During the recent COVID-19 outbreak, educational institutions have transitioned to online teaching for all students for most of the programs. Due to lack of in-person interactions and monitoring, assessments in online courses may be more susceptible to contract cheating, collusion, fabrication and other types of academic misconduct than the assessments in face-to-face courses. This situation has raised several research questions that need immediate attention, such as what are the best possible options for online assessments and how to administer online assessments so that academic integrity could be preserved. The authors have conducted a scoping study and carried out an extensive literature review on i) different types of assessments that are suitable for online courses, ii) strategies for ensuring academic integrity, and iii) methods, tools and technologies available for preventing academic misconduct in online assessments. It is evident from the literature review that there are a range of options available for designing assessment tasks to detect and prevent violations of academic integrity. However, no single method or design is enough to eliminate all sorts of academic integrity violations. After thorough research and analysis of existing literature, the authors have provided a comprehensive set of recommendations that could be adopted for ensuring academic integrity in online assessments.

With increasing world population the demand of food production has increased exponentially. Internet of Things (IoT) based smart agriculture system can play a vital role in optimising crop yield by managing crop requirements in real-time. Interpretability can be an important factor to make such systems trusted and easily adopted by farmers. In this paper, we propose a novel artificial intelligence-based agriculture system that uses IoT data to monitor the environment and alerts farmers to take the required actions for maintaining ideal conditions for crop production. The strength of the proposed system is in its interpretability which makes it easy for farmers to understand, trust and use it. The use of fuzzy logic makes the system customisable in terms of types/number of sensors, type of crop, and adaptable for any soil types and weather conditions. The proposed system can identify anomalous data due to security breaches or hardware malfunction using machine learning algorithms. To ensure the viability of the system we have conducted thorough research related to agricultural factors such as soil type, soil moisture, soil temperature, plant life cycle, irrigation requirement and water application timing for Maize as our target crop. The experimental results show that our proposed system is interpretable, can detect anomalous data, and triggers actions accurately based on crop requirements.

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