DIALOGOS: A Dialog Chatbot Framework for Eliciting and Mapping Science Misconceptions in Elementary School

Abstract

Persistent misconceptions about scientific concepts develop early in elementary education and resist traditional teaching. The diagnostic tools most commonly used to identify them, such as multiple-choice and tiered tests, reveal that an answer is incorrect but not the reasoning behind it. This article presents DIALOGOS (DIAlogic Learning Observatory for the Grasping Of Science), a conceptual framework for a teacher-guided conversational tool that elicits and maps elementary pupils' science misconceptions through natural dialogue. The framework was developed through a theory synthesis of 68 sources identified in Scopus, ERIC, and Google Scholar, comprising 62 peer-reviewed works, two preprints, three books, and one policy report, managed in Zotero and hand-coded in a five-field extraction matrix. Coded claims were integrated through conceptual translation, cross-strand confrontation, and constraint propagation across three areas: conceptual change theory, dialogic teaching, and educational chatbot research. The synthesis yields six design principles and a four-part architecture: a dialogic elicitation engine grounded in Socratic questioning and the elicit–confront–resolve sequence; a mapping module comparing pupils' reasoning against a research-based catalogue and a concept-map overlay; an individual misconception profile; and a class-level observatory for teachers. The framework repositions educational chatbots from answer providers to diagnostic listeners, shifts misconception diagnosis from item-based to conversation-based mapping, and specifies six testable propositions for empirical validation