arXiv:2607. 05403v1 Announce Type: cross Abstract: This paper aims to synthesize empirical research on AI tools used to support English as a second/foreign language (EL2) learners in Arab University classrooms (AUCs) between Jan 1st 2023 and Aug 31st 2025.
arXiv:2609.36544v1 Announce Type: cross Abstract: Generative AI has changed how students produce writing assignments. The final artifact is no longer sufficient to understand the process through whic...
High-stakes English proficiency tests treat standardized, unaided performance as evidence for score interpretations about academic English proficiency. This interpretation remains meaningful, but as target language use domains increasingly involve generative AI, the extrapolation from unaided test performance to academic communicative readiness becomes less self-evident.
Edustories is a dataset of 1,492 teacher‑written case studies that detail real elementary and high‑school classroom situations involving challenging student behavior, pedagogical interventions, and their outcomes. The collection is designed to enable research on AI assistance in collective teaching contexts, such as evaluating large language models’ ability to predict the success of teacher interventions. Comparative tests show that current models achieve 58% accuracy, below the 64% accuracy of human experts, indicating a gap between AI and human expertise in predicting classroom outcomes.
The article examines how three elementary teachers implemented a conversational AI‑based curriculum using the ToyTalk platform during a three‑week summer camp. Over 13 instructional days, teachers employed adaptive practices—repair, differentiation, translation, and balancing—to navigate tensions among technology, learners, and instruction. Their understanding of AI and instructional roles evolved throughout the camp, leading to design implications for deploying conversational AI in elementary classrooms.
arXiv:2606. 00038v1 Announce Type: cross Abstract: Artificial intelligence (AI) literacy is increasingly recognized as a foundational competency for all university graduates.
The study explores how undergraduate computing students in Saudi Arabia perceive AI‑generated writing feedback when they are explicitly told that ChatGPT, not a human instructor, produced the score and comments. Through qualitative reflections, four themes emerged: students found the feedback useful for surface‑level revisions, recognized AI’s contextual and pedagogical limits, trusted the feedback conditionally—separating its utility from its authority—and reaffirmed the human instructor’s role as the ultimate grading authority. The findings highlight a clear distinction students make between feedback usefulness and evaluative authority, treating them as separate judgments rather than opposing ends of a single approval scale.
The study examined how different designs of AI teaching assistants (AI TAs) affect students in an introductory programming course. Four AI TAs were compared based on pedagogical style (Socratic vs. Direct instruction) and context awareness (no context vs. full context). Results showed that the Socratic AI TA with full context received the lowest favorability ratings, had the highest interaction stress, the most external LLM use, and the lowest comprehension outcomes, though differences were not statistically significant.
arXiv:2606. 00040v1 Announce Type: cross Abstract: As Generative AI (GenAI) becomes integral to education, fostering GenAI literacy is critical.
arXiv:2607. 21306v1 Announce Type: cross Abstract: Large language models (LLMs) are increasingly used as tutors and thought partners, helping users reason through problems.
arXiv:2606. 28749v1 Announce Type: cross Abstract: Although most undergraduates now use large language models (LLMs), a form of generative artificial intelligence (GenAI) for academic writing, no validated method distinguishes the qualitatively different ways students rely on them.
arXiv:2608. 11259v1 Announce Type: cross Abstract: Many AI tutors leverage large language models (LLMs) today.
Artificial intelligence is transforming applied English materials from fixed paper sequences into adaptive learning systems that diagnose learners, recommend tasks, and provide formative feedback. This study presents a five‑layer architecture—knowledge mapping, learner profiling, task generation, feedback orchestration, and teacher‑side governance—for a new AI‑driven practical English textbook. In an eight‑week trial with 186 non‑English‑major undergraduates, the system improved unit completion accuracy from 72.4% to 84.9%, raised speaking task scores by 10.8 points, and cut teacher correction time by 31.6%.