Search Results (135)

The transition to Outcome-Based Education (OBE) in engineering demands instructional tools that bridge theoretical knowledge and practical engineering competencies. However, traditional Learning Management Systems (LMS) primarily function as static resource repositories, lacking the semantic structure necessary to support deep learning and precise competency tracking. To address this, this study developed a three-layer domain Knowledge Graph (KG) for Structural Geology and integrated it into the ChaoXing LMS (a widely used Learning Management System in Chinese higher education). A semester-long quasi-experimental study (N = 84) was conducted to evaluate its impact on student performance and specific graduation attribute achievement compared to a conventional folder-based approach. Empirical results demonstrate that the KG-integrated group significantly outperformed the control group (p < 0.01, Cohen’s d = 0.74). Notably, while performance on rote memorization tasks was similar, the experimental group showed marked improvement in identifying and solving complex engineering problems. LMS log analysis confirmed a strong positive correlation (r = 0.68) between graph navigation depth and academic success. KG effectively bridged the gap between theoretical knowledge and practical engineering applications (e.g., geohazard analysis). This research confirms that explicit semantic visualization acts as vital cognitive scaffolding, effectively enhancing higher-order thinking and ensuring the rigorous alignment of instruction with engineering accreditation standards. Ultimately, this approach promotes sustainable learning capabilities and prepares future engineers to address complex, interdisciplinary challenges in sustainable development. Full article

Undergraduate civil engineering students frequently struggle to transition from deterministic to probabilistic reasoning, a conceptual shift essential for modern structural design practice governed by reliability-based codes. This paper presents a design-based research (DBR) contribution and a theoretically grounded pedagogical framework that integrates AI-powered conversational tutoring with interactive simulations to scaffold this transition. The framework synthesizes cognitive load theory, scaffolding principles, self-regulated learning research, and threshold concepts theory. The design incorporates three novel elements: (1) a structured misconception inventory specific to structural reliability, derived from literature and expert elicitation, with each misconception linked to targeted intervention strategies; (2) an integration architecture connecting large language model tutoring with domain-specific simulations, where simulation states inform tutoring and misconception detection triggers targeted activities; and (3) a scaffolded module sequence building systematically from deterministic foundations through probability concepts to reliability analysis methods. Sequential modules progress from uncertainty recognition through Monte Carlo simulation and design applications. We provide technical specifications for the implementation of AI tutoring, including prompt engineering strategies, accuracy safeguards that address known limitations of large language models (LLMs), and protocols for escalation to human instructors. An assessment framework specifies concept inventory items, process measures, and practical competence tasks. Ultimately, this paper provides testable conjectures and identifies conditions under which the framework might fail, structuring subsequent empirical validation with student participants following institutional ethics approval. Full article

This study examines how business school students with no programming background develop technical literacy through a newly introduced Digital Innovation course. Addressing a gap in non-STEM education research—where little is known about how social science students experience technical literacy interventions—we draw on qualitative data from group exam reflections (n = 14) and mid-semester survey responses (n = 7). Using an inductive thematic analysis, the study investigates how students perceived, navigated, and made sense of foundational coding activities. Four themes emerged: (1) Perceived value of coding and technical literacy, (2) Hidden gaps in foundational technical literacy, (3) AI as a cognitive and pedagogical scaffold and (4) Emerging technical competence and identity formation. Framed within theories of digital literacy and constructivist learning, the findings show how limited, scaffolded exposure to web development can shift students from digital consumption toward novice digital production. The study contributes empirical insight into how coding can be meaningfully embedded within business school curricula and offers pedagogical recommendations for designing accessible technical literacy interventions. Full article

