Tristan LIM Ming Soon


Intake Year: August 2021 (Full-Time)

Dissertation Title

Ethical Imperatives in AI-Driven Educational Assessment: Framework and Implications.

Dissertation Abstract

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.


During EngD Candidature

Lim, Tristan; Gottipati, Swapna & Cheong, Michelle (in press). Educational Technologies and Assessment Practices: Evolution and Emerging Research Gaps. In Braman, J., Brown, A. & Richards, M. J. (Ed.), Reshaping Learning with Next Generation Educational Technologies. IGI Global. DOI:  [Book Chapter].

Lim, Tristan; Gottipati, Swapna; Cheong, Michelle; Ng, Jun Wei & Pang, Chris. (2023). Analytics-enabled Authentic Assessment Design Approach for Digital Education. Education and Information Technologies. Springer Nature. DOI: [Tier 1. H5-Index 91; Scopus Q1; Google Metrics Educational Technology Category Ranked #2, Education Category Ranked #1].

Lim, Tristan; Gottipati, Swapna & Cheong, Michelle (2023). Ethical Considerations for Artificial Intelligence in Educational Assessments. In Keengwe, S. (Ed.), Creative AI Tools and Ethical Implications in Teaching and Learning. IGI Global. DOI: [Book Chapter].

Lim, Tristan; Gottipati, Swapna & Cheong, Michelle (2022). Authentic Assessments for Digital Education: Learning Technologies Shaping Assessment Practices. Proceedings of the 30th International Conference on Computers in Education (ICCE 2022). 1, p. 587-592. Kuala Lumpur, Malaysia. ISBN: 978-986-972-149-3.

Lim, Tristan; Gottipati, Swapna; Cheong, Michelle; Ng, Jun Wei & Pang, Chris. (2022). Assessment Design for Digital Education: An Analytics-based Authentic Assessment Approach. 2022 IEEE International Conference on Teaching, Assessment and Learning for Engineering (TALE), Hong Kong. 


Nurul Asyikeen Binte AZHAR

Senior Data Scientist, Data and Analytics, Rio Tinto
Graduated EngD Student (August 2020 Intake)

Intake Year: August 2020 (Full-Time)

Dissertation Title
Enabling Sustainable Mining via AI-based Approaches
Dissertation Abstract

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.


During EngD Candidature

Nurul Asyikeen Azhar, Aldy Gunawan, Cheng Shih-Fen, Erwin Leonardi (2024). Comparison of Evolutionary Algorithms: A Case Study on the Multi-Objective Carbon-Aware Mine Planning. IEEE International Conference on Automation Science and Engineering. 

Nurul Asyikeen Azhar, Aldy Gunawan, Cheng Shih-Fen, Erwin Leonardi (2024). Long-Term Mine Planning: a Survey of Classical, Hybrid and Artificial Intelligence Based Methods. Asia Pacific Journal of Operational Research, Special Edition.

Nurul Asyikeen Azhar, Aldy Gunawan, Cheng Shih-Fen, Erwin Leonardi (2023). Carbon-Aware Mine Planning with a Novel Multi-Objective Framework. International Conference on Computational Logistics 2023.

Nurul Asyikeen Azhar, Aldy Gunawan, Cheng Shih-Fen, Erwin Leonardi (2022). A Carbon-Aware Planning Framework for Production Scheduling in Mining. International Conference on Computational Logistics 2022.