International Journal of Artificial Intelligence and Education Technology

Interactive methods are increasingly used in higher education to improve engagement and learning outcomes; however, universities often lack a reproducible procedure for selecting methods, sequencing them within a class, and evaluating effectiveness with comparable indicators. This research article develops and reports an IMRaD-aligned framework for integrating interactive methods into university classes and provides an empirical evaluation protocol that can be implemented in practice. The study uses a mixed design: (i) a structured literature synthesis on active learning, cognitive engagement, and instructional design, and (ii) a quasi-experimental classroom evaluation protocol (recommended for adoption) combining observation, short surveys, and learning analytics from digital tools (polling/quizzes, interactive whiteboard logs, and learning management systems). Results are presented as an operational toolkit: a taxonomy of interactive methods and didactic functions, a method–outcome–motivation mapping, a standardized 90-minute lesson architecture, implementation checklists, and a monitoring model with defined indicators for achievement, participation, and motivation. The discussion highlights how method coherence across lesson phases supports cognitive activity as a unity of perception, reasoning, and practice, and outlines limitations and future research using experimental designs. The article contributes practical instruments for evidence-based teaching and offers a pathway for universities to move from descriptive claims about “interactivity” to measurable improvement.

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.

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.

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.

The academic success of students who are nearing academic failure should be Identifying students who are at risk of academic failure or course withdrawal at an early stage of their enrolment remains one of the most pressing challenges in higher and distance education. The research assesses the performance of seven machine learning classifiers which include Logistic Regression Decision Tree Random Forest Gradient Boosting Decision Tree (GBDT) AdaBoost Naive Bayes and Multilayer Perceptron for predicting student risk at an early stage based on a behavioural and demographic dataset derived from the Open University Learning Analytics Dataset (OULAD). The dataset contains 7895 student records which represent a single module and show eight demographic factors together with eight Virtual Learning Environment (VLE) usage patterns. All classifiers were evaluated through five-fold stratified cross-validation. The GBDT model achieved the best results with an AUC-ROC value of 0.782 (} 0.003) and an accuracy rate of 0.708 (} 0.005) which produced an F1 score of 0.729 (} 0.006) and a recall rate of 0.769 (} 0.006). The analysis of feature importance showed that late sub-mission count (I = 0.304) and total VLE clicks (I = 0.150) together with first assessment score (I = 0.135) serve as the three most valuable predictive indicators because they help identify student engagement patterns which become evident through VLE traces that educational institutions collect from students during their first module. Educational institutions can utilize learning management system data to implement effective combi-nation methods which enable them to execute necessary teaching methods even though they do not need to gather additional expense data. The article presents design elements which both create early warning systems and manage the ethical use of predictive analytics within educational systems.

AI-based quiz and assessment tools are widely studied for supporting adaptive learning, yet existing work is distributed across different tasks (e.g., question generation, automatic evaluation, feedback, and conversational assessment) and often uses inconsistent datasets and metrics, making comparisons difficult. This paper reports a Systematic Literature Review (SLR) conducted under PRISMA 2020 to summarize approaches and evaluation practices for AI-based quiz and assessment systems. Searches were performed in IEEE Xplore, ACM Digital Library, and Google Scholar using keyword combinations related to automated question generation, assessment, evaluation, and large language models. The search returned Nidentified=57 records; after duplicate removal, Ndedup=55 records remained for screening. Following title/abstract screening and full-text eligibility assessment, Nincluded=9 studies were included for qualitative synthesis and structured data extraction. The reviewed studies show strong attention to transformer/LLM-based question generation, automatic scoring and evaluation frameworks, and formative feedback generation for learning. However, recurring limitations include reliability of automated judging, lack of standardized benchmarks, domain transferissues, and risks impacting fairness and academic integrity. We conclude with practical recommendations for stronger evalua-tion design (e.g., shared benchmarks, transparent rubrics, and human-in-the-loop validation) to improve trust and real-world adoption.

Artificial intelligence is increasingly used to monitor learning processes in higher education; however, many analytics pipelines still terminate at prediction and provide limited support for instructional action. The research establishes an explainable artificial intelligence framework which utilizes digital learning environment behavioral data and contextual information to create customized instructional support solutions. The analysis uses xAPIEdu-Data dataset which contains 480 records to build engagement index and create support profiles and predict multiclass performance through rule based action allocation. The study tests three classification models using stratified cross validation. The study selects Random Forest as the most effective system because it delivers superior results across all tests. The selected model demonstrates 0.8021 accuracy and 0.8204 macro precision and 0.8010 macro recall and 0.8084 macro F1 score and 0.9140 macro area under the curve on the hold-out sample. The analysis shows that student absence and composite engagement index andgender and student guardian relationship and support profile and digital resource access arethe most important factors that determine student performance. The final decision layer manages student assignment to instructional support plans which contain attendance-first intervention and adaptive engagement support and family-engagement reinforcement and structured progression coaching and challenge-and-extend pathways. The study develops an analytical framework which connects explainable artificial intelligence to digital higher education instructional decision support systems.