AI evidence extraction

Adoption of Generative AI in Higher Education: Perceptions of Journalism Students

Laura Alonso-Muñoz, Andreu Casero-Ripollés · Information · 2026

AI-generated evidence extraction, verified across multiple analytical personas. Not a substitute for the peer-reviewed original.

6/10

Relevance

1/4

Quality (LMQS)

Evidence

Citations

7.33

FWCI

Top 10%

Impact

This is an AI analysis. Read the peer-reviewed original at the publisher: https://doi.org/10.3390/info17020189

Methodology & findings

Study design

Online cross-sectional survey design with quantitative analysis of frequency distributions and ANOVA

Sample

N = 281, 4 groups

Primary method

Analysis of variance (ANOVA) with F-test and partial eta squared effect size computation. Descriptive statistics (percentages) reported for usage frequencies.

Main result

The study found that "93% of students report using Gen AI, with significantly higher usage among advanced students (i.e., 3rd and 4rth academic year Journalism degree students)" and that "77.2% of respondents use it for learning or studying, while 44.2% use it to complete class assignments." Additionally, "students primarily turn to artificial intelligence to perform tasks more efficiently and effectively and to achieve better results."

Reports effect sizes and confidence intervals.

Research paradigm

Positivist/Empiricist

Author conclusions

The authors conclude that "These findings provide valuable insights for reorienting undergraduate curricula to address the challenges of generative AI and to educate students on its ethical and appropriate use." They also note that "students acknowledge certain risks in the academic use of Gen AI, they perceive its benefits more clearly than its limitations" and "they are aware that they need more AI literacy."

Risk of bias

Selection bias: Single institution (Universitat Jaume I de Castelló) may not be representative of broader higher education population; Self-selection bias: Voluntary online survey participation; Sampling frame limited to journalism students, reducing generalizability; No control for response bias or non-response bias characteristics; No demographic weighting or stratification apparent; Selection bias: respondents may be self-selected volunteers more interested in AI; Sampling frame limited to single institution (Universitat Jaume I de Castelló, Spain); No control group for comparison; Self-reported data on AI usage susceptible to social desirability bias; Selection bias: Self-selected online survey respondents may differ systematically from non-respondents; Sampling: Single institution (Universitat Jaume I de Castelló, Spain) limits generalizability; Social desirability bias: Students may over-report Gen AI usage or underreport unethical use; Cross-sectional design: Cannot establish causal relationships

Open questions raised

The authors identify a need for curriculum reorientation and enhanced AI literacy education, though specific future research directions are not explicitly detailed in the abstract.
The authors identify the need to reorient undergraduate curricula and educate students on ethical and appropriate use of generative AI, suggesting gaps in current curriculum design and AI literacy instruction.
The authors identify the need for curriculum reorientation to address generative AI challenges and the importance of educating students on ethical and appropriate AI use. They note students recognize the need for improved AI literacy.

Data: not_statedCode: not_statedExtracted from: pdfAgreement 60%

Explore related topics

AI Literacy Training Human-AI Collaboration Survey