Ultra Strong Machine Learning (USML) refers to symbolic learning systems that not only improve their own performance but can also teach their acquired knowledge to quantifiably improve human performance. We introduce LENS (Logic Programming Explanation via Neural Summarisation), a neuro-symbolic framework that combines symbolic program synthesis with large language models (LLMs). This framework automatically generates natural language explanations of learned logic programs, replacing hand-crafted templates used in prior USML work. Using LLMs-as-judges evaluation and expert validation, we show that LENS produces higher-quality explanations than both direct LLM prompting and hand-crafted templates. We then examine whether LENS explanations suffice for achieving USML in a human trial teaching active learning strategies across three related domains. Our exploratory analysis suggests that concise, expert-written explanations may benefit learners with higher initial performance, while LLM-generated explanations provide no advantage over human self learning despite being rated as higher quality. This case study reveals that achieving USML requires methods grounded in human learning, where current LLM-generated explanations do not capture human cognitive constraints and LLMs-as-judges evaluations do not reflect what effectively supports human learning.

翻译：超强机器学习（USML）指不仅能提升自身性能，还能将其习得知识传授给人类以量化提升人类表现的一类符号学习系统。本文提出LENS（基于神经摘要的逻辑编程解释框架），这是一个将符号程序合成与大型语言模型（LLMs）相结合的神经符号框架。该框架能自动生成已习得逻辑程序的自然语言解释，取代了先前USML研究中手工设计的解释模板。通过LLM作为评判器的评估和专家验证，我们证明LENS生成的解释质量优于直接LLM提示和手工模板。随后，我们在三个相关领域开展人类教学实验，探究LENS生成的解释是否足以实现USML的教学目标——传授主动学习策略。探索性分析表明：对于初始表现较好的学习者，简洁的专家撰写解释可能更具益处；而尽管LLM生成解释在质量评分上更高，却未能展现出超越人类自主学习的效果。本案例研究揭示：实现USML需要以人类学习机制为基础的方法，当前LLM生成的解释未能捕捉人类认知约束，且LLM作为评判器的评估方式并不能反映对人类学习真正有效的支持要素。