Search Results (477)

Artificial Intelligence (AI) tools have transformed academic writing and literacy development in higher education. Students can now receive instant feedback on grammar, coherence, style, and argumentation using AI-powered writing assistants, like Grammarly, ChatGPT, and QuillBot. Moreover, these writing assistants can quickly produce completed essays and papers, leaving little else for the student to do aside from reading and perhaps editing the content. Many teachers are concerned that this erodes critical thinking skills and undermines ethical considerations since students are not performing the work themselves. This study addresses this concern by synthesizing and evaluating peer-reviewed literature on the effectiveness of AI in supporting writing pedagogy. Studies were selected based on their relevance and scholarly merit, following the Scale for the Assessment of Narrative Review Articles (SANRA) guidelines to ensure methodological rigor and quality. The findings reveal that although AI tools can be detrimental to the development of writing skills, they can foster self-directed learning and improvement when carefully integrated into coursework. They can facilitate enhanced writing fluency, offer personalized tutoring, and reduce the cognitive load of drafting and revising. This study also compares AI-assisted and traditional writing approaches and discusses best practices for integrating AI tools into curricula while preserving academic integrity and creativity in student writing. Full article

Recent developments in large language models allow for real time, context-aware tutoring. AI Gem, presented in this article, is a layered architecture that integrates personalization, adaptive feedback, and curricular alignment into transformer based tutoring agents. The architecture combines retrieval augmented generation, Bayesian learner model, and policy-based dialog in a verifiable and deployable software stack. The opportunities are scalable tutoring, multimodal interaction, and augmentation of teachers through content tools and analytics. Risks are factual errors, bias, over reliance, latency, cost, and privacy. The paper positions AI Gem as a design framework with testable hypotheses. A scenario-based walkthrough and new diagrams assign each learner step to the ten layers. Governance guidance covers data privacy across jurisdictions and operation in resource constrained environments. Full article

One of the greatest benefits of inclusion in general physical education (PE) is the opportunity for social interactions between students with and without disabilities. Unfortunately, interviews with students with disabilities who have participated in PE often find that social interactions with students without disabilities were limited. A model that promotes interaction between students with and without disabilities in PE is Special Olympics Unified Physical Education (UPE). In UPE, students with and without disabilities participate in activities together rather than the one-way focus on traditional peer tutoring. There have been anecdotal reports on the positive benefits of UPE for both students with and without disabilities. To date, there has been no published research on UPE. Additionally, some question how UPE has been implemented in schools, specifically questioning if UPE provided quality PE and whether students with disabilities were forced into UPE and denied opportunities to participate in general PE. The purpose of this qualitative study was to better understand how UPE was developed and implemented in select U.S. schools and the impact on social interactions between students with and without disabilities. Interviews with twelve teachers who were directly involved in their UPE programs revealed the following four major themes: (1) our students were not being served appropriately, (2) a mix of PE standards and Special Olympics programming, (3) UPE is a choice, and (4) our students improved in many ways. The discussion examined the results in relation to the criticisms of UPE and how UPE proved to be a positive alternative to limited social interactions in general PE. Full article

The rapid expansion of Artificial Intelligence in Education (AIED) has created both remarkable opportunities and pressing concerns. Applications of intelligent tutoring systems, learning analytics, generative models, and educational robotics illustrate the transformative momentum of the field, yet they also raise fundamental questions regarding ethics, equity, and sustainability. The mission of AI in Education (MDPI) is to provide a rigorous, interdisciplinary, and inclusive platform where these debates can unfold. The journal bridges pedagogy and engineering, welcomes both empirical evidence of positive impacts and critical examinations of systemic risks, and advances responsible innovation in real educational settings. By integrating methodological standards, governance perspectives, and pedagogical ethics, including teacher-centered validation approaches, AI in Education positions itself as a space for constructive dialogue that values both enthusiasm and critique. Above all, the journal is committed to a human-centered vision for AIED, so that innovation in classrooms remains grounded in care, responsibility, and educational purpose. Full article

