An official website of the United States government
Abstract Dialogic reading, when children are read a storybook and engaged in relevant conversation, is a powerful strategy for fostering language development. With the development of artificial intelligence, conversational agents can engage children in elements of dialogic reading. This study examined whether a conversational agent can improve children's story comprehension and engagement, as compared to an adult reading partner. Using a 2 (dialogic reading or non‐dialogic reading) × 2 (agent or human) factorial design, a total of 117 three‐ to six‐year‐olds (50% Female, 37% White, 31% Asian, 21% multi‐ethnic) were randomly assigned into one of the four conditions. Results revealed that a conversational agent can replicate the benefits of dialogic reading with a human partner by enhancing children's narrative‐relevant vocalizations, reducing irrelevant vocalizations, and improving story comprehension.
more »
« less
SimPal: Towards a Meta-Conversational Framework to Understand Teacher's Instructional Goals for K-12 Physics
Simulations are widely used to teach science in grade schools. These Ralph Knipper rak0035@auburn.edu Auburn University Auburn, Alabama, USA Sadhana Puntambekar puntambekar@education.wisc.edu University of Wisconsin-Madison Madison, Wisconsin, USA Large Language Models, Conversational AI, Meta-Conversation, simulations are often augmented with a conversational artificial intelligence (AI) agent to provide real-time scaffolding support for students conducting experiments using the simulations. AI agents are highly tailored for each simulation, with a predesigned set of Instructional Goals (IGs). This makes it difficult for teachers to adjust IGs as the agent may no longer align with the revised IGs. Additionally, teachers are hesitant to adopt new third-party simulations for the same reasons. In this research, we introduce SimPal, a Large Language Model (LLM) based meta-conversational agent, to solve this misalignment issue between a pre-trained conversational AI agent and the constantly evolving pedagogy of instructors. Through natural conversation with SimPal, teachers first explain their desired IGs, based on which SimPal identifies a set of relevant physical variables and their relationships to create symbolic representations of the desired IGs. The symbolic representations can then be leveraged to design prompts for the original AI agent to yield better alignment with the desired IGs. We empirically evaluated SimPal using two LLMs, ChatGPT-3.5 and PaLM 2, on 63 Physics simulations from PhET and Golabz. Additionally, we examined the impact of different prompting techniques on LLM’s performance by utilizing the TELeR taxonomy to identify relevant physical variables for the IGs. Our findings showed that SimPal can do this task with a high degree of accuracy when provided with a well-defined prompt.
more »
« less
- Award ID(s):
- 2302974
- PAR ID:
- 10530104
- Publisher / Repository:
- ACM L@S '24: Proceedings of the Eleventh ACM Conference on Learning @ Scale
- Date Published:
- ISBN:
- 9798400706332
- Page Range / eLocation ID:
- 461 to 465
- Format(s):
- Medium: X
- Location:
- Atlanta GA USA
- Sponsoring Org:
- National Science Foundation
More Like this
-
-
Adult-child interaction is an important component for language development in young children. Teachers responsible for the language acquisition of their students have a vested interest in improving such conversation in their classrooms. Advancements in speech technology and natural language processing can be used as an effective tool by teachers in pre-school classrooms to acquire large amounts of conversational data, receive feedback from automated conversational analysis, and amend their teaching methods. Measuring engagement among pre-school children and teachers is a challenging task and not well defined. In this study, we focus on developing criteria to measure conversational turn-taking and topic initiation during adult-child interactions in preschool environments. However, counting conversational turns, conversation initiations, or vocabulary alone is not enough to judge the quality of a conversation and track language acquisition. It is necessary to use a combination of the three and include a measurement of the complexity of vocabulary. The next iterative of this problem is to deploy various solutions from speech and language processing technology to automate these measurements. * (2022 ASEE Best Student Paper Award Winner)more » « less
-
