Automated classroom engagement recognition holds substantial promise for scalable learning analytics, yet the suitability of modern Vision-Language Models (VLMs) for this task under zero-shot conditions remains largely unexplored. We present a systematic benchmark that evaluates five widely-used VLMs: CLIP, BLIP-VQA, GPT-4o, LLaVA-1.
Personality recognition in asynchronous video interviews (AVIs) has become increasingly important due to their widespread adoption in modern recruitment. Existing approaches often rely on large language models (LLMs) to analyze textual responses of interviewees in AVI.
arXiv:2606. 16428v1 Announce Type: cross Abstract: Effective personalized AI-assisted learning demands systems that can not only generate accurate learner-specific educational materials, but also dynamically adapt their instruction to diverse learners.
By Jaward Sesay, Yue Yu, Siwei Dong, Yemin Shi, Guangyao Chen, B\"orje F. Karlsson
arXiv:2606. 01584v1 Announce Type: cross Abstract: Conversational tutoring agents have been shown to improve learning engagement and student outcomes, and large language models (LLMs) are increasingly used in these systems to provide scalable, personalized feedback.
By Aitor Arronte Alvarez, Naiyi Xie Fincham
arXiv:2608. 10448v1 Announce Type: new Abstract: Multimodal emotion recognition in conversation (MERC) requires understanding complex interactions between verbal and non-verbal cues.
By Sujung Oh, Jung Uk Kim, Sangmin Lee
arXiv:2608. 03952v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly used to provide conversational practice for English-as-a-second-language (ESL) learners.
By Dongjie Yang, Siyan Lin, Leixian Shen, Rui Sheng, Huamin Qu, Zixin Chen
arXiv:2607. 21013v1 Announce Type: new Abstract: Multimodal large language models (MLLMs) have achieved impressive performance in multimodal emotion recognition (MER) tasks and lifted MER to a new level that is complex emotion understanding with advanced video understanding abilities and natural language description.
By Lihuang Fang, Yuchen Zou, kebin Jin, Jinghui Qin
arXiv:2604. 15280v2 Announce Type: replace-cross Abstract: Understanding emotions is a fundamental ability for intelligent systems to be able to interact with humans.
By Madhav Agarwal, Sotirios A. Tsaftaris, Laura Sevilla-Lara, Steven McDonagh
arXiv:2606. 29900v1 Announce Type: cross Abstract: Personality recognition in asynchronous video interviews (AVIs) has become increasingly important due to their widespread adoption in modern recruitment.
By Tianyi Zhang, Wei Shan, Yuan Zong, Tianhua Qi, Wenming Zheng
arXiv:2511. 16107v3 Announce Type: replace-cross Abstract: Visual in-context learning (VICL) solves visual tasks by conditioning on a few input-output demonstrations without any model training.
By Shao-Jun Xia, Huixin Zhang, Zhengzhong Tu
arXiv:2608. 10492v1 Announce Type: new Abstract: Large Language Model (LLM)-based simulators often reproduce observable actions but fail to capture the underlying reasoning behind them.
By Rose Niousha, Minwoo Kang, Narges Norouzi
arXiv:2606. 04434v1 Announce Type: cross Abstract: Multimodal In-Context Learning (ICL) has emerged as a practical inference paradigm for Multimodal Large Language Models, where a small set of interleaved image-text In-Context Demonstrations (ICDs) conditions the model to solve new tasks.
By Niloufar Alipour Talemi, Hossein Kashiani, Fatemeh Afghah