Cognitive computing and 3D facial tracking method to explore the ethical implication associated with the detection of fraudulent system in online examination

Article type: Research Article

Authors: Sultanuddin, S.J.^a | Sudhee, Devulapalli^b | Prakash Satve, Priyanka^c | Sumithra, M.^d | Sathyanarayana, K.B.^e | Kumari, R. Krishna^{f; *} | Narasimharao, Jonnadula^g | Reddy, R. Vijaya Kumar^h | Rajkumar, R.ⁱ

Affiliations: [a] Department of Cyber Security, Dhanalakshmi College of Engineering, Manimangalam, Tambaram, Tamilnadu, India | [b] Department of Computer Science and Engineering, Vallurupalli Nageswara Rao Vignana Jyothi Institute of Engineering and Technology, Hyderabad Telangana, India | [c] Department of Computer Science and Engineering, Dr. Babasaheb Ambedkar Technological University, Lonere, Raigad, Maharashtra, India | [d] Department of Information Technology, Panimalar Engineering College, Chennai, Tamilnadu, India | [e] Department of Information Science and Engineering, Jawaharlal Nehru New College of Engineering, Navule, Shivamogga, India | [f] Department of Career Development Centre, College of Engineering and Technology, SRM Institute of Science and Technology, SRM Nagar, Kattankulathur, Tamil Nadu, India | [g] Department of Computer Science and Engineering, CMR Technical Campus, Kandlakoya, Medchal, Hyderabad, Telangana, India | [h] Department of Computer Science and Engineering, Koneru Lakshmaiah Education Foundation, Vaddeswaram, Andhra Pradesh, India | [i] Department of Electronics and Instrumentation Engineering, Kongu Engineering College, Perundurai, Erode, Tamilnadu, India

Correspondence: [*] Corresponding author. R. Krishna Kumari, Department of Career Development Centre, College of Engineering and Technology, SRM Institute of Science and Technology, SRM Nagar, Kattankulathur-603203, Tamil Nadu, India. E-mail: [email protected].

Abstract: Following the Covid-19 pandemic, the rapid spread of online education and tests demanded the implementation of cheating detection tools to ensure academic integrity. While advances in technology such as face recognition, face expression recognition, head posture analysis, eye gaze tracking, network data traffic analysis, and IP spoofing detection have shown promising results in detecting fraudulent behavior, their integration raises ethical concerns that must be carefully considered. This work presents a cognitive computing strategy for investigating the ethical implications of using cheating detection systems in online tests. This study attempts to examine the potential impact on students’ privacy, fairness, and trust in the examination process by employing cognitive computing, which models human cognitive capacities. A thorough literature review is used in the process to uncover existing ethical norms and regulatory frameworks linked to online assessments and cheating detection. Soft computing approaches are also used to evaluate the effectiveness and dependability of the aforementioned cheating detection strategies. The study looks into how far facial recognition and expression analysis can go in terms of privacy, as well as the possibility of bias in head posture analysis and eye gaze tracking algorithms. Furthermore, it investigates the ethical implications of monitoring network data traffic and detecting IP spoofing, with a focus on data security and user permission. The cognitive computing model, based on the analysis, presents a comprehensive framework for ethical decision-making when installing cheating detection technologies. The findings of this study contribute to the continuing discussion about the ethical concerns of using modern technologies to identify cheating in online exams. It provides educational institutions and policymakers with practical ideas for striking a balance between academic integrity and protecting students’ rights and dignity. By emphasizing ethical issues, this study aims to ensure that the implementation of cheating detection systems adheres to values of fairness, transparency, and privacy protection, promoting a trusting and supportive online learning environment for all parties involved.

DOI: 10.3233/JIFS-235066

Journal: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 5, pp. 8449-8463, 2023