When AI simulates teaching personas: Linguistic dimensions of LLM-generated classroom discourse

This study investigates how GPT-4o constructs the social role of teacher through discourse and how this discourse compares with naturally occurring classroom teaching. We generated 19,950 AI classroom teaching texts from a corpus of online college teaching using 56 teacher personas plus a baseline condition. The AI-generated texts contained the original student turns alongside the AI-teacher turns. We analyzed the resulting corpus using Lexical Multi-Dimensional Analysis (LMDA), a bottom-up corpus-linguistic method that identifies dimensions of discourse variation from recurrent patterns of lexical covariation. Five dimensions emerged: Macro-Structuring Discourse vs. Performative Stagecraft, L1-scaffolded Guidance vs. Provocative Challenge, Procedural Control vs. Rapport-driven Engagement, Evaluative Control vs. Reflective Aesthetic Engagement, and Informal Facilitation vs. Formal Deductive Exposition. Comparison with the corresponding human classroom discourse showed that GPT-4o frequently magnified teaching discourses already present in human interaction, particularly organizational, procedural, and expository discourse. It also introduced more artificial forms of teaching discourse characterized by theatrical performance, confrontation, and evaluative pressure. Mixed-effects analyses showed significant differences between human and AI discourse on all five dimensions. Persona prompting also affected dimension scores, but differentiation among the 57 conditions was uneven: many personas occupied overlapping positions, and differences occurred on only some dimensions. The findings show that GPT-4o can produce distinct forms of teaching discourse, yet many different persona prompts repeat similar discourses, suggesting a saturation point beyond which the LLM cannot yield new teaching discourses. More generally, the study demonstrates how corpus-based discourse analysis can characterize the forms of teaching that emerge when an LLM is instructed to perform a professional role.

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Publication Details

Journal
PLoS ONE
Published
2026-11-06
DOI
https://doi.org/10.1371/journal.pone.0360650
Primary Topic
Discourse Analysis in Language Studies
Type
article
Field-Weighted Citation Impact
0.00

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article

When AI simulates teaching personas: Linguistic dimensions of LLM-generated classroom discourse

Tony Berber Sardinha, Marilisa Shimazumi
4 links
PLoS ONE
Discourse Analysis in Language Studies
article

When AI simulates teaching personas: Linguistic dimensions of LLM-generated classroom discourse

Tony Berber Sardinha, Marilisa Shimazumi
article en

Abstract

This study investigates how GPT-4o constructs the social role of teacher through discourse and how this discourse compares with naturally occurring classroom teaching. We generated 19,950 AI classroom teaching texts from a corpus of online college teaching using 56 teacher personas plus a baseline condition. The AI-generated texts contained the original student turns alongside the AI-teacher turns. We analyzed the resulting corpus using Lexical Multi-Dimensional Analysis (LMDA), a bottom-up corpus-linguistic method that identifies dimensions of discourse variation from recurrent patterns of lexical covariation. Five dimensions emerged: Macro-Structuring Discourse vs. Performative Stagecraft, L1-scaffolded Guidance vs. Provocative Challenge, Procedural Control vs. Rapport-driven Engagement, Evaluative Control vs. Reflective Aesthetic Engagement, and Informal Facilitation vs. Formal Deductive Exposition. Comparison with the corresponding human classroom discourse showed that GPT-4o frequently magnified teaching discourses already present in human interaction, particularly organizational, procedural, and expository discourse. It also introduced more artificial forms of teaching discourse characterized by theatrical performance, confrontation, and evaluative pressure. Mixed-effects analyses showed significant differences between human and AI discourse on all five dimensions. Persona prompting also affected dimension scores, but differentiation among the 57 conditions was uneven: many personas occupied overlapping positions, and differences occurred on only some dimensions. The findings show that GPT-4o can produce distinct forms of teaching discourse, yet many different persona prompts repeat similar discourses, suggesting a saturation point beyond which the LLM cannot yield new teaching discourses. More generally, the study demonstrates how corpus-based discourse analysis can characterize the forms of teaching that emerge when an LLM is instructed to perform a professional role.

PLoS ONEVol. 21(11)
Universidade Estadual Paulista (Unesp) (BR), Pontifícia Universidade Católica de São Paulo (BR)
Fundação de Amparo à Pesquisa do Estado de São Paulo, Conselho Nacional de Desenvolvimento Científico e Tecnológico
Quality education
Openalex Percentile: Top 8%
Discourse Analysis in Language Studies
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