With the recent prevalence of remote education, academic assessments are often conducted online, leading to further concerns surrounding assessment misconducts. This paper investigates the potentials of online assessment misconduct (e-cheating) and proposes practical countermeasures against them. The mechanism for detecting the practices of online cheating is presented in the form of an e-cheating intelligent agent, comprising of an internet protocol (IP) detector and a behavioural monitor. The IP detector is an auxiliary detector which assigns randomised and unique assessment sets as an early procedure to reduce potential misconducts. The behavioural monitor scans for irregularities in assessment responses from the candidates, further reducing any misconduct attempts. This is highlighted through the proposal of the DenseLSTM using a deep learning approach. Additionally, a new PT Behavioural Database is presented and made publicly available. Experiments conducted on this dataset confirm the effectiveness of the DenseLSTM, resulting in classification accuracies of up to 90.7%.

Whether you realize it or not, AI has found its way into our daily life. The best examples are your smartphone's virtual assistant and Netflix's recommendation system. AI has also crept its way into education. Students use AI to improve their learning, while teachers leverage it for online assessment and identifying students' strengths and weaknesses. As we look at the future of education, we must ask the question: can AI make us smarter?

Indeed, assessment may be better than compared with a conventional test. Griffin's research has found that the tasks on his platform do not exhibit the between nation bias (or Differential Item Functioning) that questions on the standardised, international PISA assessment purportedly suffer from (Kreiner & Christensen, 2014). They are also robust to differences in background language (Vista, Care and Griffin, 2014). The fact that assessment takes a back seat here begs the following question. Of what real worth is the psychometric modelling in the background?