Generative AI Chatbots in Education Technology: A Critical Review of Feedback, Assessment, and Governance

Mahshid

doi:https://doi.org/10.54216/IJAIET.030103

Prospects for Applied Mathematics and Data Analysis | 2024

Hyperalgorithms & Superhyperalgorithms: A Unified Framework for Higher-Order Computation

An algorithm is a finite, well-defined computational procedure that transforms inputs into outputs through a structured sequence of steps, guaranteeing termination and correctness. A multialgorithm comprises multiple algorithms augmented with a selection mechanism that dynamically chooses the most appropriate procedure based on input characteristics or contextual conditions. While these concepts have deep roots in computer science and beyond, this paper introduces two novel generalizations: the Hyperalgorithm and the Superhyper- algorithm. By leveraging the mathematical frameworks of hyperstructures and superhyperstructures, respectively, we extend the classical notion of computation to higher-order operations on sets and iterated powersets. We present formal definitions, illustrative examples, and a preliminary analysis of their computational properties, laying the groundwork for a unified theory of higher-order algorithms.

groups

Takaaki Fujita mail

link https://doi.org/10.54216/PAMDA.040104

Volume & Issue

Vol. Volume 4 / Iss. Issue 1

Details open_in_new

Prospects for Applied Mathematics and Data Analysis | 2024

Rethinking Strategic Perception: Foundations and Advancements in HyperGame Theory and SuperHyperGame Theory

Mathematical structures can generally be extended into Hyperstructures and SuperHyperstructures by leveraging powerset and n-th iterated powerset constructions (cf.7, 17, 31). These frameworks are particularly effective for representing hierarchical systems across various conceptual domains. Game Theory is a mathematical discipline for analyzing strategic interactions among rational agents with conflicting or cooperative objectives and finite choices.5, 10, 26 HyperGame Theory extends this by modeling situations in which players possess misperceptions or differing beliefs about the game being played.23 These ideas can be further generalized into the concept of SuperHyperGames.15 This paper explores the mathematical properties and illustrative examples of both HyperGame Theory and SuperHyperGame Theory. We hope that this investigation contributes to future developments in the theory and application of game-theoretic frameworks.

groups

Takaaki Fujita mail

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

Volume & Issue

Vol. Volume 4 / Iss. Issue 2

Details open_in_new

International Journal of Artificial Intelligence and Education Technology | 2024

Immersive Learning with the Metaverse’s Environment to Increase Academic Success and Motivation in learning Arabic as a Second Language for Non-Native Speakers

The metaverse's environment offers a unique opportunity for immersive learning experiences that can enhance education in ways never before possible. By creating virtual environments that simulate real-world scenarios, students can actively engage with the material and practice their skills in a safe and controlled setting. This technology has the potential to revolutionize the way we learn, making education more interactive, engaging, and effective for students of all ages. The integration of the metaverse's environment into Arabic language learning can provide non-native speakers with a more engaging and interactive learning experience. By creating virtual environments that simulate real-life situations, students can practice their language skills in a more realistic and practical way. The participants were 60 learners from non-native speakers enrolled in an Arabic Language course for intermediate level in the Arabic Language Center for Non-Native Speakers at the faculty of education at Mansoura University. The findings of research found that the immersive approach could help increase students' motivation to learn Arabic as a second language, leading to greater academic success in the subject. Additionally, the use of the metaverse can also help bridge the gap between language learners and native speakers, providing opportunities for real-time communication and cultural exchange.

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Reham Mohamed Al-Ghoul mail -

Ramy Samir Mohammed ALSeragy mail

link https://doi.org/10.54216/IJAIET.030101

Volume & Issue

Vol. Volume 3 / Iss. Issue 1

Details open_in_new

Metaheuristic Optimization Review | 2026

Metaheuristic Optimization for Complex Engineering Design: A Comprehensive Review of Structural and Mechanical Challenges

Metaheuristic Optimization in Engineering has gained much attention recently because of its application in solving challenging problems and nonlinear and constrained design often encountered in structural and mechanical design. These optimization techniques are derived from natural phenomena, including Bio-evolution, Animal instincts and the physical world, necessitating efficient and inexpensive design for engineers. In conventional design processes, the design process may be tiresome and often unable to cope with large and complex engineering endeavors; however, metaheuristic algorithms exhibit high effectiveness and functionality in optimizing designs in various sectors about reinforced concrete structures and steel reinforced frames, mechanical parts, among others. This literature review explains the current metaheuristic algorithms and their applicability to solving engineering problems, particularly regarding computational time, quality and physical solution constraints. Difficulties regarding mechanical properties, structural, and dynamic performances can effectively be resolved by utilizing metaheuristic algorithms such as harmony search, teaching-learning-based optimization and other useful hybrid strategies to elevate the engineering optimization field to another level. It also emphasizes CI application in improving the design processes and offers clues on the future application of both the hybrid and the multi-objective optimization strategies in engineering.

