Leveraging Generative AI in Teaching and Learning


Mr. Keshava P Rangarajan

The workshop planned in the ICONWIL-2024 conference shall cover how “customized quizzing” based on the content covered in a course can be implemented using Generative AI, in the assessment of students.

This workshop shall make use of Generative AI to train the large language models on the content covered in the classroom, and the content available on the internet. At its core, Generative AI is a technology that can enable artificial intelligence systems to continuously learn and adapt in response to changing circumstances. This is making it very popular in digital transformation initiatives for business processes and operations in the industry. Similarly, it has huge potential for improving the teaching learning process in the education industry.

A large language model (LLM) is a deep learning algorithm that can perform a variety of NLP (Natural Language Processing) tasks. Large language models use transformer models and are trained using massive datasets - hence, large. This enables them to recognize, translate, predict, or generate text or other content. LLMs acquire the ability to generate general purpose language by learning statistical relationships from text documents during a computationally intensive self-supervised and semi-supervised training process. 

The objective of work integrated learning courses is to enable the participants to apply the concepts learnt in their work environment. The students enrolled in these courses have diverse education backgrounds and industry affiliation. The learning of the students in such cases can be assessed in terms of industry relevance by using "customised quizzing or evaluation". By this term, we mean the quizzes or evaluation components that are customised according to the domain of the program in which the student has enrolled, or according to the background of the student. For example, if we have to assess the students on concepts related to probability, the questions for students enrolled in MBA Finance can be contextualised for BFSI industry, while the questions for a student working in healthcare sector or enrolled in MBA in Hospital and Health Systems Management can be contextualised for healthcare industry.

About Mr. Keshava P Rangarajan:

Mr. Keshava P Rangarajan is a passionate, experienced and hands-on technology leader. He has extensive hands-on experience in AI, ML, DL & hybrid cloud technologies with emphasis on IBM Cloud, AWS, Azure & GCP. He is the founder of an innovative stealth startup, driving Spanda platform's AI and Quantum Computing Marketplace. He has worked in leadership roles in Technology Architecture earlier in various companies including as Client CTO in IBM, USA.

Mr. Keshava P Rangarajan has been instrumental in the formation of the Open Earth Community, which now has 430 companies and 771 projects. He has over 40 patents in areas covering analytics, optimization, search and semantics, real-time sense and respond, deep learning, artificial intelligence, automation, blockchain, online planning, dynamic pricing, optimization, cloud based algorithmic processing, simulation, production, and more.

Mr. Keshava P Rangarajan along with Dr Srinath Madasu and Dr Egidio Marotta has authored a book titled “Hybrid Data Science (HDS) Modeling Approaches for Industrial and Scientific Applications” that has been published in 2022. He is also a seasoned public speaker with extensive experience delivering keynotes as well as talks in more than 100 conferences, industry events, Webinars and Partner Events globally.