Students’ voices on generative AI: perceptions, benefits, and challenges in higher education

Cecilia Ka Yuk Chan; Wenjie Hu

doi:10.1186/s41239-023-00411-8

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Students’ voices on generative AI: perceptions, benefits, and challenges in higher education

Cecilia Ka Yuk Chan, Wenjie Hu

Springer Nature (2023) • Volume 20, Issue 1

Empirical-ClinicalCrossSectionalPDF AvailableGrade Eligible⚠️ Moderate Risk Flags

Overall Assessment

Limited Methodological Quality

Assessment created by PaperScorers Medical AI v0.1.0 on Dec 14, 2025

D

43/100

Key Takeaways

•Cross-sectional survey of 399 HK students; generally positive towards GenAI.
•Perceived benefits: personalised support, writing, research; 24/7 access.
•Key concerns: accuracy, privacy/ethics, over-reliance, jobs, values, policy gaps.
•Bivariate stats only; no confounder adjustment or multiplicity control.
•Data on request; no preregistration; COI none; convenience sample limits generalisability.

Conclusion

Useful snapshot of HK students’ GenAI perceptions; timely but methodologically modest and limited in generalisability.

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Quality Dimensions

Bias & Integrity

COI handling, outcome switching, selective reporting

C-55

Methods Rigour

Protocol clarity, sampling, controls

D44

External Validity

Generalisability, setting, population

D40

Transparency & Reproducibility

Data/code/materials availability, preregistration

D-30

Significance & Novelty

Contribution vs prior art; context if known

C60

Statistical Validity

Models, assumptions, multiplicity, CI priority

D-35

Integrity & Transparency

Integrity checks

P-hacking risk?

?

Review recommended

Signs of selective hypothesis testing or analysis choices that could inflate false positive rates.

Outcome switching?

?

Review recommended

Evidence that primary outcomes were changed during or after the study, potentially distorting results.

Conflict of interest?

?

None suspected

Authors have financial or other relationships that could bias research findings.

Data integrity issues?

?

None suspected

Concerns about the accuracy, completeness, or authenticity of the reported data.

Open science signals

Research data

?

Upon request

Raw data used in the analyses is publicly available or accessible upon request.

Analysis code

?

Not shared

Code or scripts used to analyze the data are shared for reproducibility.

Study materials

?

Not shared

Protocols, questionnaires, and other materials are publicly available.

Premise

Primary Research Question

Three RQs: familiarity; perceived benefits/challenges; effective integration of GenAI in HE.

Hypothesis is Falsifiable

?Unclear

Exploratory survey with no a priori hypotheses.

PICO Framework

Population✓Yes

UG/PG students from six HK universities; 10 faculties.

Intervention—Not specified

No intervention; observational survey.

Comparison?Unclear

Bivariate t-tests/correlations; no prespecified primary comparisons.

Outcome✓Yes

Knowledge, willingness, concerns scales and open-ended responses.

Literature Positioning

Literature Review Balanced

Well covered

Broad prior work on AI/GenAI in HE cited across domains.

Evidence: pp. 2–5 References list

Research Gap Clearly Stated

Well covered

Notes lack of GenAI-specific student perception studies.

Evidence: p. 6 (end of literature review)

Stated Contribution Clear

Well covered

Explores students’ familiarity, benefits/challenges, and integration implications.

Evidence: Abstract; p. 2 Highlights

Study Provenance

Peer reviewed venueFunding disclosedNo conflicts declared3 affiliations listed

Authors & Affiliations

University of Hong Kong, Hong Kong, China

Funding Statement

No funding has been received for this study.

Conflicts of Interest

The author declares that one has no competing interests.

Peer Review

✓Published in International Journal of Educational Technology in Higher Education (SpringerOpen).

Evidence: p. 16 (Published online: 17 July 2023)

Methodological Assessment

Observational StudySTROBEObservational

7 applicable checklist items.

