###### Abstract  
Albeit in different ways, both machine learning and gamification have transfigured the user experience of information systems. Although both are hot research topics, so far, little attention has been paid to how these two technologies converge with each other. This relation is not obvious as while it is feasible to enhance gamification with machine learning, it is also feasible to support machine learning with gamification; moreover, there are applications in which machine learning and gamification are combined yet not directly connected. In this study, we aim to shed light on the use of both machine learning in gamification and gamification in machine learning, as well as the related topics of using gamification in machine learning education and machine learning in gamification research. By performing a systematic literature mapping, we not only identify prior works addressing these respective themes, but also analyze how their popularity evolved in time, investigate the areas of application reported by prior works, used machine learning techniques and software tools, as well as the character of research contribution and the character of evaluation results for works that presented them. gamification; machine learning; SLM +
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32 papers extracted in a systematic approach from IEEE Xplore, ACM Digital Library, SpringerLink, and Science Direct digital libraries [8]. The identified papers were analyzed with respect to the usage area, distinguishing the usage of ML in gamification (unfortunately, only three areas were considered there: learning, personalization, and behavioral change, and their delimitation was somewhat ambiguous, considering that one application could at the same time belong to all of these areas), and the usage of gamification in ML; reported effects of using gamification in ML; aspects of gamification affected by ML; and benefits and challenges (unfortunately, aggregate data were only provided for the usage of ML in gamification, the review in the remaining dimensions was only performed on selected examples). The period covered in the review ends in 2019, omitting the last four years of research output, thus making it, to some extent, outdated. The second review, by [PERSON] et al., covers a one-year-longer period (ending in 2020), and is based on querying five data sources (Google Scholar, Science Direct, ACM, IEEE, and Scopus), but is limited in both its scope, as it only dealt with research on gamification applied to education, and quantity, as the authors analyzed only five published works [9]. These five works were analyzed with respect to the game style, the area of education and its level, the game architecture, and the machine learning algorithms and techniques used. The limitations of the two existing reviews provide a strong motivation for performing a new, up-to-date mapping of literature on combining machine learning and gamification. Partly inspired by the dimensions of analysis performed in the prior studies, partly addressing their deficiencies, we have developed 10 research questions, which we arranged into three categories: the general characteristics of identified studies (RQ1-3), the covered topics (RQ4-7), and the research contribution (RQ8-10):  
* How did the interest in the field evolve in time? * How is the interest in the field distributed geographically? * Is the research output in the field confined to specific publication venues? * What are the main themes of the identified publications relevant to the field and how did they evolve in time? * What are the human activity areas addressed by the identified publications? * What are the machine learning types, techniques, and software tools used in applications described in the identified publications? * What are the main technologies (other than machine learning) that the applications described in the identified publications are based on? * What is the character of the research contribution of the identified publications?