Modern Trends in the Automatic Generation of Content for Video Games

Attractive and realistic content has always played a crucial role in the penetration and popularity of digital games, virtual environments, and other multimedia applications. Procedural content generation enables the automatization of production of any type of game content including not only landscapes and narratives but also game mechanics and generation of whole games. The article offers a comparative analysis of the approaches to automatic generation of content for video games proposed in last five years. It suggests a new typology of the use of procedurally generated game content comprising of categories structured in three groups: content nature, generation process, and game dependence. Together with two other taxonomies – one of content type and the other of methods for content generation – this typology is used for comparing and discussing some specific approaches to procedural content generation in three promising research directions based on applying personalization and adaptation, descriptive languages, and semantic specifications

Towards long-term social child-robot interaction: using multi-activity switching to engage young users

Social robots have the potential to provide support in a number of practical domains, such as learning and behaviour change. This potential is particularly relevant for children, who have proven receptive to interactions with social robots. To reach learning and therapeutic goals, a number of issues need to be investigated, notably the design of an effective child-robot interaction (cHRI) to ensure the child remains engaged in the relationship and that educational goals are met. Typically, current cHRI research experiments focus on a single type of interaction activity (e.g. a game). However, these can suffer from a lack of adaptation to the child, or from an increasingly repetitive nature of the activity and interaction. In this paper, we motivate and propose a practicable solution to this issue: an adaptive robot able to switch between multiple activities within single interactions. We describe a system that embodies this idea, and present a case study in which diabetic children collaboratively learn with the robot about various aspects of managing their condition. We demonstrate the ability of our system to induce a varied interaction and show the potential of this approach both as an educational tool and as a research method for long-term cHRI

How do Pedagogical Conversational Agents affect Learning Outcomes among High School Pupils: Insights from a Field Experiment

Pedagogical conversational agents (CA) support formal and informal learning to help students achieve better learning outcomes by providing information, guidance or fostering reflections. Even though the extant literature suggests that pedagogical CAs can improve learning outcomes, there exists little empirical evidence of what design features drive this effect. This study reports on an exploratory field experiment involving 31 pupils in commercial high schools and finds that students achieved better learning outcomes when preparing for their tests with a pedagogical CA than without. However, the drivers of this effect remain unclear. Neither the use frequency of the design features nor the pupils’ expectations towards the CA could explain the improvement in marks. However, for the subjective perception of learning achievement, pupils’ expectations was a significant predictor. These findings provide support for the use of pedagogical CAs in teaching but also highlight that the drivers of better learning outcomes still remain unknown

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

Using Hexad Archetypes to Motivate Students in a Chatbot-enhanced Web-based Training

This study explores the challenge of maintaining motivation in further education for working students, who face the double burden of work and learning. To address this issue, we investigate the design and implementation of a pedagogical conversational agent (PCA) within a web-based training (WBT) platform. Drawing on literature, interviews with 11 experts, and a creative workshop with 14 working students, we use the Hexad user type framework to tailor the WBT to each user\u27s motivational archetype. We prioritize design features for each of the six archetypes and instantiate these in a prototype. In a field experiment with 17 working students using the WBT prototype for exam preparation, we observe a significant increase in intrinsic and extrinsic motivation. This study contributes to the emerging field of PCA-enhanced digital learning, highlighting the potential of personalized motivation in persuasive dialogue systems