While generative artificial intelligence (GenAI) is widely utilized for content creation, the ongoing debate regarding the interplay between human agency and GenAI remains unsettled. Grounded in Bandura’s (2001) theory of agency, this qualitative study explored a cohort of college students’ experiences with GenAI (specifically ChatGPT) for content creation by investigating the determinants and influences on the use of GenAI, as well as their agency in the GenAI-assisted content creation process. The study employed semi-structured in-depth interviews with fourteen university students, coupled with the collection of screenshot records of their GenAI activities. Drawing on thematic analyses, the findings suggested three primary factors influencing student GenAI adoption, including efficiency, high-quality content, and inspiration/ideation. Throughout the content creation process, students excised their agency as they engaged in critical thinking while using GenAI, actively thinking about, evaluating, and adapting their content creation behaviour. This study also showed that the use of GenAI yielded mixed effects on students’ decision-making ability and creativity. It emphasized the changes in students’ decision-making roles in content creation and the sense of “restriction” they experienced. These findings offer insights into how to balance students’ human agency with their use of GenAI, particularly by highlighting the importance of transparency in GenAI systems and emphasizing the need to foster students’ critical thinking throughout the GenAI-assisted content creation process.
As artificial intelligence (AI) tools become increasingly integrated into educational settings, understanding how they align with learners’ needs and support meaningful engagement has become a research priority. Among emerging AI tools, Google’s NotebookLM offers a novel, multimodal learning experience that allows students to interact with personalized content through summaries, podcasts, mind maps, and simulated dialogues. However, empirical evidence on its effectiveness from the learner’s perspective remains limited. This quantitative study examines the effectiveness of NotebookLM in enhancing student engagement and perceived academic performance, utilizing the Task-Technology Fit (TTF) model as a theoretical framework. The study involved a convenience sample of 61 third-year undergraduate students from a university in Saudi Arabia who used NotebookLM to study project management course materials. Data were collected using a structured questionnaire with measurement items adapted from previously validated instruments, complemented by a post-session quiz designed to assess students’ understanding of the instructional material. Our findings highlight that content quality played a key role in shaping students’ perceptions of how well the technology supported their learning, alongside technology features. Importantly, a higher perceived fit between tasks and technology was positively associated with higher student engagement and perceived academic performance (i.e., students’ self-reported perceptions of productivity, effectiveness, and task completion rather than objective grades), while a quiz was administered as an additional learning check. This study discusses practical implications for educators and instructional designers and offers directions for future research on the broader adoption and long-term impact of AI multimodal learning assistants in higher education.
Design thinking (DT), an innovative approach to solving complex problems, has been widely used to support creativity development. However, the potential of DT to support creative self-efficacy (CSE) and the role of design thinking mindsets (DTM) in this process remain underexplored. This study designed a gamified, scenario-driven DT intervention implemented at a university in Hong Kong, examining students’ DTM and CSE and their experiences during the intervention. An explanatory sequential mixed-methods design was employed. Pre- and post-intervention questionnaires assessed DTM and CSE, followed by qualitative interviews exploring students’ experiences. Quantitative results showed significantly higher posttest scores in both DTM and CSE compared with pretest scores. Qualitative findings further illuminated students’ DT experiences including greater tolerance for ambiguity, openness to opportunities, attention to users’ perspectives, iterative engagement with feedback, and increased creative confidence through collaborative teamwork, tangible product development, and inspirational support. The findings also suggest that scenario-driven learning provided contextual grounding for DT activities, while gamification offered motivational and social affordances for engagement. The study contributes theoretically by providing exploratory insight into the relationship between DTM and CSE within DT interventions, and practically by illustrating how gamification, scenario-based learning, prototyping, feedback, and collaborative activities can be integrated into technology-enhanced DT education.
The rapid diffusion of Artificial Intelligence (AI) is reshaping practices in higher education, particularly in distance education institutions where technology-mediated systems dominate instructional and administrative operations. However, the extent to which staff attitudes toward AI translate into collective organizational learning remains unclear. This study investigates the relationship between employees’ attitudes toward AI and organizational learning within a large-scale distance education institution in Türkiye. A total of 239 staff members, 88.3
