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Cited by 24Academic integrity and plagiarismProblem and Project Based LearningStudent Assessment and Feedback

A teamwork framework for preventing breaches of academic integrity and improving students’ collaborative skills in the AI era

Open access
Issa, Tomayess, Hall, Mahnaz·2024·Heliyon
DOI 10.1016/j.heliyon.2024.e38759PMC PMC11491901
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Abstract

Generative artificial intelligence (AI) tools have become a major challenge in the education sector in terms of the way that students use and manage them. This study examines the development, implementation, and evaluation of a teamwork framework by using academic integrity standards and formative feedback to minimise the use of generative AI tools in the Business Project Management (BPM) unit and promote students' learning skills through teamwork and self/peer evaluation. This teamwork assessment was designed to transform students into independent learners by improving their cultural awareness, self-confidence, teamwork, communication, leadership, as well as personal and interpersonal skills. The study's objectives are to determine whether a teamwork framework can help to maintain academic integrity and transform BPM students into independent learners and leaders in the era of generative AI, and to determine whether lecturers' formative feedback enhances students' skills in teamwork assessment. This research comprises an empirical study of 408 local and international BPM students from different cultural backgrounds. A mixed-methods approach was used to collect data and achieve a broader perspective of the research topic. BPM students reported their satisfaction with this type of assessment since it helped them acquire skills such as intercultural effectiveness and teamwork. Following the implementation of the teamwork framework, the number of instances of academic misconduct and requests for extensions have decreased dramatically, while the assessment's average marks increased by 10 %. A set of recommendations is offered that will ensure the successful implementation of the proposed framework for teamwork assessment and self/peer evaluation. This study was limited to the Business Project Management unit, but in 2024, the same study will be conducted involving other postgraduate units at Curtin University with a future rollout in other universities to compare how students perceive teamwork assessment.

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Integrity signals

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Crossref · 10.1016/j.heliyon.2024.e38759

Cites

53 of 53 references verified0 citation errors · 0 not checkable

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  • Generative AI tools allow students to automatically generate essays, reports, and coding solutions, increasing the risk of plagiarism and contract cheating.

    “These tools allow students to automatically gen -erate essays, reports, and coding solutions, increasing the risk of plagiarism and contract cheating [ 55, 56, 79-81].”

    From Redefining student assessment in AI-infused learning environments: a systematic review of challenges and strategies for academic integrity (2026) · cites this paper as [80]

    The Introduction says generative AI can produce text, and the Academic integrity and generative AI section says students may use ChatGPT or Bard to complete assignments, increasing the risk of plagiarism and cheating. The paper does not specifically mention generated essays, reports, or coding solutions, and it discusses contract-cheating services separately rather than saying generative AI increases contract cheating. The provided copy gives section labels but no page numbers. (LLM, conf=0.87)

    Experimental — accuracy not yet measured

  • Team-based and collaboration-oriented assessment formats have been proposed as a way to reduce opportunities for undisclosed AI reliance while building independent academic skill.

    “Team-based and collaboration-oriented assessment formats, for instance, have been proposed specifically as a way to reduce opportunities for undisclosed AI reliance while also building the kind of independent academic skill that this study's Behavioral Control findings suggest matters (Issa & Hall, 2024).”

    From Convenience Amid Crisis: Explaining Generative AI Misuse and Compliance Intention Among Myanmar University Students (2026) · cites this paper as Issa & Hall, 2024

  • The question of whether AI-generated works can be copyrighted remains an evolving legal issue with no universal clarity.

    “The question of whether AI-generated works can be copyrighted remains an evolving legal issue with no universal clarity.”

    From MAPPING GENERATIVE AI'S ETHICAL ISSUES IN HIGHER EDUCATION: A FELT-GUIDED SYSTEMATIC REVIEW [PEMETAAN ISU ETIKA GENERATIVE AI DI PENDIDIKAN TINGGI: TINJAUAN SISTEMATIS BERPANDUAN FELT] (2025) · cites this paper as Issa & Hall, 2024

    The ‘Academic integrity and generative AI’ section discusses plagiarism, cheating, and detecting AI-generated work; the Discussion addresses identifying students’ own contributions. Neither addresses whether AI-generated works can be copyrighted. Searches for copyright, ownership, and intellectual property found no passage on the claimed legal uncertainty. (LLM, conf=0.74)

    Experimental — accuracy not yet measured

  • Even seemingly innocuous text input can allow LLMs to infer user characteristics such as age, gender, income, and location, posing re-identification risks.

