An empirical study on the impact of generative artificial intelligence-based learning activities on college students’ deep learning
Year: 2025
Type: article
Abstract: Deep learning, emphasizing the comprehensive understanding and transfer of knowledge, is a critical indicator of learning quality among college students. Generative artificial intelligence (AI), through its support for efficient feedback and personalized learning, offers a new technological approach to fostering deep learning. This paper, grounded in Activity Theory, presents a deep learning activity model enhanced by generative AI technology. A quasi-experimental study was conducted over a semester, using the “Modern Educational Technology and Informatization Practice” course as a case study to validate the model’s effectiveness. The study found that the model positively contributes to learners’ learning process performance, knowledge acquisition, cognitive development, and emotional experience. This paper provides empirical evidence supporting the practical application of generative AI in promoting deep learning among college students, highlighting the need for continuous evaluation and refinement to fully harness its potential in advancing the technological transformation of higher education.
Authors: Wenxin Zhang, Bo Chen, Ci Tang
Institution: Chengdu Normal University
Language: English
Subfield: Information Systems
Field: Computer Science
Domain: Physical Sciences
SDG: Quality education
Open Access status: diamond
Funder: Chengdu Normal University
OpenAlex ID: W4414920057