Frontiers in Education: Digital Learning Innovations
6 hours 34 minutes ago
IntroductionPromoting scientific argumentation in the context of socio-scientific issues remains a major challenge in science education. Artificial intelligence offers new opportunities to provide adaptive feedback on students’ written arguments; however, little is known about learners’ perceptions of hybrid learning environments that combine AI-supported feedback with peer feedback under authentic classroom conditions. This study presents the development and pilot evaluation of SMOKEChem, a web-based learning environment designed to foster scientific argumentation through AI-supported and collaborative feedback processes.MethodsAn exploratory mixed-methods design was employed to investigate learners’ perceptions of SMOKEChem during regular chemistry lessons. A total of 130 secondary school students (mean age = 15 years) participated in the study. Quantitative data were collected using questionnaires assessing technology acceptance, cognitive load, and chatbot usability, while qualitative responses explored students’ perceptions of peer feedback.ResultsThe findings indicate that SMOKEChem was perceived as useful, user-friendly, and cognitively balanced, with low levels of extraneous cognitive load and high levels of germane cognitive load. Furthermore, students perceived AI-supported and peer feedback as fulfilling complementary functions: acceptance and usability data indicate that AI provided immediate, structured guidance for revising arguments, while qualitative data on peer feedback point to dialogic reflection, perspective-taking, and collaborative knowledge construction.DiscussionThe findings provide initial empirical evidence that hybrid feedback designs combining AI-supported and peer feedback can effectively support scientific argumentation in socio-scientific contexts. The complementary strengths of both feedback sources highlight the potential of integrating AI into collaborative learning environments rather than replacing peer interaction. These results provide practical implications for the design of AI-supported argumentation environments and inform future research on hybrid feedback approaches in science education.
Laura Celine Leppla
6 hours 34 minutes ago
Learning analytics promises early identification of struggling students, but evidence comes mainly from learning-management-system counts rather than authentic laboratory telemetry. We tested the predictive value of fine-grained process traces in a first-year digital electronics laboratory. The open Educational Process Mining dataset contains 115 engineering undergraduates observed across six simulator sessions (230,318 records) and 93 students with examination outcomes. Under a pre-specified, leakage-aware protocol—nested repeated cross-validation, pipeline-internal preprocessing, a 5×3 feature-group-by-model ablation matrix, and bootstrap confidence intervals—we predicted below-median final-examination performance. The pre-specified behavioural logistic model, using eleven features spanning volume, engagement, and interaction intensity, achieved an AUC of 0.617 (95% CI 0.491–0.737), not reliably above chance; the best non-pre-registered behavioural model reached 0.648. Four in-course-grade features achieved an AUC of 0.764 (0.644–0.854), the best matrix cell. Behaviour underperformed grades (ΔAUC −0.147, 95% CI −0.263 to −0.028), and adding telemetry to grades produced no detectable improvement (ΔAUC −0.040, −0.083 to 0.006). With one session, behavioural prediction was below chance and plateaued near 0.62 from session four, while the single grade available at session two already outperformed six sessions of telemetry; its signal concentrated in exercise coverage and workflow and partly reflected a small lower-participation group that often missed the examination. Full telemetry predicted non-participation well (AUC 0.872), but exercise coverage alone matched it (0.879). Continuous-score regression also favoured grades as standalone predictors (best MAE 21.29 vs. 22.38), although nonlinear unions modestly improved error. In this setting, telemetry is primarily a coarse disengagement flag; the course’s assessment loop remains the more trustworthy signal for participating students.
