Bijan Khosrawi-RadTechnische Universität Braunschweig · Institut für Wirtschaftsinformatik
Bijan Khosrawi-Rad
Master of Science
Design-oriented research on Game-based Learning, Pedagogical Conversational Agents, and Virtual Companionship
About
35
Publications
16,308
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131
Citations
Publications
Publications (35)
Serious games are an innovative approach to entrepreneurship education and enable practicing skills like critical thinking. Pedagogical conversational agents (PCAs) enable individual support for learners in serious games. However, research lacks comparative studies on their added value in serious games, and there is no consensus regarding which PCA...
Universities are increasingly offering courses on the development of new and innovative business models. Despite the relevance of digital teaching, they so far rarely use digital collaboration and ideation tools. Pedagogical conversation agents (PCAs) can moderate collaborative learning tasks and support creative processes. Integrating PCAs into vi...
Virtual reality (VR) is a promising technology offering immersive environments for diverse applications, including collaborative work. However, VR applications often lack user acceptance due to usability and cognitive load issues. Prescriptive design knowledge and appropriate design principles (DPs) can address these issues, but their low reusabili...
Design Science Research (DSR) is an established approach to create innovative artifacts while considering relevance and rigor. Design principles (DPs) are the most common knowledge contribution. However, DPs are often not reused in the research and practice community. In this methodology paper, we propose an approach to conduct meta-studies for des...
Pedagogical conversational agents (PCAs) can support learners, e.g., by conveying learning content. Research suggests that building a social relationship between a PCA and a user facilitates learning. However, there is not yet enough knowledge regarding which PCA role learners prefer. This paper shows the results of an experiment (n = 130) and a fo...
The increasing popularity of conversational agents such as ChatGPT has sparked interest in their potential use in educational contexts but undermines the role of companionship in learning with these tools. Our study targets the design of virtual learning companions (VLCs), focusing on bonding relationships for collaborative learning while facilitat...
Pedagogical Conversational Agents (PCAs) such as chatbots and voice assistants can be used to help learners study through intelligent dialog. For example, educators can use PCAs to facilitate creative brainstorming processes. PCAs as brainstorming facilita-tors allow learning groups to network with each other and generate or evaluate ideas with the...
Pedagogical conversational agents (PCAs) such as chatbots and voice assistants can support learners in their studies. However, interactions with PCAs are often perceived as less motivating. Gamifying PCAs has been proposed as one approach to counteract this issue and increase learners' engagement. However, there is currently little prescriptive kno...
Pedagogical conversational agents (PCAs) are intelligent dialog systems that can support students as chatbots or voice assistants. However, many users find interactions with PCAs less engaging. One solution to increase learners' engagement is to embed the PCA in a virtual world, e.g., as a humanoid avatar that facilitates collaborative learning. Su...
Design Science Research (DSR) has become a widespread paradigm in the Information Systems (IS) discipline to design and evaluate novel artifacts for relevant problems in a scientifically rigorous manner. With its potential to complement the traditional IS curriculum, DSR education is gaining popularity in academia, despite still being in its infanc...
Conversational agents are becoming increasingly intelligent due to artificial intelligence (AI) advances, improving human-machine interaction. As conversational agents are already being used in various real-world applications, research shows the enormous potential of exploring advanced areas such as companionship or learning support. Particularly,...
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 miti...
Digital Game-based Learning (DGBL) has achieved several positive results in recent years, e.g., increased fun, motivation, or learning outcome. However, many DGBL applications fail, which makes an isolated consideration of individual game elements and their influence on learning necessary to better design future DGBL applications. One widely used g...
Virtual worlds (VWs) are no novum in higher education but regain interest through COVID-19 restrictions, emerging technologies, and the metaverse hype. Therefore, we conduct a systematic literature review to gain the current status quo of research in higher and further education to identify the educational activities, research areas, learning envir...
Lifelong vocational learning in a digital context frequently falters due to a lack of motivation, structure, time management, and attention to adult students' work-life balance. In remote settings, students have further little contact with peers and feel disconnected. This paper answers how a Virtual Learning Companion (VLC) can be designed to addr...
