Conversational agents for fostering curiosity-driven learning in children

Abstract

Curiosity is an important factor that favors independent and individualized learning in children. Research suggests that it is also a competence that can be fostered by training specific metacognitive skills and information-searching behaviors. In this light, we develop a conversational agent that helps children generate curiosity-driven questions, and encourages their use to lead autonomous explorations and gain new knowledge. The study was conducted with 51 primary school students who interacted with either a neutral agent or an "incentive" agent that helped curiosity-driven questioning by offering specific semantic cues. Results showed a significant increase in the number and the quality of the questions generated with the "incentive" agent. This interaction also resulted in longer explorations and stronger learning progress.Together, our results suggest that the more our agent is able to train children's curiosity-related metacognitive skills, the better they can maintain their information-searching behaviors and the more new knowledge they are likely to acquire.

Supporting Qualitative Analysis with Large Language Models: Combining Codebook with GPT-3 for Deductive Coding

“…The recent advent of LLMs (e.g., GPT-3 [2], PaLM [3], OPT [16]) offers new capabilities, including creative writing [14], programming 1 , etc. Unlike traditional task-specific models, LLMs accept natural language prompts as input to perform various tasks [8].…”