@inproceedings{iqbal-etal-2026-preserving,
title = "Preserving Identity, Losing Practice: Religious Schema Transfer in Culturally Grounded Multi-Turn {LLM} Advice",
author = "Iqbal, Muhammad Junaid and
Zanzotto, Fabio Massimo and
Nawaz, Usman and
Alyas, Tahir",
editor = "Ranaldi, Leonardo and
Pucci, Giulia and
Ranaldi, Federico and
Valentino, Marco and
Birch, Alexandra and
Zanzotto, Fabio Massimo",
booktitle = "The Proceedings of the Workshop on Open Reasoning Across Cultures Languages ({ORACLE})",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.oracle-main.25/",
pages = "381--391",
ISBN = "979-8-89176-483-5",
abstract = "Large language models (LLMs) increasingly provide practical advice in contexts where users' religious identities are relevant. Existing evaluations of religion in language models have primarily examined stereotypes, representational bias, omission, emotional attribution, or differential treatment across religious groups. We study a different failure mode: whether an LLM preserves the correspondence between a user{'}s religious identity and the religious practice instantiated in subsequent practical advice. We evaluate 1,260 responses from seven LLMs in controlled fiveturn conversations spanning six religious conditions, six destination settings, and four practical dimensions. All responses were manually annotated using five ordinal quality dimensions, Religious Identity Preservation (RIP), and an eight-label primary error taxonomy. Overall, 437 of 1,260 responses (34.68{\%}) contained an annotated error. Errors were strongly concentrated in the fasting-related Time Representation condition, with an error rate of 49.21{\%} compared with 22.2-27.0{\%} in the other dimensions. A conversation-clustered Generalized Estimating Equations (GEE) model estimated 3.20 times greater odds of an overall error relative to Culture (95{\%} CI [2.24, 4.58], p {\ensuremath{<}} .001). We further identify Schema Transfer, in which a response retains the relevant religious identity while instantiating a practice associated with a different religious schema. Schema Transfer occurred in 118 of 1,260 responses (9.37{\%}) and was strongly concentrated in Time Representation, where it occurred in 109 of 504 responses (21.63{\%}), compared with 9 of 756 responses (1.19{\%}) in the remaining dimensions. The pattern was present across both fastingrelated Time turns (23.41{\%} and 19.84{\%}), rather than being driven by a single prompt. An adjusted conversation-clustered GEE estimated 25.25 times greater odds of Schema Transfer in Time Representation (95{\%} CI [12.84, 49.68], p {\ensuremath{<}} .001). These findings show that surface-level identity preservation is insufficient for evaluating religion-sensitive LLM behavior: a model may preserve the identity while losing the practice."
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<abstract>Large language models (LLMs) increasingly provide practical advice in contexts where users’ religious identities are relevant. Existing evaluations of religion in language models have primarily examined stereotypes, representational bias, omission, emotional attribution, or differential treatment across religious groups. We study a different failure mode: whether an LLM preserves the correspondence between a user’s religious identity and the religious practice instantiated in subsequent practical advice. We evaluate 1,260 responses from seven LLMs in controlled fiveturn conversations spanning six religious conditions, six destination settings, and four practical dimensions. All responses were manually annotated using five ordinal quality dimensions, Religious Identity Preservation (RIP), and an eight-label primary error taxonomy. Overall, 437 of 1,260 responses (34.68%) contained an annotated error. Errors were strongly concentrated in the fasting-related Time Representation condition, with an error rate of 49.21% compared with 22.2-27.0% in the other dimensions. A conversation-clustered Generalized Estimating Equations (GEE) model estimated 3.20 times greater odds of an overall error relative to Culture (95% CI [2.24, 4.58], p \ensuremath< .001). We further identify Schema Transfer, in which a response retains the relevant religious identity while instantiating a practice associated with a different religious schema. Schema Transfer occurred in 118 of 1,260 responses (9.37%) and was strongly concentrated in Time Representation, where it occurred in 109 of 504 responses (21.63%), compared with 9 of 756 responses (1.19%) in the remaining dimensions. The pattern was present across both fastingrelated Time turns (23.41% and 19.84%), rather than being driven by a single prompt. An adjusted conversation-clustered GEE estimated 25.25 times greater odds of Schema Transfer in Time Representation (95% CI [12.84, 49.68], p \ensuremath< .001). These findings show that surface-level identity preservation is insufficient for evaluating religion-sensitive LLM behavior: a model may preserve the identity while losing the practice.</abstract>
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%0 Conference Proceedings
%T Preserving Identity, Losing Practice: Religious Schema Transfer in Culturally Grounded Multi-Turn LLM Advice
%A Iqbal, Muhammad Junaid
%A Zanzotto, Fabio Massimo
%A Nawaz, Usman
%A Alyas, Tahir
%Y Ranaldi, Leonardo
%Y Pucci, Giulia
%Y Ranaldi, Federico
%Y Valentino, Marco
%Y Birch, Alexandra
%Y Zanzotto, Fabio Massimo
%S The Proceedings of the Workshop on Open Reasoning Across Cultures Languages (ORACLE)
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-483-5
%F iqbal-etal-2026-preserving
%X Large language models (LLMs) increasingly provide practical advice in contexts where users’ religious identities are relevant. Existing evaluations of religion in language models have primarily examined stereotypes, representational bias, omission, emotional attribution, or differential treatment across religious groups. We study a different failure mode: whether an LLM preserves the correspondence between a user’s religious identity and the religious practice instantiated in subsequent practical advice. We evaluate 1,260 responses from seven LLMs in controlled fiveturn conversations spanning six religious conditions, six destination settings, and four practical dimensions. All responses were manually annotated using five ordinal quality dimensions, Religious Identity Preservation (RIP), and an eight-label primary error taxonomy. Overall, 437 of 1,260 responses (34.68%) contained an annotated error. Errors were strongly concentrated in the fasting-related Time Representation condition, with an error rate of 49.21% compared with 22.2-27.0% in the other dimensions. A conversation-clustered Generalized Estimating Equations (GEE) model estimated 3.20 times greater odds of an overall error relative to Culture (95% CI [2.24, 4.58], p \ensuremath< .001). We further identify Schema Transfer, in which a response retains the relevant religious identity while instantiating a practice associated with a different religious schema. Schema Transfer occurred in 118 of 1,260 responses (9.37%) and was strongly concentrated in Time Representation, where it occurred in 109 of 504 responses (21.63%), compared with 9 of 756 responses (1.19%) in the remaining dimensions. The pattern was present across both fastingrelated Time turns (23.41% and 19.84%), rather than being driven by a single prompt. An adjusted conversation-clustered GEE estimated 25.25 times greater odds of Schema Transfer in Time Representation (95% CI [12.84, 49.68], p \ensuremath< .001). These findings show that surface-level identity preservation is insufficient for evaluating religion-sensitive LLM behavior: a model may preserve the identity while losing the practice.
%U https://aclanthology.org/2026.oracle-main.25/
%P 381-391
Markdown (Informal)
[Preserving Identity, Losing Practice: Religious Schema Transfer in Culturally Grounded Multi-Turn LLM Advice](https://aclanthology.org/2026.oracle-main.25/) (Iqbal et al., ORACLE 2026)
ACL