International Journal of Continuing Engineering Education and Life-Long Learning (9 papers in press)

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

• Open Access

  AI-powered precision training pathways for engineering ethics and professional competence in Industry 4.0 ( Free Full-text Access )

  by Shuqin Cai, Yu Lei 
  Abstract: Driven by Industry 4.0, the implementation of precision delivery pathways for engineer ideological education urgently requires deep integration with cutting-edge artificial intelligence technologies. This study proposes an AI-driven framework for “data-informed, adaptive delivery, and iterative optimization” in ideological education. The model employs natural language processing to analyze multi-source educational content, constructs knowledge graphs and dynamic learner profiles to support personalized recommendation, and optimizes teaching strategies through deep reinforcement learning. Experiments on public datasets demonstrate that this approach significantly outperforms traditional online instruction and baseline recommendation models across multiple evaluation metrics, achieving an F1-score of 0.824 on learning outcome prediction and improving interaction depth by 31.5%, with statistical significance (p < 0.01). The results validate the proposed precision delivery pathways in enhancing the targeting accuracy and pedagogical effectiveness of ideological education for Industry 4.0 engineers through AI-powered adaptive learning.
  Keywords: engineering skill training; continuing engineering education; virtual practice teaching; digital twin; ethical risk control.
  DOI: 10.1504/IJCEELL.2026.10078547
   
  

Special Issue on: OA Intelligent Learning Ecosystems AI Metaverse and Emerging Technologies for Continuing Engineering Education

• Open Access

  Construction and application effect analysis of engineering education knowledge graph based on graph convolutional network ( Free Full-text Access )

  by Aijiang Liu, Lixia Zhao 
  Abstract: To address complex knowledge associations in engineering education and the lack of quantitative teaching decision support, this study proposes a knowledge graph (KG) construction method integrating graph convolutional networks. The model employs a transferred dimension embedding approach to enhance entity representation and uses a BERT-BiLSTM-CRF architecture for entity-relationship extraction. Experimental results show the model achieves a mean reciprocal rank of 92.4% in link prediction and an F1 score of 91.2% on an engineering education dataset, significantly improving entity and relation extraction accuracy. A dynamic inference mechanism based on graph attention network also improves multi-hop reasoning performance. The constructed KG effectively maps relationships between courses and graduation requirements, providing data support for teaching intervention and curriculum optimisation.
  Keywords: graph convolutional networks; GCN; engineering education; KG; trans D; transformers; bidirectional long short-term memory networks.
  DOI: 10.1504/IJCEELL.2026.10077085
   
  

• Open Access

  Intelligent English translation scoring method based on multi-angle semantic feature calculation model ( Free Full-text Access )

  by Qiongjie Jing 
  Abstract: The research aims to construct an intelligent scoring model based on multi-angle semantic feature calculation to deal with the deficiencies of traditional methods. The research proposes a multi-angle semantic feature computing model based on a multi-level semantic collaboration mechanism (MMSF-ISM). This model organically integrates the deep semantic representation of BERT, the global attention of transformer, the long-term dependency modelling of LSTM, and the local feature extraction capabilities of convolutional networks through a semantic gating fusion mechanism, achieving four-dimensional collaborative evaluation at the lexical, grammatical, semantic, and text levels. The findings denote that the Pearson correlation coefficients between the intelligent scoring model based on multi-angle semantic feature calculation and manual scoring reach 89% and 85%, respectively, which is more than 40% higher than traditional methods. This model demonstrates good generalisation ability in practical application scenarios, making it important for advancing the progress of intelligent education technology.
  Keywords: English translation intelligent scoring; multi-angle semantic features; BERT; long short-term memory; LSTM; feature fusion; error detection.
  DOI: 10.1504/IJCEELL.2026.10077149
   
  

Special Issue on: OA Intelligent Learning Ecosystems AI, Metaverse and Emerging Technologies for Continuing Engineering Education

• Open Access

  Dynamic scene motion target segmentation method for physical education ( Free Full-text Access )

  by Tao Lei, Yi Huang, Zhijuan Zhou 
  Abstract: This paper proposes a segmentation algorithm integrating a spatial attention mechanism with a Mask R-CNN to address safety risks in dynamic physical education scenes and improve motion target segmentation. The method first performs target segmentation using Mask R-CNN, enhances feature representation through spatial attention, and completes monitoring and localization via a visual processor and a conditional convolution instance segmentation model. Experiments show strong performance: a segmentation boundary value of 0.967, F1-score of 96.75%, and 3.27% average absolute error on the YouTube-VOS dataset, with false negative and false positive rates of 1.47% and 1.81%. On the CDnet 2014 dataset, pixel accuracy reaches 95.42% with an iteration time of 4.95 s. On a self-constructed dataset, the method achieves 0.972 average precision and 0.984 panoptic quality. These results demonstrate accurate and efficient motion target segmentation, supporting intelligent physical education applications.
  Keywords: dynamic scene; motion target; physical education; spatial attention mechanism; mask region-based convolutional neural network; MRCNN; volume integral; conditional convolutions for instance segmentation; CondInst.
  DOI: 10.1504/IJCEELL.2026.10077490
   
  