Conversational agents (CAs) are getting smarter thanks to advances in artificial intelligence, which opens the potential to use them in educational contexts to support (working) students. In addition, CAs are turning toward relationship-oriented virtual companions (e.g., Replika). Synthesizing these trends, we derive the virtual learning companion...
Zusammenfassung
Startups verfolgen in ihrer Anfangsphase das Ziel, schnell zu wachsen, um sich einen großen Kundenstamm aufzubauen und sich langfristig am Markt behaupten zu können. Während die Digitalisierung einen schnellen Markteinstieg ermöglicht, ist es in der New Economy für junge Unternehmen mit begrenzten Ressourcen jedoch schwierig, von po...
Dieser Beitrag hat zum Ziel, eine problemzentrierte Taxonomie für das selbstregulierte Lernen zu entwickeln. Das Vorgehen nach Nickerson zur Taxonomie-Entwicklung und wissenschaftliche Literatur aus der Lernpsychologie und Pädagogik fungieren zusammen mit der Kerntheorie des selbstgesteuerten Lernens als Wissensbasis. Das resultierende Artefakt, un...
Personalisiertes Lernen ermöglicht es Lernenden, nach ihren eigenen Lernpräferenzen und-stilen zu lernen. Conversational Agents (CAs) bieten eine vielversprechende Möglichkeit zur Unterstützung der Lernenden. CAs können Lernstile im Dialog mit den Nutzer:innen erkennen sowie passende Lern-Empfehlungen bereitstellen. Eine Herausforderung besteht jed...
Many people globally experience the feeling of loneliness and struggle with its consequences. A modern way to deal with this loneliness and lack of companionship is to use empathetic and emotional conversational agents. Often referred to as virtual companions, these agents can engage in human-like conversations with their users and build relationsh...
Pedagogical Conversational Agents (PCAs) offer the potential to increase educational equity worldwide by making learning accessible to all as a service for good, often enabled by artificial intelligence (AI). Yet, there are ethical challenges to the design and use of PCAs that hinder the achievement of individual and social goals. However, in addit...
Due to significant technological progress in the field of artificial intelligence, conversational agents have the potential to become smarter, deepen the interaction with their users, and overcome a function of merely assisting. Since humans often treat computers as social actors, theories on interpersonal relationships can be applied to human-mach...
International students often have difficulties in getting connected with other students (from their host country), or in fully understanding the lectures due to barriers such as interacting in a foreign language or adjusting to a new campus. eLearning Companions (eLCs) act as virtual friends, accompany students with dialog-based support for learnin...
Learners are faced with the challenge of processing a large amount of knowledge. However, they often lack individual support, and teaching is not tailored to their learning styles. Conversational Agents (CAs) could be a way to identify personal learning styles through a dialog between the CA and the learner, and to support him/her accordingly. This...
Conversational Agents (CAs) are widely spread in a variety of domains, such as health and customer service. There is a recent trend of increasing publications and implementations of CAs in education. We conduct a systematic literature review to identify common methodologies, pedagogical CA roles, addressed target groups, the technologies and theori...
Zusammenfassung
Begünstigt durch die wachsende Relevanz digitaler Unterstützungsangebote für das Lehren und Lernen, bspw. in Schulen und Universitäten, gewinnt der Forschungszweig der Conversational Agents im Bildungskontext an Bedeutung. Sogenannte Lern-Companions, also virtuelle Lern-Gefährten, bieten das Potenzial einer nutzeradaptiven sowie ört...
Questions
Question (1)
Hello everyone,
I've got a question regarding within-subject experiments, in which two or more variants of a prototype (e.g., chatbot) are evaluated with respect to different constructs, I.e. classic A/B testing experiments of different design options. For both versions, the same items are used for comparability.
Before the final data analysis, I plan to perform tests for validity, reliability and factor analysis. Does anyone know if I need to calculate the corresponding criteria (e.g., Cronbach's alpha, factor loadings, KMO values) for both versions separately, or only once aggregated for the respective constructs? And how would I proceed with the exclusion of items? Especially when there are a lot of control conditions, it might be difficult to decide whether to exclude an item if it is below a certain criterion.
In reviewing the literature of papers with a similar experiment design, I couldn't identify a consistent approach so far.
Thank you very much for your help! If anyone has any recommendations for tools or tutorials, I would also appreciate it as well.