AI evidence extraction

ChatGPT and the rise of generative AI: Threat to academic integrity?

Damian Eke · Journal of Responsible Technology · 2023

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

9/10

Relevance

1/4

Quality (LMQS)

Evidence

476

Citations

16.93

FWCI

Top 10%

Impact

This is an AI analysis. Read the peer-reviewed original at the publisher: https://doi.org/10.1016/j.jrt.2023.100060

Methodology & findings

Study design

Narrative commentary and literature review examining the implications of ChatGPT for academic integrity.

Main result

The paper finds that ChatGPT represents a significant threat to academic integrity because "students as well as researchers can start outsourcing their writing to ChatGPT" and "some of the responses are so lucid, well-researched and decently referenced" that they could easily pass as original student work. The study also notes that "there is a greater concern for institutions in Low-and-middle-income countries where Turnitin and other plagiarism tools are yet to be integrated as measures for academic integrity," making these regions particularly vulnerable to AI-assisted cheating.

Research paradigm

Critical analysis and normative argumentation

Author conclusions

The authors conclude that "the way ChatGPT and other AI powered text generators are used could surely undermine academic integrity. They are also capable of revolutionising academia. It is the responsibility of all of us humans to ensure that the risks to academic integrity are mitigated for greater maximisation." They further argue that "Academic writing, essay assignments and technical coding assessments may not be dead but it is time to reimagine critical changes to ensure sustainable integrity in academia" and call for a "multi-stakeholder effort; from the technical developers, policy makers in academic institutions, publishers, professors, lecturers to students."

Risk of bias

Selection bias in cited examples (cherry-picked successful ChatGPT responses); Author positionality bias (single academic perspective rather than multi-stakeholder evidence); Limited empirical evidence on prevalence of ChatGPT misuse in actual academic settings; Lack of quantitative data on detection rates or actual incidence of academic integrity violations; Author perspective appears supportive of technology integration while acknowledging risks, which could bias framing; Limited empirical evidence cited—primarily reliant on anecdotal reports and early observations; Selection of sources may reflect available early commentary rather than systematic literature search

Limitations

The paper acknowledges that while "ChatGPT has more use cases than GPT-3," it "lacks the ability to truly understand the complexity of human language and conversation" and "can occasionally generate incorrect information, produces harmful instructions or biased content and has limited knowledge because of the data it was trained on." Additionally, the authors note that detection tools remain problematic: OpenAI's AI text classifier has been "described as an 'imperfect tool' by OpenAI who warned that it should not be used as a primary decision-making tool."

Open questions raised

Lack of clarity in academic integrity policies regarding AI-generated text use
Absence of harmonized guidelines for acknowledging ChatGPT use in academic writing
No reliable, validated tool to detect dishonest use of AI text generators in academia
Limited education and capacity building among staff and students on responsible ChatGPT use
Need for rethinking assessment methods beyond traditional essay writing
Lack of clear academic integrity policies addressing AI-generated text use

Extracted from: pdfAgreement 79%

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