## 3 Results
### Topic Analysis
#### 3.2.2 Areas of Application  
Analyzing the collected publications, we were able to determine 16 activity areas targeted by the application of machine learning and gamification described therein (note that some papers addressed more than one area). As can be observed in Figure 7, the most popular area was education (addressed by over 40% of the papers in the analyzed pool), followed by research and development (a little above 1/4 of the papers), and healthcare (about 23% of the papers); no other area has been addressed by more than seven papers. Below, selected examples of applications of gamification and machine learning in areas for which at least two applications have been identified are presented. An interesting example of an application in the area of education (yet also Augmented Reality) is ARsinoe, which combines gamification and machine learning in an Augmented  
Figure 6: Evolution of publication themes. Figure 7: Addressed areas of application. Reality mobile app helping K12 students in studying Egyptian culture and history, and in the learning of languages with non-Latin writing systems [19]. In the area of research and development (yet also pertaining to healthcare), an interesting application of gamification and machine learning has been developed within the SUITCEYES project in the form of an intelligent assistive wearable empowering people with deafblindness, helping them recognize faces and detect objects [20]. Another example of an application targeted at healthcare worthy of mention is GameAAL, a system for monitoring the elderly daily life activities and providing neurocognitive stimulation games, promoting social interaction and mobility of its end-users using gamification and machine learning techniques [5]. Among the several applications dedicated to Human Resources Management (yet also pertaining to R&D), one particularly interesting is the one described by [PERSON] et al., in which Deep-Reinforcement-Learning agents emulate human-like behavior based on the OCEAN personality traits model, navigating, interacting, and solving tests in a three-dimensional online escape room [21]. An example of an application in the area of entertainment is provided by [PERSON] et al., who developed an automatic generator of quiz games based on a given topic, leveraging the DBpedia database and using machine-learned ranking algorithms for recommending similar resources given a particular resource, at the same time serving the purpose of cleaning the publicly maintained data repositories of DBpedia and Wikipedia [22]. As regards the area of UX improvement, 360-MAM-Affect aims at this goal in the context of a recommender system for educational video content, utilizing ML for sentiment analysis and gamification techniques to increase user participation with digital content [23]. In the area of environment protection and sustainability, the survey on smart grid mobile apps by [PERSON] et al. covers not one but five applications combining gamification and machine learning with an aim to lower and shape energy consumption patterns to achieve peak load reduction, load smoothing, and hence carbon emission curtailment [24]. Regarding the cybersecurity area, [PERSON] et al. investigate the problem of backdoor attacks consisting in creating a vulnerability in a machine learning model by \"poisoning\" the training set by selectively mislabeling images containing a backdoor object. They introduce the Backdoor Game, in which users can interact with different poisoned classifiers and upload their own images containing backdoor objects in an engaging way. Thus, obtained results can be helpful in determining the effectiveness of different backdoor objects and increasing the safety of future AI systems [25]. The ThinklFirst framework targets both Customer support/Recommender system and Environment protection/Sustainability areas, as it aims at encouraging online shoppers to behave in a more targeted and environmentally conscious manner while purchasing goods [26].