Dan Wu

Digital cultural heritage (DCH) platforms deliver complex multimedia narratives, but their cognitive demands remain insufficiently evaluated. We recorded electroencephalography (EEG) and eye tracking (ET) from 19 valid participants (of 30 recruited) interacting with seven authentic material formats from a national public service platform. Labels were three tiers derived from material-level NASA-TLX means; modeling used leave-one-subject-out validation (LOSO) with data-driven steps fitted within training folds. The primary three-tier model, using ET with random forest, reached 0.669 accuracy (permutation p = 0.0033; 95% CI [0.595, 0.743]; macro-F1 0.66). A complementary two-tier analysis retaining all trials reached 0.737 accuracy (macro-F1 0.73). Nested LOSO with inner-fold model selection yielded three-tier accuracy 0.632 (95% CI [0.564, 0.707]; macro-F1 0.630) and two-tier accuracy 0.684 (95% CI [0.632, 0.744]; macro-F1 0.676). EEG differentiated material formats at the group level (frontal theta, p = 0.0023) but did not improve trial-level classification. Fold-internal TreeSHAP and permutation importance converged on attentional narrowing under high load: shorter saccades, longer dwell, and fewer unique gaze regions. Objective information-density measures attributed the highest load to cross-channel integration demand, not information amount. These findings support design principles for cognitively sustainable digital cultural platforms and delineate the evidentiary boundaries of small-sample multimodal load modeling.

Artificial intelligence (AI) is reshaping libraries from traditional resource repositories into intelligent, adaptive, and user-centered knowledge service hubs. Across public, academic, and special libraries, AI technologies are increasingly embedded in information discovery, reference services, knowledge organization, user interaction, decision support, and operational workflows. These developments create new opportunities for digital library innovation, while also raising critical questions about trust, accountability, data privacy, algorithmic fairness, professional ethics, and inclusive access. This workshop examines the future of libraries in the age of AI through the dual lenses of service transformation and governance challenges. Rather than viewing AI simply as a tool for improving efficiency, it explores how AI reconfigures library services, reshapes user–library interactions, and challenges existing models of institutional governance and professional responsibility. The workshop will bring together digital library researchers, LIS scholars, library practitioners, AI researchers, and policy-oriented stakeholders to discuss empirical findings, theoretical frameworks, case studies, design approaches, and governance models. By identifying emerging research agendas and practical pathways, the workshop aims to advance sustainable, equitable, accountable, and human-centered library development in the AI age.

Future Collections, Libraries, Archives, and Museums (FCLAM) is an international, peer-reviewed, open access journal dedicated to advancing theory, practice, and innovation within galleries, libraries, archives, and museums (GLAM) (Kwiecien et al

With the rapid development of public digital cultural resources, the lack of cross-lingual information retrieval (CLIR) services catering to multilingual users in practical applications has created significant language barriers. This hinders the promotion of public digital culture and results in the underutilization of relevant resources. To address this need, this paper constructs M-APE, a shared semantic model that operates without reliance on parallel corpora. Through a three-step process comprising the generation, fine-tuning, and optimization of a shared semantic space, M-APE establishes a common semantic framework for diverse languages. The model utilizes a Chinese semantic space, transferred and trained on authentic public cultural corpora, as its input. Evaluation based on bilingual dictionary induction quality demonstrates that M-APE significantly enhances semantic sharing performance between Chinese and Indo-European languages, represented here by English and French, achieving an average cross-family transformation accuracy of 56.6%. Furthermore, focusing on the CLIR needs of multilingual users within China’s public cultural engineering projects, this study develops a Chinese-English-French cross-lingual information retrieval framework by integrating M-APE into public cultural domain tasks. Experimental results indicate that the proposed method achieves superior cross-lingual retrieval performance in terms of average metrics.

Introduction. While AI-assisted writing tools gain widespread educational adoption, their differential impacts on English as a foreign language (EFL) learners across proficiency levels remain underexplored. This study investigates how AI writing assistance affects EFL learners at different proficiency levels to inform differentiated instructional design. Method. A controlled experiment with 17 Chinese university students divided into low-, intermediate-, and high-proficiency groups employed mixed methods, analyzing 85 writing samples and 56 human-AI interaction dialogues using SPSS and NVivo. Analysis. Findings revealed striking proficiency-dependent effects: low-proficiency learners showed significant writing improvements after AI-assisted training, while intermediate- and high-proficiency learners demonstrated minimal gains. Interaction analysis uncovered distinct usage patterns—low- and intermediate-proficiency learners relied on AI primarily for linguistic compensation, whereas high-proficiency learners engaged in extensive prompt refinement to align AI output with their writing goals. Conclusion(s). Results challenge one-size-fits-all approaches to AI writing instruction. Current AI tools excel at addressing basic linguistic needs but struggle with higher-order writing skills required by advanced learners. Findings emphasise the need for proficiency-responsive AI interventions and enhanced explainability to transform AI assistance into sustainable writing competence.