    “Even seemingly innocuous text input can allow Large Language Models (LLMs) to infer user characteristics such as age, gender, income, and location, posing re-identification risks.”

    From MAPPING GENERATIVE AI'S ETHICAL ISSUES IN HIGHER EDUCATION: A FELT-GUIDED SYSTEMATIC REVIEW [PEMETAAN ISU ETIKA GENERATIVE AI DI PENDIDIKAN TINGGI: TINJAUAN SISTEMATIS BERPANDUAN FELT] (2025) · cites this paper as Issa & Hall, 2024

    The Academic integrity and generative AI section discusses plagiarism, cheating, and AI-generated assignments, not inference of personal characteristics from text. Searches for age, gender, income, location, and demographic inference found no passage in our copy addressing re-identification risks. No page numbers were provided for these passages. (LLM, conf=0.76)

    Experimental — accuracy not yet measured

  • Universities risk losing intellectual property (IP) rights or disclosing confidential research information if it is fed into institutionally unsupported AI applications.

    “Universities risk losing intellectual property (IP) rights or disclosing confidential research information if it is fed into institutionally unsupported AI applications.”

    From MAPPING GENERATIVE AI'S ETHICAL ISSUES IN HIGHER EDUCATION: A FELT-GUIDED SYSTEMATIC REVIEW [PEMETAAN ISU ETIKA GENERATIVE AI DI PENDIDIKAN TINGGI: TINJAUAN SISTEMATIS BERPANDUAN FELT] (2025) · cites this paper as Issa & Hall, 2024

    The Academic integrity and generative AI section discusses plagiarism, cheating, and students’ reliance on AI tools, not the loss of IP rights or disclosure of confidential research information. Searches for intellectual property, confidentiality, privacy, and unsupported AI applications found no passage addressing those risks. Page numbers were not available in our copy. (LLM, conf=0.75)

    Experimental — accuracy not yet measured

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Cites

Flagged first, then by citations

Research works printed but not linked: 4

  1. [9]Vana P.K. Vottero B.A. Altmiller G. Quality and Safety Education for Nurses: Core Competencies for Nursing Leadership and Care Management 2022 Springer Publishing Company
  2. [37]Khanal J. Gaulee U. Challenges of international students from pre-departure to post-study: a literature review J. Int. Stud. 9 2 2019 560 581
  3. [44]Dawson P. Defending Assessment Security in a Digital World: Preventing E-Cheating and Supporting AcademicIintegrity in Higher Education 2020 Routledge
  4. [55]Wiggins B. Confronting the Dilemma of mixed methods journal of theoretical and Phil. Psychol. 31 1 2011 44 60

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Related

Authors
Issa, T., & Hall, M.
Affiliations
Curtin University (AU)
Corresponding author
Issa, Tomayess
ORCID
Issa, Tomayess
Venue
Heliyon
Publisher
Elsevier BV
Published
2024-10
Volume
10
Issue
19
Pages
e38759
ISSN
2405-8440 (ISSN-L)
DOI
10.1016/j.heliyon.2024.e38759
PMC
PMC11491901
OpenAlex
W4403081770
Cited by
12
References
53
Citations by year
2026: 6 · 2025: 6
FWCI
4.97
Type
journal article
Language
en
Open access
Licence
Locations
  • Heliyon (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by-nc-nd)
  • PubMed (publishedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
  • eSpace (Curtin University) (submittedVersion, cc-by)
Topics
  • Social Sciences › Safety Research › Academic integrity and plagiarism
  • Social Sciences › Education › Problem and Project Based Learning
  • Social Sciences › Education › Student Assessment and Feedback
Keywords
academic integrity, generative AI, artificial intelligence in education, formative feedback, self-assessment, peer assessment, business project management, self-directed learning, teamwork skills, academic misconduct, intercultural effectiveness, graduate students, assessment design

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