Mingyu Wu
6 hours 34 minutes ago
Artificial Intelligence (AI)-generated feedback is increasingly transforming mathematics education by providing timely, personalized, and adaptive learning support. However, limited evidence exists on the behavioural mechanisms through which students’ perceptions of AI-generated feedback influence mathematics learning outcomes, particularly in Sub-Saharan Africa. This study examined the direct and indirect relationships among Perceived AI Feedback Quality (PAQ), AI Dependence (AID), and Mathematics Learning Outcomes (MLO) among 360 undergraduate students in Ghana. A quantitative cross-sectional survey design was adopted, and data were analysed using Structural Equation Modelling (SEM) with IBM SPSS Statistics 23 and AMOS 23. The measurement model demonstrated satisfactory reliability and validity. The structural model showed that PAQ significantly predicted AID (β = 0.673, p < .001) and MLO (β = 0.449, p < .001), while AID also significantly predicted MLO (β = 0.441, p < .001). Bootstrapping analysis further revealed that AID partially mediated the relationship between PAQ and MLO (β = 0.314, p < .001). The findings indicate that students who perceive AI-generated feedback as accurate, relevant, and useful are more likely to depend on AI, and this dependence is in turn associated with stronger perceived mathematics learning outcomes. The study extends Feedback Theory and Social Cognitive Theory by identifying AI Dependence as a key behavioural mechanism linking AI feedback quality to mathematics learning. The findings provide practical implications for educators, instructional designers, and policymakers seeking to promote the effective and responsible integration of AI technologies into mathematics education.
Emmanuel Kusi
1 day 7 hours ago
IntroductionArtificial intelligence (AI) is rapidly transforming research practices in higher education, yet empirical evidence on how AI supports research productivity among students remains limited, particularly in developing contexts. This study examined the influence of AI tools used during the research preparation, analysis, and dissemination stages on the research productivity of postgraduate students in public universities in Nigeria. The study was guided by the Unified Theory of Acceptance and Use of Technology 2 (UTAUT2) and Experiential Learning Theory.MethodsThe study adopted a descriptive survey design involving 312 respondents. Data were collected using the AI Tools Utilisation and Research Productivity Questionnaire (AITURPQ) and analysed using simple linear regression. The analysis examined the predictive effects of AI-assisted research preparation, analytical, and dissemination tools on research productivity.ResultsThe findings reveal that AI-assisted research preparation tools significantly predicted research productivity (R² = .241, p < .05). AI-supported analytical tools also exerted a significant positive influence on research productivity (R² = .137, p < .05), while AI-driven dissemination tools demonstrated the strongest predictive effect (R² = .329, p < .05).DiscussionThe findings underscore the role of AI across the research lifecycle in supporting knowledge creation, analytical competence, and scholarly dissemination. They highlight the need to strengthen AI literacy and institutional support and promote responsible AI integration to enhance postgraduate research productivity.
Edet E. Okon
5 days ago
Generative artificial intelligence (GenAI) and artificial intelligence (AI) are transforming STEAM education by reshaping instructional design, learning facilitation, assessment, creativity, and interdisciplinary learning. Their rapid evolution presents both opportunities and challenges for educators and educational institutions. This article synthesized contemporary scholarship on GenAI, AI, pedagogy, ethics, and digital innovation. Structured searches of Scopus, Web of Science, ERIC, and Google Scholar, supplemented by the UNESCO Digital Library and the OECD iLibrary and by citation chaining, identified 107 records, of which 15 met the eligibility criteria and were included in the reviewed corpus; additional literature cited in the findings and discussion attaches the pedagogical argument and contextualizes the synthesis. The primary search window covered 2016–2025, with foundational theoretical works purposively retained. Records were screened using explicit inclusion and exclusion criteria in two stages. The synthesis examined the conceptual foundations of Gen AI and AI in STEAM education, pedagogical theories supporting responsible integration of Gen AI and AI, and Gen AI and AI tools applicable to STEAM classrooms. Findings highlight applications in lesson planning, personalized learning, inquiry- and project-based learning, formative assessment, feedback, academic integrity, coding, media generation, and interdisciplinary learning. GenAI and AI can enhance teacher productivity, creativity, collaboration, adaptive instruction, and learner engagement when supported by sound pedagogy, AI literacy, and ethical governance. The article proposes an integrated framework for responsible human–AI collaboration linking pedagogical principles, ethical considerations, and discipline-specific applications. It offers practical guidance for fostering innovative, equitable, and contextually responsive STEAM education in an increasingly AI-enabled educational environment.
