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Cited by 6Academic integrity and plagiarismArtificial Intelligence in EducationDoctoral Education Challenges and Solutions

The Postgraduate Student Perspective on Academic Misconduct in the Era of Essay Mills and Generative AI: A Case Study from Northeast England

Strachan, Rebecca, Oguna, Cynthia, Oruche, Ugochukwu·2024·2024 IEEE Global Engineering Education Conference (EDUCON)
DOI 10.1109/educon60312.2024.10578821
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Abstract

Globally the number of students at university has been growing with UNESCO reporting in 2023, that there are now 235 million university students across the world, double the number from 20 years ago. Higher education is now a key area of economic growth for many countries and thus has also become a target for exploitation, evidenced by the growing numbers of essay mills and similar services, and even more recently by some of the generative Artificial Intelligence (AI) tools. The UK has also seen a growth in student numbers, particularly at postgraduate and for international students. In computing for example, UK PGT student numbers have increased rapidly with the UK Higher Education Statistics Agency reporting 25,225 computing PGT students in 2019/2020 rising to 47,410 in 2021/22, with 69% of these students being classed as international. These students can find it challenging to adapt to education in the UK and can have differing levels of abilities including digital literacy. Alongside this growth, the variety/incidence of student academic misconduct (AM) has also been rising. Previous research has tended to focus on plagiarism but there is an increasing need to explore the implications arising from the widespread availability of essay mills and generative AI tools. This study aims to provide a greater understanding of AM from the perspective of the computing PGT student. Adopting a case study approach, computing PGT students (n=358) were surveyed at one UK university in Spring 2023 with a follow up focus group. The study employed two PG students as researchers and this enabled a more trusted and student-centered approach to the survey, focus group and analysis. Thematic analysis of the data from the survey (responses n=26) and focus group (n=7) show students believe AM affects academic standards and understand the reasons behind this. They believe the university is providing clear AM guidance, but have more mixed opinions on whether they think the AM process is appropriate/fair. The analysis demonstrates the need for a holistic and concerted effort between staff and students based around six main areas: assessment; educational provision; staff attitudes/support; student opportunity/motivation; student belonging/engagement; and student-friendly AM guidance/resources. Future work is building on these recommendations to create a framework and set of practical interventions to promote academic integrity, and address the current AM challenges.

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Crossref · 10.1109/educon60312.2024.10578821

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Authors
Strachan, R., Oguna, C., & Oruche, U.
Affiliations
Northumbria University (GB)
ORCID
Venue
2024 IEEE Global Engineering Education Conference (EDUCON)
Publisher
IEEE
Published
2024-05-08
Pages
1-7
DOI
10.1109/educon60312.2024.10578821
OpenAlex
W4400411509
Cited by
4
References
31
Citations by year
2026: 1 · 2025: 3
FWCI
1.83
Type
conference paper
Language
en
Open access
closed
Licence
Locations
IEEE Global Engineering Education Conference (EDUCON) (publishedVersion)
Topics
  • Social Sciences › Safety Research › Academic integrity and plagiarism
  • Physical Sciences › Computer Science › Information Systems › Artificial Intelligence in Education
  • Health Sciences › Health Professions › General Health Professions › Doctoral Education Challenges and Solutions
Keywords
academic misconduct, essay mills, generative AI, graduate students, computing students, academic integrity, student perceptions, international students, digital literacy, academic standards, school belonging, assessment

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