Evaluating the Effectiveness of tutorial dialogue instruction in a Explotary learning context

[Proceedings of] ITS 2006, 8th International Conference on Intelligent Tutoring Systems, 26-30 June 2006, Jhongli, Taoyuan County, TaiwanIn this paper we evaluate the instructional effectiveness of tutorial dialogue agents in an exploratory learning setting. We hypothesize that the creative nature of an exploratory learning environment creates an opportunity for the benefits of tutorial dialogue to be more clearly evidenced than in previously published studies. In a previous study we showed an advantage for tutorial dialogue support in an exploratory learning environment where that support was administered by human tutors [9]. Here, using a similar experimental setup and materials, we evaluate the effectiveness of tutorial dialogue agents modeled after the human tutors from that study. The results from this study provide evidence of a significant learning benefit of the dialogue agentsThis project is supported by ONR Cognitive and Neural Sciences Division, Grant number N000140410107proceedingPublicad

Aerospace medicine and biology: A continuing bibliography with indexes (supplement 361)

This bibliography lists 141 reports, articles and other documents introduced into the NASA Scientific and Technical Information System during Mar. 1992. Subject coverage includes: aerospace medicine and physiology, life support systems and man/system technology, protective clothing, exobiology and extraterrestrial life, planetary biology, and flight crew behavior and performance

Integration of Action and Language Knowledge: A Roadmap for Developmental Robotics

“This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. In most cases, these works may not be reposted without the explicit permission of the copyright holder." “Copyright IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.”This position paper proposes that the study of embodied cognitive agents, such as humanoid robots, can advance our understanding of the cognitive development of complex sensorimotor, linguistic, and social learning skills. This in turn will benefit the design of cognitive robots capable of learning to handle and manipulate objects and tools autonomously, to cooperate and communicate with other robots and humans, and to adapt their abilities to changing internal, environmental, and social conditions. Four key areas of research challenges are discussed, specifically for the issues related to the understanding of: 1) how agents learn and represent compositional actions; 2) how agents learn and represent compositional lexica; 3) the dynamics of social interaction and learning; and 4) how compositional action and language representations are integrated to bootstrap the cognitive system. The review of specific issues and progress in these areas is then translated into a practical roadmap based on a series of milestones. These milestones provide a possible set of cognitive robotics goals and test scenarios, thus acting as a research roadmap for future work on cognitive developmental robotics.Peer reviewe

Generative AI and Its Educational Implications

We discuss the implications of generative AI on education across four critical sections: the historical development of AI in education, its contemporary applications in learning, societal repercussions, and strategic recommendations for researchers. We propose ways in which generative AI can transform the educational landscape, primarily via its ability to conduct assessment of complex cognitive performances and create personalized content. We also address the challenges of effective educational tool deployment, data bias, design transparency, and accurate output verification. Acknowledging the societal impact, we emphasize the need for updating curricula, redefining communicative trust, and adjusting to transformed social norms. We end by outlining the ways in which educational stakeholders can actively engage with generative AI, develop fluency with its capacities and limitations, and apply these insights to steer educational practices in a rapidly advancing digital landscape.Comment: This is a preprint version of an edited book chapter to appear in Kourkoulou, D., O. Tzirides, B. Cope, M. Kalantzis, (eds) (2024). Trust and Inclusion in AI-Mediated Education: Where Human Learning Meets Learning Machines, Springe

Simulating activities: Relating motives, deliberation, and attentive coordination

Activities are located behaviors, taking time, conceived as socially meaningful, and usually involving interaction with tools and the environment. In modeling human cognition as a form of problem solving (goal-directed search and operator sequencing), cognitive science researchers have not adequately studied “off-task” activities (e.g., waiting), non-intellectual motives (e.g., hunger), sustaining a goal state (e.g., playful interaction), and coupled perceptual-motor dynamics (e.g., following someone). These aspects of human behavior have been considered in bits and pieces in past research, identified as scripts, human factors, behavior settings, ensemble, flow experience, and situated action. More broadly, activity theory provides a comprehensive framework relating motives, goals, and operations. This paper ties these ideas together, using examples from work life in a Canadian High Arctic research station. The emphasis is on simulating human behavior as it naturally occurs, such that “working” is understood as an aspect of living. The result is a synthesis of previously unrelated analytic perspectives and a broader appreciation of the nature of human cognition. Simulating activities in this comprehensive way is useful for understanding work practice, promoting learning, and designing better tools, including human-robot systems

Examining Cognitive Empathy Elements within AI Chatbots for Healthcare Systems

Empathy is an essential part of communication in healthcare. It is a multidimensional concept and the two key dimensions: emotional and cognitive empathy allow clinicians to understand a patient’s situation, reasoning, and feelings clearly (Mercer and Reynolds, 2002). As artificial intelligence (AI) is increasingly being used in healthcare for many routine tasks, accurate diagnoses, and complex treatment plans, it is becoming more crucial to incorporate clinical empathy into patient-faced AI systems. Unless patients perceive that the AI is understanding their situation, the communication between patient and AI may not sustain efficiently. AI may not really exhibit any emotional empathy at present, but it has the capability to exhibit cognitive empathy by communicating how it can understand patients’ reasoning, perspectives, and point of view. In my dissertation, I examine this issue across three separate lab experiments and one interview study. At first, I developed AI Cognitive Empathy Scale (AICES) and tested all empathy (emotional and cognitive) components together in a simulated scenario against control for patient-AI interaction for diagnosis purposes. In the second experiment, I tested the empathy components separately against control in different simulated scenarios. I identified six cognitive empathy elements from the interview study with first-time mothers, two of these elements were unique from the past literature. In the final lab experiment, I tested different cognitive empathy components separately based on the results from the interview study in simulated scenarios to examine which element emerges as the most effective. Finally, I developed a conceptual model of cognitive empathy for patient-AI interaction connecting the past literature and the observations from my studies. Overall, cognitive empathy elements show promise to create a shared understanding in patients-AI communication that may lead to increased patient satisfaction and willingness to use AI systems for initial diagnosis purposes

Promoting Learning by Inducing and Scaffolding Cognitive Disequilibrium and Confusion through System Feedback

Learners frequently experience uncertainty about how to proceed during learning. These experiences cause learners to enter a state of cognitive disequilibrium and its affiliated affective state of confusion. Cognitive disequilibrium and confusion have been found to frequently occur during complex learning and provide opportunities for deeper learning. In the current thesis, a learning environment that induces confusion was investigated. In the environment, learners engaged in a dialogue on scientific reasoning with an animated pedagogical agent. Confusion was induced through false feedback provided by the tutor agent (e.g., when learners responded correctly and were told their response was incorrect). Self-reports of confusion during the training session indicated that false feedback was an effective method for inducing confusion. False feedback was also found to increase learners’ ability to apply this knowledge to new and novel situations, under certain conditions. Implications for the design of learning environments are also discussed