International Journal of Continuing Engineering Education and Life-Long Learning (7 papers in press)

Special Issue on: OA AI and Digital Technology driven Innovation in Continuing Engineering Education and Lifelong Learning Part Two

• Open Access

  Pathways for the digital transformation of ideological and political education in engineering education in the context of industry 4.0 ( Free Full-text Access )

  by Huibin Wei 
  Abstract: Industry 4.0 gives a new twist to the engineering professional competence framework. Digital transformation of engineering ideological and political education is not to simply add nonscientific factors but to reconstruct the dynamic balance of digital mechanism and ideological and political education. This paper constructs a four-dimensional driving spiral model to reveal the mechanism of technology empowerment. It designs a comprehensive ideological and political system to embed ethical competence throughout the entire cycle. Relying on three paths: scenario simulation, ethical sandbox, and agent platform, empirical measurements have shown that the efficiency of emotional transfer is 78%, the optimization rate of decision-making has increased by 75.5%, the transparency of the process has risen to 89%, and the comprehensive efficiency index has reached 106.8%. In response to tensions such as dataism and algorithm dependence, an innovative technical humanistic balance degree constraint index has been proposed.
  Keywords: Industry 4.0; engineering ideological and political education; digital transformation; four-dimensional driving model; ethical decision-making.
  DOI: 10.1504/IJCEELL.2026.10081318
   
  

Special Issue on: OA Immersive Technologies Redefining Education Transforming Learning in the Digital Age

• Open Access

  Using deep learning to analyse automatic grading and personalised feedback of student assignments ( Free Full-text Access )

  by Xuan Zhao 
  Abstract: Homework assessment faces challenges including lengthy scoring time, subjective variability, and lack of targeted feedback. This study constructs a Transformer-based deep learning model to overcome these limitations. The model uses a dual-channel encoder to extract deep semantic representations of student homework content and scoring criteria, with cross-channel attention enabling accurate alignment. A scoring sub-network based on knowledge-point sensitivity factors produces scores in a unified representation space. A conditional Transformer decoder then generates tailored feedback conditioned on these scores, integrating homework understanding, criterion alignment, scoring, and feedback generation. Results show a Pearson correlation of 0.930 for automatic scoring, an average error coverage index of 0.90 (most samples near 1.00) for feedback, and a BERT score of 0.89, indicating accurate weakness identification and semantically consistent feedback.
  Keywords: automatic rating; personalised feedback; deep learning; dualchannel transformer; semantic alignment; knowledge tracing.
  DOI: 10.1504/IJCEELL.2026.10080845
   
  

• Open Access

  Multidimensional application scenarios and key technical challenges of AI enabling digital transformation in distance education ( Free Full-text Access )

  by Ping Yang 
  Abstract: The rapid development of artificial intelligence (AI) technology is triggering a profound change in education, from text to content, and the application of AI technology in distance education is becoming increasingly widespread. This paper discusses the innovative applications of AI-based intelligent devices in distance education, including intelligent teaching assistants, personalised learning path design, intelligent assessment and feedback systems, and the construction of virtual experimental and practice environments. By analysing the existing research results and practice cases, this paper summarises the significant advantages of AI technology in enhancing the efficiency of distance education, optimising the learning experience, and promoting educational equity, and proposes future research directions and challenges. As the functions of AI devices are enriched and improved, they will be better suited to the characteristics of distance education and drive innovation in teaching scenarios.
  Keywords: artificial intelligence; distance education; smart devices; digitalisation of education.
  DOI: 10.1504/IJCEELL.2026.10081242
   
  

