## 1 Introduction  
The integration of Artificial Intelligence (AI) into education has introduced new approaches for evaluating student engagement, particularly in Science, Technology, Engineering, Arts, and Mathematics (STEAM) programs. This paper explores the development of an AI-based text sentiment analysis system designed to evaluate learning engagement levels by analyzing textual data from students, such as responses, feedback, and discussions. By utilizing natural language processing techniques, this system aims to provide educators with actionable insights to enhance teaching strategies and foster a more interactive and effective STEAM learning environment.  
[PERSON]'s taxonomy, introduced in 1956, originally categorized learning objectives into six ascending levels. A recent study [1] updated the taxonomy to encompass these levels: remembering, understanding, applying, analyzing, evaluating, and creating, as illustrated in Figure 1. According to [PERSON]'s framework, the highest level of learning is the ability to produce original work. This study adopted this framework to design its research objectives [1].  
STEM integrates the disciplines of science, technology, engineering, art, and mathematics, and STEAM education assists in developing problem-solving skills by fostering cross-disciplinary integration [2; 3]; hands-on STEM activities designed through 6E orientation can help enhance technological literacy [3]. Computational thinking (CT) is designed to train problem-solving methods, help students understand the nature of the problem, and develop possible solutions to complex problems. These solutions are then presented in a manner that can be understood by computers, humans, or both [4]. Therefore, the main focus of CT is to learn to program problems, solve them systematically, and practice creativity [5; 6]. To reach the sixth level of [PERSON]'s learning goals, the students must be able to create their own products [1]. Further, inquiry-based learning involves asking questions, making observations, conducting research to discover information that has been recorded, developing experimental methods and tools for data collection, collecting, analyzing, and interpreting data, outlining possible explanations, and creating predictions for future research. This helps learners explore learning topics, achieve deeper learning, and create their own work [7].  
Text sentiment analysis, also known as opinion mining or emotion AI, involves studying people's opinions, sentiments, evaluations, appraisals, attitudes, and emotional states, such as happiness, fear, and surprise, etc. [8]. Sentiment analysis is a valuable tool for gaining insights from social networks [9] and has been widely applied in various domains, including assessing learning engagement in STEAM education, analyzing social media trends in financial markets [10], evaluating customer satisfaction [11], and measuring employee engagement during digital transformation [12].  
Learning engagement is crucial for enhancing learning performance, as highly engaged students are more likely to actively participate in class, understand and retain information, and achieve superior learning outcomes. An AI-based sentiment analysis system can evaluate learners' engagement by analyzing the sentiments expressed in their written feedback, forum posts, or other textual communications. Therefore, the purpose of this study is to develop an AI-based sentiment analysis system to evaluate learners' engagement levels in education.  
Figure 1: [PERSON]’s taxonomy of learning [1].