• Open Access

  Learning path planning method for engineering education based on course group knowledge graph and learner portrait ( Free Full-text Access )

  by Nanping Wang, Yuanlin Wang, Ming Jiang 
  Abstract: With the increasing demand for personalised learning support and intelligent recommendation in engineering education, traditional learning path planning methods still face challenges in handling multi-source learner profiles and complex course associations, resulting in low path matching rates and high recommendation latency. To address these issues, this study proposes a learning path recommendation framework integrating knowledge graph modelling and multi-source behaviour perception. The framework combines dynamic knowledge tracing with hierarchical regularised user modelling and knowledge graph embedding with an upper confidence bound strategy to achieve accurate path recommendation. Experimental results show that the proposed model achieves an F1-score of 93.22% on a public dataset, while the average response delay is within 2 seconds. Under noise disturbances, the fluctuation of path similarity remains within ±2.5%, and the course completion rate reaches 97.3% across four engineering disciplines, demonstrating strong accuracy, real-time performance and adaptability.
  Keywords: engineering education; learning path planning; knowledge graph; user profiling; intelligent recommendation.
  DOI: 10.1504/IJCEELL.2026.10077493
   
  

• Open Access

  Evaluation of mechanical engineering classroom teaching effectiveness based on improved MTCNN algorithm ( Free Full-text Access )

  by Chengyu Xiang 
  Abstract: To address the challenge of monitoring student engagement in mechanical engineering classrooms, this study proposes an enhanced multi-task cascaded convolutional neural network combined with particle filtering and a support vector machine. The model integrates facial expression and head pose recognition to evaluate classroom attention. Experimental results show face detection accuracy reached 91% on the FDDB dataset, while expression recognition achieved 92% accuracy and 90% F1-score on CK+. The combined method improved behaviour recognition accuracy by 13.28% and increased frame rates by 13.6-25% compared to single-method approaches. Practical application demonstrated a 22% increase in homework completion, 28.46% rise in assignment scores, and doubled interaction frequency. The model effectively assesses teaching effectiveness and provides data-driven support for improving instructional content and student engagement.
  Keywords: classroom teaching effectiveness evaluation; facial detection; facial expression recognition; MTCNN algorithm; particle filter.
  DOI: 10.1504/IJCEELL.2026.10077704
   
  

• Open Access

  Campus network public opinion monitoring method based on emotional feature extraction and classification ( Free Full-text Access )

  by Xue Li, Shanshan Li, Yunge Gao, Kang Liu 
  Abstract: This study proposes a multimodal approach for timely campus network public opinion monitoring amidst increasing data. The method uses convolutional neural networks, bidirectional encoders, and Mel-frequency cepstral coefficients to extract features from images, text, and speech, which are then fused using a Bidirectional Gated Recurrent Unit (BiGRU) for sentiment classification. Comparative experiments demonstrated the superiority of the BiGRU and self-attention mechanism algorithm, achieving an F1 score of 0.85 and accuracy of 0.87. This method consistently achieved the highest accuracy, averaging close to 0.90, with minimal variation as sample size increased. It also maintained the shortest, most stable response time. The proposed monitoring method demonstrates significant performance and high accuracy, offering valuable support for campus management.
  Keywords: bidirectional encoder representations from transformers; multi-modal features; Mel-frequency cepstral coefficients; MFCC; bidirectional gated recurrent unit; GRU; campus network public opinion monitoring.
  DOI: 10.1504/IJCEELL.2026.10078485
   
  

• Open Access

  Teaching content generation and semantic information extraction for art and design courses targeting AIGC ( Free Full-text Access )

  by Bo Zhang 
  Abstract: Traditional methods for generating course content rely on manual rules, lacking dynamic knowledge support and semantic monitoring, leading to deviations between generated content and teaching objectives. Therefore, this study proposes an AI-powered framework for generating art and design course content that integrates knowledge graphs and multimodal semantic information extraction. Results show that, in tests on a self-built dataset, the framework achieved knowledge accuracy of 91.21%, knowledge graph relationship consistency of 89.91%, and graph-text relevance of 0.33%, representing average improvements of 39.3%, 29.8%, and 13.8% compared to the best baseline. The model converges quickly with a response time as low as 1.0 millisecond, a service error rate of only 4%, and maintains 92% stability under 100 concurrent requests. This framework demonstrates excellent scalability and service robustness, providing core technical support for the transformation of art and design education in the era of artificial intelligence.
  Keywords: artificial intelligence generation; art and design courses; knowledge graph; multi-modal semantic information extraction; teaching content generation; adversarial optimisation; intelligent teaching assistance system.
  DOI: 10.1504/IJCEELL.2026.10078590
   
  

• Open Access

  Immersive learning path design of mixed reality technology in business English education ( Free Full-text Access )

  by Liwen Liu 
  Abstract: This paper proposes an immersive learning system based on mixed reality technology to address the lack of authenticity, interaction, and personalisation in traditional business English teaching. The system integrates 3D scene modelling, multimodal interaction, and reinforcement learning-driven adaptive path planning. Experimental results show stable technical performance with 58.2 FPS, 93.4% gesture recognition accuracy, and 97.8% conflict resolution success rate. Teaching effectiveness includes a 42.7% improvement in oral fluency, a 146.7% increase in terminology use, and 82.4% long-term memory retention. The system enhances learners language application and practical skills.
  Keywords: business English; personalised feedback; mixed reality; immersion; multimodal interaction.
  DOI: 10.1504/IJCEELL.2026.10078591