AI evidence extraction

“We Need to Analyze Students GenAI Use”: Towards an AI Adoption Framework for Higher Education

Lasse Bischof, Eva-Maria Schön, Maria Rauschenberger, Michael Neumann · SerWisS (Hochschule Hannover) · 2025

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

7/10

Relevance

1/4

Quality (LMQS)

Evidence

Citations

0.00

FWCI

This is an AI analysis. Read the peer-reviewed original at the publisher: https://doi.org/10.25968/opus-3794

Methodology & findings

Study design

Case study with mixed methods approach using a questionnaire that included both quantitative and qualitative questions conducted at the University of Applied Sciences and Arts Hannover

Sample

N = 151, 4 groups

Primary method

not_stated (quantitative and qualitative data mentioned but specific statistical methods not detailed in abstract)

Main result

The study found that "129 (n=151) of the students use GenAI tools in their studies," demonstrating high adoption rates among learners. Based on the synthesis of results, the authors created "a systematic description for GenAI integration into higher education" to address the gap between student adoption and institutional frameworks.

Reports effect sizes and confidence intervals.

Research paradigm

mixed_methods (quantitative and qualitative)

Author conclusions

The authors conclude that "with the AI Adoption Framework, higher education institutions will be able to review and adapt their regulations and curricula in relation to GenAI to keep up with the pace of change in the field," offering specific solutions for institutional integration of generative AI tools.

Risk of bias

Selection bias: Case study at single institution may not be generalizable; Self-selection bias: Students completing questionnaire may differ from non-respondents; No mention of response rate or sampling strategy; Cross-sectional design limits causal inference; Selection bias: single institution case study may not be generalizable; Potential non-response bias from questionnaire administration; Self-reported use of GenAI tools subject to recall and social desirability bias; Self-reported data from questionnaire; No control group for comparison

Limitations

null_value_not_explicitly_stated

Open questions raised

The authors identify a gap between rapid adoption of GenAI tools among students and the lag in institutional frameworks and curricula in higher education institutions.
The authors identify the gap that "institutional frameworks in higher education often lag behind" student adoption of GenAI tools, necessitating systematic integration approaches.
The authors identify that "institutional frameworks in higher education often lag behind" student adoption of GenAI tools, and that there is a need for systematic frameworks to integrate GenAI into higher education curricula and regulations.

Data: not_statedCode: not_statedExtracted from: pdfAgreement 60%

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