ChatGPT as an Assessment Design Tool in Higher Education: Evaluating Item Quality, Bloom’s Taxonomy Coverage, and Faculty Acceptance Across Academic Disciplines

Nadia; Rabia

doi:https://doi.org/10.54216/IJAIET.050106

Fusion: Practice and Applications | 2026

Uncertainty-Aware Radar-LiDAR Fusion for PoE-Constrained Smart Infrastructure Perception with Asynchronous Sensing

Infrastructure-based autonomous perception operates under fundamentally different constraints than vehicle mounted systems: elevated-mounting geometries producing depression-angle-dependent sparse point clouds, a 12.95 W IEEE 802.3af Power-over- Ethernet (PoE) power ceiling, and distributed asynchronous sensing governed by IEEE 1588v2 precision time protocol (PTP) synchronization uncertainty. Existing automotive radar–LiDAR fusion frameworks assume abundant power, dense sensing, and synchronous measurements — assumptions that all fail in fixed infrastructure deployments. This paper presents XADAR, an uncertainty-aware multi-modal fusion framework designed for these infrastructure-specific constraints. XADAR makes three princi-pal contributions: (1) a covariance inflation mechanism that propagates PTP synchronization uncertainty continuously through the fusion pipeline, replacing hard synchronization thresholds with a smooth degradation curve proportional to temporal offset; (2) adap-tive sensor-specific fusion weights derived from modality covariance matrices that account for IWR6843 77 GHz FMCW radar Doppler ambiguity and ground-reflection multipath, and TFS20-L ToF LiDAR atmospheric scattering and range-zone limitations; and (3) a complete reproducible architecture including an IEEE 802.3af-compliant power budget (5.78 W maximum concurrent load; 41.6% PoE safety margin), quantitative 77 GHz propagation analysis based on ITU-R P.676-12 and P.838-3 (10.7 dB fade margin at 100 m under 50 mm/hr rain), and an MIL-STD-1629 FMEA covering twelve failure modes with severity classifications. A structured five-stage validation pathway from synthetic temporal-offset experiments to sixmonth field trials is defined for future empirical work.

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Mostafa Borhani mail

link https://doi.org/10.54216/FPA.210229

Volume & Issue

Vol. Volume 21 / Iss. Issue 2

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Journal of Intelligent Systems and Internet of Things | 2026

Thermal Vehicle Detection and Tracking for Intelligent Transportation Systems: A Modular IoT Architecture and Staged Deployment Roadmap

Automated vehicle monitoring in intelligent transportation systems must operate reliably around the clock, including under conditions that routinely cripple conventional visible-light cameras: night, glare, shadows, and adverse weather. This paper proposes a modular Internet of Things (IoT) architecture for thermal-based vehicle detection, classification, and trajectory analysis, together with a four-phase deployment roadmap that connects public-dataset evaluation to live-traffic field validation. The system integrates longwave infrared (LWIR) imaging (8–14 𝜇m) with YOLO-family deep learning detectors (YOLOv8/v11/v12) and multi-object tracking algorithms (ByteTrack, BoTSORT, StrongSORT), deployed across NVIDIA Jetson edge devices and cloud infrastructure through JSON/MQTT formalized data contracts. The primary novel contribution is a system-level integration framework that bridges the gap between component-level algorithmic research and operational deployment. Concretely, this work: (i) defines five functionally independent modules with explicit interface specifications and latency budgets not previously formalized in the thermal-ITS literature; (ii) introduces quantified decision gates linking progression criteria directly to published benchmark values; (iii) provides region-specific operational availability estimates derived from empirical weather-degradation data; and (iv) integrates domain adaptation, GDPR compliance, edge hardware budgets, and regulatory WIM frameworks within a single coherent system blueprint. Domain adaptation strategies reported in peer-reviewed literature recover 20–50% of cross-dataset mAP degradation (typically 10–30%) caused by sensor and scene variability; these figures are literature benchmarks, not results obtained in this work. An optional weight-estimation module (Module 4) based on recent vision-based and bridge WIM validation studies is treated as an exploratory extension requiring site-specific validation.

