Large language models (LLMs) offer a range of new possibilities, including adapting the text to different audiences and their reading needs. But how well do they adapt? We evaluate the readability of answers generated by four state-of-the-art LLMs (commercial and open-source) to science questions when prompted to target different age groups and education levels. To assess the adaptability of LLMs to diverse audiences, we compare the readability scores of the generated responses against the recommended comprehension level of each age and education group. We find large variations in the readability of the answers by different LLMs. Our results suggest LLM answers need to be better adapted to the intended audience demographics to be more comprehensible. They underline the importance of enhancing the adaptability of LLMs in education settings to cater to diverse age and education levels. Overall, current LLMs have set readability ranges and do not adapt well to different audiences, even when prompted. That limits their potential for educational purposes.
LLaVA-Docent: Instruction Tuning with Multimodal Large Language Model to Support Art Appreciation Education
Korea University（高丽大学）；University of Nebraska-Lincoln（内布拉斯加大学林肯分校）；Seoul Metropolitan Office of Education（首尔市教育厅）；Seoul National University（首尔国立大学）；Seoul National University of Education（首尔国立教育大学）
Knowledge-Based Design Requirements for Generative Social Robots in Higher Education
Zurich University of Applied Sciences（应用科学大学苏黎世）；University of Applied Sciences and Arts Northwestern Switzerland（西北瑞士应用科学与艺术大学）；Bielefeld University（比勒菲尔德大学）
From Prediction to Application: Language Model-based Code Knowledge Tracing with Domain Adaptive Pre-Training and Automatic Feedback System with Pedagogical Prompting for Comprehensive Programming Education
Enuma Inc（Enuma公司）；University of Memphis（孟菲斯大学）；Daegu National University of Education（大邱教育大学）；Seoul National University（首尔大学）；Seoul Metropolitan Office of Education（首尔市教育厅）；Korea University（高丽大学）
