Guidelines for Applying Artificial Intelligence to Promote Self-Directed Learning Among Students in the Digital Age

Abstract

The objectives of this study were: (1) to examine the current state of artificial intelligence (AI) usage in students’ self-directed learning, (2) to investigate the effects of AI use on students’ self-directed learning competencies, and (3) to explore appropriate approaches for applying AI to enhance students’ self-directed learning competencies in the digital era. The sample consisted of 420 undergraduate students selected through multi-stage sampling, as well as 15 instructors with experience integrating AI into their teaching, who participated in in-depth interviews. The research instruments included (1) a questionnaire on students’ AI use and self-directed learning and (2) an in-depth interview guide for instructors. Quantitative data were analyzed using descriptive statistics, while qualitative data were analyzed through content analysis. Research findings: (1) Most students had experience using artificial intelligence (AI) technologies for self-directed learning, with intelligent chatbots being the most commonly used tools. However, the frequency of AI use remained relatively low. Students primarily used AI to explain complex concepts, complete assignments, and practice language skills. Common interactions included requesting the generation of practice exercises, editing written work, and answering content-related questions. Additionally, students employed various strategies to enhance the quality of AI responses, such as asking for step-by-step explanations or requesting illustrative examples. (2) The use of AI had a significantly positive impact on students’ self-directed learning competencies, especially in academic achievement, motivation and attitudes, learning planning, and cognitive processes. Notably, students improved their ability to apply knowledge, set clear learning goals, and learn according to their individual pace and preferences. (3) The study recommends several AI application strategies, including personalized learning, promoting critical thinking and AI literacy, fostering lifelong learning skills, knowledge creation, purposeful integration of AI into learning activities, ethical and technical AI literacy training, process-based learning assessments, and the development of digital learning environments that support self-directed learning.