Artificial intelligence (AI) is increasingly embedded in education through adaptive platforms, intelligent tutoring systems, and generative tools. While these technologies promise efficiency and personalization, they also raise concerns about pedagogical deskilling, reduced teacher autonomy, and ethical risks. This paper conceptualizes the potential impacts of AI on teaching expertise and instructional design through the lens of Cognitive Load Theory (CLT). The aim is to conceptualize how AI may reshape the management of intrinsic, extraneous, and germane cognitive loads. The study proposes that AI may effectively scaffold intrinsic load and reduce extraneous distractions but displace teacher judgment in ways that undermine germane learning and reflective practice. Additionally, opacity, algorithmic bias, and inequities in access may create new forms of cognitive and ethical burden. The conceptualization presented in this paper contributes to scholarship by foregrounding teacher cognition, an underexplored dimension of AI research, conceptualizing the teacher as a cognitive orchestrator who balances human and algorithmic inputs, and integrating ethical and equity considerations into a cognitive framework. Recommendations are provided for teacher education, policy, and AI design, emphasizing the need for pedagogy-driven integration that preserves teacher expertise and supports deep learning. Full article

Generative artificial intelligence (GenAI) tools are transforming art education by enabling instant creation of textual, visual, audio, and multimodal outputs. This systematic review synthesizes research on GenAI applications in art education from January 2019 to August 2025. Following PRISMA 2020 guidelines, 19 peer-reviewed empirical studies across six databases (Web of Science, ScienceDirect, Springer, Taylor & Francis, Scopus, and ERIC) met inclusion criteria, which required clear pedagogical implementation with students or educators as active participants. Research accelerated from two studies in 2023 to 14 in 2025, with most studies examining higher education and East Asia contexts through mixed methods approaches and grounded in constructivist and cognitive learning theories. Text-to-image generation models (DALL-E, Midjourney, Stable Diffusion) and conversational AI (ChatGPT) were most frequently implemented across creative production, pedagogical scaffolding, and instructional design applications. Findings from this emerging body of research suggest that GenAI has the potential to improve learning achievement, creative thinking, engagement, and cultural understanding when integrated through structured pedagogical frameworks with intentional instructor design. However, these positive outcomes represent early-stage implementation trends in well-resourced contexts rather than broadly generalizable conclusions. Successful integration requires explicit instructional frameworks, clear ethical guidelines for human-AI collaboration, and evolved assessment methods. Full article

As an emerging learning support technology, large language model-powered pedagogical agents demonstrate significant potential in enhancing video learning effectiveness, yet the underlying cognitive mechanisms remain inadequately elucidated. This study employed a multimodal approach combining EEG and eye-tracking to investigate the effects of AI-generated mind maps and text summaries on learning performance and cognitive processing. Following data screening, 80 valid datasets from education majors were randomly assigned to three groups: mind map summary (PA-MMS, n = 27), text summary (PA-TS, n = 28), and control (NPA, n = 25). Results showed both experimental groups achieved significantly higher post-test scores than controls, with PA-MMS demonstrating the strongest performance (d = 3.78). EEG evidence indicated pedagogical agents reduced Theta activity (decreased working memory load) while PA-MMS enhanced Alpha activity (superior attention control). Eye-tracking revealed differentiated strategies: PA-MMS exhibited networked fixation patterns facilitating integration; PA-TS demonstrated linear scanning. Delayed testing showed PA-MMS achieved the highest retention (96.8%). Correlations confirmed posttest scores negatively correlated with Theta (r = −0.46) and extraneous load (r = −0.61), positively with germane load (r = 0.54). Mind maps simultaneously reduced extraneous load (d = 1.26) while enhancing germane processing (d = 1.15), representing a shift from static scaffolds to AI-mediated generative support. Full article

This research-to-practice study examines how Generative Artificial Intelligence (GenAI) can be integrated into live case studies to enhance experiential learning in higher education. It explores GenAI’s potential as an agent to learn with scaffolding reflection and engagement and addresses gaps in existing applications that often focus narrowly on content generation. To explore GenAI’s agentive potential, the methodology illustrates this approach in a UK postgraduate operations management module. Students engaged in a live case study of a local ethnic restaurant to refine its business model and operations. The data sources used to examine students’ results included module materials, outputs, and feedback surveys. Thematic analysis was employed to assess how GenAI facilitated experiential learning. The findings suggest that GenAI integration facilitated exploration, reflection, conceptualisation, and experimentation. Students reported that the activity was engaging and relevant, facilitating critical decision-making and understanding of operations management. However, the outcomes varied according to GenAI literacy and student participation. Although GenAI-enriched learning is beneficial, human agency and contextual knowledge remain crucial. Overall, this study integrates GenAI as a cognitive partner throughout Kolb’s ELC. This study offers a transferable framework for active learning, illustrating how technology can enhance critical and reflective learning in authentic educational contexts. However, limitations include uneven student participation and engagement, resource constraints, overreliance on artificial intelligence outputs, differentiated impact on learning outcomes, and a single-case report, which must be addressed before the framework can be scaled up. Future research should test this through multi-case studies while developing GenAI literacy, measuring GenAI impact, and implementing ethical practices in the field. Full article

