What do systematic literature reviews say about connectedness towards virtual agents in an educational context?10 answersSystematic literature reviews indicate that connectedness towards virtual agents in an educational context is influenced by several factors, including the design and implementation of the agents, their perceived human-likeness, and their ability to adapt to the socio-cultural environment of the learners. The design features of virtual agents, such as their voice, non-verbal behaviors, and gender, play a significant role in facilitating and improving the learning process by enhancing the learners' engagement and academic self-evaluations. Moreover, the human-like qualities of these agents, including their appearance and interactive capacities, are crucial for creating a more effective learning experience, as these features contribute to the agents' believability as human interlocutors.
Research also highlights the importance of the adaptability of virtual agents to the socio-cultural requirements of students, which can foster commitment, motivation, and responsibility, thereby improving learning outcomes. The use of virtual agents in virtual reality (VR) environments and their integration into hybrid virtual learning scenarios further underscores the potential of these agents to provide immersive and situated learning experiences that can significantly impact learners' motivation and engagement.
The effectiveness of pedagogical agents is also contingent upon their roles within the learning environment, whether as tutors, companions, or facilitators, and how these roles are perceived by the learners. Additionally, the interaction between the agents' appearance and behavior has been found to impact knowledge acquisition, suggesting that a careful consideration of these elements is necessary for maximizing the educational value of virtual agents.
In summary, systematic literature reviews suggest that connectedness towards virtual agents in an educational context is multifaceted, relying on the agents' design, human-likeness, adaptability, and the roles they embody within the learning environment. These factors collectively contribute to the agents' potential to enhance the learning experience by fostering a sense of connectedness and engagement among learners.
How important are agents in language models?4 answersAgents play a crucial role in language models (LMs) by enabling them to execute computer tasks guided by natural language commands efficiently. LMs, such as large language models (LLMs), can automate tasks and assist in problem-solving, enhancing productivity. Despite being trained solely on text data, LMs can infer and represent properties of agents likely to have produced the text, allowing them to model intentional communication and understand the relationship between language and use. Moreover, LLMs have shown potential in automated testing, serving as testing assistants that provide valuable information and drive the testing process, showcasing their importance in software development. Overall, agents are integral components of LMs, enabling them to perform diverse tasks, understand human intentions, and enhance software testing processes.
Are children moral agents?5 answersChildren can be considered moral agents as they demonstrate the ability to make sociomoral judgments based on factors like intent, past behavior, and situational constraints. Research indicates that even young children as young as 4 years old consider intent when making moral judgments, showing sensitivity to distinguishing intentional harms from accidental ones. Additionally, children at this age can use complex moral reasoning to evaluate whether harmful behavior towards antisocial individuals is justified based on past transgressions. Their evaluations of moral and epistemic agents are influenced by discerning intentional actions and monitoring situational constraints, reflecting practices of forgiveness, blame, trust, and objection. While perspectives on children's moral agency vary across disciplines, a developmental psychology perspective dominates, highlighting the importance of integrating different viewpoints to advance interdisciplinary knowledge on childhood ethics.
How the design of pedagogical AI agents helps to solve existing problems associated with using generative AI in education?4 answersThe design of pedagogical AI agents helps to solve existing problems associated with using generative AI in education by addressing challenges and improving learning outcomes. Pedagogical conversational agents (PCAs) based on common ground theory facilitate effective communication between learners and AI agents, leading to personalized learning and improved learning processes and outcomes. Additionally, the use of hyper-realistic human video generation technology in creating pedagogical agents (PAs) allows for the examination of how design choices and student perceptions impact learning motivation. Furthermore, the inclusion of enthusiasm in the design of human-AI collaboration and communication is crucial for generating multimodal enthusiastic communicative presentations, enhancing user experience and engagement. These design approaches contribute to the development of AI agents that support individualized learning, address conversational breakdowns, and create immersive and engaging learning environments.
Who mainly uses AI conversational agents?5 answersConversational AI agents are being used by various domains and industries, including e-commerce, customer services, and companies that want to communicate directly with end-users. Major IT companies like Google, IBM, Microsoft, and Amazon have developed powerful tools for creating conversational agents. Additionally, conversational AI agents are also being used in the healthcare sector for tasks such as text-based question answering, text summarization, and visual question answering. Furthermore, large-scale conversational AI agents like Alexa, Siri, and Google Assistant are widely used by users for tasks such as automatic speech recognition, natural language understanding, and entity resolution. Therefore, conversational AI agents are utilized by a wide range of users, including individuals, businesses, and industries, to facilitate communication and automate conversations.
What are the effects of conversational agents on children's social development?5 answersConversational agents have been found to have positive effects on children's social development. They can guide children in their quest for online resources, allowing them to develop their technical and critical thinking abilities as they interact with technology in various use cases. Additionally, conversational agents that help children generate curiosity-driven questions and encourage autonomous explorations have been shown to increase the number and quality of questions generated, resulting in longer explorations and stronger learning progress. These agents can train children's curiosity-related metacognitive skills, helping them maintain their information-searching behaviors and acquire new knowledge. Overall, conversational agents can play a valuable role in supporting children's social development by facilitating their learning, promoting curiosity, and enhancing their engagement with online resources.