EngD Alumni

This dissertation looks into the ethical challenges of integrating AI into education, revealing a significant gap in the literature concerning AI's ethical imperatives in educational assessments. It aims to understand the technologies behind assessments, clarify the relationship between AI, ethics, and assessments, and develop a framework for addressing AI's ethical challenges in this setting. The research contributes a detailed examination of AI's role and its ethical consequences in educational assessments, presenting a framework to aid stakeholders to address these complexities. It also calls for further interdisciplinary research and responsible AI application to enhance educational practices ethically and effectively.

The Doctor of Engineering program at SMU is distinguished by its exemplary supervision and robust support systems. My thesis supervisors, Prof. Swapna Gottipati, Prof. Michelle Cheong and Prof. David Lo, provided invaluable guidance, instrumental in both my academic and professional growth. The diverse expertise of the thesis committee members enriched research and professional perspectives, fostering an environment where I felt both challenged and supported both as an academic and industry practitioner.

Additionally, commendations to the diligent logistical and procedural support from the academic administrative staff, in particular Ms. Yeo Lip Pin and Ms. Diana Koh, who were instrumental in streamlining procedural requirements and minimizing administrative hurdles for EngD students. 

I am profoundly grateful for the mentorship and resources that have been pivotal to my education in SMU. This intellectually stimulating programme effectively combines rigorous academic training with comprehensive support, preparing students for impactful careers in their fields.

Government agencies prioritize citizen service delivery to foster trust with the public. Technological advancements, particularly in Artificial Intelligence (AI), hold promise for improving service provision and aligning government operations with citizens' needs. This dissertation contributes a framework for the development of an AI-enabled recommender system known as AI Based Citizen Question-Answer Recommender (ACQAR). Further research was done with the implementation of this system within a Singaporean government agency to enhance the agency’s citizen service delivery. ACQAR integrates Empath X SLA predictor, Citizen Question-Answer system (CQAS), and ChatGPT to generate contextually aware responses for customer service officers. The study aims to optimize government-citizen interactions in the digital age, where citizens expect efficient, personalized, and empathetic services. 

The Doctor of Engineering program (EngD) at Singapore Management University serves as a bridge between industry practices and academic rigor. It fosters and elevates my knowledge and skills in practical implementation within my current work domain, furthering my passion in utilizing Artificial Intelligence and Technology to serve the citizens of Singapore. This program is an ideal choice for those seeking a practical yet academically rigorous learning experience to further their career. 

Deepest gratitude goes to the esteemed supervisory committee members, Vice Provost (Education) Venky Shankararaman, Associate Professor (Education) Ouh Eng Lieh, and Associate Professor Hady Wirawan Lauw. Their boundless patience, unwavering guidance, and steadfast support have been the guiding light of the EngD journey. Professor Michelle Chong and the EngD administration office also deserve heartfelt appreciation for their endless patience and assistance in navigating logistical hurdles and coordinating conference funding arrangements, allowing for full immersion in research endeavors.

The precedence-constrained production scheduling problem (PCPSP) in Long-Term Mine Planning is acknowledged to be NP-hard and conventionally prioritizes the Net Present Value (NPV) of profits. Even so, heightened sustainability concerns necessitate heightened sustainable practices. Yet, research still lags. Hence, we tackled how sustainability elements can be incorporated into the PCPSP, focusing on environmental sustainability of carbon dioxide emissions (or carbon costs).

To begin, our systematic review examined the techniques for the PCPSP for commonalities, trends and sustainability inclusion. We then assessed two Multi-Objective Optimization (MOO) approaches to trade off the NPV of profits against carbon costs -- decomposition-based and domination-based. Under the decomposition-based approach, we used a bounded objective function method and proposed the novel hybrid Temporally Decomposed Greedy Lagrangian Relaxation (TDGLR) algorithm. Next, the domination-based approach compared two popular Multi-Objective Evolutionary Algorithms (MOEAs) of Non-dominated Sorting Genetic Algorithm II and Pareto-Envelope Based Selection Algorithm II using novel heuristics within them. Lastly, we put forth a framework to assess uncertainty within a dual MOEA setup. Overall, our research provides future direction when trading off sustainability elements and incorporating uncertainties for this real-world problem.

The programme was a good bridge between industry and academia whereby research was focused on real-world problems. It instilled the rigour, depth and inquisition that can be applied to and elevate my work as a Data Scientist. The programme also allowed me to connect with other researchers locally and globally so as to expand and exchange ideas.