Cited by 0Academic integrity and plagiarismEthics in Business and EducationArtificial Intelligence in Education

The DOI matches its Crossref record; 20 of the 28 references it cites were found in a registry or an index, and 8 are not checked yet; 1 of the 51 sources its claims cite were checked for support.

Drivers of academic dishonesty and multi-level prevention strategies: an umbrella review

Open access
Estrella-Bote, Valeria de Rosario, Vallespir-Adillón, Maria, Julián, Martín, Comas-Forgas, Ruben·2026·Revista Digital de Investigación en Docencia Universitaria

Abstract

Introducción: Academic dishonesty remains persistent in higher education, exacerbated by online assessment, contract cheating markets and generative AI. Objective: This umbrella review synthesised review-level evidence on drivers of academic dishonesty and prevention strategies recommended for higher education. Methodology: Searches were conducted in Scopus and Web of Science Core Collection from database inception to June 2025, without date restrictions, and complemented with citation searching. Seventy-eight peer-reviewed reviews were included. A structured extraction and coding instrument was piloted; review-level text segments were coded through keyword-assisted content analysis, independent coding and consensus. Findings are reported as non-exclusive review-level frequencies, not causal effect sizes. Results: The most recurrent driver domains were motivation, attitudes and moral reasoning (43.6%), pressure, stress and workload (35.9%), assessment design and teaching context (33.3%), institutional governance gaps (28.2%) and technology-mediated opportunity (26.9%). The most frequent strategy domains were technology/AI and online-integrity guidance (73.1%), embedded integrity education (66.7%), institutional governance and policy (60.3%), monitoring controls (39.7%) and culture/communication (37.2%). Conclusions: The findings support a multi-level integrity programme combining assessment redesign, curriculum-embedded integrity education, staff development, transparent policy, proportionate monitoring and continuing evaluation.

Verified 2026-11-11 · 8 references not checked yet

Overview

AI snapshot

Written from the paper’s full text.

TL;DR

This umbrella review synthesized 78 peer-reviewed reviews (1998–2025) on drivers of academic dishonesty and recommended prevention strategies in higher education. It reports non-exclusive review-level frequencies, with motivation/attitudes/moral reasoning and technology/AI guidance as the most recurrent cause and strategy domains, respectively.

Key findings

  • Most recurrent cause domains were motivation, attitudes and moral reasoning (43.6%), pressure, stress and workload (35.9%), assessment design and teaching context (33.3%), institutional governance gaps (28.2%) and technology-mediated opportunity (26.9%); peer norms and social influence was 24.4%, competition/performance orientation 23.1%, skills deficits/misunderstanding 14.1%, and opportunity/low perceived detection risk 12.8%.
  • Most frequent strategy domains were technology/AI and online-integrity guidance (73.1%, 57/78), embedded integrity education (66.7%, 52/78), institutional governance and policy (60.3%, 47/78), detection/monitoring/technological controls (39.7%, 31/78) and culture/communication/shared responsibility (37.2%, 29/78); assessment redesign was 33.3% (26/78), student skills support 29.5% (23/78), student engagement mechanisms 23.1% (18/78), staff development 16.7% (13/78), supportive learning environment 15.4% (12/78), and evaluation/continuous improvement 9.0% (7/78).
  • The 78 included reviews covered 1998–2025: 53 (67.9%) were published 2020–2025, 16 (20.5%) 2015–2019, and 9 (11.5%) before 2015; at least 30 were classified as systematic reviews, four as scoping reviews, and four as bibliometric reviews, with the remainder narrative, integrative or broadly labelled literature reviews.
  • Findings support a multi-level integrity programme combining assessment redesign, curriculum-embedded integrity education, staff development, transparent policy, proportionate monitoring and continuing evaluation; frequencies are non-exclusive review-level indicators, not causal effect sizes.

Methods

  • Umbrella review design, reported with PRISMA 2020 and PRIOR principles; searches in Scopus and Web of Science Core Collection in June 2025 from database inception, with no date restriction, plus backward and forward citation searching; inclusion required peer-reviewed English-language reviews focused on higher education.
  • Selection: 256 records were exported and deduplicated, removing 123 duplicates and 10 additional records, leaving 123 for title/abstract screening; 29 were excluded, 94 full texts were sought, five were not retrieved, 89 were assessed for eligibility, 11 were excluded (not higher-education focused, n = 7; not available in English full text, n = 4), yielding 78 included reviews.
  • Thematic synthesis coded review-level text segments using a piloted structured extraction/coding instrument, keyword-assisted content analysis, independent coding and consensus; interrater reliability was Krippendorff’s alpha 0.82; AMSTAR 2 was used where review methods were sufficiently reported, and results are reported as non-exclusive review-level frequencies.

