Search Results (51)

This conceptual review article explores the intersection of multimodal writing and multilingualism in a contemporary educational context, with a focus on both secondary and post-secondary classrooms. As digital tools, media platforms, and global communication in interconnected spaces reshape literacy practices, students increasingly communicate and express themselves through a range of modes—visual, audio, textual, and gestural—often in more than one language. This article argues for reimagining and reconceptualizing writing to be a multifaceted literacy practice that integrates multimodal digital tools and that invites multilingual literacy opportunities. Drawing on classroom examples and current research on multimodal writing and translanguaging practices in multilingual spaces, the article explores how educators can support students in developing critical literacy skills through multimodal projects that honor linguistic diversity, cultural identity, and multiple means of expression. The article offers practical strategies for scaffolding multimodal writing in multilingual space, creating inclusive literacy environments where multilingualism and multimodality are seen as a resource, not a barrier. Full article

Studying the luminescent properties and the light amplification capabilities are fundamental investigations for newly synthesized organic compounds intended to act as chromophores. These studies are conducted for compounds in the form of solutions, solids, and also molecules stabilized with the aid of polymers. One of the methods used to study amplification is the generation of amplified spontaneous emission (ASE) using stripe-shaped light beam excitation. This process can lead to the generation of ASE, but also, with the coexistence of microcrystals and scatterers, to the generation of laser action with random feedback, known as random lasing (RL). However, when the degree of light scattering is too high, it can lead to the inhibition of laser emission. Therefore, as an alternative in studying amplification properties, we developed a protocol that allows the investigation of laser action generation using rapidly prototyped polymer waveguides with an embedded dye. The setup used was based on Direct Laser Writing (DLW), which enables the controlled fabrication of multimode optical waveguides. We demonstrated that the use of this technique will allow for the study of the performance of dyes from strictly structured resonators, enabling measurements of gain and lasing threshold. This allowed us to lower the lasing thresholds while maintaining the directionality of emission. Full article

This study investigates the pedagogical potential of artificial intelligence (AI)-supported multimodal writing instruction to foster both English language proficiency and sustainability awareness among middle school learners. Adopting a qualitative case study design, this research was conducted over an eight-week period in a public middle school in northern Turkey. A total of 42 seventh-grade students participated in weekly English writing sessions that incorporated AI tools as well as multimodal materials including infographics, videos, and observation logs. The instructional design was grounded in Task-Based Language Teaching (TBLT) principles and included topics aligned with the United Nations Sustainable Development Goals (SDGs). Data were collected through pre- and post-intervention written reflections and classroom observations and analyzed thematically using MAXQDA 2020. The findings revealed three key developmental shifts: (1) stronger learner engagement and intrinsic motivation in writing tasks, (2) more strategic and reflective use of AI tools across the writing process, and (3) enhanced global and ecological awareness expressed through student writing. The findings hold implications for curriculum designers, language educators, and policymakers seeking to align language education with the broader goals of sustainable development and 21st-century skill formation. Full article

This study aims to explore teachers’ perceptions and students’ strategies when integrating AI-mediated informal digital learning of English tools (AI-IDLE) into career ESL writing instruction. This case study involved six university instructors and over 300 students in an English writing course. Although AI-IDLE has broadened English access beyond classrooms, existing research on writing skills often neglects students’ diverse strategies that correspond to their professional aspirations, as well as teachers’ perceptions. The data included a demographic questionnaire, think-aloud protocols for real-time assessment of cognitive processes during the task, and semi-structured interviews for teachers’ validation. Findings reveal three major student strategies: (1) explicit genre understanding, (2) student-driven selection of digital multimodal tools—such as Grammarly, ChatGPT, Canva with Magic Write, and Invideo—to integrate text with images, sound, and layout for improved rhetorical accessibility, and (3) alignment with students’ post-graduation career needs. Students’ work with these AI tools demonstrated that when they created projects aligned with professional identities and future job needs, they became more aware of how to improve their writing; however, the teachers expressed hopes and doubts about the tools’ effectiveness and authenticity of the students’ work. Suggestions to use AI-IDLE to improve writing were provided. Full article