Niroj Dahal
6 days 6 hours ago
BackgroundLarge language models (LLMs) are increasingly being considered for assessment support in health professions education; however, evidence of their performance in essay-style examinations remains limited. In particular, little is known about the reproducibility and operational stability of LLM-based grading under different conditions.ObjectiveThis study compared the grading performance of several LLMs with that of human examiners in a master's-level public health course and assessed the consistency of LLM-based grading across repeated sessions and different file upload volumes.MethodsWe conducted a method comparison and validation study using anonymized student submissions from a 5-hour essay-style examination in a master's-level course in public health, empowerment, and health promotion. Four LLMs—ChatGPT, Gemini, LeChat, and Kimi—were prompted to assign final grades on an A-F scale using the same grading guidance as human examiners. The agreement between the LLM- and human-assigned grades in a single-file upload setting was assessed using weighted Cohen's kappa. Internal consistency across file upload volumes was assessed using Krippendorff alpha. Repeated grading across multiple sessions was performed for the best-aligned model.ResultsAgreement with human examiners was limited in fast mode and improved in reasoning or thinking modes; however, reproducibility across sessions and implementation conditions remained limited. In the single-file upload setting, ChatGPT showed the strongest agreement with human examiners (weighted kappa 0.718, 95% CI 0.578–0.859), followed by Kimi (weighted kappa 0.571, 95% CI 0.373–0.768). ChatGPT achieved 50.0% exact agreement and 90.6% agreement within ±1 grade, with corresponding values of 28.1% and 78.1% for Kimi. Gemini and Gemini Pro showed the highest internal consistency across the file upload conditions. Repeated grading by ChatGPT across 5 days showed moderate variation. LLMs used fewer extreme grades than did human examiners.ConclusionsSome LLMs, particularly ChatGPT and Kimi, showed moderate alignment with human examiners, but the alignemnet was not consistently stable across repeated sessions or operational settings. LLMs may currently be better suited as supervised grading assistants than as autonomous graders.
Asgeir Brevik
6 days 6 hours ago
Teaching Virtual Reality (VR) design software to students with disabilities remains challenging in Ecuadorian higher education, where tool complexity, accessibility constraints, and diverse learning needs can limit participation. This study examined Hercules VR, a pedagogical framework integrating gamification, co-design, and biomimicry to support Unreal Engine learning among university students with cognitive, sensory, and motor disabilities. Using a single-group, mixed-methods pre-post pilot design (quasi-experimental, no control group), 26 students participated in a 16-week semester including 12 structured instructional sessions. The framework combined a co-design stage adapting activities to participants’ needs, a gamified progression system, and a biomimetic approach inspired by the life cycle of the Dynastes hercules beetle to make complex software concepts more accessible. Pre- and post-test instruments assessed VR design comprehension, collaborative participation, and intrinsic motivation. Results showed statistically significant within-group improvements across all self-reported dimensions (Wilcoxon signed-rank tests, all exact p < .001), with very large effect sizes (Rosenthal’s r = 0.86–0.87), computed from a dataset independently verified during revision. Post-test scores across the three outcomes were not significantly correlated, suggesting they changed relatively independently rather than as a single cohesive process. Given the absence of a control group and the combined delivery of the three intervention components, findings are interpreted as descriptive evidence of within-group change rather than confirmation of a causal effect or of any individual component's specific contribution. Qualitative findings indicated that participants perceived the biomimetic model as helpful for understanding abstract concepts and experienced the learning process as more accessible, while the co-design component was associated, in participants’ accounts, with greater autonomy and active participation. As an exploratory pilot study, these results suggest Hercules VR may offer a promising pedagogical approach for inclusive digital design education in similar higher-education contexts, though controlled, multi-institutional research is needed before broader replicability claims can be made. The framework aligns with Sustainable Development Goal 4 (Quality Education).
