Role of AI in different fields of Research- Dr JD Singh.pptx

Artificial intelligence (AI) accelerates academic research by automating literature reviews, enhancing data analysis and visualization, assisting in the development of research ideas and hypotheses, and supporting content creation through writing and editing. AI enhances research across diverse fields by automating data analysis, generating insights from complex datasets, speeding up discovery, and predicting outcomes. It aids in hypothesis generation, experimental design, and scientific writing, but also presents challenges such as ethical considerations, bias in algorithms, and the need for human oversight to ensure reliability and integrity.
AI is a big turning point for this world – from compelling research areas and unprecedented applications to staggering levels of human augmentation.

1. Dr. J D Singh Associate Professor G V (PG) College of Education (CTE) Sangaria-335063, Rajasthan Email: [email protected] Cell: [phone] Role of AI in Different Fields of Research Presented as keynote Speaker on 7 Oct. 2025 on the theme ‘Role of Artificial Intelligence in different fields of Research’ in the International Seminar at Department of Education and Physical Education, Tantia University, Sri Ganganagar (Rajasthan), India

2. Content Outline Artificial Intelligence (AI) Origin and Development of AI Types of Artificial Intelligence Focus on AI based Research in NEP 2020 Present Status of Research in India AI Machine/ System with Function/ Work Role of AI in Different Areas of Research Selected AI Tools for Supporting Educational Research Advantages and Disadvantages of AI in Research Conclusion

3. Artificial Intelligence (AI) Artificial Intelligence (AI) is the branch of computer science that creates machines or programs capable of thinking, learning, and making decisions like humans. It aims to make computers intelligent able to: • Think (reasoning and problem-solving) • Learn (from data and experience) • Perceive (through vision, speech, or sensors) • Understand language (natural language processing) • Act intelligently (decision-making and automation) In simple terms, AI is called “ मानव जैसी बुद्धिमत्ता वाली कृत्रिम प्रणाली” It enables computers to analyze complex data, identify patterns, and provide intelligent solutions—something that has completely changed the landscape of modern research .

4. Origin and Development of Artificial Intelligence Artificial Intelligence (AI) originated in the mid-20th century with the goal of creating machines capable of performing tasks that require human intelligence. The term “Artificial Intelligence” was first coined by American computer scientist John McCarthy (1927–2011) in 1955, who is known as the Father of AI. The first major step in AI development occurred during the Dartmouth Conference (1956), which formally established AI as a research field. Over the decades, AI evolved through advances in computer science, machine learning, and data processing. Today, AI powers technologies like speech recognition, robotics, and virtual assistants, data analysis, transforming every sector of society.

5. Types of Artificial Intelligence AI can be classified in two main ways — (A) based on capability and (B) functionality. A. Based on Capability 1. Narrow AI (Weak AI) • Works for a specific task only. • Example: Siri , Alexa , ChatGPT , Google Assistant.   2. General AI (Strong AI) • Can think, learn, and perform any intellectual task like a human. • Still a future goal, not yet achieved. 3. Super AI • Hypothetical AI that surpasses human intelligence. • It could think, create, and act beyond human capacity.

6. Types of Artificial Intelligence B. Based on Functionality 1. Reactive Machines – respond only to present input (e.g., IBM’s Deep Blue chess computer). 2. Limited Memory – learn from past data (e.g., self-driving cars). 3. Theory of Mind – understand emotions and human thinking (still developing). 4. Self-Aware AI – has consciousness and self-awareness (not yet created).

7. Focus on AI based Research in NEP 2020 The New Education Policy (NEP) 2020 integrates Artificial Intelligence (AI) by promoting its inclusion in the curriculum to foster essential digital literacy, coding, computational thinking, and 21st-century skills in students. National Curriculum Framework :  The NCERT is working on a new curriculum framework to explore the introduction of AI at the secondary level.  CBSE Initiatives:  The Central Board of Secondary Education (CBSE) has already introduced AI as a subject in Class IX and Class XI in its affiliated schools.  Digital Infrastructure:  The  Samagra Shiksha Abhiyan ( SmSA )is proposed to provide funding for digital infrastructure, like computer labs and offline AI applications, to address connectivity challenges in rural areas. 

