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Effect of large language model assistance on undergraduate art history question-answering performance: a randomized
Yunting Zhang1,2, Fan Zhang3, Zili Zhang4
1School of Fine Arts, Jining University, Jining, China.
Introduction:
Large language models (LLMs) are increasingly used in higher education, yet empirical evidence for their effectiveness in art education remains scarce. This study aimed to evaluate whether LLM assistance could improve undergraduate art history question-answering performance and explanatory support.
Methods:
This study developed the Art History Theory Question Set (AHTQS), comprising 104 single-choice items with Bloom-level annotations, and benchmarked three LLMs (ChatGPT-4o, DeepSeek-V3, and Qwen2.5-Plus). DeepSeek-V3 showed the highest accuracy (96.2%) and lowest observed run-to-run variability and was selected for a randomized crossover pilot study with six undergraduates. The primary outcome was the change in examination accuracy from independent to LLM-assisted answering. A Likert-scale evaluation involving nine students and three instructors was also conducted to assess the clarity and coherence of LLM-generated explanations.
Results:
A one-sided Wilcoxon signed-rank test showed significant improvement with LLM support [W(6) = 21.0, p = 0.0156, r = 0.879], with median accuracy increasing from 43.3% to 93.3% (median gain = 42.3%). Five of six students showed higher accuracy under the LLM-assisted condition, and no clear evidence of a sequence or carryover effect was detected (Mann-Whitney U = 7.0, p = 0.3758). Domain-level analyses indicated significant gains in all four categories (p < 0.05). Error-frequency analysis further showed marked reductions in high-frequency mistakes. The Likert-scale evaluation indicated high perceived clarity and coherence of LLM explanations, with favorable but more cautious instructor ratings.
Discussion:
These pilot findings suggest that supervised LLM assistance may support art history question-answering and explanatory feedback. Future studies should validate these findings in larger cohorts, assess delayed learning retention, and examine open-ended, image-based, and higher-order art history tasks before curriculum-level implementation.
