Research on the Implementation of Algorithmic Thinking and Sentiment Analysis by Applying the Knowledge-Based System
Yan Li · Artificial intelligence · 2025
Artificial intelligence technology is becoming increasingly mature and is being widely implemented and applied in academia and industry to solve practical problems. Due to the gradual growth and high development of AI, a large number of universities lack artificial intelligence literacy teaching for students and faculty members, especially the lack of educational innovation and exploration of algorithmic thinking and sentiment analysis for researchers. AI literacy is an emerging field that aims to equip individuals with the knowledge and skills to understand, interact with, and make informed decisions about AI technologies. This study aims to practice and reform the BERT-based model to develop an algorithmic thinking education innovation exploration curriculum and conduct sentiment analysis. To improve AI literacy and the efficiency of undergraduate students’ programming ability, reduce the burden of data retrieval work on students, and enhance the efficiency of students’ programming learning, this research has developed a series of algorithmic thinking courses based on the BERT model and aims to develop a knowledge graph-based question-answer system to enable undergraduate students to understand the operating rules and basic syntax of programming. The study finds that the system collects data from student and teacher portals and uses these data for sentiment analysis to optimize the system and allow researchers to efficiently obtain information for self-directed learning.