ChatGPT and Gemini participated in the Korean College Scholastic Ability Test -- Earth Science I

TL;DR

This study evaluates the scientific reasoning capabilities and limitations of advanced LLMs like GPT-4o and Gemini in the context of the Korean College Scholastic Ability Test, revealing key perception and reasoning flaws to inform AI-resistant assessment design.

Contribution

The paper provides a detailed analysis of LLMs' performance on a real-world science test, identifying specific cognitive weaknesses and proposing strategies for AI-resistant assessments.

Findings

Models struggle with unstructured inputs due to OCR errors.

Perception errors dominate, highlighting a perception-cognition gap.

Models perform calculations well but fail to grasp underlying concepts.

Abstract

The rapid development of Generative AI is bringing innovative changes to education and assessment. As the prevalence of students utilizing AI for assignments increases, concerns regarding academic integrity and the validity of assessments are growing. This study utilizes the Earth Science I section of the 2025 Korean College Scholastic Ability Test (CSAT) to deeply analyze the multimodal scientific reasoning capabilities and cognitive limitations of state-of-the-art Large Language Models (LLMs), including GPT-4o, Gemini 2.5 Flash, and Gemini 2.5 Pro. Three experimental conditions (full-page input, individual item input, and optimized multimodal input) were designed to evaluate model performance across different data structures. Quantitative results indicated that unstructured inputs led to significant performance degradation due to segmentation and Optical Character Recognition (OCR) failures. Even under optimized conditions, models exhibited fundamental reasoning flaws. Qualitative analysis revealed that "Perception Errors" were dominant, highlighting a "Perception-Cognition Gap" where models failed to interpret symbolic meanings in schematic diagrams despite recognizing visual data. Furthermore, models demonstrated a "Calculation-Conceptualization Discrepancy," successfully performing calculations while failing to apply the underlying scientific concepts, and "Process Hallucination," where models skipped visual verification in favor of plausible but unfounded background knowledge. Addressing the challenge of unauthorized AI use in coursework, this study provides actionable cues for designing "AI-resistant questions" that target these specific cognitive vulnerabilities. By exploiting AI's weaknesses, such as the gap between perception and cognition, educators can distinguish genuine student competency from AI-generated responses, thereby ensuring assessment fairness.