Artificial Intelligence and its role in revolutionising education

New Delhi: All of us are quite familiar with YouTube, Netflix etc. You are actually using AI. Siri, Alexa and Spotify are also examples of artificial intelligence. In the past few years, AI has emerged as a game changer in various fields, including education. 

Its use in the education sector has grown phenomenally. AI is revolutionising the way we think, teach and learn, ushering in an era of incomparable development of technology. 

A dynamic integration of AI in the form of a collaborative tool can pave the way for making education accessible to all, empowering students to harness their cognitive potential. 

Before we delve into discussing AI's revolutionary effects on education, it is pertinent to note a basic understanding of AI and its genesis.

Artificial intelligence means the development of human intelligence through artificial means. In the words of John McCarthy, the father of AI, ‘AI is the science of making intelligent machines for the future with the help of computer software and hardware.'

AI consists of two words artificial and Intelligence. Intelligence is related to humans and the computer is the artificial medium created by humans for solving complex problems with speed and accuracy. In essence, it is an electro-mechanical process of a machine that thinks, understands a language and adapts to a situation, and anticipates a problem to solve it. 

Background

The historical development of AI spans decades, marked by milestones, breakthroughs, and evolving concepts. Its groundwork was laid in 1940s-50s with pioneers like Alan Turing, John von Neumann, and Claude Shannon exploring concepts of computation, logic, and machine intelligence. Turing's paper on "Computing Machinery and Intelligence" (1950) proposed the Turing Test as a measure of machine intelligence and raised fundamental questions about the capabilities of artificial systems.

The term "Artificial Intelligence" was coined at the Dartmouth Summer Research Project in 1956. Participants included John McCarthy, Marvin Minsky, Allen Newell, and Herbert Simon, who laid the foundation for AI as a distinct field of study. 

The 1950s and 1960s saw the development of early AI programmes, including the Logic Theorist (1956) by Newell and Simon, which could prove mathematical theorems. Other notable AI projects included the General Problem Solver (GPS) and the first AI-based board game-playing programmes.

Symbolic AI, also known as "Good Old-Fashioned AI," focused on symbolic representation, logic, and rule-based system. The development of expert systems, such as MYCIN (1972) for medical diagnosis and DENDRAL (1965) for chemical analysis, demonstrated early successes in knowledge-based AI.

The 1980s and 1990s saw a resurgence of interest in neural networks and connectionist models, inspired by biological brain structures. Backpropagation algorithms, pioneered by Geoffrey Hinton, David Rumelhart, and Ronald Williams, revolutionised training neural networks for pattern recognition and learning. This was also a period of scepticism and reduced funding for AI research, known as the "AI Winter."

It underwent a reinvention with the emergence of subfields like machine learning, natural language processing, computer vision, and robotics, leading to renewed interest. The proliferation of big data, computational power, and cloud computing in the 2000s facilitated advancements in AI, particularly in deep learning.

Deep learning techniques, such as convolutional neural networks (CNNs) and recurrent neural networks (RNNs), achieved breakthroughs in image and speech recognition, and natural language understanding.

Applications of AI expanded across industries, including healthcare, finance, transportation, education, and entertainment, leading to the current era of AI-driven innovation and automation. As AI technologies became more pervasive, ethical considerations regarding bias, transparency, accountability, privacy, and job displacement have gained prominence. Efforts are underway to develop ethical guidelines, regulations, and frameworks for responsible AI deployment and governance.

The historical development of AI reflects a continuous evolution of ideas, techniques, and applications, shaped by interdisciplinary collaboration, technological advancements, societal challenges, and ethical considerations. 

At present, 47% of learning tools are AI enabled. Earlier the library was the only source of information but today all means of learning and teaching are just a click away. The use of technology has revolutionised education with better accessibility and improved results. We strongly believe that the role of teachers in education is irreplaceable and AI will help in enhancing their skills and make their work easier. Through AI, students can get personalised courses, tests and learning methods.

AI offers personalised learning opportunities by tailoring educational experiences to unique needs, preferences, and learning styles of individual students. AI-powered adaptive learning platforms analyse student's performance, preferences, and learning patterns to deliver personalised learning pathways and content. These platforms adjust the difficulty level, pace, and sequence of lessons based on individual progress, ensuring optimal learning outcomes.

Intelligent Tutoring Systems:

ITS use AI algorithms to provide personalised guidance, feedback, and remediation to students in real-time. These systems identify areas of strength and weakness, offer customised learning activities, and track progress to support mastery of concepts.