Richard P. Sánchez Sánchez
1 week ago
BackgroundHandwriting is central to children's written-language development, but conventional assessment usually emphasizes the completed product and provides limited information about the processes that produced it. Digital technologies can capture handwriting as an unfolding motor and orthographic activity.ObjectiveThis scoping review mapped technologies for capturing children's handwriting process data, the raw signals and derived features obtained, the assessment constructs supported by process measures, and their educational, developmental, and clinical applications.MethodsFollowing scoping-review guidance and PRISMA-ScR, we searched Scopus, PubMed, ERIC, Web of Science Core Collection, and IEEE Xplore and conducted backward citation searching of 11 relevant review or framework articles. Of 198 reports sought for full-text assessment, one could not be retrieved, 197 were assessed, 39 were excluded, and 158 were included. All included reports were charted with the same 16-field framework. Independent verification by a second researcher covered randomly selected samples of 40 of 197 full-text eligibility decisions (20.3%; 97.5% agreement; Cohen's κ = 0.918) and 32 of 158 extractions (20.3%).ResultsDigitizing or graphics tablets were used in 136 reports (86.1%), and 117 (74.1%) used Wacom hardware. Position or trajectory (145, 91.8%) and time or timestamps (124, 78.5%) were the most common raw signals, followed by pen pressure or normal force (82, 51.9%) and pen-state or in-air information (71, 44.9%). Common derived feature families included speed or velocity (97, 61.4%), temporal or duration measures (84, 53.2%), spatial or geometric measures (67, 42.4%), and pressure- or force-derived measures (59, 37.3%). Developmental or normative characterization (75, 47.5%) and clinical characterization or differential assessment (74, 46.8%) were the most frequent applications. Latin-alphabetic handwriting appeared in 114 reports (72.2%), followed by Hebrew (23, 14.6%), Chinese (19, 12.0%), and Arabic (5, 3.2%).ConclusionsDigital handwriting assessment is well established in process capture but less consistently translated into applied educational or clinical decisions. Classification performance should not be equated with diagnostic validity. A theory-informed and review-refined four-level framework organizes raw signals, derived features, assessment constructs, and empirical interpretation or action without implying a validated developmental or psychometric hierarchy.
Siyuan Liu
1 week ago
IntroductionDigital technologies and artificial intelligence (AI) are reshaping teacher education faster than many programmes can pedagogically respond. This scoping review examined how digital technologies are incorporated into teacher education, the advantages and barriers associated with these approaches, and how recent AI-focused research may help reinterpret persistent challenges.MethodsTwo analytically distinct Scopus-derived subsets were analysed. The RQ1/RQ2 subset comprised 155 studies on digitally supported teacher education, while the RQ3 subset included 40 AI-focused studies, yielding an overall evidence base of 195 publications. Data charting combined primary-category coding, descriptive frequency analysis, cross-category synthesis, and multi-label coding for the AI-focused evidence.ResultsWithin the RQ1/RQ2 subset, competence and ICT-integration frameworks were the most frequent strategy (46.5%). Digital competence development was the most frequently reported advantage (76.1%), whereas infrastructure and access represented the main barrier (45.2%). In the AI subset, professional development and teacher accompaniment (35.0%) and instructional design and assessment (32.5%) were the predominant approaches.DiscussionAcross both evidence streams, technological affordances became educationally meaningful when mediated by pedagogical design, disciplinary knowledge, critical and ethical judgement, human oversight, professional agency, and institutional support. The findings support an AI-responsive approach to teacher education that emphasises pedagogically justified and professionally accountable AI use rather than tool adoption alone.
John Fredy Patiño Hernández
1 week ago
BackgroundDigital tools are increasingly integrated into higher education, yet evidence from Peruvian higher technological education remains limited.MethodsWe conducted an observational, analytical, cross-sectional study among 153 students from a public technological higher education institute in Callao, Peru. Data were collected in March–April 2025 using simple random sampling. Digital tools were assessed using an overall score and three domains: asynchronous interactive tools, synchronous interactive tools, and didactic resources. Meaningful learning was analyzed as a continuous score. Spearman correlations and linear regression models with HC3 robust standard errors were used. Multivariable models were adjusted a priori for sex and age group.ResultsMedian digital tools and meaningful learning scores were 94 (IQR: 85–103) and 85 (IQR: 75–96), respectively. Overall digital tools use correlated with meaningful learning (ρ=0.54; p < 0.001) and remained associated after adjustment (aβ=0.52; 95% CI: 0.41–0.64). Synchronous interactive tools showed the strongest independent association (aβ=0.81; 95% CI: 0.50–1.11), followed by didactic resources (aβ=0.46; 95% CI: 0.18–0.75). The association for asynchronous tools was attenuated after adjustment (aβ=0.21; 95% CI: −0.12 to 0.55; p = 0.213).ConclusionGreater digital tool use was associated with higher meaningful learning scores. Synchronous tools and didactic resources showed independent associations, whereas asynchronous tools did not retain an adjusted association. Longitudinal multicenter studies are needed to confirm these findings.