Background: Pre-retrofit auditing and pre-demolition auditing (PRA/PDA) are important in material reuse, waste reduction, and regulatory compliance in the building sector. An emphasis on sustainable construction practices has led to a higher requirement for PRA/PDA. However, traditional auditing processes demand substantial time and manual effort and are more easily to create human errors. As a developing technology, artificial intelligence (AI) can potentially assist PRA/PDA processes. Objectives: This scoping review aims to review the potential of AI in assisting each sub-stage of PRA/PDA processes. Eligibility Criteria and Sources of Evidence: Included sources were English-language articles, books, and conference papers published before 31 March 2025, available electronically, and focused on AI applications in PRA/PDA or related sub-processes involving structured elements of buildings. Databases searched included ScienceDirect, IEEE Xplorer, Google Scholar, Scopus, Elsevier, and Springer. Results: The review indicates that although AI has the potential to be applied across multiple PRA/PDA sub-stages, actual application is still limited. AI integration has been most prevalent in floor plan recognition and material detection, where deep learning and computer vision models achieved notable accuracies. However, other sub-stages—such as operation and maintenance document analysis, object detection, volume estimation, and automated report generation—remain underexplored, with no PRA/PDA specific AI models identified. These gaps highlight the uneven distribution of AI adoption, with performance varying greatly depending on data quality, available domain-specific datasets, and the complexity of integration into existing workflows. Conclusions: Out of multiple PRA/PDA sub-stages, AI integration was focused on floor plan recognition and material detection, with deep learning and computer vision models achieving over 90% accuracy. Other stages such as operation and maintenance document analysis, object detection, volume estimation, and report writing, had little to no dedicated AI research. Therefore, although AI demonstrates strong potential in PRA/PDA, particularly for floor plan and material analysis, broader adoption is limited. Future research should target multimodal AI development, real-time deployment, and standardized benchmarking to improve automation and accuracy across all PRA/PDA stages. Full article

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. Full article

As technology continues to shape how students read and write, digital literacy practices have become increasingly multimodal and complex—posing new challenges for researchers seeking to understand these processes in authentic educational settings. This paper presents three qualitative studies that use multimodal analyses and visual modeling to examine digital reading and writing across age groups, learning contexts, and literacy activities. The first study introduces collaborative composing snapshots, a method that visually maps third graders’ digital collaborative writing processes and highlights how young learners blend spoken, written, and visual modes in real-time online collaboration. The second study uses digital reading timescapes to track the multimodal reading behaviors of fifth graders—such as highlighting, re-reading, and gaze patterns—offering insights into how these actions unfold over time to support comprehension. The third study explores multimodal composing timescapes and transmediation visualizations to analyze how bilingual high school students compose across languages and modes, including text, image, and sounds. Together, these innovative methods illustrate the power of multimodal analysis and visual modeling for capturing the complexity of digital literacy development. They offer valuable tools for designing more inclusive, equitable, and developmentally responsive digital learning environments—particularly for culturally and linguistically diverse learners. Full article

With the global surge in patent filings, accurately evaluating similarity between patent documents has become increasingly critical. Traditional similarity assessment methods—primarily based on unimodal inputs such as text or bibliographic data—often fall short due to the complexity of legal language and the semantic ambiguity that is inherent in technical writing. To address these limitations, this study introduces a novel multimodal patent similarity evaluation framework that integrates weak AI techniques and conceptual similarity analysis of patent drawings. This approach leverages a domain-specific pre-trained language model optimized for patent texts, statistical correlation analysis between textual and bibliographic information, and a rule-based classification strategy. These components, rooted in weak AI methodology, significantly enhance classification precision. Furthermore, the study introduces the concept of conceptual similarity—as distinct from visual similarity—in the analysis of patent drawings, demonstrating its superior ability to capture the underlying technological intent. An empirical evaluation was conducted on 9613 patents in the manipulator technology domain, yielding 668,010 document pairs. Stepwise experiments demonstrated a 13.84% improvement in classification precision. Citation-based similarity assessment further confirmed the superiority of the proposed multimodal approach over existing methods. The findings underscore the potential of the proposed framework to improve prior art searches, patent examination accuracy, and R&D planning. Full article

