AI literacy is an emerging research area that increasingly incorporates new forms of computational intelligence, creating opportunities to enrich human learning and interactive experiences. Our scoping review examines design interventions and discourses within AI literacy and games to identify and characterize the learning experiences that prior research has sought to support. Drawing from 45 papers, we identified and analyzed 48 unique design artifacts, including game‑based learning prototypes and gamified systems. We constructed a comprehensive matrix charting each artifact’s game or gamification approach, platform, AI literacy focus, game design elements, player requirements, modality, and open‑source availability, providing a detailed view of how AI literacy is represented across these systems. Building on this matrix, we identify nine design suggestions that illustrate how specific design choices shape learners’ knowledge, skills, and experiences with AI. Our work clarifies game‑based AI literacy interventions and offers actionable suggestions for designing future systems that effectively leverage games and gamification to support AI learning.

Abstract Presence, as a psychological state, is typically assessed using questionnaires. While many researchers in this field assume that these self-report instruments are standardized, the reliability of such questionnaires remains uncertain. This knowledge gap challenges the accuracy and validity of data derived from studies assessing presence. Ensuring reliable and precise data collection and reporting is essential for the credibility of findings in presence research, because inaccuracies may cause errors in conclusions, which affects theoretical understandings, methodological approaches and practical applications. To address this issue, we conducted a systematic analysis of 397 empirical quantitative studies on presence. We investigated the use of presence scales, including applications, modifications, a variety of measures and reporting practices. We found that the majority of the presence studies modify questionnaires, do not re-validate them and improperly report their methods. Based on these findings, we propose solutions to enhance transparency and validation of the presence measurements.

Abstract Formalized literature reviews are crucial in human–computer interaction (HCI) because they synthesize research and identify unsolved problems. However, current practices lack transparency when reporting details of a literature search. This restricts replicability. This paper introduces the INSPIRE framework for HCI research. It focuses on the search stage in literature reviews to support a search that prioritizes transparency and quality-of-fit to a research question. It was developed based on guiding principles for successful searches and precautions advised by librarian experts in HCI (n=8) for search strategies in (primarily systematic) literature reviews. We discuss how their advice aligns with the HCI field and their concerns about computational AI tools assisting or automating these reviews. Based on their advice, the framework outlines pivotal stages in conducting a literature search. These essential stages are: (1) defining research goals, (2) navigating relevant databases and (3) using searching techniques (like divergent and convergent searching) to identify a set of relevant studies. The framework also emphasizes the importance of team involvement, transparent reporting, and a flexible, iterative approach to refining the search terms.

Video game onboarding faces the challenge of teaching game mechanics in a fun and engaging way. Artificial intelligence (AI) solutions have become a quick fix to help users understand technology. However, little is known about how AI supports player onboarding in video games. To address this knowledge gap, this research explores player perspectives on AI-supported onboarding. We conducted a qualitative user study (n=20) to investigate player expectations, attitudes, and concerns about AI-supported learning experiences. Players learn primarily through the lived experience of a game and value personalized guidance during onboarding. Participants emphasized the importance of maintaining control over how AI is used during onboarding and the freedom to choose their support level. Our results suggest that players want future AI-supported onboarding systems to prioritize their agency, encourage active learning, and maintain transparency throughout the learning process. We contribute to game design research by proposing balanced, player-centric AI-supported onboarding experiences in video games.

Figure 1: In our cooperative co-located exergame Space Scavenger Squad, players work together to catch orbs that appear on the ExerCube's panels.In a user study, we compared three cooperative mechanics:  free , where players share one task;  coupled , where players perform specific tasks; and  concurrent , where players have to sync their actions to succeed.