This article investigates the use of digital technologies for visualizing educational content in programming education, with a particular focus on developing logical thinking skills. In the context of rapid GenAI development—where AI can both support and hinder cognitive engagement—the study proposes a new instructional model (MDLTP). The model integrates visualization-based learning, scaffolding, spiral learning, and GenAI-supported tools into a unified end-to-end digital ecosystem tailored to novice programmers. The purpose of this study is to develop and evaluate a digital instructional model that effectively fosters logical thinking in novice programmers through visualization technologies and GenAI-supported tools. The novelty of the study lies in the systematic alignment of Bloom’s taxonomy with digital tools, the structured integration of algorithm-visualization technologies into a MOOC + Moodle environment, and the defined pedagogical boundaries for appropriate GenAI use in early-stage cognitive development. Statistical analysis of learning outcomes and survey data from 329 students confirms the effectiveness of the proposed approach in enhancing motivation, comprehension, and logical reasoning. Full article

This study examined whether the systems thinking approach integrating information and communication technology (ICT) and digital tools (hereafter referred to as the STICT approach) improves technological and engineering literacy (TEL) and related outcomes for pre-service preschool teachers. Although there is an expectation for preschool teachers to develop TEL, evidence-based models that systematically combine systems thinking with digital tools and ICT support remain scarce. Using a quasi-experimental design (n = 44; one-semester experiment), the experimental group explicitly integrated systems thinking and digital tools, while the comparison control group followed the traditional approach to teaching design, technology, and engineering (DTE) content; both groups focused on making products for preschoolers. The outcomes included multidimensional literacy, attitudes towards DTE, self-reported systems thinking, aspects of engagement, and focus group reflection. The analyses (ANCOVA/MANCOVA, regression/PLS, multi-group tests, thematic analysis) revealed notable results, including a higher post-test literacy for the experimental group and a lower perceived difficulty with technology. Both groups improved in the self-assessment of systems thinking, with no differences between them. The qualitative findings supported the educational value of the approach. In this pilot classroom experiment (n = 44), findings are consistent with an advantage of the STICT approach on the TEL composite and with lower perceived difficulty of technology, whereas self-assessed systems thinking improved similarly in both groups. Given the small sample and multiple outcomes, estimates carry considerable uncertainty and should be read as preliminary. We theorise that TEL gains arise primarily from systems thinking processes applied during design/evaluation, with ICT functioning as a cognitive-and-motivational scaffold that makes relations/feedback explicit and reduces perceived difficulty; self-assessed systems thinking improved in both groups. Full article

Reading and mathematics are intricately linked through shared cognitive processes that underpin developmental relationships across domains. Despite extensive research on early-grade links between reading and basic arithmetic, gaps persist in understanding how reading comprehension (RC) supports applied math problem-solving (AMP) in older students and non-English contexts. The current study investigates the grade-level relationship between RC and AMP in typically developing Hebrew-speaking fourth (N = 41) and eleventh graders (N = 43), focusing on the contributions of working memory (WM), reading fluency, and arithmetic fluency. Results indicated significant positive associations between RC and AMP in both age groups. In fourth graders, arithmetic fluency partially statistically mediated the RC-AMP relationship in a cross-sectional mediation model. This indicates that students rely on computational proficiency to translate textual understanding into solutions. In contrast, eleventh graders exhibited a direct RC-AMP link, reflecting advanced comprehension and metacognitive strategies as computational skills are automatized. WM showed stronger correlations with RC and AMP among younger students, whereas these associations were weaker in older students. These findings support a Developmental Linguistic–Cognitive Scaffold Model, highlighting age-related shifts in cognitive and linguistic mechanisms supporting AMP. The results emphasize the need for integrated curricula incorporating RC strategies to enhance mathematical reasoning, particularly in morphologically rich languages like Hebrew. Full article