AI-driven recommendation systems analyse student behavior, interests, and past interactions to suggest relevant learning materials, resources, and activities. These recommendations can include multimedia content, articles, quizzes, simulations, and interactive exercises aligned with learning objectives. 

NLP (natural language processes) technologies enable AI systems to analyse and interpret student responses, essays, and discussions to provide personalised feedback. AI can identify misconceptions, provide explanations, offer suggestions for improvement, and facilitate meaningful dialogue.

AI-driven data analytics tools analyse large volumes of educational data, including assessment results, engagement metrics, and learning behaviors. Educators can gain valuable insights into student's progress, identify trends, and make data-informed decisions to enhance instructional strategies and interventions.

AI-powered gamification platforms create personalised game-based learning experiences tailored to individual preferences and skill levels. These platforms use AI to adjust game mechanics, challenges, and rewards to maintain engagement and promote learning. AI-enhanced VR and AR applications create immersive, personalised learning environments that adapt to student interactions and responses.

These technologies provide hands-on experiences, simulations, and scenarios that cater to diverse learning styles. AI systems can recognise emotions, sentiment, and engagement levels through facial expression analysis, voice tone analysis, and behavioral cues. Educators can use this data to provide personalised emotional support, motivation, and encouragement to students. 

AI has the potential to contribute significantly to equity and equality by addressing various societal challenges, reducing biases, and promoting inclusivity. AI algorithms can help reduce bias in decision-making processes, such as recruitment, admissions, lending, and criminal justice. By analysing data objectively and identifying patterns based on facts rather than subjective judgments, AI can mitigate human biases that may lead to discrimination. 

AI-powered adaptive learning platforms and personalised tools can cater to diverse learning needs, abilities, and backgrounds, promoting equal access to quality education. AI can also assist in identifying barriers and implementing targeted interventions to help marginalised or disadvantaged students. 

AI is increasingly integrated into educational content and curriculum to equip students with essential knowledge, skills, and competencies for the AI-driven world. AI curriculum covers foundational concepts such as machine learning, deep learning, neural networks, algorithms, data analytics, and NLP. Students learn about AI principles, ethics, bias mitigation, responsible AI use, and societal implications of AI technologies.

AI curriculum includes hands-on projects, coding exercises, and AI toolkits to apply theoretical concepts to real-world problems. Students work on AI projects, such as image recognition, chatbots, recommendation systems, predictive analytics, and autonomous robots. They learn programming languages commonly used in AI development, such as Python, R, TensorFlow, PyTorch, and scikit-learn.

AI curriculum includes exposure to AI development platforms, cloud services, and tools for data collection, preprocessing, modeling, and innovation. AI education integrates data science concepts, including data acquisition, cleaning, transformation, feature engineering, and model evaluation. Students gain skills in data visualisation, exploratory data analysis, statistical techniques, and big data analytics relevant to AI applications.

AI curriculum covers supervised, unsupervised, reinforcement and ensemble learning algorithms. Students learn about regression, classification, clustering, dimensionality reduction, decision trees, support vector machines, neural networks, and deep learning architectures.

AI content addresses ethical considerations, fairness, transparency, accountability, privacy, bias detection, and mitigation strategies. Students discuss AI's impact on society, job displacement, ethical dilemmas, cybersecurity risks, and responsible AI governance frameworks.

AI education includes domain-specific applications such as healthcare (medical imaging, diagnostics), finance (algorithmic trading, fraud detection), education (adaptive learning, intelligent tutoring), and robotics (autonomous systems, human-robot interaction). Students explore case studies, industry applications, and interdisciplinary AI projects aligned with their interests and career goals.

AI curriculum encourages collaborative teamwork, problem-solving, critical thinking, and communication skills through group projects, hackathons, and AI competitions. Students participate in AI challenges, hackathons, coding contests, and research initiatives to showcase their AI skills and creativity.

AI education emphasises lifelong learning, continuous skill development, and staying updated with AI trends, research, and advancements. Students engage in self-directed learning, online courses, workshops, seminars, and networking opportunities with AI professionals and researchers. AI content and curriculum in education prepare students to become AI-literate, adaptive, innovative, and responsible contributors to workforce and society.

AI-driven systems can play a transformative role in supporting differently-abled students by providing personalised learning experiences, accessibility solutions, and assistive technologies. AI-powered adaptive learning platforms tailor educational content, pace, and activities based on individual learning styles, preferences, and abilities. These platforms adjust difficulty levels, provide multi-modal content (text, audio, video), and offer customised assessments to accommodate diverse needs.

