Augmented intelligence (AuI) integrates human intelligence (HI) and artificial intelligence (AI) to harness their strengths and mitigate their weaknesses. The combination of HI and AI has seen to improve both human and machine capabilities, and achieve a better performance compared to separate HI and AI approaches. In this paper, we present a survey of literature to understand how AuI has been applied in the literature, including the roles of HI and AI, AI approaches, features, and applications. Due to the limited literature related to this topic, we also present a survey of expert opinion to answer four main questions to understand the experts’ implications of AuI, including: a) the definition of AuI and the significance of HI in AuI; b) the roles of HI in AuI; c) the current and future applications of AuI in research, industry, and public, as well as the advantages and shortcomings of AuI; and d) end users’ view of the application of AuI. We also present recommendations to improve AuI, and provide a comparison between the findings from the surveys of both literature and expert opinion. The discussion of this paper shows the promising potential of AuI compared to separate HI and AI approaches.

Growing concerns about online examinations have led to various investigations of techniques for improvement. With most higher education institutions shifting to online learning and examination amid COVID-19, these concerns, including the academic dishonesty, validity, reliability, and anxiety of online examination, are more critical than ever. This paper presents the outcomes of the survey to elicit the perceptions of undergraduate students from two universities in South Korea and Malaysia towards undertaking online exams and the associated concerns. Additionally, the study explores the potential of artificial intelligence (AI) in addressing these concerns. There are three main research questions: 1) How has AI been adopted to tackle the four main concerns in online exams? 2) What are the students’ perceptions regarding these concerns? Are there any differences between South Korean and Malaysian students? 3) What is the extent of the stress level when webcam proctoring and timers are implemented during online exams? The survey results show that both South Korean and Malaysian students agree that online exams make cheating more accessible than in-person exams. They also suggest that selecting questions randomly from a question bank could discourage cheating. Moreover, the study highlights that both groups of students experience moderate stress levels when webcam proctoring is used over Zoom during online exams, and they experience a high-stress level when timers are set for each question.

This study addresses the current issues in online assessments, which are particularly relevant during the Covid-19 pandemic. Our focus is on academic dishonesty associated with online assessments. We investigated the prevalence of potential e-cheating using a case study and propose preventive measures that could be implemented. We have utilised an e-cheating intelligence agent as a mechanism for detecting the practices of online cheating, which is composed of two major modules: the internet protocol (IP) detector and the behaviour detector. The intelligence agent monitors the behaviour of the students and has the ability to prevent and detect any malicious practices. It can be used to assign randomised multiple-choice questions in a course examination and be integrated with online learning programs to monitor the behaviour of the students. The proposed method was tested on various data sets confirming its effectiveness. The results revealed accuracies of 68% for the deep neural network (DNN); 92% for the long-short term memory (LSTM); 95% for the DenseLSTM; and, 86% for the recurrent neural network (RNN).

The rapid expansion of online learning has ushered in new educational opportunities, but concurrently introduced challenges in preserving academic integrity during assessments. This transition accentuates the need to address the heightened risks of e-cheating, encompassing various forms of academic dishonesty and potential misuse of advanced tools such as ChatGPT. To tackle these challenges, this paper proposes a pioneering multi-stage detection system that synergises geographical verification with behavioural analysis, offering a nuanced understanding of cheating behaviours in diverse online learning scenarios. The core innovation is an intelligent agent (multi-IA) incorporating an IP Detector for geographical verification and a deep learning-based Behavioural Monitor (known as DenseLSTM) for analysing response behaviours. This novel amalgamation enhances adaptability and accuracy in e-cheating detection, thereby fortifying the credibility and integrity of online assessments. Empirical analysis validates the IA system’s efficacy, demonstrating remarkable accuracy in classifying candidate behaviours and effectively discerning between normal and potentially fraudulent responses. These findings underscore the system’s potential as an invaluable asset for educational institutions and educators in preserving the integrity of online assessments, and supporting the growth of online learning.

This paper presents a method for processing and digitizing the current signal information output from biosensors and a hardware Artificial Intelligence (AI) model design that classifies data using low-power and compact AI algorithms to minimize the high-power consumption and on chip area of the Convolution Neural Network (CNN) models used in biosensors. The dataset was built using a data sampling method that digitizes the current signal values from the biosensor by sampling them 16 times with a 16-bit Analog-to-Digital Converter (ADC), enabling feature extraction in advance. To reduce power consumption and area, a BNN-MLP model without an extraction layer was designed, and to improve accuracy, a dense layer was added to the final layer. This approach enhances accuracy while using binary weights. BNN-MLP model was designed using TensorFlow for the software model and implemented in hardware at the Register Transfer Level (RTL). To verify the similarity between the hardware-implemented BNN-MLP model and the software BNN-MLP model, classification was performed on a test dataset of 1,000 samples using a Field Programmable Gate Array (FPGA), and the classification accuracy for each class was compared. When the When the BNN-MLP model is implemented on an FPGA, it utilizes 7,994 LUTs and 9,780 FFs, occupying fewer resources compared to previous studies. Additionally, it operates at a lower power consumption of 0.157 W, demonstrating the highest power efficiency performance of 407.6 GOPS/W. Finally, the inference time for a single data point was 0.018 ms, which is much faster that in previous studies, confirming its potential for low-power and compact applications in biosensors.