Educational chatbots are increasingly deployed to scaffold student learning, yet educators lack scalable ways to assess the cognitive depth of these dialogues in situ. Bloom’s taxonomy provides a principled lens for characterizing reasoning, but manual tagging of conversational turns is costly and difficult to scale for learning analytics. We present a reproducible high-confidence pseudo-labeling pipeline for multi-label Bloom classification of Socratic student–chatbot exchanges. The dataset comprises 6716 utterances collected from conversations between a Socratic chatbot and 34 undergraduate statistics students at Nanyang Technological University. From three chronologically selected workbooks with expert Bloom annotations, we trained and compared two labeling tracks: (i) a calibrated classical approach using SentenceTransformer (all-MiniLM-L6-v2) embeddings with one-vs-rest Logistic Regression, Linear SVM, XGBoost, and MLP, followed by per-class precision–recall threshold tuning; and (ii) a lightweight LLM track using GPT-4o-mini after supervised fine-tuning. Class-specific thresholds tuned on 5-fold cross-validation were then applied in a single pass to assign high-confidence pseudo-labels to the remaining unlabeled exchanges, avoiding feedback-loop confirmation bias. Fine-tuned GPT-4o-mini achieved the highest prevalence-weighted performance (micro-F1 $=0.814$), whereas calibrated classical models yielded stronger balance across Bloom levels (best macro-F1 $=0.630$ with Linear SVM; best classical micro-F1 $=0.759$ with Logistic Regression). Both model families reflect the corpus skew toward lower-order cognition, with LLMs excelling on common patterns and linear models better preserving rarer higher-order labels, while results should be interpreted as a proof-of-concept given limited gold labeling, the approach substantially reduces annotation burden and provides a practical pathway for Bloom-aware learning analytics and future real-time adaptive chatbot support. Full article

The integration of large language models (LLMs) into project-based learning (PBL) holds significant potential for addressing enduring pedagogical challenges in engineering education, such as providing scalable, personalized support during complex problem-solving. Grounded in Self-Determination Theory (SDT), this study investigates how different LLM usage strategies impact student learning within a blended engineering geology PBL context. A one-semester quasi-experiment (N = 120) employed a 2 (usage mode: individual/shared) × 2 (interaction restriction: restricted/unrestricted) factorial design. Mixed-methods data, including surveys, interaction logs, and reflective reports, were analyzed to assess learning engagement, psychological needs satisfaction, cognitive interaction levels, and project outcomes. Results demonstrate that the individual use strategy significantly outperformed shared use in enhancing engagement, needs satisfaction, higher-order cognitive interactions, and final project scores. The restricted interaction strategy effectively served as a metacognitive scaffold, optimizing the learning process by promoting deliberate planning. Notably, individual autonomy did not undermine collaboration but enhanced it by improving the quality of individual contributions to group work. Students also developed robust critical verification habits to navigate LLM “hallucinations.” This research identifies “individual autonomy” as the core mechanism and “moderate constraint” as a crucial design principle for LLM integration, providing an empirically supported framework for harnessing generative AI to foster both motivational and cognitive outcomes in engineering PBL. Full article

Most adaptive learning systems personalize around content sequencing and difficulty adjustment rather than transforming instructional material within the lesson itself. This paper presents the STREAM (Semantic Transformation and Real-Time Educational Adaptation Multimodal) framework. This modular pipeline decomposes multimodal educational content into semantically tagged, pedagogically annotated units for regeneration into alternative formats while preserving source traceability. STREAM is designed to integrate automatic speech recognition, transformer-based natural language processing, and planned computer vision components to extract instructional elements from teacher explanations, slides, and embedded media. Each unit receives metadata, including time codes, instructional type, cognitive demand, and prerequisite concepts, designed to enable format-specific regeneration with explicit provenance links. For a predefined visual-learner profile, the system generates annotated path diagrams, two-panel instructional guides, and entity pictograms with complete back-link coverage. Ablation studies confirm that individual components contribute measurably to output completeness without compromising traceability. This paper reports results from a tightly scoped feasibility pilot that processes a single five-minute elementary STEM video offline under clean audio–visual conditions. We position the pilot’s limitations as testable hypotheses that require validation across diverse content domains, authentic deployments with ambient noise and bandwidth constraints, multiple learner profiles, including multilingual students and learners with disabilities, and controlled comprehension studies. The contribution is a transparent technical demonstration of feasibility and a methodological scaffold for investigating whether within-lesson content transformation can support personalized learning at scale. Full article