AI-driven NLP technologies enable text-to-speech (TTS), speech-to-text (STT), and language translation for students with visual impairments, dyslexia, or language barriers. NLP tools also support voice commands, dictation, and interactive dialogue systems for hands-free interaction.

AI-powered systems assist students with visual impairments by describing images, recognising objects, and providing audio descriptions. AI-driven tools generate real-time captions for videos, lectures, and multimedia content to enhance accessibility for students with hearing impairments.

AI-based assistive technologies support students with motor disabilities by enabling alternative input methods, gesture recognition, and adaptive interfaces. These technologies include eye-tracking devices, head-controlled mice, sip-and-puff devices, and customised input devices tailored to individual needs.

AI-driven learning analytics tools monitor student progress, engagement, and performance to identify learning gaps, provide feedback, and track individualised learning pathways. Educators can use data-driven insights to adjust instructional strategies, interventions, and accommodations for differently-abled. 

AI systems can recognise emotions, facial expressions, and behavioral cues to provide personalised emotional support, motivation, and mental health resources. Emotion-aware tutoring systems can adapt feedback, encouragement, and interventions based on students' emotional states and well-being. By leveraging AI-driven systems, educational institutions can create inclusive learning environments, empower differently-abled students to thrive academically and socially, and promote equity and accessibility in education.

AI has transformed traditional Learning Management Systems (LMS) into intelligent and personalised platforms that enhance the learning experience for students, streamline administrative task for educators, and provide data-driven insights for institutions.

These platforms analyse student data, including learning styles, preferences, performance, and progress. AI then generates personalised learning pathways, recommends relevant content, adapts instructional materials, and assigns tailored assessments. 

AI-driven adaptive learning algorithms adjust the difficulty level, pace, and sequence of learning activities based on real-time student performance and mastery of concepts. Students receive targeted feedback, remediation, and enrichment activities to optimise learning outcomes and promote self-paced learning.

AI algorithms analyse content consumption patterns, engagement metrics, and user feedback to recommend relevant educational resources, videos, articles, quizzes, and interactive materials. Content recommendation engines enhance student engagement, motivation, and exploration of diverse learning materials aligned with curriculum objectives.

AI automates grading processes for assessments, quizzes, and assignments, reducing manual workload for educators and providing immediate feedback to students. AI-driven feedback mechanisms analyse student responses, identify misconceptions, offer explanations, and suggest next steps for improvement.

AI-powered LMS platforms use predictive analytics to forecast student performance, identify at-risk students, and flag potential learning challenges or gaps. Educators can intervene early, provide targeted support, and implement personalised interventions to prevent academic setbacks and promote student success.

AI-driven chatbots, virtual assistants, and NLP technologies facilitate interactive communication and support for students, educators, and administrators. NLP enables voice commands, text-based queries, automated responses, and intelligent conversational interfaces for seamless interaction and support. 

AI-driven learning analytics tools aggregate and analyse data from various sources within the LMS, including student interactions, engagement levels, assessment results, and learning behaviors. Institutions gain actionable insights, trends, and reports to inform instructional strategies, curriculum design, resource allocation, and decision-making for continuous improvement.

By using AI as LMS, educational institutions can create dynamic, adaptive, and student-centric learning environments that foster personalised learning experiences, improve academic outcomes, and support lifelong learning initiatives. Like many technological tools and techniques, AI has also issues and challenges. Some of these are:

1 Resistance from society and parents. 
2 Educational institutions adapting to AI enabled systems. 
3 No dedicated policy on national level. 
4 Sustained electricity supply. 
5 Absence of emotions. 
6 Teachers place and respect at risk. 
7 Threat to cybersecurity. 
8 Potential threat to human civilisation. 
9 Sharp decline in teachers' employment. 

There is no doubt that AI is going to play most dominant role in education realising the potential of AI. CBSE has prescribed AI syllabus for classes 8 to 12. NITI Ayog has also released India’s National strategy for Artificial Intelligence (NSAI).

Overall, the integration of AI in education represents a revolutionary transformation towards a more inclusive, equitable and efficient teaching besides learning ecosystem in India. By integrating AI in education, we can harness the full potential of students and promises to achieve a bright future across the nation. 

Let’s adopt this technological marvel whole heartedly and be part of this movement.

(The writer is an educationist, author and social worker.)