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Mostafa Borhani mail

link https://doi.org/10.54216/JISIoT.180231

Volume & Issue

Vol. Volume 18 / Iss. Issue 2

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International Journal of Artificial Intelligence and Education Technology | 2025

Predicting Academic Outcomes in Secondary Education: Ensemble Classification with Grade Trajectories, Attendance Behaviour, and Socioeconomic Context

Early identification of students at risk of academic failure is a persistent challenge in educational technology, with direct implications for student retention, institutional equity, and the allocation of support resources. Although supervised machine learning has been widely applied to student outcome prediction, the relative merit of competing algorithm classes and the degree to which demographic and behavioural features contribute predictive power beyond prior academic assessments remain incompletely resolved in the secondary school context. This paper presents a structured comparative evaluation of five supervised classifiers trained on a rich combination of periodic grades, attendance records, sociodemographic characteristics, and lifestyle indicators drawn from secondary school students. A dual importance analysis— combining impurity-based measures with held-out permutation importance—disentangles the distinct predictive roles of grade trajectories, absenteeism, parental background, and lifestyle variables. Ensemble methods demonstrate consistent superiority across all evaluation criteria, with prior periodic assessments and attendance emerging as the dominant predictors. Parental education level introduces a socioeconomic gradient that operates independently of student controlled factors, generating structural inequities that standard grade-monitoring systems are unlikely to address. These findings provide both a methodological benchmark for secondary school prediction tasks and practical guidance for institutions designing equitable and evidence-based early warning interventions.

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Jehad Mousa mail -

Abdallah Salama mail

link https://doi.org/10.54216/IJAIET.040201

Volume & Issue

Vol. Volume 4 / Iss. Issue 2

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International Journal of Artificial Intelligence and Education Technology | 2025

Governed Early-Warning Analytics for Student Success in Digital Higher Education: A Business-Oriented Evidence Model

Student-success analytics has moved from experimental prediction toward an institutional capability for reducing attrition, allocating support resources, and improving digital learning governance. This paper develops a business oriented early-warning model for education technology environments in which predictive performance, interpretability, intervention priority, and governance are treated as joint design requirements. The study uses a public student-success dataset from a higher education institution and evaluates decisive outcome prediction for dropout and graduation, while preserving a wider discussion of the enrolled group as an unresolved operational state. The proposed model combines a transparent predictive layer, a risk-to-action prioritization layer, and a governance layer that restricts how predictions are translated into student support decisions. The results show that a parsimonious logistic specification can provide competitive performance compared with more complex tree based models, while producing clearer accountability for academic advising and digital student-success units. The discussion argues that student-success technology should not be judged by accuracy alone, but by whether the analytics pipeline produces timely, explainable, privacy-aware, and operationally usable support signals.

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Nahla Moussa mail -

Low Hon Loon Alfred mail

link https://doi.org/10.54216/IJAIET.040202

Volume & Issue

Vol. Volume 4 / Iss. Issue 2

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International Journal of Artificial Intelligence and Education Technology | 2025

Public Education Investment, Instructional Resources, and Student Achievement: Cross-National Evidence from PISA 2022 in the Context of Sustainable Development Goal 4

All people must have access to educational opportunities which meet their needs through Sustainable Development Goal 4. The goal requires education systems to obtain sufficient funds and use their resources properly. The relationship between public education spending and student academic performance remains disputed because different countries achieve different results from their spending levels. The study employs PISA 2022 country-level scores which represent the first international assessment data published after COVID-19 to analyze public education expenditure as a GDP share together with pupil–teacher ratio and per-capita GDP in relation to student academic performance across three subjects. The study found that public education funding as percentage of GDP does not connect with PISA score results across 35 countries, showing no statistical link to tests (r = −0.095, p = 0.586). The pupil–teacher ratio serves as an effective predictor because it shows a strong negative relationship to student performance (β&circ; = −4.097, R2 = 0.312, p < 0.001). A three-variable regression model which combines expenditure share with pupil–teacher ratio and GDP per capita explains 59% of cross-country score variance (R2 = 0.592). High-income economies dominate the upper achievement tier, but several upper-middle-income systems— notably Estonia and Poland—substantially outperform their GDP-predicted  scores. The results show that organizations should focus their resources on developing teaching skills.

