Harnessing Generative AI in Education: From Theory to Real-World Impact

The rise of generative artificial intelligence (GenAI) is transforming the education industry. GenAI models, particularly large language models (LLMs), have emerged as powerful tools capable of driving innovation, improving efficiency, and delivering superior services for educational purposes. This paper provides an overview of GenAI for educational purposes, from theory to practice. We have developed a chatbot for summarizing dialogues. In our research work, we have used strategies like zero-shot, one-shot, and few-shot inferencing and also fine-tuned the FLAN-T5 model to serve our purpose of summarization for educational tasks using PEFT (Parameter-Efficient Fine-Tuning) techniques like LoRA (Low-Rank Adaptation) and Prompt Tuning. We have also utilized the technique of Reinforcement Learning with PPO (Proximal Policy Optimization) and PEFT to generate less toxic summaries. The model's performance is quantitatively evaluated using the ROUGE metric and toxicity evaluation metrics. The chatbot can summarize dialogues and is of immense interest to users in the real world. In our research work, our findings demonstrate significant improvements in summarization quality and toxicity reduction, contributing to the development of safer and more effective AI systems.