An active instructional approach based on the SAMR framework: Integrating AIGC into undergraduate freshmen learning

Abstract

The question of how to use artificial intelligence generated content (AIGC) properly to enhance learning among college students is a key concern for contemporary educators. Although previous studies have discussed the influence of AIGC on college teaching and student learning and its functions in this context, there remains a lack of discussions regarding ways of guiding students' use of AIGC and studies on the specific topic of helping college freshmen use AIGC properly. Based on the substitution, augmentation, modification and redefinition (SAMR) model, this study develops a progressively active teaching framework that integrates AIGC into learning. This framework is used to design learning activities for general education courses targeting freshmen. This exploratory study was conducted in the context of a 16-week course. During the teaching process, AIGC interaction log data and AIGC experience records were collected from students, following which data processing was conducted using the discourse analysis, quantitative statistical analysis, and epistemic network analysis (ENA) methods to obtain the ultimate results of this study: (1) A combination of active teaching with the SAMR model can improve the quality of interactions between students and AIGC; (2) teaching strategies rooted in active learning can enhance students' ability to use AIGC; and (3) improvements in students' technical skills strengthen the quality of their interactions with AIGC. This study makes novel contributions to the literature on active learning strategies for teachers and curriculum designers, and it offers practical guidance for educational practitioners and college students regarding the integration of AI technology into both teaching and learning.