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

Toward A Theory Of Procedural Rhetorical Systems: Demonstrations Of Player Agency In Uptake Of Rules In Video Games

This paper expands Ian Bogost’s (2007) procedural rhetoric by broadening the rhetorical view of games to encompass the arguments that they make not just about the material, but about themselves. The theory of procedural rhetorical systems (PRSes) views game systems as arguing toward how players should be following their rules, and like in any form of rhetoric, players possess agency in how they take up these arguments and how closely they follow rules. To demonstrate this, this paper analyzes a specific game, the 1996 platformer Super Mario 64, alongside various digital artifacts demonstrating how players have taken it up, including videos, forum discussions, wiki entries, and comments. This paper divides the different ways players can view PRSes into three uptake lenses (ULs), which are standard, speedrunner, and modder uptake. Where standard uptake represents taking up a game’s PRSes according to their exact argument, speedrunner and modder uptake represent taking them up in alternative ways, either with intent of beating the game as fast as possible or with the knowledge that rules can be modified and even transplanted from one place to another. These varied ULs prove that game rules are argued to players via PRSes and that players have agency in how they take them up

QuizMASter – A Multi-Agent Game-Style Learning Activity

The research paper proposes a practical and promising game-based learning pattern which may create a new direction on how to create an effective game-based learning. The agent-based technologies can be generalized to generic online learning systems The future work proposed in the paper is interesting. More work on functionality testing and integration with Moodle and other existing systems and 3D environments should be done.This paper describes a research project in progress of developing a Multi-Agent System-based educational game QuizMASter for e-learning that would help students learn their course material through friendly competition. We explore the use of perceptive pedagogical agents that would be able to determine the learner’s attitudes; to assess learners’ emotional states through examining learner’s standing, response timing, and history, and banter; and to provide appropriate feedback to students in order to motivate them

A multi-agent approach to adaptive learning using a structured ontology classification system

Diagnostic assessment is an important part of human learning. Tutors in face-to-face classroom environment evaluate students’ prior knowledge before the start of a relatively new learning. In that perspective, this thesis investigates the development of an-agent based Pre-assessment System in the identification of knowledge gaps in students’ learning between a student’s desired concept and some prerequisites concepts. The aim is to test a student's prior skill before the start of the student’s higher and desired concept of learning. This thesis thus presents the use of Prometheus agent based software engineering methodology for the Pre-assessment System requirement specification and design. Knowledge representation using a description logic TBox and ABox for defining a domain of learning. As well as the formal modelling of classification rules using rule-based approach as a reasoning process for accurate categorisation of students’ skills and appropriate recommendation of learning materials. On implementation, an agent oriented programming language whose facts and rule structure are prolog-like was employed in the development of agents’ actions and behaviour. Evaluation results showed that students have skill gaps in their learning while they desire to study a higher-level concept at a given time