The article discusses how machine learning exercises can be designed for automated assessment tools, framing them as deterministic input-output tasks. It emphasizes that this approach does not create a new grading system but enables existing platforms (e.g., VPL for Moodle, Codeforces, MOJ) to support AI education more effectively. The authors argue that integrating theory with practice through such exercises can foster dynamic, interactive AI courses.
By Artur Jordao
arXiv:2607. 22598v1 Announce Type: cross Abstract: Educational chatbots powered by large language models (LLMs) show promising effects on learning outcomes, yet most systems delegate pedagogical decisions such as content selection and didactic structuring implicitly to the LLM, making tutoring strategies difficult to trace, evaluate, and reproduce.
By Laurent Brisson (IMT Atlantique - DSD), Maria Segarra (IMT Atlantique - INFO, Lab-STICC\_MOTEL), Gr\'egory Smits (IMT Atlantique - INFO, Lab-STICC\_MOTEL)
Large Language Models: A Plain-Language Overview for Engineers explains that LLMs are neural networks trained on vast text collections to predict subsequent words, and outlines key concepts such as Transformers, pre‑training, fine‑tuning, reinforcement learning from human feedback, retrieval‑augmented generation, chain‑of‑thought prompting, and tool‑using agents. It also discusses efficiency techniques like FlashAttention and QLoRA, provides a life‑cycle figure and comparative tables, and offers a checklist for evaluating LLM claims, including checks for data contamination, model‑based grading, long‑context handling, and hallucination. The article aims to give engineers a clear picture of how training changes and prompts affect generative AI tools like ChatGPT.
By Bahaeddin T\"urko\u{g}lu
The paper introduces Universal Textual Teaching (UTT), a parameter‑update‑free framework that distills knowledge gaps between a Teacher and Student large language model into a reusable natural‑language artifact called a Primer. UTT identifies representative gap cases, then iteratively refines the Primer through a cycle of student attempts, prompter‑generated teaching instructions, teacher demonstrations, and synthesizer consolidation. Experiments on math (Omni‑MATH‑2) and code generation (KernelBench) show significant accuracy gains, outperforming prompt engineering and parameter‑based knowledge distillation, and the Primer generalizes across different Teacher‑Student pairs.
By Zhanyi Lu, Huan Wang
AI University (AI‑U) is a flexible framework that uses a fine‑tuned large language model (LLM) combined with retrieval‑augmented generation (RAG) and a reasoning synthesis model to produce style‑aligned responses from lecture videos, notes, and textbooks. In a graduate‑level finite‑element‑method (FEM) course, the authors created a pipeline to generate course‑grounded training data, fine‑tuned an open‑source LLM with Low‑Rank Adaptation (LoRA), and applied RAG‑based synthesis. Evaluation through cosine similarity, LLM‑based assessment, expert review, and user studies showed that the expert model outperformed the base model in alignment with course materials, with 86 % of test cases scoring higher and human users preferring the expert model roughly twice as often.
whyItMatters":"The study demonstrates a practical method for building course‑specific learning assistants that improve alignment with instructional content, offering a template that can be extended across STEM fields."
By Mostafa Faghih Shojaei, Rahul Gulati, Benjamin A. Jasperson, Shangshang Wang, Simone Cimolato, Manas Vardhan, Dangli Cao, Willie Neiswanger, Krishna Garikipati
The article introduces Large Language Models (LLMs) to scientists and professionals, covering core concepts such as tokenisation, embeddings, training, inference, context windows, prompting, and instruction hierarchies without delving into mathematics. It emphasizes the limitations of LLM outputs—hallucinations, uncertainty, reproducibility, bias—and stresses the importance of systematic verification, human oversight, and responsible handling of sensitive data. Practical exercises guide readers through running a local LLM with Ollama and Open WebUI, focusing on evidence retrieval, source verification, and distinguishing plausible language from scientifically supported conclusions.
By Umberto Michelucci
The paper proposes that large language models (LLMs) encode high‑level concepts as linear directions within their activation space and that they can use subspaces and vector algebra to perform tasks. By analyzing functional modules and residual streams during in‑context learning (ICL), the authors find that LLMs can create evidence‑accumulating subspaces and solve ICL tasks through simple algebraic operations within those subspaces.
By Jung H. Lee, Sujith Vijayan
arXiv:2607. 13041v1 Announce Type: cross Abstract: Large Language Model (LLM) based AI educational content generation systems are increasingly being developed, yet no standardised benchmark exists to systematically evaluate them.
By Ravidu Suien Rammuni Silva, Ahmad Lotfi, Isibor Kennedy Ihianle, Golnaz Shahtahmassebi, Jordan J. Bird
The paper evaluates Large Language Models for automatically analyzing responses in digital teacher simulations. Experiments compare DeBERTaV3 and Llama 3 across zero‑shot, few‑shot, and fine‑tuning settings, revealing that performance varies by characteristic and that Llama 3 consistently outperforms DeBERTaV3, especially when new characteristics must be identified. The findings suggest Llama 3 is preferable for dynamic simulation environments where teacher educators introduce new evaluation criteria.
By David de-Fitero-Dominguez, Mariano Albaladejo-Gonz\'alez, Antonio Garcia-Cabot, Eva Garcia-Lopez, Antonio Moreno-Cediel, Erin Barno, Justin Reich
The paper presents a multi‑source Retrieval‑Augmented Generation (RAG) AI tutor designed to help non‑native English speaker (NNES) students in computing courses. It can process questions in foreign languages—currently Mandarin—and give bilingual answers, generate glossaries of technical terms, and translate queries. In tests on 91 Piazza posts, the tutor answered 83.52 % correctly, translated 76.92 % of questions, and produced glossaries for 64.47 % of them.
By Yiyin Shen, Xinyi Ai, Weibing Wang, Rogers Jeffrey Leo John, Meenakshi Syamkumar
arXiv:2607. 13094v1 Announce Type: cross Abstract: The rise of Artificial Intelligence (AI) enables automatic analysis of large amounts of data.
By Lynn Vonderhaar, Juan Couder, Siri Siqveland, Omar Ochoa, James Pembridge
arXiv:2606. 26103v1 Announce Type: cross Abstract: Large Language Models (LLMs) have rapidly influenced many aspects of society, particularly education, due to their demonstrated ability to complete assignments and examinations across a wide range of subjects.
By Tanner Culleton, Hung-Fu Chang