Edgar Pfuño-Ramos
1 week 2 days ago
Digital entrepreneurship has emerged as one of the most transformative forces reshaping economies, labour markets, and educational expectations globally, yet initial teacher education (ITE) programmes have been conspicuously slow to respond. This article reports a systematic review of peer-reviewed literature published between 2015 and 2025, examining the extent to which digital entrepreneurship has been integrated into ITE curricula and what implications that integration, or its absence, carries for curriculum design. Searches were conducted across ERIC, Scopus, Web of Science, Google Scholar, and SciELO, yielding 312 initial results. Following rigorous inclusion and exclusion screening, 37 sources were retained and thematically analysed. Four interrelated themes emerged: the conceptual ambiguity surrounding digital entrepreneurship as an educational construct; the persistent gap between policy ambition and ITE curriculum reality; the inadequacy of pre-service teacher preparation for teaching digital entrepreneurship across conceptual, pedagogical, and technological dimensions; and the structural, cultural, and resource-related barriers that impede meaningful curriculum reform. Drawing on the EntreComp and DigCompEdu frameworks, the review concludes that ITE has not yet arrived at the destination its own policy documents have signposted. Fundamental redesign is required, not the incremental addition of technology modules, but a rethinking of what it means to prepare teachers for a digitally entrepreneurial world. The urgency of this task is compounded by the rise of generative artificial intelligence, acute youth unemployment across the Global South, and new evidence that pre-service teachers consistently underperform on precisely the digital competence dimensions most relevant to entrepreneurial practice. Five evidence-grounded curriculum implications are advanced: conceptual clarity before content delivery; integration rather than addition; the modelling imperative; assessment alignment; and structural partnership with the digital economy.
Beatrice Ngulube
1 week 5 days ago
Generative artificial intelligence is increasingly used to produce formative feedback in programming education; however, whether students find such feedback useful, actionable, and worth using remains underexamined. This study presents a user-centred evaluation of feedback produced by an automated, rubric-based large language model assessment of programming responses, using a two-layer instrument, namely, response-level feedback on individual answers rated on five dimensions (clarity, specificity, accuracy, actionability, and usefulness) and a consolidated performance report rated on six dimensions (overall satisfaction, relevance, personalisation, cognitive load, intention to use, and motivation). Using a cross-sectional design, 144 students across secondary, short-cycle higher education (CTeSP—professional higher technical courses), and undergraduate programming rated 893 response-level feedback instances, and 140 of them rated 237 consolidated reports. Ratings were favourable across all dimensions, with student-level means of 4.24–4.43 (response level) and 4.11–4.38 (report level). At the response level, clarity and perceived accuracy were rated highest, and actionability and usefulness lowest. Cognitive load was the lowest at the report level. Only the between-context comparison for perceived accuracy reached statistical significance, with lower ratings in CTeSP (ε2 = 0.076); the small secondary sample limits conclusions about similarity. Perceived usefulness was most strongly associated with actionability and perceived accuracy (R2 = 0.76), and intention to use with motivation and personalisation (R2 = 0.53). These findings characterise the correlates of perceived usefulness and intention to use within each feedback layer, supporting human-centred designs that preserve student judgement and instructor oversight.
