Scholarlink

<p>Generative Artificial Intelligence (GenAI) is increasingly embedded in university courses, shaping students’ learning experiences, cognitive engagement, and productivity. Despite its rapid diffusion, it remains unclear to what extent students’ individual dispositions influence their perceptions of GenAI, alongside motivation, perceived usefulness, and attitudes. To address this gap, the present study examines how three traits—Critical Openness, Growth Mindset, and Innovativeness—affect students’ evaluations of GenAI. A mixed-methods design was adopted, combining open-ended responses and Likert-scale questionnaires from a sample of 43 university students. Content analysis identified four main dimensions of students’ experiences with GenAI: Cognitive (Support for Creativity and Learning; Errors and Inaccuracies), Emotional (Positive Attitudes; Emotional and Psychological Impact), Operational (Efficiency and Time-Saving; Accessibility and Use; Technical Limitations in Use; Output Quality), and Ethical-social (Ethical and Social Concerns). Chi-square analyses revealed significant associations between Critical Openness and both Output Quality and Errors and Inaccuracies, between Growth Mindset and Efficiency and Time-Saving, and between Innovativeness and Efficiency and Time-Saving. Hierarchical binary logistic regression indicated that Critical Openness predicted Output Quality, while Innovativeness and age predicted Efficiency and Time-Saving. The findings highlight implications for the design of AI literacy programs aimed at fostering critical and reflective engagement with AI-generated outputs and supporting more effective pedagogical integration of GenAI in higher education.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Exploring Student Dispositions and Experiences with Generative Artificial Intelligence: An Exploratory Mixed-Methods Study in Higher Education

  • 期刊论文

  • 发表日期：

    2026年1月27日

查看全文
  • Vanessa Donadel,
  • Marco Zuin

摘要

Generative Artificial Intelligence (GenAI) is increasingly embedded in university courses, shaping students’ learning experiences, cognitive engagement, and productivity. Despite its rapid diffusion, it remains unclear to what extent students’ individual dispositions influence their perceptions of GenAI, alongside motivation, perceived usefulness, and attitudes. To address this gap, the present study examines how three traits—Critical Openness, Growth Mindset, and Innovativeness—affect students’ evaluations of GenAI. A mixed-methods design was adopted, combining open-ended responses and Likert-scale questionnaires from a sample of 43 university students. Content analysis identified four main dimensions of students’ experiences with GenAI: Cognitive (Support for Creativity and Learning; Errors and Inaccuracies), Emotional (Positive Attitudes; Emotional and Psychological Impact), Operational (Efficiency and Time-Saving; Accessibility and Use; Technical Limitations in Use; Output Quality), and Ethical-social (Ethical and Social Concerns). Chi-square analyses revealed significant associations between Critical Openness and both Output Quality and Errors and Inaccuracies, between Growth Mindset and Efficiency and Time-Saving, and between Innovativeness and Efficiency and Time-Saving. Hierarchical binary logistic regression indicated that Critical Openness predicted Output Quality, while Innovativeness and age predicted Efficiency and Time-Saving. The findings highlight implications for the design of AI literacy programs aimed at fostering critical and reflective engagement with AI-generated outputs and supporting more effective pedagogical integration of GenAI in higher education.