Qualitative case study reveals cognitive hurdles and AI-dependence risks during Python learning in media undergraduates, highlighting the need to integrate computational thinking with domain practice.
HRMARS - Taking the course New Media Data Analysis and Applications as a case study, this research adopts a qualitative approach to explore the challenges faced by humanities and social sciences students in learning Python-based data analysis and the role of artificial intelligence-assisted programming tools. Data were collected through semi-structured in-depth interviews with 15 undergraduate students majoring in Journalism and Internet and New Media, combined with teachers’ reflexive materials. The findings reveal three major challenges in students’ Python learning process. First, students experienced cognitive barriers due to difficulties in establishing meaningful connections between programming learning and media-related professional practices. Second, limited computational training experience and continuous difficulties in code debugging contributed to emotional resistance towards programming learning. Third, while artificial intelligence-assisted tools lowered learning barriers and provided immediate feedback, they also created potential dependence on AI-generated code, which may weaken students’ independent analytical abilities. This study argues that media data analysis courses designed for media students should not primarily aim to develop programming skills for software development, but should instead emphasise the integration of computational thinking, data analysis abilities, and disciplinary practices.
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Zhou Fang (2026) studied this question.
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