Amortised Design Optimization for Item Response Theory

Abstract

I was fortunate to engage in numerous meetings, brainstorming sessions, and discussions with a variety of colleagues at Aalto University. I wish to express my gratitude to Jyri Kivinen, Vikas Verma, Mert Celikok, Tomi Peltola, and all the members of the Probabilistic Machine Learning (PML) group for their insightful and valuable contributions to these discussions. Furthermore, I had the pleasure of participating in many enriching conversations with Professor Antti Oulasvirta and the members of the Computational Behavior Lab at Aalto, led by Professor Oulasvirta.I am also grateful to Isak Westerlund, who has worked alongside me during my PhD project, taking on a significant amount of responsibility in coding the applications and training the various AI models. My thanks also go to Ariel Kwiatkowski, who contributed to our second paper.I am deeply grateful to the pre-examiners, Professor Anna Rafferty and Dr. Xiaoyang Wang, for their essential insights and constructive feedback on my thesis. In addition, I am thankful to Dr. John Williamson for willingly taking on the role of the opponent I want to thank Bitville Oy CEO Jouko Ranta, as well as all my colleagues at Bitville Oy, for making it possible for me to concentrate practically fulltime on the research projects during these years.I had the unique opportunity to share my research journey with my son, Oskar, who has now embarked on his own PhD journey at Aalto University as I write this preface. Thank you for the hundreds of hours of brainstorming, discussion, and reflection over nearly the last 10 years, and for your invaluable contribution as a member of the research team. Our discussions have been of the utmost importance to me, whether they were about mathematical details or the extensive influence of AI on society.Last but not least, I want to extend my heartfelt thanks to my daughter, Erika, and my wife, Sofia, for their encouragement and patience. I am aware that discussions about AI have prominently featured in our family conversations over the last few years. It is evident that this project could not have reached its conclusion without your unwavering support.

The Potential of Machine Learning Methods in Psychological Assessment and Test Construction

“…Of course, the applications described above are only a small selection of possible applications of ML in the area of psychological assessment and test construction. Others, for example, include enhancing item response modeling and CAT using ML and AI techniques such as deep learning, reinforcement learning, and (Bayesian) optimal experimental design (Keurulainen et al, 2023; Mujtaba & Mahapatra, 2020). Current hindrances to the use of ML in psychological assessment and test construction include a lack of transparency and interpretability (“black box models”) and – especially in the case of CDSS – a lack of trust in AI-based systems.…”