Various types of user data such as heart rate, age, eye tracking data, body mass index, conversational data and more can be used to model a user. Intelligent systems can user these models to recognize and adapt. For example, by recognizing if someone is bored or scared while playing games based on a model of their engagement and using that to adapt the difficulty of a game [1]. However, this survey will focus on modelling user decision-making and reasoning. It will thus answer the question: "How do intelligent systems acquire and use user data to model decision-making and reasoning, and how are these models applied for recognition and adaptation?" To answer these questions, a systematic literature review was conducted. Using the key concepts Intelligent Systems, Recognition, Adaptation, User Modelling, and Human Decision-Making and Reasoning, queries have been formulated for 4 different databases. Papers have been screened and extracted using the PRISMA guidelines [2], resulting in the usage of 52 articles. The results reveal that intelligent systems are dominated by recommender systems. They use user preferences to recommend items in all sorts of domains. These user preferences can be modelled using other models such as user demographics, behaviour, emotions and characteristics. Besides making the decision-making process easier and thereby improving user experience, they also deal with other challenges such as fairness, privacy, usability and prediction accuracy. While being less prevalent, other intelligent systems such as decision support systems, assistance systems and social robots sometimes serve even more important purposes and have the potential of solving diverse problems. However, their underutilization suggests an opportunity for research in this area.

This paper is a systematic literature review (SLR) investigating how intelligent systems leverage learning-related information to adapt their behaviour. This paper is done according to PRISMA guidelines, which ensures reproducibility. For this review, we analysed 58 papers published after 2023. The review focused on types of inputs, system objectives, and application domains. The systems in this survey adapt their behaviour based on various inputs, such as facial expression, gestures, eye gaze, and user preferences. These systems enhance the learning experience by increasing user engagement and motivation. Many of these papers target the education domain, such as STEM, but there are also papers focused on motor skills training or cognitive training. Time constraints limited the scope of the review, particularly in identifying long-term trends. But the result can be a solid base for future research into adaptive learning and training platforms.

In an increasingly digital world, the ability of systems to adapt to individual users has become essential. Among cognitive variables informing such systems, the human short-term memory plays a crucial role, as it is responsible for perceiving, storing and retrieving data. This study explores the modalities in which various information about memory-related processes has been used in the field of Human-Centered AI (HCAI) by analyzing input methods, objectives, application domains and nature of implemented adaptations. By conducting a systematic literature review, the paper aims to address this gap, while identifying current trends and challenges. The 45 papers included in this study revealed that increasing or decreasing complexity of content or adjusting difficulty levels based on accuracy rates or time periods can improve human performance and support learning in computer-based environments. Such personalization are especially useful in the educational and healthcare domains. These results provide a basis of design guidelines for future research of adaptive mechanisms considering memory-related information. This survey is anticipated to be a starting point for upcoming developments in this field or succeeding reviews that could incorporate a wider range of the memory scope.

Recent developments in Artificial Intelligence offer new possibilities for the development of systems which adapt to human motivation or emotion. These can have a variety of applications such as making therapy more accessible or boosting student motivation or engagement. In order to gain an overview these applications, of adaption strategies and inputs are used and challenges researchers are facing during development, a Systematic Literature Review was conducted. The review follows PRISMA guidelines and includes from Scopus, Web of Science and IEEE Explore. Due to time limitations, only papers published in 2024 and 2025 were considered. The final review includes 139 papers, out of which only 6 target motivation. 46 were chatbots, and 53 were recommendation systems. Researchers primarily struggled with the interpretation of complex emotions, and the need for more adaptation options. Lastly, a strong need for extensive user testing and comprehensive data privacy measures was identified.

With an increased demand for personalized systems, adaptive systems can support its users by recognizing their cognitive state, and adapt different elements to improve the user's mental state. With a literature survey, the following question has been answered: how do intelligent systems acquire and use information related to user attention? The answer was found by following the PRISMA guidelines, using the identification flowchart, to ensure reproducibility of the identification and screening of the papers, in addition to applying consistent criteria to which articles could be included in the review. This process resulted in 74 papers that fit the criteria. The results showed a large variety in the sensor input, modeling, objectives and domain, while the adaptation strategies could be summarized by five categories: UI change, feedback timing, automation level, difficulty adjustment and behavioral feedback. Combinations of categories were also present. UI changes and feedback timing were the most popular categories, especially from 2015 onward. Difficulty adjustments were surprisingly rarely utilized, especially in articles focused on education, possibly because the adaptation requires additional complexity to be added to the system. Challenges described in the literature were mainly focused on short term improvements, instead of long term issues.

This study introduces a new metric for evaluating the disassociation between superstar and non-superstar researchers. Superstar researchers are defined as those in the top 0.1\% by h-index. Leveraging a large dataset, this paper analyzes the data and aims to flatten the discrepancy between superstars and non-superstars, in terms of innovation and popularity. Some authors that publish innovative papers and who haven't collaborated with superstars, tend to be left in the shadows, compared to the ones that have collaborated with superstars from an early stage. The new metric indicates the disassociation between such authors, by factoring in certain parameters that were put into perspective with the help of a Multiple Linear Regression model. The findings reveal significant differences in dissociation scores between researchers and superstar researchers, offering new insights into the dynamics of academic innovation and collaboration. This metric provides a robust tool to identify where an author stands in terms of dissociation and what needs to be done to diminish the discrepancy.

In the scientific community, a few prominent researchers, known as "superstars," receive most of the attention, citations, and resources. However, it is unclear whether they promote true innovation. This study replicates and extends previous work analyzing how superstars influence their collaborators, focusing on the field of computer science. Using the Semantic Scholar Academic Graph dataset, we confirm that while connected researchers in computer science tend to publish more and receive more citations, their ideas are often less innovative. Unlike in the work we replicate, however, we observe this effect even before dissociating the connected researchers from superstars. We also develop a new metric to further clarify the impact of superstars on research diversity. The findings provide insights into the role of superstars in scientific innovation.

Superstar researchers - those who author research papers which are far more widely cited than average - are generally well-respected within their fields and are frequently sought by new researchers for advice on career development and for collaborations. Though the effect of collaboration with superstar researchers on associated researchers is not well-researched yet, it is believed that collaboration with superstar researchers increases the research output of associated researchers, but may decrease their originality and innovation. This project explores a method of visualizing the career development of researchers associated with superstars. Using a dataset of authors, papers, and paper citations, a graph has been created with papers as edges and nodes as authors to visualize the career development of associate authors after collaboration with a superstar. The utility of this visualization is evaluated using heuristics.