BELLA

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Evidence · no claims cited

Description

BELLA is a symbolic-AI learning-by-teaching tutoring system developed by Lenat et al. that models the user and controls a diagnostic tutee avatar to support pre-algebra learning.

Components

• Microtheory-based representation of interaction contexts enabling temporal reasoning about user and tutee models: BELLA represents each step of the user–Elle dialogue as a new reified context (microtheory) that snapshots the user model, the Elle model, and the problem state at that moment. As the article describes, "a growing graph of interaction contexts is built" that provides a way to refer to past states, back up a step, and serve as grist for BELLA to induce conclusions about the user. Several mt-types are distinguished: Elle-belief mts, user-model snapshot mts, time-point mts, process mts, math belief mts, quantitative relationship mts, problem interpretation mts, tactician mts and strategist mts. (Lenat et al. (2014))

Claims

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Key Sources

• Lenat, D. B., & Durlach, P. J. (2014). Reinforcing Math Knowledge by Immersing Students in a Simulated Learning-By-Teaching Experience. Int J Artif Intell Educ. https://doi.org/10.1007/s40593-014-0016-x

Page metadata

Field	Value
type	product
id	bella
title	BELLA
description	BELLA is a symbolic-AI learning-by-teaching tutoring system developed by Lenat et al.
status	review
product_kind	software
generated	by process:settle-candidates on 2026-10-28
trust	unverified — no one has confirmed this page's content

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