Adopting Artificial Intelligence Tools in Higher Education Student Assessment
Produktdetaljer
Biografisk notat
Thangavel Murugan is Assistant Professor in the Department of Information Systems and Security, College of Information Technology, and a Faculty Fellow of the Center for Excellence in Teaching and Learning at United Arab Emirates University. He holds a Doctorate from Madras Institute of Technology, Anna University, Chennai, and has over 11 years of teaching and research experience. His research specialization includes information security, high-performance computing, blockchain, and educational technology.
Karthikeyan Periasamy is Associate Professor at Thiagarajar College of Engineering (TCE), Madurai, since 2007. He completed his Ph.D. program in Information and Communication Engineering at Anna University in 2015. His research interests include computational intelligence algorithms and educational technology (ET). He has published over 55 papers in international journals, conferences, and book chapters.
A.M. Abirami is Associate Professor at Thiagarajar College of Engineering. She holds a PhD in Text Analytics and Semantic Web. Her interests include data analytics, natural language processing, and engineering education. She has several publications in these areas and has received awards for her work in engineering education.
This edited volume explores the adoption of artificial intelligence (AI) tools in higher education, specifically focusing on student assessment. It examines the integration of various AI tools within higher education, discussing the challenges and opportunities they present and the innovative solutions they offer.
The chapters explore various issues surrounding the use of AI in higher education and propose potential solutions. The book begins with a systematic exploration of AI's potential, presenting innovative ways to ensure fair and accurate assessments that enhance the overall quality of education. It highlights the benefits of AI-powered grading systems that streamline assessment processes, provide timely feedback, and promote fair evaluations. The text discusses how machine learning algorithms can revolutionize assessment methods, allowing individualized, adaptive testing tailored to each student's unique needs. Furthermore, it examines natural language processing (NLP) techniques for evaluating student essays by analyzing linguistic features such as grammar and semantic coherence. Moreover, it highlights AI-powered virtual assistants offering personalized feedback and learning recommendations. Case studies illustrate successful AI assessment implementations and methods for improving AI-based evaluations. The book also addresses engagement and success in the post-COVID-19 context. It raises concerns about plagiarism and academic integrity, comparing AI solutions to traditional methods while exploring the challenges associated with adopting AI in education.
Educators, administrators, academics, and technology experts working in institutes of higher learning will find this volume compelling. It is also suitable for students taking courses in educational technology, e-learning, and digital learning.
This edited volume explores the adoption of Artificial Intelligence (AI) tools in higher education, specifically focusing on student assessment. This book will appeal to educators, administrators, academics, and technology experts working in institutes of higher learning.
Table of Contents
Editor Biographies
Contributors Biographies
Preface
Chapter 1. AI-Driven Evaluation Techniques: Revolutionizing Student Practices
Sajida Sultana. Sk, Dr. Renugadevi Rajaram, Maridu Bhargavi and Shaik Abdul Afzal BIyabani
Chapter 2. Inclusive Learning and Assessment in the era of AI
Minal Patil, R H Goudar, Anand Nayyar, Dhananjaya G M and Vijayalaxmi N Rathod
Chapter 3. Automated Grading Systems: Enhancing Efficiency and Consistency in Student Assessments
SC Vetrivel, VP Arun, Ramya Ambikapathi, TP Saravanan
Chapter 4. The Potential and Drawbacks of Machine Learning for Student Assessment
K.Kartheeban, Aanandaram V, Lakshminarasimman D, Kabalishwaran D
Chapter 5. NLP-Driven Approaches to Automated Essay Grading and Feedback
Nikhil V, Annamalai R and Senthil Jayapal
Chapter 6. Enhancing Learning Outcomes for the dyslexic students using AI-Powered Assistants
Anjali Mathur
Chapter 7. Transforming Education Through AI-Powered Personalized Assessment Models
Kanthimathi S, Prayasha Nanda, Shravan Venkatraman, Varsana Renganayagan and Sivaraman Eswaran
Chapter 8. Enhancing Student Engagement and Success in Post-Covid19 through AI Technologies
A. Peter Soosai Anandaraj, V. Murugananthan, A. Judith Arockiya Gladies, S. Sridevi, V. Vijayalakshmi and B. Senthilkumaran
Chapter 9. AI Tools for Plagiarism Detection and Academic Integrity
S. Baghavathi Priya, B. Kavya Sai, and TamilSelvi Madeswaran
Chapter 10. Unlocking Potential: Personalizing Learning and Assessment with Cutting-Edge Technologies
Renugadevi. R, Maridu Bhargavi, G.Kalaiarasi, P.Ranjith Kumar, A. Arul Edwin Raj and B.Saritha
Produktdetaljer
Biografisk notat
Thangavel Murugan is Assistant Professor in the Department of Information Systems and Security, College of Information Technology, and a Faculty Fellow of the Center for Excellence in Teaching and Learning at United Arab Emirates University. He holds a Doctorate from Madras Institute of Technology, Anna University, Chennai, and has over 11 years of teaching and research experience. His research specialization includes information security, high-performance computing, blockchain, and educational technology.
Karthikeyan Periasamy is Associate Professor at Thiagarajar College of Engineering (TCE), Madurai, since 2007. He completed his Ph.D. program in Information and Communication Engineering at Anna University in 2015. His research interests include computational intelligence algorithms and educational technology (ET). He has published over 55 papers in international journals, conferences, and book chapters.
A.M. Abirami is Associate Professor at Thiagarajar College of Engineering. She holds a PhD in Text Analytics and Semantic Web. Her interests include data analytics, natural language processing, and engineering education. She has several publications in these areas and has received awards for her work in engineering education.