Yes 3 · No 3 · Unclear 1

Study Design & Definitions

✓

Participant Selection Described

Convenience online sampling across six HK universities described.

Evidence: p. 6 Methodology

✓

Variable Definitions Clear

Likert items and constructs shown in Tables 2–4.

Evidence: pp. 7–9 Tables 2–4

✗

Confounding Addressed

Only bivariate tests; no adjustment for confounding.

Evidence: pp. 7–9 Results

✗

Temporality Established

Cross-sectional design; temporality not established.

Evidence: p. 6 Design

✓

Exposure Definition Anchored

Use-frequency categories defined (never/rarely/sometimes/often/always).

Evidence: p. 7 Table 1

Bias Mitigation

✗

Selection Bias Addressed

Sampling bias acknowledged; no mitigation/weighting.

Evidence: pp. 15–16 Limitations

?

Misclassification Assessed

No measurement validity/reliability metrics reported.

Evidence: p. 6–7 Methods

Qualitative ResearchQualitative RigorQualitative

6 applicable checklist items.

Yes 1 · No 4 · Unclear 1

✗

Sampling Strategy Justified

Qual component from open-ended survey; no qualitative sampling rationale.

Evidence: p. 6 Methods

✗

Data Saturation Evidenced

No statement of saturation.

Evidence: p. 6 Methods

✗

Reflexivity Addressed

No reflexivity or positionality discussion.

Evidence: pp. 6–11

✗

Triangulation or Member Checking

No triangulation or member checking reported.

Evidence: p. 6 Methods

✓

Coding Process Transparent

Two coders; 50-response subset; discrepancies resolved; codebook created.

Evidence: p. 6 Methods

?

Thick Description for Transferability

Findings summarised; limited depth/transferability detail.

Evidence: pp. 9–13

Modules without applicable checklist items (8)

These designs were fully marked as not applicable but remain available for reference.

Abstract

This study explores university students’ perceptions of generative AI (GenAI) technologies, such as ChatGPT, in higher education, focusing on familiarity, their willingness to engage, potential benefits and challenges, and effective integration. A survey of 399 undergraduate and postgraduate students from various disciplines in Hong Kong revealed a generally positive attitude towards GenAI in teaching and learning. Students recognized the potential for personalized learning support, writing and brainstorming assistance, and research and analysis capabilities. However, concerns about accuracy, privacy, ethical issues, and the impact on personal development, career prospects, and societal values were also expressed. According to John Biggs’ 3P model, student perceptions significantly influence learning approaches and outcomes. By understanding students’ perceptions, educators and policymakers can tailor GenAI technologies to address needs and concerns while promoting effective learning outcomes. Insights from this study can inform policy development around the integration of GenAI technologies into higher education. By understanding students’ perceptions and addressing their concerns, policymakers can create well-informed guidelines and strategies for the responsible and effective implementation of GenAI tools, ultimately enhancing teaching and learning experiences in higher education.

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Study Overview

Study Design

Cross Sectional

Primary classification used to evaluate this research.

Population

Undergraduate and postgraduate students from six Hong Kong universities across disciplines.

Brief summary of who or what was studied.

Sample Size

399

Number of participants included in the study analyses.

Pre-registration

Not registered

Indicates whether the protocol or trial was registered before data collection.

Primary Outcome

Unclear whether the primary outcome was pre-specified.

Effect Size

correlation: r=0.326 (p<0.001) willingness vs frequency of use

Reviewer Notes

Cross-sectional online survey; convenience sampling; descriptive stats, t-tests and correlations; no intervention or follow-up.

Publication Details

DOI

10.1186/s41239-023-00411-8

Published

July 16, 2023

Citations

1225 citations

FWCI

64.09

External Resources

DOI Link OpenAlex

Disclaimer: This assessment is generated by AI and should not be the sole basis for clinical or research decisions. Always review the original paper and consult with domain experts.

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