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Nima Khodadadi mail -

Aria Rabet mail

link https://doi.org/10.54216/MOR.070102

Volume & Issue

Vol. Volume 7 / Iss. Issue 1

Details open_in_new

Metaheuristic Optimization Review | 2026

Intelligent Healthcare Optimization Using Metaheuristic Algorithms: A Review of Emerging Methods and Applications

Machine learning and optimization techniques have significantly changed the healthcare industry, especially in finding and managing essential and dangerous diseases like lung cancer, breast cancer, diabetes as well as heart disease. Lung cancer, which is among the common fatal cancers, requires proper subtyping before proper management is made. This has been achieved through machine learning alongside radiomics, where detailed imaging characteristics of the tumor from CT scans are retrieved without invasive procedures. In the same way, machine learning has provided much higher detection, diagnosis and treatment levels of breast cancer, diabetes and heart disease. This literature review sums up the priorities of studies showing the benefits of using machine learning and bio-inspired optimization methods to address the challenges posed by disease classification and prediction. Such complications have proved great potential in improving the diagnostic methods used for early intervention and, thereby, accurate and efficient diagnosis of a problem, developing an appropriate treatment plan and, thus, improving the patient caring methods and scenario, which has played an imperative role determining the future of modern-day health care.

groups

Arian Rabet mail -

Ehsan khodadadi mail

link https://doi.org/10.54216/MOR.070103

Volume & Issue

Vol. Volume 7 / Iss. Issue 1

Details open_in_new

Metaheuristic Optimization Review | 2026

Artificial Intelligence and Deep Learning in Hantavirus Research: A Comprehensive Review

Hantavirus remains an important zoonotic threat because of its association with severe human diseases, including hemorrhagic fever with renal syndrome and hantavirus pulmonary syndrome. Its transmission is strongly influenced by rodent reservoirs, environmental conditions, human exposure patterns, and regional ecological variability. Recent advances in artificial intelligence (AI) and deep learning have created new opportunities for improving Hantavirus detection, outbreak prediction, ecological risk mapping, diagnostic support, and public health surveillance. This review examines the role of AI-driven methods in Hantavirus research, with emphasis on how machine learning, deep learning, image-based analysis, epidemiological modeling, and data-driven surveillance can support earlier detection and more informed decision-making. The review also discusses the potential of AI to integrate heterogeneous data sources, including clinical records, environmental variables, remote sensing indicators, genomic information, and epidemiological reports. Despite these advances, several challenges remain, including limited datasets, geographic bias, model generalization, lack of clinical validation, data imbalance, interpretability concerns, and the need for real-time deployment. Overall, AI and deep learning offer promising tools for strengthening Hantavirus surveillance and response, but their practical value depends on transparent models, high-quality data, interdisciplinary validation, and integration into public health systems.

groups

Elham Edkndarnia mail

link https://doi.org/10.54216/MOR.070104

Volume & Issue

Vol. Volume 7 / Iss. Issue 1

Details open_in_new

Metaheuristic Optimization Review | 2026

InsightX Platform: An Integrated Web-Based System for Trading Analysis, Marketing Analytics, and AI-Powered Decision Support

The rapid expansion of digital data in financial markets and business environments has increased the need for intelligent, accessible, and integrated analytical systems. Traditional tools for trading analysis, marketing analytics, and user assistance often operate separately, forcing users to move between different platforms and making data interpretation more complex. This project presents InsightX, a web-based analytical platform that combines trading analysis, marketing analytics, and an AI-powered chatbot within a unified interface. The system is designed to support data-driven decision-making by transforming raw financial and marketing data into interactive visualizations, performance indicators, and user-oriented insights. The platform consists of three main modules. The Trading Analysis Module enables users to examine market data through real-time charts, historical comparisons, and technical indicators such as moving averages and RSI. The Marketing Analysis Module supports customer data analysis, segmentation, campaign performance evaluation, and KPI-based insight generation. The AI Chatbot Module enhances usability by allowing users to ask questions, receive explanations, and navigate analytical results through natural language interaction. InsightX is implemented using Python and Streamlit, supported by libraries such as Pandas, NumPy, Plotly, yfinance, and AI API integration for chatbot functionality.

groups

Osama Abo Elela mail

link https://doi.org/10.54216/MOR.070105

Volume & Issue

Vol. Volume 7 / Iss. Issue 1

Details open_in_new

International Journal of Artificial Intelligence and Education Technology | 2026

Investigating the Readiness of Pre-Service Teachers Towards Information and Communication Technology (ICT) Integration in Teaching at Federal Universities in North-Eastern Nigeria

