Academic Profile : Faculty

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Asst Prof Tanmay Sinha
Assistant Professor, National Institute of Education - Learning Sciences and Assessment


I am looking for PhD/EdD students for a January 2025 start. If you are interested in doing impactful work at the intersection of emotions, learning through problem-solving, and AI for education, please reach out for any queries and discussions on overlapping research interests! Applications open in May 2024.


Dr. Tanmay Sinha is an assistant professor at the National Institute of Education, Nanyang Technological University in Singapore. He obtained a master’s degree in computer science from Carnegie Mellon University and completed his doctoral work in the learning sciences at ETH Zurich. Tanmay most recently served as executive director for the first ETH-EPFL joint doctoral program in the learning sciences in Switzerland (2021-2023), where he co-developed the academic program strategy, formulated and taught courses on learning sciences foundations and artificial intelligence in education.

Tanmay is interested in sensemaking-focused pedagogies that can enhance students' preparation for future learning. His research has advanced the instructivist versus constructivist debate in the learning sciences by empirically showing that deliberate, guided failure in problem-solving prior to instruction does not hurt conceptual understanding and transfer of learning. Tanmay's work has challenged normative conceptions of socio-emotional factors such as curiosity and shame in learning by bringing typically neglected aspects like peer influence and goal-conduciveness to the fore. His empirical portfolio of work draws on observations and interventions spanning pedagogies such as peer and dialog-based tutoring, individual and collaborative problem-solving for students from middle school to the university. Across his projects, Tanmay has applied varied methods ranging from applied statistics, social network analysis and multimodal machine learning to design-based research and meta-analysis.

Tanmay's recent explanatory account of the role of emotions in problem-solving is one of the most widely read articles in Journal of the Learning Sciences. Building on this interdisciplinary line of research, Tanmay is continuing to investigate the light and dark side of emotions in learning and forge new lines of inquiry to build AI-focused technological interventions that can personalize and improve the social context of learning through failure-driven problem-solving. This work is rooted in the idea that education should not only challenge student understanding but also foster significant social learning experiences, which may sometimes involve setbacks and failures, trigger emotions such as shame and anger, yet ultimately empower students to embrace resilience in their future learning endeavors.

Tanmay's research has appeared in flagship avenues such as Journal of the Learning Sciences, Journal of Educational Psychology, Review of Educational Research, Learning and Instruction, and Cognitive Science. His research has received further accolades at three international conferences, including Empirical Methods for Natural Language Processing (shared task winner in modeling large scale social interaction in MOOCs), Intelligent Virtual Agents (best student paper), European Conference on Technology Enhanced Learning (best paper nominee). Tanmay's work has also garnered attention from prestigious media outlets like The New York Times, Times Higher Education, and the World Economic Forum.
  • Improving students' sensemaking and their affinity towards mistakes and challenges in instructional designs for STEM learning
Courses Taught
MLT 917 Artificial Intelligence for Education: A Pedagogical Spectrum (**forthcoming new course!**)
MLT 909 Research Methodologies for the Learning Sciences
MLT 901 Foundations of the Learning Sciences

ILA 0021 Introduction to Artificial Intelligence

MSL 906 Educational at the Intersection of Artificial Intelligence and Neuroscience
MSL 907 Translating Educational Neuroscience
MSL 909 Integrative Project