8. Focus on AI based Research in NEP 2020 The National Education Policy (NEP) 2020 promotes integrating Artificial Intelligence (AI) into education by encouraging AI awareness, introducing it as a subject in the curriculum from Class 6, and expanding teacher training to effectively use AI tools. Research in AI within India is growing, supported by government initiatives and institutions like NITI Aayog , focusing on applications like personalized learning, AI-driven assessment , and developing the ethical and responsible use of AI in various sectors.  Key initiatives include enhancing  digital literacy, promoting   computational thinking , establishing AI Centers of Excellence, and focusing on the use of AI within the academic ecosystem. 

9. Focus on Research & Innovation in NEP 2020 According to the NEP 2020, the current status of research and development in India is considered inadequate. NEP 2020 encourages Higher Education Institutions to establish Research and Development Cell (R&D Cell) to promote the quality research. The Parliament of India passed the Anusandhan National Research Foundation (NRF) Bill 2023 on August 9, 2023. The NRF is a key recommendation of the NEP 2020 to promote research in all fields of education. NRF, as an apex body, aims to provide “high-level strategic direction for research, innovation and entrepreneurship”, and enhance “India's national research infrastructure, knowledge enterprise, and innovation potential, for scientific pursuit”. National Research Foundation (NRF) will funding to outstanding peer-reviewed research and to actively seed research in universities, colleges and Laboratories. NEP 2020 also aims to encourage research on Indian Knowledge System (IKS).

10. Present Status of Research & Innovation India's research output has seen a significant surge in recent years, making it one of the fastest growing research hubs globally. Presently 9 research/ Techno parks in India (while more than 150 in china, USA, UK and Japan, 18 in south Korea) Research and development (R&D) is the most important department of an organization. Budget 2024-25 of research and development (R&D) is among lowest in the world (0.64%). While China (2.4%), Germany (3.1%), USA (3.5%), South Korea (4.8%) etc. Global Innovation Index (GII) : India ranked 39th out of 133 economies in the GII 2024.  Network Readiness Index (NRI) : India ranked 60th in the NRI 2023, up from 79th in 2019. Nature Index: India is ranked 9th overall and 8th in natural sciences overtaking Australia and Switzerland.

11. Present Status of Research & Development (R&D) Education & Research in private sector is an industry of making money (most of them). Providing education is second option for them. 1.5 lacs Ph.D. degree awarded in 2022 (mostly doubtful) Poor Quality of Research Papers (19 % contribution in the world but fewer citation) According to Scopus (a multidisciplinary abstract and citation database) and QS 2023 data (from 2017 to 2022), India is currently the world's fourth largest producer of research papers (1.3 million), behind only China (4.5 million), USA (4.4 million) and UK (1.4 million). But its research often receives fewer citations per paper compared to other leading nations. Lack of Fund and effective monitoring & evaluation

12. Challenging Issues in Research & Development The estimated budget for education is Rs 1,25,638 crore out of Rs. 48,20,512 crore for 2024-25.  The 2024-25 budget for education (2.6%) in India is a 7.7% decrease from the previous year (2.9%) . No touching target Investment 6% of GDP as per NEP 2020 (currently allotted 2.6 % in 2024-25 budget). While Global average is 4.9%. (As per record, USA spent 5.44%, South Korea 4.8% China and Japan 4% each) Reduced funding for higher education : The budget allocated Rs 47,620 crore for the Department of Higher Education increased by 8 % over last year’s budget, but 17% lower than the revised spending estimates of the previous year 2023-24. The UGC's budget was reduced almost 61%  in the 2024-25 Union Budget, dropping from Rs 6,409 crore to Rs 2,500 crore , which is expected to affect projects and initiatives related to higher education, such as scholarships, research and infrastructure.

