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AI evidence extraction

A survey on students’ perceptions of GenAI in higher education

O. Noroozi, N. Taghizadeh Kerman, S.K. Banihashem · Socio-Environmental Systems Modeling · 2024

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

7/10
Relevance
1/4
Quality (LMQS)
E
Evidence
0
Citations
0.00
FWCI

Methodology & findings

Study design

Online survey administered to higher education students

Sample

N = 940, 1 group

Main result

The study found that "students generally found GenAI tools useful, easy to use, and moderately trustworthy." Additionally, "they reported that GenAI tools help with task completion and improve learning performance" and "effort expectancy was high, with students finding the tools intuitive and easy to learn." However, "motivation levels were moderate, and there was variability in intention to use."

Reports effect sizes.

Research paradigm

Positivist/empiricist

Author conclusions

"These findings suggest that while GenAI tools show promise in supporting students' performance, there are challenges related to student motivation and long-term intention to use." The authors further note that "the study provides insights for educators and institutions aiming to integrate GenAI tools effectively into higher education."

Risk of bias

Selection bias: Sample limited to life sciences courses at one Dutch university; Self-selection bias: Students who completed the survey may differ from non-respondents; Social desirability bias: Possible in self-reported perceptions; Selection bias: Participants were self-selected survey respondents from a single institution; Attrition: Unknown response rate from the 940 surveyed students; Confounding: No control group mentioned; causality cannot be inferred; Geographic limitation: Single Dutch university, limiting generalizability; Selection bias: participants were self-selected through voluntary online survey participation; Potential response bias: limited to students willing to complete surveys; Geographic limitation: single Dutch university may limit generalizability; Temporal specificity: conducted in 2023-2024 academic year, GenAI tools rapidly evolving; No control group for comparison

Open questions raised

  • The authors identify that "we still lack insight into their views on its use and potential applications" of GenAI tools in higher education, and they suggest future work is needed to address challenges in student motivation and long-term adoption intentions.
  • The authors identify the need for research on long-term sustainability of GenAI tool usage and strategies to enhance student motivation toward these tools in educational contexts.
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