Reading and writing in the 21st century have evolved from traditional text-based formats to multimodal literacy, integrating linguistic, visual, auditory, and spatial modes to enhance communication and comprehension. While multimodal reading has been widely studied, multimodal writing remains underexplored, despite its growing importance in K–12 education across disciplines. Multimodal composing demands advanced self-regulation as students navigate multiple digital tools and platforms. Self-regulated learning strategies, particularly the self-regulated strategy development model, offer a promising approach to support students in planning, monitoring, and revising multimodal compositions. However, a comprehensive framework linking self-regulation and multimodal composition is lacking. This article addresses this gap by synthesizing findings from two studies—one in preservice teacher education and another in a first-grade classroom—along with existing research to propose a self-regulated multimodal composing framework. This framework aims to guide educators in fostering students’ autonomy and competence in multimodal composing. By integrating self-regulation strategies with multimodal composition processes, the SRMC framework provides actionable insights for instructional practices, helping teachers support diverse learners in today’s digitally mediated classrooms. The article discusses implications for pedagogy and future research, advocating for greater emphasis on self-regulated multimodal composing in literacy education. Full article

To address the complex challenge of recognizing mixed handwritten text in practical scenarios such as examination papers and to overcome the limitations of existing methods that typically focus on a single category, this paper proposes MHTR, a Multimodal Handwritten Text Adaptive Recognition algorithm. The framework comprises two key components, a Handwritten Character Classification Module and a Handwritten Text Adaptive Recognition Module, which work in conjunction. The classification module performs fine-grained analysis of the input image, identifying different types of handwritten content such as Chinese characters, digits, and mathematical formula. Based on these results, the recognition module dynamically selects specialized sub-networks tailored to each category, thereby enhancing recognition accuracy. To further reduce errors caused by similar character shapes and diverse handwriting styles, a Context-aware Recognition Optimization Module is introduced. This module captures local semantic and structural information, improving the model’s understanding of character sequences and boosting recognition performance. Recognizing the limitations of existing public handwriting datasets, particularly their lack of diversity in character categories and writing styles, this study constructs a heterogeneous, integrated handwritten text dataset. The dataset combines samples from multiple sources, including Chinese characters, numerals, and mathematical symbols, and features high structural complexity and stylistic variation to better reflect real-world application needs. Experimental results show that MHTR achieves a recognition accuracy of 86.63% on the constructed dataset, significantly outperforming existing methods. Furthermore, the context-aware optimization module demonstrates strong adaptive correction capabilities in various misrecognition scenarios, confirming the effectiveness and practicality of the proposed approach for complex, multi-category handwritten text recognition tasks. Full article

Addressing the stability requirements of photonic integrated devices operating over wide temperature ranges, this work achieves controlled fabrication of femtosecond-laser direct-written Type II double-line waveguides and Type III depressed-cladding tubular waveguides within ultra-low-expansion LAS glass-ceramics. The light-guiding mechanisms were elucidated through finite element modeling. The influences of laser writing parameters and waveguide geometric structures on guiding performance were systematically investigated. Experimental results demonstrate that the double-line waveguides exhibit optimal single-mode guiding performance at 30 μm spacing and 120 mW writing power. For the tubular depressed-cladding waveguides, both single-mode and multi-mode fields are attainable across a broad processing parameter window. Large-mode-area characteristics manifested in the 50 μm core waveguide, exhibiting an edge-shifted intensity profile for higher-order modes that generated a hollow beam, enabling applications in atom guidance and particle trapping. Full article

