With the rapid development of Multimodal Large Language Models (MLLMs) in education, their applications have mainly focused on content generation tasks such as text writing and courseware production. However, automated assessment of non-exam learning outcomes remains underexplored. This study shifts the application of MLLMs from content generation to content evaluation and designs a lightweight and extensible framework to enable automated assessment of students’ multimodal work. We constructed a multimodal dataset comprising student essays, slide decks, and presentation videos from university students, which were annotated by experts across five educational dimensions. Based on horizontal educational evaluation dimensions (Format Compliance, Content Quality, Slide Design, Verbal Expression, and Nonverbal Performance) and vertical model capability dimensions (consistency, stability, and interpretability), we systematically evaluated four leading multimodal large models (GPT-4o, Gemini 2.5, Doubao1.6, and Kimi 1.5) in assessing non-exam learning outcomes. The results indicate that MLLMs demonstrate good consistency with human evaluations across various assessment dimensions, with each model exhibiting its own strengths. Additionally, they possess high explainability and perform better in text-based tasks than in visual tasks, but their scoring stability still requires improvement. This study demonstrates the potential of MLLMs for non-exam learning assessment and provides a reference for advancing their applications in education.

While the majority of scholarly investigations concerning pedagogical behavior predominantly underscore verbal communication, the significance of nonverbal behavior remains inadequately examined. This research endeavors to propose a theoretical framework pertaining to educators’ nonverbal communication and introduces two artificial intelligence-based recognition models: GestureNet, which focuses on hand gestures, and JointBDOE, which pertains to body orientation. Utilizing video data derived from 32 high-caliber classroom sessions, the research meticulously analyzes gesture patterns through the application of descriptive statistics, sequence analysis, and variance testing. The results indicate a notable prevalence of descriptive and pointing gestures alongside a predominance of frontal orientations. Marked disparities in gesture utilization were identified across various educational stages and subjects, with primary school educators exhibiting more frequent gesture sequences. These findings contribute to the enhancement of automated behavior analysis and provide practical implications for professional development and the design of classroom interactions.

Student engagement analysis is closely linked with learning outcomes, and its precise identification paves the way for targeted instruction and personalized learning. Current student engagement methods, reliant on either head pose estimation with facial landmarks or eye-trackers, are hardly generalized to authentic classroom teaching environments with high occlusion and non-intrusive requirements. Based on empirical observations that student body orientation and head pose exhibit a high degree of consistency in classroom settings, we propose a novel student engagement analysis algorithm incorporating human body orientation estimation. To better suit classroom settings, we develop a one-stage and end-to-end trainable framework for multi-person body orientation estimation, named JointBDOE. The proposed JointBDOE integrates human bounding box prediction and body orientation into a unified embedding space, enabling the simultaneous and precise estimation of human positions and orientations in multi-person scenarios. Extensive experimental results using the MEBOW dataset demonstrate the superior performance of JointBDOE over the state-of-the-art methods, with an MAE reduced to 10.63° and orientation accuracy exceeding 91% at 22.5°. With the more challenging reconstructed MEBOW dataset, JointBDOE maintains strong robustness with an MAE of 16.07° and an orientation accuracy of 88.3% at 30°. Further analysis of classroom teaching videos validates the reliability and practical value of body orientation as a robust metric for engagement assessment. This research showcases the potential of artificial intelligence in intelligent classroom analysis and provides an extensible solution for body orientation estimation technology in related fields, advancing the practical application of intelligent educational tools.

As critical elements in argumentative writing, argument components and strategies significantly influence argument quality. However, the existing research lacks an in-depth exploration of how students construct and utilize these elements in argumentative writing. This study first evaluates the performance of leading large language models (LLMs) in identifying argument components and strategies using three approaches: single-task learning (STL), chain-of-thought (CoT), and multi-task learning (MTL). With the aid of learning analytics methods (Epistemic Network Analysis (ENA) and two-mode network), the study further reveals the intrinsic mechanisms linking argument components, strategies, and writing quality. Specifically, the research trains and evaluates LLMs on 226 argumentative essays, encompassing 4726 components and 4837 strategies. Compared to basic STL, the CoT and MTL methods significantly improve LLMs’ performance in both tasks. Moreover, learning analytics indicate that high-quality essays possess rich and complex logical relations, presenting multidimensional and multi-layered reasoning structures, whereas low-quality essays predominantly rely on simple and repetitive connections, lacking deeper logical support. These findings have significant implications for the automated analysis of argumentative writing and the sustainable development of education, not only providing valuable insights for educators in argumentation instruction but also contributing to the systematic enhancement of students’ argumentative abilities and critical thinking.