Pedro C. Mendonça
2 weeks ago
This study addresses the challenge of providing scalable and immersive environments for spoken English training in foreign language education. To this end, we designed and implemented a virtual reality (VR)-based instructional system that integrates immersive scenarios, avatar-based interaction, and task-based learning activities to support oral communication practice. A convergent parallel mixed-method design was employed to evaluate the effectiveness of the proposed instructional framework. Using convenience sampling, 60 university students from two intact English classes voluntarily participated in the study. One class (n = 30) received VR-supported instruction, whereas the other class (n = 30) received traditional instruction. Quantitative data were collected through pre- and post-intervention IELTS speaking assessments, while qualitative data were obtained from questionnaires and interviews to examine learners’ perceptions and experiences. Quantitative analyses, including independent-samples t-tests and ANCOVA, revealed no statistically significant difference in speaking proficiency between the two groups. However, a small effect size (Cohen's d = 0.2085) suggested a modest advantage for the VR group. Qualitative findings indicated high levels of learner engagement and perceived improvement, with over 90% of participants reporting increased confidence, vocabulary use, and opportunities for authentic communication. Learners particularly valued the immersive environments, avatar-based interaction, and immediate feedback provided through the VR platform. Rather than developing a new VR engine, this study contributed a structured instructional framework that demonstrates how existing immersive technologies can be systematically integrated into spoken English instruction. The findings provide practical insights into the pedagogical design, implementation, and classroom integration of VR technologies for language learning.
Hungche Chen
2 weeks 6 days ago
Generative artificial intelligence (GenAI) is rapidly transforming pedagogical practices in higher education by generating explanations, feedback, simulations, learning resources, and dialogic prompts. Existing AI frameworks in education predominantly conceptualize AI through functional roles, such as tutoring, assessment-centric models, institutional governance principles, or learner literacy perspectives. However, higher education institutions often regard GenAI as a complementary tool while simultaneously framing it as a threat to academic integrity, triggering reactive responses such as prohibition, surveillance, and detection. These framings leave a theoretical gap, offering limited insight into how GenAI redefines pedagogical agency, responsibility, and knowledge work in everyday interactions among instructors, students, and institutional structures. To address this gap, the present study proposes a nested instructor-student-GenAI triadic conceptual model for higher education. The model is derived through a focused integrative interdisciplinary synthesis that brings together literature from higher education, educational technology, learning sciences, instructional design, human-computer interaction, cognitive psychology, policy, ethics, and institutional governance. The model positions GenAI as a bounded didactic-pedagogical mediator operating within a shared didactic mediation space. Higher education institutions are conceptualized as the governance layer that enables, constrains, and legitimizes triadic practice through policies, infrastructure, regulations, and accountability mechanisms, while wider stakeholders shape external expectations. The study further formulates researchable propositions and discipline-sensitive implications to support future empirical validation and responsible GenAI integration in higher education.
Sharmila Rani Moganadas
2 weeks 6 days ago
IntroductionWith the growing integration of generative artificial intelligence (GenAI) into higher education, research attention must shift from the frequency of tool use to how these tools are used by learners. This study examines how GenAI literacy is related to learners' patterns of GenAI use and cognitive engagement during GenAI interactions.MethodsDrawing on questionnaire data from a large, diverse international sample of academic learners (N = 848), hierarchical multiple regression analyses were conducted to examine the relationships between dimensions of GenAI literacy and actual usage behaviors. Cluster analysis was then used to identify learner profiles based on patterns of GenAI use, literacy, monitoring, and reliance.ResultsThe findings indicate a dual role for critical-ethical awareness, a dimension of GenAI literacy that is associated with both monitoring GenAI outputs and using GenAI as a cognitive shortcut. Cluster analysis identified three learner profiles: Engaged and Literate users, who integrate monitoring and reliance; Uncritical Reliant users, who show high reliance but limited monitoring; and Skeptical Minimal users, who have low GenAI use but high reliance when they do engage. These patterns point to an “illusion of cognitive independence,” in which learners report high levels of independent thinking while engaging in cognitive offloading by relying on GenAI outputs without sufficient monitoring, particularly among Uncritical Reliant users.DiscussionCollectively, these findings suggest that GenAI literacy does not necessarily reduce reliance on GenAI but instead shapes how learners combine monitoring with reliance. Conceptually, this positions GenAI literacy as a regulatory mechanism of human–AI interaction, extending self-regulated learning to contexts in which cognitive processes are distributed between learners and systems. These results highlight the importance of developing GenAI literacy as a practice of reflective and regulated use rather than focusing solely on increasing technology adoption.
