Incorporating AIGC into design ideation: A study on self-efficacy and learning experience acceptance under higher-order thinking

Abstract

The concept of higher-order thinking skills (HOTS) is universally acknowledged as a key component for the development of exceptional talents in the 21st century. Addressing the challenge of how to accurately and effectively nurture these skills in students has emerged as a pivotal topic in educational discourse. This research delves into the practical use of artificial intelligence-generated content (AIGC) in the realm of design thinking pedagogy, investigating its potential impact in bolstering students' HOTS. The research aims to discern the differences in student self-efficacy and receptivity to learning experiences between AIGC and conventional teaching methodologies, particularly across various student competency levels. An empirical study involving 119 students was conducted, offering both quantitative and qualitative insights into their self-efficacy and receptiveness to learning experiences, highlighting notable distinctions among diverse student groups. The findings suggest a significant edge of AIGC in enhancing student self-efficacy, with consistent results across different skill levels. However, variations in learning experience receptivity were observed among students of different abilities. An extensive analysis and discussion of these data underscore the necessity for educators to intricately understand and address the unique needs and challenges of students when devising and implementing AIGC-based teaching strategies. This approach ensures a more targeted and effective support for the learning and development of students with varying capabilities. The study offers empirical evidence and theoretical direction for the application of AIGC in educational settings, empowering educators to more precisely cater to the diverse learning needs and developmental trajectories of their students.