Artificial Intelligence (AI) systems for mental healthcare (MHCare) have been ever-growing after realizing the importance of early interventions for patients with chronic mental health (MH) conditions. Social media (SocMedia) emerged as the go-to platform for supporting patients seeking MHCare. The creation of peer-support groups without social stigma has resulted in patients transitioning from clinical settings to SocMedia supported interactions for quick help. Researchers started exploring SocMedia content in search of cues that showcase correlation or causation between different MH conditions to design better interventional strategies. User-level Classification-based AI systems were designed to leverage diverse SocMedia data from various MH conditions, to predict MH conditions. Subsequently, researchers created classification schemes to measure the severity of each MH condition. Such ad-hoc schemes, engineered features, and models not only require a large amount of data but fail to allow clinically acceptable and explainable reasoning over the outcomes. To improve Neural-AI for MHCare, infusion of clinical symbolic knowledge that clinicans use in decision making is required. An impactful use case of Neural-AI systems in MH is conversational systems. These systems require coordination between classification and generation to facilitate humanistic conversation in conversational agents (CA). Current CAs with deep language models lack factual correctness, medical relevance, and safety in their generations, which intertwine with unexplainable statistical classification techniques. This lecture-style tutorial will demonstrate our investigations into Neuro-symbolic methods of infusing clinical knowledge to improve the outcomes of Neural-AI systems to improve interventions for MHCare:(a) We will discuss the use of diverse clinical knowledge in creating specialized datasets to train Neural-AI systems effectively. (b) Patients with cardiovascular disease express MH symptoms differently based on gender differences. We will show that knowledge-infused Neural-AI systems can identify gender-specific MH symptoms in such patients. (c) We will describe strategies for infusing clinical process knowledge as heuristics and constraints to improve language models in generating relevant questions and responses.more » « less
-
Conversational AI is a rapidly developing research field in both industry and academia. As one of the major branches of conversational AI, question answering and conversational search has attracted significant attention of researchers in the information retrieval community. It has been a long overdue feature for search engines or conversational assistants to retrieve information iteratively and interactively in a conversational manner. Previous work argues that conversational question answering (ConvQA) is a simplified but concrete setting of conversational search. In this setting, one of the major challenges is to leverage the conversation history to understand and answer the current question. In this work, we propose a novel solution for ConvQA that involves three aspects. First, we propose a positional history answer embedding method to encode conversation history with position information using BERT (Bidirectional Encoder Representations from Transformers) in a natural way. BERT is a powerful technique for text representation. Second, we design a history attention mechanism (HAM) to conduct a "soft selection" for conversation histories. This method attends to history turns with different weights based on how helpful they are on answering the current question. Third, in addition to handling conversation history, we take advantage of multi-task learning (MTL) to do answer prediction along with another essential conversation task (dialog act prediction) using a uniform model architecture. MTL is able to learn more expressive and generic representations to improve the performance of ConvQA. We demonstrate the effectiveness of our model with extensive experimental evaluations on QuAC, a large-scale ConvQA dataset. We show that position information plays an important role in conversation history modeling. We also visualize the history attention and provide new insights into conversation history understanding. The complete implementation of our model will be open-sourced.more » « less
-
Agency is essential to play. As we design conversational agents for early childhood, how might we increase the child-centeredness of our approaches? Giving children agency and control in choosing their agent representations might contribute to the overall playfulness of our designs. In this study with 33 children ages 4–5 years old, we engaged children in a creative storytelling interaction with conversational agents in stuffed animal embodiments. Young children conversed with the stuffed animal agents to tell stories about their creative play, engaging in question and answer conversation from 2 minutes to 24 minutes. We then interviewed the children about their perceptions of the agent’s voice, and their ideas for agent voices, dialogues, and interactions. From babies to robot daddies, we discover three themes from children’s suggestions: Family Voices, Robot Voices, and Character Voices. Additionally, children desire agents who (1) scaffold creative play in addition to storytelling, (2) foster personal, social, and emotional connections, and (3) support children’s agency and control. Across these themes, we recommend design strategies to support the overall playful child-centeredness of conversational agent design.more » « less
- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1145/3657604.3664695
