The swift progression of technology has radically reshaped our lives, becoming a big part of our daily routines and paving the way for advancements in communication, automation, and information processing. OpenAI, a company at the forefront of artificial intelligence since 2015, has made remarkable strides towards making AI accessible and beneficial for all (OpenAI, n.d.). A notable accomplishment in their journey has been the development of Chat Generative Pre-trained Transformers (ChatGPT). This study aims to identify and explore the factors influencing students' acceptance and use of ChatGPT in academic settings. Despite the rising prominence of ChatGPT across various disciplines, understanding its acceptance and utilization, particularly within the sphere of higher education, remains limited. ChatGPT holds immense potential as a valuable asset for both students and educators. Utilizing the Unified Theory of Acceptance and Use of Technology (UTAUT) and a quantitative research approach, investigating these factors. The results suggest that student acceptance and use lies in Behavioral Intention, while Behavioral Initiation is influenced by both Effort Expectancy and Performance Expectancy.

<p dir="ltr">Unmanned Aerial Vehicles (UAVs) are increasingly applied in agricultural tasks such as crop monitoring, especially with AI-driven enhancements significantly increasing their autonomy and ability to execute complex operations without human interventions. However, existing UAV systems lack efficiency, intuitive user interfaces using natural language processing for command input, and robust security which is essential for real-time operations in dynamic environments. In this paper, we propose a novel solution to create a secure, efficient, and user-friendly interface for UAV control by integrating Large Language Model (LLM) with the case study on agricultural environment. In particular, we designed a four-stage approach that allows only authorized user to issue voice commands to the UAV. The command is issued to the LLM controller processed by LLM using API and generates UAV control code. Additionally, we focus on optimizing UAV battery life and enhancing scene interpretation of the environment. We evaluate our approach using AirSim and an agricultural setting built in Unreal Engine, testing under various conditions, including variable weather and wind factors. Our experimental results confirm our method's effectiveness, demonstrating improved operational efficiency and adaptability in diverse agricultural scenarios.</p>