C. P. and J., J., editors, Foundations of in-telligent tutoring systems, pages 1 — -19. Hillsdale: Lawrence Erlbaum Associates.

[4] Chen, C.-M. and Chen, M.-C. (2009). Mobile formative assessment tool based on data mining techniques for supporting web-based learning. Computers & Educa-tion, 52(1):256–273..

[5] Iglesias, A., Mart´ınez, P., Aler, R., and Fernandez,´ F. (2008). Learning teaching strategies in an Adap-tive and Intelligent Educational System through Rein-forcement Learning. Applied Intelligence, 31(1):89– 106..

[6] Sande, B. V. D. (2013). Properties of the Bayesian Knowl-edge Tracing Model. Journal of Educational Data Mining, 5(2):1–10.

[7] Xu, B., Recker, M., and Flann, N. (2013). Clustering Edu-cational Digital Library Usage Data : A Comparison of Latent Class Analysis and K-Means Algorithms. Journal of Educational Data Mining, 5(2):38–68..

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About:

Sudarshan Srinivas, Krishna Moorthy, and Aarthi Thiru are undergraduate researchers working with Dr. Khurana.

Dr. Sukant Khurana runs an academic research lab and several tech companies. He is also a known artist, author, and speaker. You can learn more about Sukant at www.brainnart.com or www.dataisnotjustdata.com and if you wish to work on biomedical research, neuroscience, sustainable development, artificial intelligence or data science projects for public good, you can contact him at skgroup.iiserk@gmail.com or by reaching out to him on linkedin https://www.linkedin.com/in/sukant-khurana-755a2343/.

Here are two small documentaries on Sukant and a TEDx video on his citizen science effort.

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