In the era of generative artificial intelligence, writing has evolved into a programmable practice capable of generating sustainable narratives and preserving cultural heritage through poetic prompts. This study proposes “Writing Is Coding ” as a paradigm for sustainability education, exploring how students engage with AI-mediated multimodal creation to address environmental challenges. Using grounded theory methodology with 57 twelfth-grade students from technology-integrated high schools, we analyzed their experiences creating environmental stories and digital cultural artifacts using MidJourney, Kling, and Sora. Data collection involved classroom observations, semi-structured interviews, and reflective journals, analyzed through systematic coding procedures (κ = 0.82). Five central themes emerged: writing as algorithmic design for sustainability (89.5%), emotional scaffolding for environmental awareness (78.9%), aesthetics of imperfection in cultural preservation (71.9%), collaborative dynamics in sustainable creativity (84.2%), and pedagogical value of prompt literacy (91.2%). Findings indicate that AI deepens environmental consciousness and reframes writing as a computational process for addressing global issues. This research contributes a theoretical framework integrating expressive writing with algorithmic thinking in AI-assisted sustainability education, aligned with SDGs 4, 11, and 13. Full article

While poetry has long relied on musical and visual elements for its communicative power, numerous contemporary poets are drawing so dramatically on the resources of the visual arts and on elements of musical scoring that their poems become inter-arts hybrids. The interdisciplinary character of environmental writing and its attachment to material conditions of planetary life particularly invite the use of visual and/or audio technologies as documentation or as prompts toward multisensory attention that may shift readers’ perceptions of the more-than-human world. This essay examines four recent works of ecopoetry from the US to explore some of the diverse ways in which, by integrating into volumes of poetry their own visual and musical art, poets are expanding the environmental imagination and enhancing their environmental messaging. The visual and musical elements, I argue, offer fresh perceptual lenses that help break down cognitive habits bolstering separations of Western humans from more-than-human realms or dampening awareness of social and cultural norms that foster environmental degradation and violations of environmental justice. The multi-modal works discussed are Jennifer Scappettone’s The Republic of Exit 43, JJJJJerome Ellis’s Aster of Ceremonies, Danielle Vogel’s Edges & Fray, and Jonathan Skinner’s “Blackbird Stanzas.” Full article

Background: This project focuses on the relevance of using a health literacy approach to educating children about obesity prevention. The Habits of Health and Habits of Mind© model was used to write Electronic Texts for Health Literacy© to encourage actions that support obesity prevention. Guided by the Integrative Theory of Behavioral Prediction, the design template for a new genre of digital texts called Electronic Texts for Health Literacy© emerges for exploring children’s frame of mind, health literacy skills, and behavioral intentions toward obesity prevention. Methods: Online materials from selected websites were strategically reviewed for improving obesity prevention and child health literacy. The digital resources were juxtaposed with the Electronic Texts for Health Literacy©, with the latter written by and for children. Discussion: Health educators who use a constructivist pedagogy can help students to write health literacy narratives about obesity prevention, then read and talk about their multimodal compositions to further the practice and development of their health literacy skills. Children with obesogenic body frames can also gain from cowriting visual–textual–gestural health narratives with their peers or health professionals. Co-constructed narratives can help children make deeper connections about their identity, frame of mind, and social agency. Summary: Although this untested resource is available as a new genre of digital text, health educators could nudge children toward developing a stronger frame of mind and behavioral intentions toward obesity prevention when they write health literacy narratives that focus on decision making, goal setting, and communication in the context of eating nutritious foods and participating in physical activities. Full article

Transmodality—the process of transforming a text or section of a text into another mode or modes—enables readers to engage deeply and imaginatively with literature through interpretation and response. It is a valuable pedagogical approach in initial teacher education, where pre-service teachers are developing dispositions towards reading and cultivating knowledge of literature. In this article, two case studies are presented of undergraduate and post-graduate courses that aimed to engage pre-service teachers with children’s literature by asking them to respond to texts through embodied and multimodal modes. The work is underpinned by theories that highlight the role of semiotic modes in reading and writing, with a focus on the gestural, spatial, and auditory modes. The first case study examines the ways in which gesture and space worked to create multimodal ensembles that communicate and make meaning. The second case study considers pre-service teachers engaged in transferring meaning across linguistic and aural modes as they read a classic literary text and composed a soundscape. In both cases, we consider how mode-switching developed and demonstrated pre-service teachers’ aesthetic, cognitive, and affective engagement as part of their embodied experience with literary texts. This research has implications for the way teachers and teacher educators can inspire engagement with children’s literature through embodied and multimodal ways in English curriculum contexts and initial English teacher education. Full article