13. AI Machine/ System with Function/ Work 1. Siri (Apple) Voice-based personal assistant that understands speech, answers questions, sets reminders, and performs phone tasks. 2. Alexa (Amazon) Smart home assistant that controls devices, plays music, provides weather updates, and answers questions. 3. Google Assistant AI assistant that performs searches, manages schedules, and controls smart devices through voice commands. 4. ChatGPT ( OpenAI ) Conversational AI that generates human-like responses, helps with writing, learning, and problem-solving. Helps in brainstorming, summarizing research papers, generating drafts, and refining academic writing. 5. Tesla Autopilot Self-driving car system that detects objects, follows lanes, and drives safely with minimal human input.

14. AI Machine/ System with Function/ Work 6. Sophia Robot (Hanson Robotics) Humanoid robot capable of facial recognition, conversation, and expressing emotions. 7. IBM Watson AI system that analyzes big data, supports decision-making in healthcare, business, and research. 8. Google Translate Uses AI and machine learning to translate text and speech between multiple languages. 9. Drones with AI Used in agriculture, defense, and delivery systems for navigation and image recognition. 10. Smart Security Cameras Detect movement, recognize faces, and alert users about unusual activities.

15. Robot / Project Where, What it does/ Role in Teaching 1. Shalu Shalu is an artificially intelligent, multilingual, and social humanoid robot developed by Dinesh Kunwar Patel, a computer science teacher in Mumbai, India. Made from recycled waste materials, Shalu can serve as a teacher, receptionist, and conversational companion.  India ( Kendriya Vidyalaya , IIT Bombay teacher’s project) Speaks 9 Indian + 38 foreign languages, conducts quizzes, answers questions on GK, maths , helps in solving equations. Designed to be a robot-teacher in classroom environments. 2. Eagle (Robot Teacher) "AI Eagle" refer to a series of humanoid teaching robots, including Eagle 2.0 and Eagle 5.0, developed by the  Eagle Robot Lab  for use in India's  Indus International Schools (Hyderabad, Bengaluru , Pune etc.) Works alongside human teachers. Teaches science & humanities for grades ~5-11. Clarifies doubts, asks questions, does assessments, works in many languages.

16. Robot / Project Where, What it does/ Role in Teaching 3. Iris India's first Humanoid AI teacher robot and a research assistant tool developed under India's  Atal Tinkering Lab (ATL) project in April 2024. Kerala, India (KTCT Higher Secondary School) Speaks three languages, interacts with students, answers questions, tells stories. Moves its head/hands, listens via voice, gives personalised learning. 4. Social Robot “Pepper” + LLM ( ChatGPT ) The world's first social humanoid robot able to recognize faces and human emotions was introduced in Tokyo on 5 June 2014. Pepper is optimized for human interaction through conversation and his touch screen.   In a high-school classroom (outside India) – research/academic project

17. Role of AI in Different Areas of Research AI in Scientific Research AI has become a powerful tool for scientists in fields like physics, chemistry, and biology. It helps in data analysis, pattern recognition, and prediction. In drug discovery, AI predicts molecular structures and helps develop new medicines faster. In astronomy, it assists in analyzing cosmic images and identifying new stars and galaxies. Example: Google’s DeepMind used AI to solve the protein folding problem, a major breakthrough in biological research.

18. Role of AI in Different Areas of Research 3. AI in Commercial Research AI plays a transformative role in banking research by accelerating data analysis, automating tasks, enhancing cybersecurity , and personalizing customer experiences.  AI is transforming commercial research by automating data collection and analysis, providing predictive insights into market trends and consumer behavior, and enabling real-time monitoring of competitors and sentiment. Example: AI tools analyze large volumes of text, such as social media posts and customer reviews, to determine public sentiment, opinions, and attitudes toward brands, products, or services.  

