Growing levels of “micro-cheating” by academics are being ignored as universities focus on detecting more serious allegations of scientific misconduct and students’ unauthorised use of artificial intelligence (AI), a leading educationalist has claimed.

In a new paper in the journal , Bruce Macfarlane, dean of the Faculty of Education at the Education University of Hong Kong, argues increased efforts to tackle more blatant types of academic fraud, such as falsification, fabrication or plagiarism, and a “moral panic” over student cheating using AI have led scholars to become more accepting of “more subtle forms of cheating that are harder to detect and attract less public attention”.

Often described as “questionable research practices”, Macfarlane contends that behaviour such as “double dipping” – when an academic publishes two papers that are substantially the same – and excessive self-citation (“citing oneself gratuitously even when others are recognised as more significant authorities in the academic field”) should instead be labelled “micro-cheating”.

Other examples include “symbolic citation” in which scholars cite publications through relying on others’ reading lists rather than reading the original text oneself, explains the paper titled “Micro-cheating practices and scholarly hypocrisy”.

ߣߣ��Ƶ

“This is a deliberate attempt to mislead the reader into thinking that the author has read the original source when in fact they have not read it and probably relied on a secondary interpretation which is more accessible and gives them enough superficial information to lay claim to a mastery they do not possess,” Macfarlane told ߣߣ��Ƶ.

“It is misrepresenting oneself as an expert when one is not an expert in regard to a text,” he continued, stating this practice is common because “the literature review section of a modern paper is often written as an afterthought following the completion of the other main elements such as the methodology and the findings”.

ߣߣ��Ƶ

“Effectively the literature review is back-filled and might be done in a hurry as a result,” explained Macfarlane.

Asked why the term “micro-cheating” might be helpful to highlight this type of behaviour, Macfarlane explained it would “help to bring out into the open a range of shady practices that the academic community needs to acknowledge go beyond just poor scholarship”.

“They represent forms of cheating that are common but lie under the radar. It is important to call out such behaviour as it is far more common than the exposure of the occasional dramatic case of outright cheating,” he said, adding that these more serious cases resulting in retractions “give a false impression that cheating is rare when it is in fact endemic”.

In the case of symbolic citation, it was important to call out this practice because students are increasingly accused of using ChatGPT and other generative AI systems to summarise the academic literature that they are discussing, continued Macfarlane.

ߣߣ��Ƶ

“It’s academic hypocrisy for academics to criticise students for so-called surface learning of concepts when clearly many of them enter into exactly the same behaviour themselves,” he said.

Asked how micro-cheating could be called out when academics can claim these breaches were, at worst, an inconsequential referencing error, Macfarlane agreed journals are unlikely to take action.  

“It is very difficult to police symbolic citation but reviewers should be on the lookout for superficial referencing which is akin to little more than name-checking the well-known authors in the field. But it is really poor scholarship as well as micro-cheating.”

“Policing self-plagiarism may be easier as many journals use anti-plagiarism software such as Turnitin to check submissions,” said Macfarlane. “But the fact is, though, that they rarely do. In academic life we only generally use Turnitin or equivalent in respect to checking for plagiarism in student work. This is a classic example of academic hypocrisy. It is based on the totally false assumption that only students cheat,” he said.

ߣߣ��Ƶ

Other forms of “micro-cheating” in qualitative research highlighted by Macfarlane include when researchers falsely claim to have reached the point of “data saturation” that means they do not need to collect more data or excessive “rater bias” when a single individual analysing a large dataset fails to remain neutral as they seek to find evidence to support their own ideological beliefs. In both cases, these infractions could easily be excused as the result of poor research skills or a subjective decision made by a researcher, Macfarlane accepted.

By using the term ”micro-cheating” for these distortions of research results, said Macfarlane, it was easier to discuss the often-deliberate nature of these academic integrity breaches.

ߣߣ��Ƶ

“The term micro-cheating is not intended to condone these types of behaviours but to bring them out into the open from the shadows,” he said.

jack.grove@timeshighereducation.com