Limitations

  • Stated in the paper: the review was restricted to Scopus and Web of Science and to English-language reviews, potentially underrepresenting regional literature, non-English scholarship and grey literature; primary-study overlap was monitored but not quantified through a corrected covered area because many reviews did not provide complete primary-study lists; appraisal was complicated by heterogeneous review types, so methodological quality was used as a sensitivity lens rather than a uniform exclusion criterion; review-level frequencies depend on how strongly each topic has attracted review activity and do not establish causal magnitude or intervention effectiveness.
  • Evident in the text: frequencies are non-exclusive and do not sum to 100%; the corpus includes systematic, scoping, bibliometric, narrative and integrative reviews with different search procedures, disciplinary scopes and synthesis logics, and some reviews focused on specific modalities while others addressed academic integrity more broadly.
  • Evident in the text: incomplete descriptive information was coded as not reported and not imputed, and reports with incomplete descriptive information were retained if eligible, which the authors say affected interpretive weight.

Model-written summary. Check it against the paper before relying on it.

Integrity signals

Registry

Crossref · 10.19083/ridu.2026.2492

Cites

20 of 28 references verified0 citation errors · 0 not checkable

Statistics

Nothing to recompute.

Figures

Image 1
CC-BY-NC-SA · From the PDF

Verification

Claims the paper makes

43 Own result · 33 No basis given · 22 Own method · 13 Not checked yet · 11 Own interpretation · 10 Sources not found · 9 Check pending · 1 Partly supported · 1 No full text of the sources · accuracy not yet measured

Verify all 22 references — free account
  • It may state two separate claims as one. 54
  • Probably not a substantial claim. 65
  • The quote only partly states this claim. 1
91 of 91
  • The paper relies on this without support.

    The frequency with which a driver or strategy is mentioned across included reviews indicates its importance or the strength of recommendation.

    “Findings are reported as non-exclusive review-level frequencies, not causal effect sizes.”
  • The paper relies on this without support.

    The prevention strategies recommended across reviews are appropriate or effective for reducing academic dishonesty in higher education.

    “a multi-level integrity programme combining assessment redesign, curriculum-embedded integrity education, staff development, transparent policy, proportionate monitoring and continuing evaluation.”
  • The paper relies on this without support.

    The 78 included reviews are a sufficiently comprehensive and representative sample of the review literature on academic dishonesty in higher education to support the synthesis.

    “The 78 included reviews covered the period 1998-2025.”
  • The paper relies on this without support.

    The recommendations reported across reviews reflect evidence-based practice rather than authors' opinions.

    “Across the 78 reviews, recommendations strongly favoured portfolio approaches that combine educative, assessment-based and governancebased measures.”
4 of 4

Citations

Cites

Show all 20 references

Research works printed but not linked: 4

  1. He, F. X., Fanaian, M., Zhang, N. M., Lea, X., Geale, S. K., Gielis, L., Razaghi, K., & Evans, A. (2024). Acade- mic dishonesty in university nursing students: A scoping review. International Journal of Nursing References
  2. Page, M. J., & Welch, V. A. (Eds.). (2022). In Cochrane handbook for systematic reviews of interventions (Version 6.3). Cochrane. https://training.cochrane. org/handbook
  3. Krippendorff, K. (2018). Content analysis: An introduction to its methodology (4th ed.). Sage.
  4. s15326985ep4103_1 · DOI · Paper page

Cited by

Still being checked.

Metadata

Authors
Estrella-Bote, V. D. R., Vallespir-Adillón, M., Julián, M., & Comas-Forgas, R.
Affiliations
Universidad Europea de Valencia (ES)
Venue
Revista Digital de Investigación en Docencia Universitaria
Published
2026-06-15
Volume
20
Issue
1
Pages
e2492
ISSN
2223-2516 (ISSN-L)
OpenAlex
W7202068685
Cited by
0
References
20
FWCI
0.00
Type
journal article
Language
en
Open access
Locations
Revista Digital de Investigación en Docencia Universitaria (publishedVersion, cc-by-nc-sa)
Funders
  • European Regional Development Fund (10.13039/501100011033, 13039/501100011033, MICIU/ AEI /10.13039/501100011033, AEI/10.13039/501100011033, 501100011033, MICIU/AEI/10)
  • Agencia Estatal de Investigación
Topics
  • Social Sciences › Education › Academic integrity and plagiarism
  • Social Sciences › Business, Management and Accounting › Organizational Behavior and Human Resource Management › Ethics in Business and Education
  • Social Sciences › Education › Artificial Intelligence in Education
Keywords
academic dishonesty, academic integrity, higher education, prevention, online assessment, contract cheating, generative AI, integrity education, assessment design, student motivation, workload and stress, institutional governance, academic integrity policy

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