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Reshma Shaik mail -

Hanadi Osman Diab mail

link https://doi.org/10.54216/IJAIET.040203

Volume & Issue

Vol. Volume 4 / Iss. Issue 2

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International Journal of BIM and Engineering Science | 2026

BIM-Integrated Semantic Risk Intelligence for Construction Safety Severity Prediction Using Incident Narratives and 4D Work-Zone Attributes

Construction safety management increasingly depends on the ability to connect static building information models with dynamic evidence from site operations. This paper proposes a BIM-integrated semantic risk intelligence model that translates accident narratives into work-zone risk indicators and uses them to infer safety severity. The model links textual incident evidence with BIM-relevant descriptors, including construction phase, spatial zone, temporary protection status, energy isolation, and proximity to safety constraints. A formal risk-scoring layer is combined with supervised severity learning to provide interpretable decision support for safety planning and 4D coordination. The study contributes a reproducible methodology for converting unstructured safety reports into BIM-actionable risk representations, supporting early hazard prioritisation, design-for-safety review, and site control planning. The findings indicate that semantic evidence becomes more useful when it is explicitly fused with BIM phase and spatial context, rather than being treated as disconnected textual data.

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Esam El-Mekawy mail

link https://doi.org/10.54216/IJBES.120205

Volume & Issue

Vol. Volume 12 / Iss. Issue 2

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International Journal of BIM and Engineering Science | 2026

BIM Integration Across Engineering Disciplines: A Systematic Review of Methodological Advances, Interoperability Challenges, and Emerging Digital Frameworks

This paper provides a comprehensive systematic review of Building Information Modeling (BIM) integration across ten engineering disciplines, synthesising publications from January 2020 to January 2026. It identifies convergent trends, persistent knowledge gaps, and translational barriers that separate research prototypes from scalable industry practice. A PRISMA-guided systematic review was conducted across Scopus, Web of Science, ASCE Library, and ScienceDirect. An initial corpus of 4,712 records was screened and quality-assessed, yielding 63 papers for quantitative synthesis and a broader qualitative corpus of 293 studies spanning ten sub-domains: BIM–digital twin integration, BIM and artificial intelligence/machine learning, interoperability and IFC, structural engineering, MEP and building services, facility management and operations, BIM–GIS for smart cities, off-site and modular construction, adoption barriers, and energy and sustainability analysis. Annual BIM publications grew by approximately 256% between 2019 and 2024. BIM–AI/ML and BIM–digital twin integration are the two fastest-growing sub-domains, yet both remain constrained by data standardisation deficiencies and a shortage of domain-specific training datasets. IFC-based interoperability has matured significantly, but real-time bidirectional exchange across disciplines remains nascent. Structural engineering applications exhibit the highest technology readiness, while BIM–GIS integration for smart-city applications shows the widest gap between published prototypes and commercial deployment. The review delivers a thematic roadmap and a consolidated evidence base for prioritizing investment in digital workflows, standards development, and workforce training. An original four-layer integrated framework is proposed that connects engineering code provisions, AI/ML analytics, digital twin synchronisation, and automated quantity extraction within a single traceable workflow.

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Ann Wolter mail -

Paul Bailey mail -

Raja Ahmed Hassan mail -

Wipitha Mazungwi mail

link https://doi.org/10.54216/IJBES.120206

Volume & Issue

Vol. Volume 12 / Iss. Issue 2

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International Journal of Artificial Intelligence and Education Technology | 2026

A Systematic Review of AI-Powered Uzbek Short-Answer Grading Using NLP and Teacher-Annotated Datasets

This paper presents a Systematic Literature Review (SLR) of AI-powered automated short-answer grading, with a particular focus on low-resource languages such as Uzbek. The review follows the PRISMA 2020 guidelines to ensure transparency and methodological rigor. Relevant peer-reviewed studies published between 2018 and 2025 were systematically identified, screened, and analyzed across multiple academic databases. In total, 33 studies were included in the final synthesis. The reviewed literature indicates that transformer-based models, including mBERT and XLM-R, generally achieve stronger performance than traditional machine learning approaches, while recent large language models show potential in few-shot and zero-shot grading scenarios. The findings also highlight that the limited availability of teacher-annotated datasets remains a major challenge for developing reliable automated grading systems in low-resource educational contexts.