This paper investigates the readiness of pre-service teachers towards ICT integration in teaching at federal universities in the North-Eastern Nigeria. A descriptive survey design was adopted for the study. The population of the study consist of 7885 pre-service teachers of Federal Universities in the North-Eastern Nigeria. The sample size is 381 pre-service teachers selected across the Federal Universities in North-Eastern Nigeria using a random sampling technique. Three research questions and three hypotheses were generated and answered in the study. The instrument for data collection in this study was ICT Attitude, Access and Competency (ICT-AAC). The instrument was validated by four (4) experts who checked the suitability and clarity of these items. The Cronbach Alpha reliability coefficient was computed for the instrument (ICT-AAC) which yielded an internal consistency reliability index of 0.82, 0.88 and 0.73 for cluster A, B, and C respectively, with an overall reliability index of 0.89. The research questions were answered using descriptive statistics and Spearman’s correlation coefficient using SPSS version 25. Findings of the study showed that most of the pre-service teachers have positive attitude towards ICT integration in teaching. It was also found that pre-service teachers have no Access to most of the ICT facilities. It was further confirmed that the pre-service teachers were Competent in manipulating ICT facilities. The study further indicated a significant relationship between pre-service teachers’ Attitude, Accessibility and ICT Competency. Finally, a number of challenges that might hinder the integration of ICT facilities in teaching were found and recommendations proffered, which include; improvement of pre-service teachers’ Access to ICT facilities such as projector, smart/interactive white boards and the software required for the effective use of the boards. This should be aimed at increasing their Competency of the newly acquired or available facilities and how to integrate them in teaching.

groups

Salisu Usman mail -

Muhammed Bello mail -

Ibrahim Abubakar mail

link https://doi.org/10.54216/IJAIET.050201

Volume & Issue

Vol. Volume 5 / Iss. Issue 2

Details open_in_new

International Journal of Artificial Intelligence and Education Technology | 2024

The Effectiveness of Learning through Gamification with Artificial Intelligence on Mental Health (Anxiety) and Building Learning Habits for College Learners

Gamification is the process of incorporating game-like elements, such as scoring and competition, into non-game activities to increase engagement and motivation. Artificial intelligence (AI) refers to the simulation of human intelligence in machines that are programmed to think and learn like humans. When these two concepts are combined, they can help students overcome anxiety and develop effective learning habits, and the use of AI technology can provide personalized feedback and support, ultimately improving overall mental well-being and academic success. These innovative approaches to learning have the potential to revolutionize traditional education methods and create a more engaging and effective learning environment for students. This research used gamification with artificial intelligence in learning content in an eLearning environment. The participants were 60 learners enrolled in the vocational diploma program in educational technology specialization at the faculty of education at Mansoura University. The findings of research found that incorporating game-like elements and personalized learning experiences could help reduce stress and increase motivation among students. This innovative approach to education shows promise in improving student outcomes and overall academic performance.

groups

Reham Mohamed Al-Ghoul mail -

Ramy Samir Mohammed ALSeragy mail

link https://doi.org/10.54216/IJAIET.030102

Volume & Issue

Vol. Volume 3 / Iss. Issue 1

Details open_in_new

International Journal of Artificial Intelligence and Education Technology | 2024

Generative AI Chatbots in Education Technology: A Critical Review of Feedback, Assessment, and Governance

Generative artificial intelligence (AI) chatbots have become a disruptive education technology because they combine dialogue, content generation, reasoning support, and immediate feedback in a single interface. Their rapid adoption has created a strategic challenge for schools, universities, and professional education providers: the same tools that can expand formative support and learner agency can also weaken assessment validity, privacy protection, academic integrity, and equitable participation. This review synthesises peer-reviewed literature published from 2020 to 2023 to examine how generative AI chatbots should be understood within education technology. The paper focuses on three interdependent areas: feedback and learner support, assessment redesign, and institutional governance. The review finds that the most defensible use of generative AI is not tool substitution but learning-design augmentation: chatbots can support explanation, drafting, questioning, and formative feedback when teachers define learning goals, evidence requirements, and acceptable use conditions. The main risks are not limited to plagiarism or inaccurate outputs; they also include hidden inequity, over-reliance, weakened disciplinary judgement, and policy-practice misalignment. The paper proposes a responsible adoption framework that links pedagogical affordances, assessment redesign, AI literacy, and governance controls. The contribution is a publication-ready conceptual synthesis for institutions seeking to adopt generative AI in education technology without reducing learning to automated content production.

groups

Mahshid Manouchehri mail

link https://doi.org/10.54216/IJAIET.030103

Volume & Issue

Vol. Volume 3 / Iss. Issue 1

Details open_in_new