Meital Amzalag
2 weeks 6 days ago
Extended reality (XR) enables users to interact with and control simulated 3D elements, creating opportunities to support meaningful learning experiences, but it also introduces cognitive load due to its novel interactions. Researchers and designers have attempted to address these challenges by manipulating the user experience through the design of the virtual environment or by varying the level of interaction complexity. While traditional media benefit from established instructional frameworks, immersive media such as XR lack similarly refined design guidelines. In this perspective article, we argue that Mayer’s multimedia learning principles cannot be directly transferred to XR learning environments without reinterpretation, as XR introduces distinctive cognitive and interaction demands, including embodiment, spatial interaction, presence, agency, and environmental complexity. From this perspective, we organize XR design considerations around cognitive load processes and discuss how these choices may either support or hinder learning. We then propose key considerations for designing effective XR-based learning experiences, including content representation (e.g., 2D and 3D elements, color, positioning), virtual environment settings (e.g., background elements, haptic feedback, sound, voice interactions), and the structure of learning materials (e.g., pre-training materials, interactions and tasks, assessment).
Pedro Acevedo
3 weeks ago
This study determined the impact of a personalized educational platform based on machine learning (ML) on the cognitive learning of secondary education students at a public institution in Piura, Peru. The research followed an applied, quantitative approach with a quasi-experimental design. A sample of 56 fourth-grade students was distributed into a control group (n = 26, traditional teaching) and an experimental group (n = 30, platform-assisted teaching), using two pre-existing classrooms randomly assigned to each condition. Cognitive learning was assessed across four dimensions (attention, memory, reasoning, and comprehension) with pretest and posttest exams administered at the start and end of the same two-hour session per topic, over three consecutive weeks. The platform combined an unsupervised clustering model for cognitive profiling with a supervised classifier that predicted, in real time, the optimal content level for each student. Analyses were based on improvement scores (posttest - pretest); intergroup comparisons used the Mann–Whitney U test with rank-biserial effect sizes, and the relationship between platform metrics and the posttest score was examined through Spearman correlations. Results showed significant differences favoring the experimental group across the three topics (p < .001), with increasing effect sizes (r = 0.527, 0.610, 0.738), confirmed through a sensitivity analysis that controlled for the pretest. The experimental group achieved an average improvement of 8.29 points out of 20, versus 4.90 for the control group. Requested adaptations showed a moderate association on average with the posttest score (ρ = 0.485, p < .001), strengthening over the course of the intervention (up to ρ = 0.588), while the predictive weight of the initial micro-diagnostic declined progressively. The platform was associated with a significant, progressive, and dimension-differentiated improvement, although the single-classroom-per-condition design and the within-session measurement warrant interpretive caution. This work contributes to inclusive education in resource-limited contexts and aligns with Sustainable Development Goals 4 and 10.
Anel Enmita Córdova Zurita
3 weeks ago
IntroductionAlthough metacognitive scaffolding enhances self-regulated learning (SRL), its continuous impact on learners' internal cognitive states remains difficult to assess using traditional outcome-based measures.MethodsTo investigate these latent regulatory dynamics during a computer-supported learning task, we applied non-linear pupillary phase-space reconstruction to track the continuous cognitive engagement of university students (N = 82).ResultsLinear Mixed-Model analyses indicated a distinct divergence in pupillary dynamics: rather than uniformly reducing cognitive load, metacognitive prompts were associated with sustained physiological mobilization. Specifically, the scaffolded cohort demonstrated significantly elevated dynamic intensity (p < 0.001) and greater structural trajectory curvature (p < 0.001). Notably, these pupillary dynamics showed no significant linear association with final post-test scores (all p > 0.4).DiscussionWe interpret these pupillary dynamics as candidate physiological correlates of sustained regulatory micro-adjustment; however, because pupillometry is sensitive to arousal, effort, attention, and task-related factors, they cannot be equated with metacognitive monitoring directly. Immediate performance metrics alone may therefore fail to capture the physiological dynamics that accompany digital learning, although no definitive process–outcome dissociation can be inferred from the present data. By introducing non-linear pupillary dynamics as candidate continuous correlates of regulatory effort, this analytical framework provides a method for evaluating instructional designs and informing the development of real-time adaptive tutoring systems.