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Sanjar Raximjonov mail -

Eugene Q. Castro mail

link https://doi.org/10.54216/IJAIET.050104

Volume & Issue

Vol. Volume 5 / Iss. Issue 1

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International Journal of Artificial Intelligence and Education Technology | 2026

Evaluating Microsoft Teams, Blackboard, Canvas, and Zoom for Online Teaching Effectiveness: A Multi-Dimensional Comparative Study in Higher Education

The rapid institutionalisation of online and hybrid delivery models in higher education has left instructors and academic administrators managing a fragmented landscape of dedicated learning management systems, video conferencing platforms, and collaborative productivity suites that overlap substantially in function but differ markedly in pedagogical affordance. Selecting a platform or combination of platforms is consequential for instructor workload, student engagement, and learning outcomes, yet the evidence base for such decisions remains limited to narrow singleplatform evaluations or anecdotal comparisons. This paper presents a systematic multi-dimensional comparative evaluation of four widely adopted platforms—Microsoft Teams, Blackboard, Canvas, and Zoom—drawing on original survey data from 284 instructors and 642 students across five higher education institutions. Nine evaluation dimensions are examined: content delivery, real-time collaboration, assessment and feedback, usability, technical reliability, student engagement support, accessibility, analytics and reporting, and third-party integration. Quantitative analyses include one-way analysis of variance across all nine dimensions, Bonferroni post-hoc comparisons, Pearson correlation analysis, and multiple regression modelling of the predictors of instructor overall satisfaction. Canvas achieves the highest composite scores for usability, analytics, and integration; Blackboard leads on assessment and reporting depth; Microsoft Teams leads on real-time collaboration; and Zoom leads on content delivery in synchronous sessions but performs poorly on the asynchronous dimensions where dedicated learning management systems are strongest. The paper synthesizes findings into a platform selection framework and eight evidence-based recommendations for practitioners designing or evaluating technology-enhanced teaching environments.

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Tariq Saali mail -

Tassawar Kamran mail

link https://doi.org/10.54216/IJAIET.050105

Volume & Issue

Vol. Volume 5 / Iss. Issue 1

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International Journal of Artificial Intelligence and Education Technology | 2026

ChatGPT as an Assessment Design Tool in Higher Education: Evaluating Item Quality, Bloom’s Taxonomy Coverage, and Faculty Acceptance Across Academic Disciplines

The emergence of large language models capable of generating coherent, contextually grounded text at scale has created a new and contested tool for higher education assessment design: instructors can now produce examination questions, assignment prompts, and feedback rubrics in seconds rather than hours. Whether the items produced by these systems meet the quality standards required for valid, reliable, and pedagogically appropriate higher education assessment is an empirical question that the literature has only partially addressed. This paper reports a three-study investigation of ChatGPT as an assessment design tool in higher education, covering item quality, cognitive level coverage, student performance, and faculty acceptance. Study 1 presents an expert-panel evaluation of 360 assessment items—180 generated by ChatGPT and 180 created by experienced instructors across six academic disciplines and four item types, rated on seven quality dimensions including content accuracy, Bloom’s taxonomy alignment, linguistic clarity, and originality. Study 2 reports a faculty survey of 186 instructors examining adoption rates, perceived benefits, concerns, and the predictors of acceptance. Study 3 compares the performance of 412 students on counterbalanced ChatGPT-generated and instructor-created assessment items. ChatGPT-generated items score significantly below instructor-created items on Bloom’s taxonomy alignment and originality, but perform comparably or above on linguistic clarity and difficulty calibration. Student performance is modestly but significantly higher on ChatGPT-generated items, a finding that challenges simple assumptions about AI-generated assessment difficulty. Academic integrity concerns and higher-order cognitive coverage are the dominant faculty concerns, while time savings—averaging 77% reduction in item-writing time—is the most consistently cited benefit. The paper contributes a validated multi-dimensional item quality framework, a faculty acceptance model, and eight evidence-based guidelines for the responsible integration of ChatGPT in assessment design workflows.

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Nadia Iftikhar mail -

Rabia Muslu mail

link https://doi.org/10.54216/IJAIET.050106

Volume & Issue

Vol. Volume 5 / Iss. Issue 1

Details open_in_new