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The maturity of artificial intelligence technology and the emergence of AI-generated content (AIGC) tools have endowed college students with a human-AIGC tools collaboration learning mode. However, there is still a great controversy about its impact on learning effect. This paper is aimed at investigating the impact of the human-AIGC tools collaboration on the learning effect of college students.

In this paper, a hypothesized model was constructed to investigate the effects of dependence, usage purpose, trust level, frequency, and proficiency of using AIGC tools on the learning effect, respectively. This paper distributed questionnaires through random sampling. Then, the improved Analytic Hierarchy Process (AHP) was used to assign weights and normalize data. Lastly, one-way ANOVA and multiple linear regression analyses were used to measure and analyze variables, revealing the mechanism of influence.

The usage purpose, frequency, and proficiency of using AIGC tools have a significant positive effect on learning. Being clear about the usage purpose of AIGC tools and matching the specific study tasks will enhance the learning effect. College students should organically integrate AIGC tools into each learning process, which is conducive to building a learning flow applicable to oneself, improving efficiency, and then enhancing learning effects. The trust level in AIGC tools is significant, but positively and weakly correlated, indicating that college students need to screen the generated content based on their knowledge system framework and view it dialectically. The dependence on AIGC tools has a negative and significant effect on learning effect. College students are supposed to systematically combine self-reflection and the use of AIGC tools to avoid overdependence on them.

Based on the findings, the learning suggestions for college students in human-machine collaboration in the AIGC era are proposed to provide ideas for the future information-based education system. For further research, scholars can expand on different groups, professions, and fields of study.

Previous studies have focused more on the impact of AIGC on the education system. This paper analyzed the impact of the various factors of using AIGC tools in the learning process on the learning effect from the perspective of college students.

Digital transformation is a confidence booster in intrapreneurship, but few have examined its impact on intrapreneurship. Further, quantitative analyses exploring the impact of Chinese enterprises' digital transformation on intrapreneurship at the micro-level are rare. Most enterprises do not have the dividend for digital transformation, and few enterprises have successfully achieved digital transformation through intrapreneurship, internal management re-engineering and technological innovation. This study investigates the effect of digital transformation on intrapreneurship in Chinese real economy enterprises.

The study develops and tests a theoretical model that digital transformation impacts intrapreneurship by promoting working capital turnover and furtherly influencing labor input. Panel data of 1,638 Chinese-listed companies between 2007 and 2020 were used to complete the empirical test.

Digital transformation impacted labor input, with an inverted-U shaped relationship between the two, and labor input significantly stimulated intrapreneurship. This effect promoted labor input's impact on working capital. Chinese real economy enterprises generally increase labor investment to promote intrapreneurship. Heterogeneity analysis revealed that enterprises' asset scale and ownership attributes uniformly affected labor input.

This study provided empirical evidence of the promotional effect of real economy enterprises' digital transformation on intrapreneurship. Further, it advanced the literature by examining this relationship at the micro-level. Moreover, the data sample was long-term and included most industries, thus providing representative results with practical implications.