19. Role of AI in Different Areas of Research 4. AI in Agricultural Research AI contributes significantly to precision agriculture and crop research. Drones and sensors collect data about soil, water, and crop health. AI models predict crop yields and detect diseases early. It supports climate modeling and sustainable farming research. Example: Microsoft’s AI for Earth project supports agricultural and environmental studies globally.

20. Role of AI in Different Areas of Research 5. AI in Engineering and Industrial Research AI accelerates product design, testing, and automation. In mechanical and civil engineering, AI is used for simulation, fault detection, and predictive maintenance. In electrical and computer engineering, AI enhances robotics, communication systems, and smart manufacturing. Example: Tesla uses AI-based simulations for self-driving car research and performance improvement.

21. Role of AI in Different Areas of Research 6. AI in Social Science and Behavioral Research AI tools analyze social media trends, human behavior, and public opinion. It helps in sociological and psychological data analysis. AI-driven analytics predict social trends, voting behaviors, and economic patterns. Example: Sentiment analysis is widely used in communication and marketing research.

22. Role of AI in Different Areas of Research 7. AI in Environmental and Climate Research AI assists in monitoring deforestation, tracking pollution, and predicting climate change. Machine learning models forecast weather patterns and natural disasters. It supports research in renewable energy optimization and biodiversity conservation. Example: AI-based climate models by NASA help in predicting global warming effects.

23. Role of AI in Different Areas of Research 8. AI in Humanities and Linguistics AI supports research in language, literature, and history through: Text analysis and translation (Natural Language Processing). Digital preservation of manuscripts and cultural heritage. Example: Google’s AI translation system and heritage restoration projects.

24. Role of AI in Different Areas of Research 9. AI in Educational Research AI helps researchers understand learning patterns, student behavior, and assessment methods. Adaptive learning platforms personalize education for each student. AI analyzes data to improve teaching strategies and curriculum design. In educational psychology, it helps in studying cognitive and emotional responses of learners. Example: AI-powered systems like ChatGPT assist in content generation, data interpretation, and educational resource creation .

25. Role of AI in Educational Research AI plays a transformative role in educational research by enhancing data analysis, personalizing learning, and improving decision-making. It allows researchers and educators to better understand how students learn, what teaching methods work best, and how educational systems can be improved. 1 . Data Collection and Analysis AI helps collect and analyze large sets of educational data (student performance, learning patterns, feedback). It supports evidence-based decision-making and predictive analysis. Example: Using AI tools to predict student dropout risk or performance trends. 2. Personalized and Adaptive Learning Research AI enables researchers to study adaptive and personalized learning environments. AI helps design systems that adjust teaching speed, style, or content. Researchers can analyze how personalized methods affect motivation and achievement. Example: Intelligent tutoring systems like Khanmigo or Coursera’s adaptive AI.

26. Role of AI in Educational Research 3. Assessment and Evaluation Studies AI can automatically assess written or spoken responses, grade assignments, and give feedback. Researchers can use this to study the reliability and validity of automated assessment methods. Example: AI-based essay evaluation systems or speech recognition in language learning. 4. Educational Policy and Planning AI helps researchers simulate and predict the impact of educational policies. Machine learning models forecast outcomes of policy changes like curriculum reforms or funding allocation. This supports data-driven policymaking in education. 5. Learning Analytics and Behavioural Studies AI-powered analytics platforms help researchers understand student engagement, attention, and cognitive load. Eye-tracking, facial recognition, and emotion analysis tools help study learning behaviour in real-time.

