The recent high performance of ChatGPT on several standardized academic tests has thrust the topic of artificial intelligence (AI) into the mainstream conversation about the future of education. As deep learning is poised to shift the teaching paradigm, it is essential to have a clear understanding of its effects on the current education system to ensure sustainable development and deployment of AI-driven technologies at schools and universities. This research aims to investigate the potential impact of AI on education through review and analysis of the existing literature across three major axes: applications, advantages, and challenges. Our review focuses on the use of artificial intelligence in collaborative teacher–student learning, intelligent tutoring systems, automated assessment, and personalized learning. We also report on the potential negative aspects, ethical issues, and possible future routes for AI implementation in education. Ultimately, we find that the only way forward is to embrace the new technology, while implementing guardrails to prevent its abuse.

The COVID-19 pandemic has impelled the majority of schools and universities around the world to switch to remote teaching. One of the greatest challenges in online education is preserving the academic integrity of student assessments. The lack of direct supervision by instructors during final examinations poses a significant risk of academic misconduct. In this paper, we propose a new approach to detecting potential cases of cheating on the final exam using machine learning techniques. We treat the issue of identifying the potential cases of cheating as an outlier detection problem. We use students’ continuous assessment results to identify abnormal scores on the final exam. However, unlike a standard outlier detection task in machine learning, the student assessment data requires us to consider its sequential nature. We address this issue by applying recurrent neural networks together with anomaly detection algorithms. Numerical experiments on a range of datasets show that the proposed method achieves a remarkably high level of accuracy in detecting cases of cheating on the exam. We believe that the proposed method would be an effective tool for academics and administrators interested in preserving the academic integrity of course assessments.

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<p class="3">The increasing costs of higher education (HE), growing numbers of flexible anytime, anywhere learners, and the prevalence of technology as a means to up-skill in a competitive job market, have brought to light a rising concern faced by graduate students and potential graduate employers.  Specifically, there is a mismatch of useful skills obtained by students through HE institutions which is evident upon graduation.  Faced with this dilemma, “graduate students,” or more specifically newly graduated students, with a with bachelor’s degree, and a growing number of employers are turning to Massive Open Online Courses, or MOOCs, as a complimentary mechanism through which this skills gap may be bridged. </p><p class="3">It is found in the literature that MOOCs are often discussed within the capacity of their development, their retention rates, institutional policies regarding their implementation, and other such related areas.  Examinations into their broader uses, benefits, and potential pitfalls have been limited to date.  Therefore, this paper aims to analyse the literature highlighting the use of MOOCs as a means to reduce the mismatch in graduate skills.  As such, this literature analysis reviews the following relevant areas: higher education and graduate skills gap, today’s graduates and employability, and MOOCs and graduate skills.  Through analysing the literature in these areas, this paper identifies gaps in the existing literature.                  </p>

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