• Open Access

  5G and smart sports: an online sports teaching and training mechanism based on blockchain and edge computing ( Free Full-text Access )

  by Wenyu Zhang, Lihao Guan, Dan Huang 
  Abstract: The rapid development of the internet has promoted education reform, with online teaching gaining prominence, especially during the COVID-19 pandemic. This paper studies an online physical education (PE) system based on blockchain and edge computing (EC). It introduces the online teaching framework and smart sports concept, then proposes combining blockchain with edge technology. Simulation experiments compare task completion time and energy consumption. Results show that when task volume ranges from 800-1,800, the common method’s completion time drops from 4.6 s to 3.5 s, while mobile edge computing (MEC) remains stable at 4 s. For tasks of 500-1,500, MEC energy consumption stays within 9-10, whereas the common method fluctuates between 614, proving MEC’s greater stability. Thus, the blockchain-EC-based system enhances data processing and significantly improves online education effectiveness.
  Keywords: online sports; teaching and training; blockchain service; edge technology; edge computing; data security; mobile edge computing; MEC.
  DOI: 10.1504/IJCEELL.2026.10081264
   
  

• Open Access

  Facial expression recognition in online classrooms based on lightweight convolutional neural networks ( Free Full-text Access )

  by Yiwen Zhang 
  Abstract: To improve facial expression recognition in online classrooms under variable lighting, occlusion, and pose variations, this paper proposes a lightweight algorithm based on MobileNetV3. It introduces three modules: a multi-branch adaptive lighting compensation module that dynamically fuses gamma correction and histogram equalisation; a sparse spatial attention module that focuses on unoccluded facial regions, such as eyes and eyebrows; and a pose calibration module that uses deformable convolution to map non-frontal faces to a canonical pose. The method achieves 89.6% accuracy with only 2.3 M parameters and 0.24 G computation. On challenging samples with abnormal lighting, occlusion, and large poses, accuracy drops by only 8.4%, 10.7%, and 7.2%, respectively. This effectively enhances the robustness of lightweight models for real-time classroom analysis.
  Keywords: online classroom facial expression recognition; attention mechanism; illumination compensation; pose calibration; convolutional neural network; CNN.
  DOI: 10.1504/IJCEELL.2026.10081519
   
  

• Open Access

  Interactive online English self-learning platform system based on computer vision ( Free Full-text Access )

  by Wei Feng, Fan Yang 
  Abstract: This paper presents a lightweight English self-learning platform designed to address the limitations of conventional cloud-based systems, including latency, limited visual feedback, and limited interaction. The system minimises reliance on the cloud by integrating lip tracking with keypoint extraction, a CTC acoustic model for pronunciation alignment, and geometric features to provide visual feedback on articulation (e.g., lip closure). It incorporates a lightweight Transformer and MediaPipe Hands for few-shot custom gesture recognition and natural interaction. Through model pruning and quantisation, the platform achieves real-time, low-power operation in weak network environments. Experiments show a lip-tracking error of ≤ 1.5 pixels, inter-frame drift within ± 0.1 pixels, a gesture recognition rate > 95% (mean latency < 10.5 ms), and ~30 FPS on-device performance. The visual feedback and enhanced natural interaction significantly improve the applicability and user experience of interactive self-learning systems.
  Keywords: English self-learning platform; interactive learning system; computer vision technology; lip shape correction feedback; edge-side lightweight deployment.
  DOI: 10.1504/IJCEELL.2026.10081520
   
  

• Open Access

  Design and application of teaching demonstration model based on intelligent multimedia and human-computer interaction ( Free Full-text Access )

  by Chunhong Zhang, Yuehua Bai, Yanping Li 
  Abstract: Current teaching demonstration systems face static content, limited interaction, and poor context adaptability. This paper proposes a three-module model: intelligent multimedia generation, human-computer interaction, and state collaboration. The generation module aligns scripts semantically, produces images and 3D scenes via diffusion and Gaussian splatting, and stores them in a FAISS index with contrastive alignment. The interaction module fuses voice, gesture, and touch via random forest to output composite intent, using gaze and emotion for disambiguation. The collaboration module retrieves resources based on intent, prioritises scheduling using historical frequency and confidence, applies LOD and mesh deformation, and updates the learning profile. Experiments show average cross-modal retrieval hit rate up to 96.1%, composite intent accuracy up to 97.9%, and median response latency as low as 375.94 ms, demonstrating effective integration of dynamic supply, multimodal fusion, and adaptive scheduling.
  Keywords: teaching demonstration model; intelligent multimedia generation; human-computer interaction; multimodal fusion; adaptive scheduling.
  DOI: 10.1504/IJCEELL.2026.10081691