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An innovative process that assists users in non-quantitative problem solving is presented. The process, called Ramic, employs the idea of psychological projection in an innovative way to help users focus, express and think through problems. Its applicability ranges from assisting with simple non-analytic decision-making to developing and assessing strategies.

In the virtual realm, harnessing the power of psychological projection for problem solving has been attempted in the form of a process called Sand Tray. Attempts at virtualization have garnered little traction potentially due to encumbrance of the interface. Ramic, in contrast, is innately set up for digital use through a relatively simple interface.

A key question this paper explores is how to quantitatively measure the value of Ramic in relation to the well-established process of Sand Tray. Even though these processes operate on qualitative problems, a preference analysis tool called conjoint analysis is used to build an experiment and derive specific user utilities for each process.

To perform the study, both processes required testing in the physical domain. A 32-person study is presented and indicates the Ramic projective process to have a 23% higher user utility than Sand Tray in the area of problem solving. As such, it presents an opportunity to explore a new way in which individuals can approach non-analytical problem solving and how computers can assist them in the task.

Attention Deficit Hyperactivity Disorder (ADHD) is a frequent learning disorder affecting about 5%–8% of the student population globally. Currently, the traditional methods for ADHD diagnosis are not fully specified, due to difficulties in identifying the particular factors that cause this disorder. In this paper, we present a novel system for diagnosing ADHD, which does not need special equipment. Instead, it is based on the application of machine learning (ML), using data gathered from gameplay sessions of a serious game named “ADHD360”, developed for this purpose. Participants were recruited with particular criteria in order to generate data for the study. The benefits of our approach include less subjectivity in the decision process, cost-efficiency and easier accessibility than the typical procedure. To this end, special data preprocessing steps and ML techniques were applied. Our models achieved up to 85.7% F1-score performance metric in predicting correctly a user’s label (ADHD or not) from his/her gameplay session in ADHD360. Our method also proved to be efficient using only a small amount of data for the training procedure. The results of our systems are very promising, indicating notable ability of the tool to distinguish players that probably suffer from ADHD than those who do not.