Lee cited and described a fascinating thought experiment originally conjured up by Winston (1992). He asked us to imagine trying to explain to someone how to use a pencil to solve a mathematics problem. Then, the goal is to imagine trying to teach a computer to do the same thing. Coming up with a theory as to why, despite the enormous investment in educational software technology over the last decade, there has been little change in how and what students learn. Lee suggested that “unlike the pencil, the computer is most remarkable in its capabilities. However, it is exactly these capabilities that make it difficult for us to instruct the computer what to do for a particular mathematics problem, and how to learn from it.” This comparison underscores an important issue; if we can find a way to harness the abilities of computers to learn and think, we will have made real progress. Although it may be a long and windy path to get there, AI could very well be the tool that we use to instruct and teach the computer how to solve mathematics problems, and then learn from them. This suggests that the biggest changes resulting from AI in education might be on the way mathematics is taught, and the way students learn it.

AI is rapidly changing the future of learning and education, and as Mark Lee and his co-authors emphasize in the text they named “Artificial Intelligence in Education,” it is inevitable that AI will have a great impact on the classroom and change the way teachers teach and students learn. This text attempts to delve into the different ways in which AI is changing the field of education as a whole, while also considering the impact that the new development will have specifically on mathematics education. It will provide insight for educators into the ways technology will change the classroom, and it will open up the issues and challenges that will surface as a result of changes.

Benefits and Challenges of AI in Education

Targeted Learning: One of the benefits of AI in Education can be personalised studying for college students. As every scholar is totally different there is no such thing as a method that a single trainer can cater to the person studying wants of a whole class. This is a significant problem in a modern full of huge class sizes and it is a frequent reason behind student/parental dissatisfaction. The outcome of that is dropping attention, disruptive behaviour and low grades. With AI it is potential for an individualised curriculum with a tutor that may hypothetically be nearly as good as a number of high-quality personal tutors. This begins with diagnostic testing, and dealing from the outcomes the system assign work and construct a curriculum. Student and educator would be capable of monitor progress and there can be much better communication on how a baby is definitely doing compared to now, the place there are often unreliable claims primarily based on a small quantity of statistics. The development of this can doubtlessly scale back, special wants courses and behavioural/dyslexic college students integrated in normal courses might be taught at their degree with out feeling singled out. AI tutors by no means get annoyed and are affected person, the instructing is constant and college students will likely be much less prone to surrender a challenging task. Also data retrieval is improved, the system is a gigantic database it by no means forgets what’s and is not efficient. This means designs of future programs could be continually bettered. Overall particular person and international understanding of college students studying what works and what would not, can doubtlessly revolutionise schooling. Personalised studying would give potential for homeschooling, for each an remoted scholar and people who are merely not happy with what’s out there within the present education system. This is also a bridge to a future the place examination based mostly schooling is switched for steady assessment.

Hand over on a lifetime skill: Knowledge is for a lifetime – Even if the students fail out of the system, the tutor can be available in a similar sort, while a new instructor might redesign and repeat work already lined. Teachers come and go, however the tutorial system can potentially span many lots of years. A.I. Can be adapted a couple of occasions and worksheets/tests/quizzes can be regenerated for unlimited makes an attempt, utilizing new problems on the same thought so that students aren’t merely memorising a solution, however actually studying to know the fabric. This can be certain that students who briefly battle in any subject are given the chance to grasp it, and reflection on missed concepts can hold occurring for many years, because the knowledge of the fabric continues to enhance. This technique is good for privateness too, as a result of it is going to reduce the necessity for a private tutor, the place the educator may be an grownup or peer of the scholar. The system could have academics for each sort of learner, and college students could be free to study within the comfort of their own properties. This will occur anyplace in the world, for an Iranian child learning a subject might need a different end result sequence to a French child, there might be translated educator roles and the scholars can meet to debate and examine their solutions. This technique is great in terms of covid and lockdowns, where there’s no danger of spreading illness and no disruption to any students learning, anyplace is a doable research location; residence, library, pc room, utilizing college or public Wi-Fi.

Future Prospects of AI in Education

The future of education will be significantly affected by artificial intelligence. There are several reasons to believe that AI can be applied to education in ways that will lead to learning that is more meaningful and powerful, preparing young people for life in this unpredictable and rapidly changing 21st century. The prospect of developing intelligent tutoring systems has been a major goal of AI in education from the outset. If it can be realized, the potential payoffs are clear. Students in schools and colleges often have no human tutor available and are attempting to learn on their own in a poorly structured domain (for example, learning a foreign language in an introductory class). An intelligent tutoring system would provide one-on-one instruction to the student, identifying his or her strengths and weaknesses, and tailoring the instruction to the individual’s needs. Systems like this can often outperform human tutors both in terms of cost and knowledge, and have been met with very positive results in controlled experiments. Professor John Hattie, Director of the Melbourne Education Research Institute at the University of Melbourne said in 2012 that the average effect size of all the tutoring methods he studied in a book on tutoring was only 0.29, whereas modern ITS systems have been able to achieve an average effect size of around 0.64 (an effect size of 0.40 is roughly equivalent to one year of learning). An ideal intelligent tutoring system will involve more than just natural language processing, and would attempt to figure out how much the student knows about a particular subject, and what the student’s goals are. This is often a complex and ambiguous task, but the ITS can use some common AI methods to do this, such as using a decision tree to map out possible methods of approaching the student, and goal-based reasoning.


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