The cultivation of aesthetic appreciation through engagement with exemplary artworks constitutes a fundamental pillar in fostering children’s cognitive and emotional development, while simultaneously facilitating multidimensional learning experiences across diverse perceptual domains. However, children in early stages of cognitive development frequently encounter substantial challenges when attempting to comprehend and internalize complex visual narratives and abstract artistic concepts inherent in sophisticated artworks. This study presents an innovative methodological framework designed to enhance children’s artwork comprehension capabilities by systematically leveraging the theoretical foundations of audio-visual cross-modal integration. Through investigation of cross-modal correspondences between visual and auditory perceptual systems, we developed a sophisticated methodology that extracts and interprets musical elements based on gaze behavior patterns derived from prior pilot studies when observing artworks. Utilizing state-of-the-art deep learning techniques, specifically Recurrent Neural Networks (RNNs), these extracted visual–musical correspondences are subsequently transformed into cohesive, aesthetically pleasing musical compositions that maintain semantic and emotional congruence with the observed visual content. The efficacy and practical applicability of our proposed method were validated through empirical evaluation involving 96 children (analyzed through objective behavioral assessments using eye-tracking technology), complemented by qualitative evaluations from 16 parents and 5 experienced preschool educators. Our findings show statistically significant improvements in children’s sustained engagement and attentional focus under AI-generated, artwork-matched audiovisual support, potentially scaffolding deeper processing and informing future developments in aesthetic education. The results demonstrate statistically significant improvements in children’s sustained engagement (fixation duration: 58.82 ± 7.38 s vs. 41.29 ± 6.92 s, p < 0.001, Cohen’s d ≈ 1.29), attentional focus (AOI gaze frequency increased 73%, p < 0.001), and subjective evaluations from parents (mean ratings 4.56–4.81/5) when visual experiences are augmented by AI-generated, personalized audio-visual experiences. Full article

This theoretical paper introduces the Verbal–Cognitive Scaffold (VCS) Model, a cognitively inclusive framework which proposes the cognitive architectures underlying computational thinking (CT). Moving beyond monolithic theories of cognition (e.g., executive-function and metacognitive control models), the VCS Model posits inner speech (InSp) as the predominant cognitive pathway supporting CT operations in neurotypical populations. Synthesizing interdisciplinary scholarship across cognitive science, computational theory, neurodiversity research, and others, this framework articulates distinct mechanisms through which InSp supports CT. The model specifies four primary pathways linking InSp to CT components: verbal working memory supporting decomposition, symbolic representation facilitating pattern recognition and abstraction, sequential processing enabling algorithmic thinking, and dialogic self-questioning enhancing debugging processes. Crucially, the model posits these verbally mediated pathways as modal rather than universal. Although non-verbal architectures are acknowledged as possible alternative routes, their precise mechanisms remain underspecified in the existing literature and, therefore, are not the focus of the current theoretical exploration. Given this context, this manuscript focuses on the well-documented verbal support provided by InSp. The VCS Model’s theoretical contributions include the following: (1) specification of nuanced cognitive support systems where distinct InSp functions selectively enable particular CT operations; (2) generation of empirically testable predictions regarding aptitude–pathway interactions in computational training and performance; and (3) compatibility with future empirical efforts to inquire into neurodivergent strategies that may diverge from verbal architectures, while acknowledging that these alternatives remain underexplored. Individual variations in InSp phenomenology are theorized to predict distinctive patterns of CT engagement. This comprehensive framework, thus, elaborates and extends existing verbal mediation theories by specifying how InSp supports and enables CT, while laying the groundwork for possible future inquiry into alternative, non-verbal cognitive pathways. Full article