Artificial Intelligence (AI) is transforming teaching, learning, and educational management. This course, "Integration of Artificial Intelligence in Education Practices," offers a practical understanding of how AI can make education more efficient, personalized, and engaging.
The course begins with an introduction to AI concepts and tools used in education, explaining how they differ from traditional methods. It explores how AI supports lesson planning, content delivery, and the creation of personalized learning experiences tailored to individual students' needs.
It highlights the role of AI in enhancing student engagement and motivation through tools like gamification and interactive platforms. The course also delves into AI-driven assessments, focusing on real-time feedback and automated grading while maintaining the essential involvement of educators.
The use of AI in analyzing student performance is another key focus, showcasing its ability to identify at-risk learners and provide actionable insights to improve outcomes, all while addressing ethical considerations like data privacy. AI's impact on collaboration and communication is examined, including tools like virtual and augmented reality that enable immersive and interactive learning experiences.
Understand the basic concepts of AI and its application in education, and differentiate between AI tools and traditional educational technologies.
Explain how AI enhances teaching, learning, and pedagogical practices through personalized learning and adaptive systems.
Apply AI tools to support lesson planning, content delivery, and assessment techniques in educational settings.
Analyze the impact of AI on student engagement, motivation, and the effectiveness of adaptive learning systems.
Evaluate the ethical considerations, challenges, and potential biases in using AI for educational purposes.
Design AI-driven strategies for enhancing collaboration, communication, and personalized learning experiences in classrooms.
| Course Status : | Ongoing |
| Course Type : | |
| Language for course content : | English |
| Duration : | 8 weeks |
| Category : |
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| Credit Points : | 3 |
| Level : | Undergraduate/Postgraduate |
| Start Date : | 20 Jan 2025 |
| End Date : | 15 May 2025 |
| Enrollment Ends : | 28 Feb 2025 |
| Exam Date : | 18 May 2025 IST |
| Translation Languages : | English |
| NCrF Level : | 4.5 |
Note: This exam date is subject to change based on seat availability. You can check final exam date on your hall ticket.
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swayam@nitttrc.edu.in, swayam@nitttrc.ac.in
Rosé, C. P., Martínez-Maldonado, R., Yacef, K., & Chan, T.-W. (Eds.). (2020). Artificial intelligence in education. Springer. https://doi.org/10.1007/978-3-030-52240-7
Holmes, W., Bialik, M., & Fadel, C. (2019). Artificial intelligence in education: Promises and implications for teaching and learning. Center for Curriculum Redesign.
Woolf, B. P. (2009). Building intelligent interactive tutors: Student-centered strategies for revolutionizing e-learning. Morgan Kaufmann.
Sclater, N. (2017). Learning analytics explained. Routledge. https://doi.org/10.4324/9781315679568
Baker, R. S., & Siemens, G. (2014). Educational data mining and learning analytics. Cambridge University Press.
As an Associate Professor at NITTTR, Chennai, I bring over two decades of expertise in technical education, curriculum development, and digital transformation initiatives. My leadership in implementing ERP systems, advocating digital accessibility, and mentoring educators on emerging technologies reflects my dedication to innovation in education.
With a Ph.D. in SOA and Semantic-based IoT middleware and expertise in Cognitive Computing, Artificial Intelligence, Web Technologies, IoT, Operating Systems, and Distributed Systems, I have guided numerous impactful projects and authored over 40 publications in prestigious journals and conferences. My deep understanding of Operating Systems has enabled me to develop efficient resource management strategies and multitasking frameworks, while my work in Distributed Systems focuses on scalability, fault tolerance, and distributed computing paradigms, crucial for modern cloud and IoT infrastructures.
I have supervised Ph.D. candidates, contributed as an expert committee member for doctoral evaluations, and actively engaged in sponsored research and consultancy projects. My leadership in fostering an AI lab equipped with state-of-the-art NVIDIA-powered infrastructure demonstrates my capability to lead transformative research in Generative AI and LLMs, while my expertise in Distributed Systems strengthens collaborative and decentralized computing initiatives.
As a senior member of IEEE and ACM, I align institutional goals with global priorities, ensuring excellence in education and research. My involvement in the Board of Studies and the development of outcome-based frameworks underscores my commitment to empowering educators and driving advancements in technical education, particularly in fields critical to today’s technology landscape.
"The SWAYAM Course Enrolment and
learning is free. However, to obtain a certificate, the learner must register
and take the proctored exam in person at one of the designated exam centres.
The registration URL will be announced by NTA once the registration form
becomes available. To receive the certification, you need to complete the
online registration form and pay the examination fee. Additional details,
including any updates, will be provided upon the publication of the exam
registration form. For more information about the exam locations and the terms
associated with completing the form, please refer to the form itself."
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