Kangning Xie
3 weeks 1 day ago
IntroductionGenerative artificial intelligence has become increasingly integrated into higher education; however, its adoption raises challenges related to authorship, transparency, content verification, privacy, and student responsibility. The objective of this study was to identify the factors associated with and structurally contributing to the ethical use of generative artificial intelligence among students at the State University of Milagro.MethodsA quantitative, applied, non-experimental, cross-sectional, correlational, explanatory-predictive, and confirmatory study was conducted. The sample comprised 980 students selected through non-probability purposive sampling. Data were collected using a 44-item questionnaire.ResultsThe proposed model demonstrated an excellent fit (CFI = 0.998; SRMR = 0.021; RMSEA = 0.008). The six predictor constructs jointly explained 44.0% of the variance in ethical GenAI use (R2 = 0.440), and all structural paths were positive and statistically significant. Academic integrity and transparency exhibited the strongest effect (β = 0.231), followed by ethical and technical literacy (β = 0.181), critical thinking and content verification (β = 0.177), institutional guidance and ethical education (β = 0.159), self-regulation and academic responsibility (β = 0.119), and data protection, privacy, and risk management (β = 0.114). Bivariate correlations between the six predictor constructs and the ethical use of generative artificial intelligence ranged from r = 0.370 to r = 0.507 (all p < 0.001).ConclusionThe ethical use of generative artificial intelligence was associated with the interaction of individual competencies, academic principles, and institutional conditions. These findings highlight the need to strengthen clear institutional policies, integrate ethics education across the curriculum, promote critical verification of AI-generated content, encourage transparent disclosure of AI use, and reinforce data protection practices. Given the cross-sectional design, the findings should be interpreted as predictive associations rather than evidence of causal relationships.
Yaima Beatriz Tabares-Cruz
3 weeks 6 days ago
BackgroundInclusive music-making requires instruments that support varied bodies, abilities, musical backgrounds, and forms of participation. Digital musical instruments provide diverse approaches to sound creation, and fabric-based interfaces offer an alternative interaction modality that may support participation for some users and contexts. Their tactile and deformable properties enable forms of interaction that differ from conventional rigid or screen-based controllers and may offer inclusive possibilities in particular settings.ObjectiveThis paper presents HarmonicThreads as a formative interaction-design case of a fabric-based digital musical instrument. The prototype explores how tactile cues, fabric deformation, projected visual feedback, and assisted accompaniment can support low-barrier music-making. We examine first-use discovery, perceived musical agency, and design considerations intended to inform the development of future fabric-based, accessible digital musical instruments.MethodsWe conducted a formative mixed-methods evaluation with 20 adult participants. Participants used HarmonicThreads in independent play, where conductive threads directly triggered notes, and assisted play, where sustained contact generated automated melodic accompaniment. Data included observations, SUS and UEQ-S responses, semi-structured interviews, and participant-generated design sketches.ResultsParticipants treated the fabric surface as an instrument, using raised threads to identify playable regions, form spatial pitch expectations, and experiment with chords, rhythm, and sustained notes. Qualitative results show that assisted play supported immediate musical engagement, while independent play supported greater perceived control. The study also revealed accessibility-relevant barriers, including uncertain activation thresholds, perceived fragility, thin and closely spaced threads, posture and fatigue, ambiguous visual feedback, and limited expressive mappings.ConclusionHarmonicThreads contributes a formative design case for inclusive digital music technology. We identify design considerations for tactile legibility, low-effort activation, ergonomic support, transparent multimodal feedback, configurable scaffolding, richer sonic mappings, and future participatory evaluation with intended users and real music-making contexts.
Ellie Nguyen