Special Issue Editorial: Adaptive and Intelligent Gamification Design

This editorial provides an overview of the three accepted papers for the AIS THCI special issue on adaptive and intelligent gamification designs. The first paper examines conversational agents and how one can use gamification to make the design more engaging. The second study focuses on mobile fitness apps and analyzes the role that personality plays in apps and game designs. Finally, the third paper examines gamification in a virtual laboratory environment. Aligned with current work, we present future research directions that involve generative AI, the metaverse, and a shift in gamification research and practice in the future

A Design Framework for Adaptive Gamification Applications

The application of gamification does not always achieve the expected results due to the shortcomings of the quite common one size fits all approach of standard gamification concepts. We therefore propose a design framework that can inform systematic development of adaptive gamification applications. The developed framework draws on the current body of gamification literature, focusing on the emerging research stream of adaptive gamification. It provides design paths and design principles that translate the individual elements into concrete guidelines to assist the design practice. The framework has been successfully applied to the design and implementation of a prototype application using gamification to incentivize knowledge exchange on an existing online platform for physicians in practical medical training. The evaluation in a case study indicated positive user acceptance and increased system usage after the introduction of the developed adaptive gamification solution

Adaptive Gamification for Learning Environments

(Scimago Q1, ATIEF A+)International audienceIn spite of their effectiveness, learning environments often fail to engage users and end up under-used. Many studies show that gamification of learning environments can enhance learners' motivation to use learning environments. However, learners react differently to specific game mechanics and little is known about how to adapt gaming features to learners' profiles. In this paper, we propose a process for adapting gaming features based on a player model. This model is inspired from existing player typologies and types of gamification elements. Our approach is implemented in a learning environment with five different gaming features, and evaluated with 266 participants. The main results of this study show that, amongst the most engaged learners (i.e. learners who use the environment the longest), those with adapted gaming features spend significantly more time in the learning environment. Furthermore, learners with features that are not adapted have a higher level of amotivation. These results support the relevance of adapting gaming features to enhance learners' engagement, and provide cues on means to implement adaptation mechanisms

Game-inspired Pedagogical Conversational Agents: A Systematic Literature Review

Pedagogical conversational agents (PCAs) are an innovative way to help learners improve their academic performance via intelligent dialog systems. However, PCAs have not yet reached their full potential. They often fail because users perceive conversations with them as not engaging. Enriching them with game-based approaches could contribute to mitigating this issue. One could enrich a PCA with game-based approaches by gamifying it to foster positive effects, such as fun and motivation, or by integrating it into a game-based learning (GBL) environment to promote effects such as social presence and enable individual learning support. We summarize PCAs that are combined with game-based approaches under the novel term “game-inspired PCAs”. We conducted a systematic literature review on this topic, as previous literature reviews on PCAs either have not combined the topics of PCAs and GBL or have done so to a limited extent only. We analyzed the literature regarding the existing design knowledge base, the game elements used, the thematic areas and target groups, the PCA roles and types, the extent of artificial intelligence (AI) usage, and opportunities for adaptation. We reduced the initial 3,034 records to 50 fully coded papers, from which we derived a morphological box and revealed current research streams and future research recommendations. Overall, our results show that the topic offers promising application potential but that scholars and practitioners have not yet considered it holistically. For instance, we found that researchers have rarely provided prescriptive design knowledge, have not sufficiently combined game elements, and have seldom used AI algorithms as well as intelligent possibilities of user adaptation in PCA development. Furthermore, researchers have scarcely considered certain target groups, thematic areas, and PCA roles. Consequently, our paper contributes to research and practice by addressing research gaps and structuring the existing knowledge base

Trends in virtual reality technologies for the learning patient

NextMed convened the Medicine Meets Virtual Reality 22 (MMVR 22) conference in 2016. Since 1992, the conference has brought together a diverse group of researchers to share creative solutions for the evolving challenge of integrating virtual reality tools into medical education. Virtual reality (VR) and its enabling technologies utilize hardware and software to simulate environments and encounters where users can interact and learn. The MMVR 22 symposium proceedings contain projects that support a variety of learners: medical students, practitioners, soldiers, and patients. This report will contemplate the trends in virtual reality technologies for patients navigating their medical and healthcare learning. The learning patient seeks more than intervention; they seek prevention. From virtual humans and environments to motion sensors and haptic devices, patients are surrounded by increasingly rich and transformative data-driven tools. Applied data enables VR applications to simulate experience, predict health outcomes, and motivate new behavior. The MMVR 22 presents investigations into the usability of wearable devices, the efficacy of avatar inclusion, and the viability of multi-player gaming. With increasing need for individualized and scalable programming, only committed open source efforts will align instructional designers, technology integrators, trainers, and clinicians. Curriculum and InstructionCurriculum and Instructio

Adaptive and Personalized Gamification Designs: Call for Action and Future Research

Gamification refers to the use of game-like elements in non-entertainment contexts to make activities more engaging and enjoyable to improve utilitarian outcomes. The gamification literature and the use of gamification in practice suggest that gamification can be a useful tool to support behavioral and psychological changes. Recent developments show that there is potential for new waves of gamification research. Therefore, we conducted a workshop at the International Conference on Wirtschaftsinformatik (WI) 2021 to discuss the future of gamification with interested scholars. The discussion with 25 gamification experts led to a research agenda that supports the need for adaptive and personalized gamification designs. Together with the experts, we identify three clusters for future research: 1) the personalization of gamification concepts, 2) theories and concepts for gamified human-computer interaction, and 3) the “dark side” of gamification (e.g., addiction). We also address what the gamification concept means. Aligned with the three clusters, we provide valuable starting points for future research inquiries to help researchers better understand the nature of gamification. We also discuss the capabilities and limits of gamification

Integrating knowledge tracing and item response theory: A tale of two frameworks

Traditionally, the assessment and learning science commu-nities rely on different paradigms to model student performance. The assessment community uses Item Response Theory which allows modeling different student abilities and problem difficulties, while the learning science community uses Knowledge Tracing, which captures skill acquisition. These two paradigms are complementary - IRT cannot be used to model student learning, while Knowledge Tracing assumes all students and problems are the same. Recently, two highly related models based on a principled synthesis of IRT and Knowledge Tracing were introduced. However, these two models were evaluated on different data sets, using different evaluation metrics and with different ways of splitting the data into training and testing sets. In this paper we reconcile the models' results by presenting a unified view of the two models, and by evaluating the models under a common evaluation metric. We find that both models are equivalent and only differ in their training procedure. Our results show that the combined IRT and Knowledge Tracing models offer the best of assessment and learning sciences - high prediction accuracy like the IRT model, and the ability to model student learning like Knowledge Tracing

A process for designing algorithm-based personalized gamification

n/