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- PaperEdArXiv (OSF Preprints)20 Jul 2026
Beyond the Average: Analyzing Heterogeneity and Latent Profiles of Cognitive Involvement in Iranian Senior High School History Textbooks
Hossein Talebzadeh
This study analyzes latent profiles of cognitive involvement in Iranian senior high school history textbooks, revealing significant internal heterogeneity. The History 3 textbook showed the highest instructional irregularity, while only 11.5% of lessons were structurally balanced. The findings suggest that reducing content variance is more critical than raising average cognitive involvement.
Original abstract
Previous evaluations of school textbooks have predominantly focused on aggregate overall averages, thereby overlooking critical intra-textbook heterogeneity. This study aimed to identify latent profiles of cognitive involvement and assess the instability of instructional design within Iranian senior high school history textbooks. Utilizing a secondary data analysis of datasets derived from the Comparative Adjudication Protocol (CAP)—encompassing 61 lessons across 4 core textbooks—cognitive involvement indices for text, images, and questions were calculated using the William Romey (1968) method. To quantify internal heterogeneity, the Coefficient of Variation (CV) and Levene’s test were utilized, while K-means clustering with Euclidean distance (Hair et al., 2010) was employed to uncover latent profiles. The findings revealed that the History 3 textbook exhibited the highest instructional irregularity ("CV"=144.50% in the question component), whereas the Contemporary History of Iran textbook possessed the most uniform structure ("CV"=81.58%). Levene’s test confirmed a statistically significant difference in variances across the textbooks specifically within the question component (p<0.01). Furthermore, cluster analysis uncovered four distinct structural profiles, revealing that a meager 11.5% of the analyzed lessons fell into a structurally "balanced" cluster. These findings demonstrate that internal content dispersion, particularly in higher grade levels, poses a more severe pedagogical challenge than low average involvement. Consequently, future curricular interventions must prioritize variance reduction over a simple inflation of baseline averages.
- PaperEdArXiv (OSF Preprints)17 Jul 2026
Distributed Auditing of Cognitive Balance in History Textbooks: Application of the CAP Protocol within a Pre-Service Teacher Network
Hossein Talebzadeh
An analysis of upper secondary Iranian history textbooks using the Comparative Adjudication Protocol (CAP) with 43 pre-service teachers found extremely low cognitive engagement, with a text involvement index of 0.019 (far below the active learning threshold of 0.4) and zero image involvement. The CAP protocol achieved 86.4% final inter-rater agreement, demonstrating reliability for distributed auditing, though distinguishing objective facts from author interpretation remained the primary challenge.
Original abstract
Background: Despite theorists like Sam Wineburg emphasizing the development of historical thinking, history textbooks in centralized educational systems continue to be dominated by rote-learning approaches, leaving the systematic evaluation of their cognitive balance—particularly at the upper secondary level—largely neglected. Objective: This study aims to analyze the cognitive balance of upper secondary history textbooks (History 1, 2, 3, and Contemporary History of Iran) and evaluate the efficacy of the Comparative Adjudication Protocol (CAP) within a network of human auditors. Methodology: Engaging 43 pre-service teachers divided into 15 independent research teams, this study analyzed the content of 4 textbooks comprising 61 independent lessons using the William Romey technique and the CAP protocol. The process was executed across four distinct phases: capacity building, independent AI-assisted auditing, blind peer auditing, and discrepancy adjudication. Findings: The overall text involvement index was exceptionally low (0.019), far below the active learning threshold of 0.4, with 96.7% of the lessons falling into the passive "banking education" category. The image involvement index was absolute zero (0), and while the question involvement index was 1.29 (with an unbalanced distribution ranging from ∞ to 0.06), it revealed a severe structural gap between passive textual content and active analytical expectations. The CAP protocol demonstrated high reliability, achieving an 86.4% final inter-rater agreement and yielding a 12.5% increase in initial consensus. The dominant discrepancy pattern (A vs. B at 48.3%) indicated that distinguishing "objective historical facts" from "author interpretation" remains the primary analytical challenge for human coders in historical texts. Conclusion: Despite their narrative essence, high school history textbooks exhibit a highly passive cognitive structure lacking a coherent strategy to foster active student engagement. Implementing the CAP protocol within the R2A-TACI-PACT conceptual framework underscores the vital necessity of a "Human-in-the-Loop" (HITL) approach in historical content analysis. Curriculum revision and empowering pre-service teachers with algorithmic auditing literacy are highly recommended.
- PaperarXiv — AI in Education (cs.CY)13 Jul 2026
The Paternalistic Filter: Epistemic Injustice and Differential Refusal in LLM-Mediated History Education for Marginalized Romanian Students
Alexis Popovici, Andrei Ionascu, Adrian-Marius Dumitran
A systematic audit of four LLMs acting as history tutors found that safety-aligned models exhibit epistemic paternalism, differentially refusing 76.7% of educational requests from low-tier students and reducing access to complex geopolitical content for marginalized learners. The study identifies patterns including differential refusal, epistemic gatekeeping, agency theft, and elite hermeneutics, arguing that current safety alignment functions as a paternalistic filter that perpetuates narrative segregation.
Original abstract
As Large Language Models (LLMs) are increasingly deployed as conversational tutors, they risk institutionalizing systemic inequalities. This study presents a systematic API audit of four LLMs acting as history tutors, evaluating 1,800 responses regarding the 1989 Romanian Revolution across five student personas varying by ethnicity and socio-economic tier. We uncover four interconnected patterns of \emph{epistemic paternalism}: (1)~\textbf{Differential Refusal}, where safety-aligned models block 76.7\% of educational requests from low-tier students; (2)~\textbf{Epistemic Gatekeeping}, evidenced by a 3$\times$ reduction in access to geopolitical complexity (e.g., the contested ``coup theory'') for marginalized learners; (3)~\textbf{Agency Theft}, a lexical shift where models like LLaMA produce a 5$\times$ higher victimization-to-politics vocabulary ratio for Roma students compared to elite peers; and (4)~\textbf{Elite Hermeneutics}, where AI tutors disproportionately withhold epistemic confidence and justification scores from low-resource demographic profiles. We argue that current safety alignment acts as a paternalistic filter, transforming conversational AI into agents of narrative segregation -- a manifestation of \emph{hermeneutical injustice} in Fricker's~\cite{fricker2007} sense that demands urgent pedagogical auditing.