27. Role of AI in Educational Research 6. Research in Inclusive and Special Education AI assists in designing assistive technologies for students with disabilities. Researchers use AI to explore accessibility tools such as text-to-speech, speech-to-text, and emotion-detection systems. Tools such as speech-to-text, text-to-speech, and emotion recognition software are being researched and refined to assist differently- abled learners. 7. Automation in Research Process AI tools like ChatGPT , Scite.ai, and Semantic Scholar help researchers in: Literature review summarization Plagiarism detection Data coding and thematic analysis Academic writing assistance This automation saves time and enhances the accuracy and efficiency of research work.

28.   Selected AI Tools for Supporting Educational Research Literature Review and Research Writing ChatGPT ( OpenAI ) Helps in brainstorming, summarizing research papers, generating drafts, and refining academic writing. Scite.ai Provides “Smart Citations” that show whether a paper supports or contradicts another study — useful for evidence-based referencing. Elicit.org An AI research assistant that helps find relevant papers, summarize findings, and extract key information. Research Rabbit Helps visualize relationships between research papers and discover connected studies. Semantic Scholar Uses AI to find the most relevant and credible research literature quickly.

29.   Selected AI Tools for Supporting Educational Research 2. Data Analysis and Coding IBM SPSS Modeler (AI-assisted) Supports statistical and predictive modeling for educational data. RapidMiner AI-powered platform for data mining and educational data analytics. NVivo with AI Assistant Helps in qualitative data analysis by auto-coding themes and summarizing interview transcripts. Power BI (with AI insights) Visualizes complex educational data and supports data-driven research conclusions.

30.   Selected AI Tools for Supporting Educational Research 3. Academic Writing and Editing GrammarlyGO AI writing assistant for grammar correction, academic tone, and clarity. QuillBot AI paraphrasing tool for rewriting, summarizing, and enhancing readability of research content. Writefull Checks academic writing style and provides feedback based on published research standards. 4. Survey and Experimental Research Google Forms (with AI add-ons) Simplifies survey design and auto-analyzes responses. SurveySparrow (AI-powered) Creates engaging, conversational AI surveys for research data collection. Qualtrics XM Advanced AI-driven survey platform for analyzing participants’ responses and sentiments.

31.   Selected AI Tools for Supporting Educational Research 5. Visualization and Presentation ChatGPT + Canva Magic Studio Helps create AI-based research infographics , graphs, and visual summaries. Tableau (with AI insights) Visual analytics platform that interprets large educational datasets automatically. Beautiful.ai AI tool for creating professional research presentations automatically.

32.   Advantages (Pros) of AI in Research Accessibility 24/7 Availability Cost-Effectiveness Real-time feedback Efficiency and Speed Handles high-risk tasks Minimizes human error Improved customer experiences Enhanced Data Analysis and Productivity Improved student engagement and motivation Accelerates innovation and scientific discovery Raising academic standards and educational quality Automated administrative tasks and New Capabilities Continuous evaluation and improvement in the long run

33.   Disadvantages (Cons) of AI in Research Algorithm Bias  Environmental Impact Reduced Human Interaction Ethical and Privacy Concerns Lack of Emotional Intelligence Job Displacement and Skill Gap  Loss of human decision-making High Costs and Accessibility Issues Misinformation and Manipulation Over-Reliance and Lack of Scrutiny Lack of Human Creativity and Intuition Lack of Transparency (The "Black Box" Problem) Risk of plagiarism and decreased academic integrity

34. Conclusion To conclude, Artificial Intelligence is not just a technology; it is a revolutionary research companion by introducing precision, adaptability, and innovation. From medicine to agriculture, from commerce to science, from engineering to education — AI enhances accuracy, speed, and creativity in every discipline. As technology continues to evolve, AI will remain a cornerstone for advancing research and shaping the future of global education. However, ethical considerations, data privacy, and responsible use of AI must remain a priority to ensure that technology serves humanity positively. Therefore, AI and human collaboration in research should leverage distinct strengths, with AI handling data processing and pattern recognition and humans providing critical thinking, creativity, and ethical oversight, leading to more efficient and innovative results.

35. Thank you !