Simplification and LLM-assisted reworking
A modular prompting approach for the comprehension of expository texts
DOI:
https://doi.org/10.59471/raia2026236Keywords:
text simplification, prompt engineering, reading comprehension, nominalization, large language modelsAbstract
Academic expository texts pose persistent comprehension challenges for university students, largely due to features such as high informational density, syntactic complexity, and abstract linguistic structures like nominalizations and impersonal constructions. Drawing on cognitive psychology and psycholinguistics, this study argues that text difficulty stems not only from conceptual complexity but from how that content is linguistically encoded: agent-action-object structures aligned with event order facilitate processing, while nominalized, passive, and syntactically dense constructions increase inferential load, particularly for novice readers. Building on recent frameworks for characterizing prompting in academic contexts, this paper proposes a modular protocol for assisted text simplification using large language models. The protocol comprises five transformation modules—denominalization of processes, explicitation of agents, syntactic segmentation, explicitation of logical relations, and integration of disciplinary examples—plus a transversal meta comprehension module that makes transformations visible to the reader. Each module is structured around a theoretical rationale, target linguistic feature, transformation rule, control criteria, and an operative prompt. Four illustrative cases (sociology, biology, economics, law) demonstrate the protocol's application. The approach distinguishes conceptual from discursive complexity, offering a systematic yet flexible alternative to traditional rule-based simplification. Limitations include the current absence of empirical validation with users, identified as a priority for future research.
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