This study investigates the factors influencing undergraduate students’ self-directed learning (SDL) abilities in generative Artificial Intelligence (AI)-driven interactive learning environments. The advent of generative AI has revolutionized interactive learning environments, offering unprecedented opportunities for personalized and adaptive education. Generative AI supports teachers in delivering smart education, enhancing students’ acceptance of technology, and providing personalized, adaptive learning experiences. Nevertheless, the application of generative AI in higher education is underexplored. This study explores how these AI-driven platforms impact undergraduate students’ self-directed learning (SDL) abilities, focusing on the key factors of teacher support, learning strategies, and technology acceptance. Through a quantitative approach involving surveys of 306 undergraduates, we identified the key factors of motivation, technological familiarity, and the quality of AI interaction. The findings reveal the mediating roles of self-efficacy and learning motivation. Also, the findings confirmed that improvements in teacher support and learning strategies within generative AI-enhanced learning environments contribute to increasing students’ self-efficacy, technology acceptance, and learning motivation. This study contributes to uncovering the influencing factors that can inform the design of more effective educational technologies and strategies to enhance student autonomy and learning outcomes. Our theoretical model and research findings deepen the understanding of applying generative AI in higher education while offering important research contributions and managerial implications.

Free-text data with long duration and continuity have great potential for studying environmental concerns and emotional expressions in child development. Based on ecosystem theory, using topic modeling and sentiment analysis in text mining to mine 4556 free-text writing data from first to sixth grade in elementary school, this study aims to reveal concerned topics’ evolutionary trends and sentiment expression differences in topics during elementary school children’s development. The results show the following: (1) Children prefer to focus on the topics of school and family in elementary school; (2) With the growth of grades, the proportion of family topics continues to decline, while that of social culture topics keeps rising; (3) When describing school, family, social culture, and interest, children mostly express negative emotions, and when describing peers and ability they mostly express positive emotions; (4) As the grade increases, the emotional expression on social culture topics become negative, while that on ability and interest topics become positive, and there are more differences in emotion expression between topics in junior and senior elementary grades. Discussion and conclusion are discussed at the end.