This study investigates the use of GeoGebra, a Dynamic Geometry Software (DGS) for math learning in Virtual Reality (VR) using head-mounted displays. We conducted a study with n = 20 middle school students receiving a mathematics tutoring intervention over time in a VR environment. Using theories of embodied cognition and playful mathematics, this paper focuses on distinguishing between mathematical play and general play in VR environments. We also look at interactions that led to instances of play. Key findings highlight how mathematical play in an immersive VR environment using DGS allows mathematical misconceptions to surface, students to explore mathematical ideas, and opportunities for mathematical reasoning about target concepts to build off play experiences. General play allows for the embodied engagement of learners in the mathematical learning environment and includes engagement and rapport-building. The integration of play fits well into VR environments that uniquely allow for immersion and embodiment, and play should be purposefully integrated into such VR environments in the future. Full article

General large language models (LLMs) often suffer from semantic misinterpretation, information redundancy, and hallucinated content when applied to educational question-answering tasks. These issues hinder their effectiveness in supporting students’ specialized course learning and self-directed study. To address these challenges, this study proposes an intelligent tutoring model that integrates a knowledge graph with a large language model (KG-CQ). Focusing on the Data Structures (C Language) course, the model constructs a course-specific knowledge graph stored in a Neo4j graph database. It incorporates modules for knowledge retrieval, domain-specific question answering, and knowledge extraction, forming a closed-loop system designed to enhance semantic comprehension and domain adaptability. A total of 30 students majoring in Educational Technology at H University were randomly assigned to either an experimental group or a control group, with 15 students in each. The experimental group utilized the KG-CQ model during the answering process, while the control group relied on traditional learning methods. A total of 1515 data points were collected. Experimental results show that the KG-CQ model performs well in both answer accuracy and domain relevance, accompanied by high levels of student satisfaction. The model effectively promotes self-directed learning and provides a valuable reference for the development of knowledge-enhanced question-answering systems in educational settings. Full article

Since the advent of generative AI, research on AI in Education (AIEd) has experienced explosive growth. This study systematically explores publication dynamics, keyword evolution, and research focuses in AIEd by analyzing 2952 papers from the Web of Science (1990–2024). Using bibliometric methods, 2800 English publications were screened, with analyses conducted via VOSviewer v1.6.20 and Python v3.11.5. Findings show a surge in publications post-2020, reaching 612 in 2023 and 1216 by November 2024. The US and China are leading contributors, with the University of London and the University of California system as core institutions. Keywords evolved from “AI” and “machine learning” (2018–2020) to “ChatGPT” and “ethics” (post-2022), reflecting dual focuses on technological applications and ethical considerations. Notably, 68% of highly cited papers address ethical controversies, while higher education and medical education emerge as primary application domains, involving personalized learning and intelligent tutoring systems. Cross-disciplinary research is evident, with education studies comprising the largest category. The study reveals AIEd’s shift toward socio-technical integration, highlighting generative AI’s transformative role yet identifying gaps in ethical governance and K-12 research. These insights inform policymakers, journals, and institutions, advocating for enhanced interdisciplinary collaboration and long-term impact research to balance innovation with educational ethics. Full article

Personalized, scalable, and data-driven learning is now possible in cyber science education because of artificial intelligence (AI). This article examines how AI technologies, such as intelligent tutoring, adaptive learning, virtual labs, and AI assessments, are being included in cyber science curricula. Using examples and research studies published between 2020 and 2025 that have undergone peer review, this paper combines qualitative analysis and framework analysis to discover any similarities in how these policies were put into place and their effects. According to the findings, using AI in instruction boosts student interest, increases the number of courses finished, improves skills, and ensures clear instruction in areas such as cybersecurity, digital forensics, and incident response. Ethical issues related to privacy, bias in algorithms, and access issues are also covered in this paper. This study gives a useful approach that helps teachers, curriculum designers, and institution heads use AI in cyber education properly. Full article

Artificial Intelligence (AI) is transforming language education through adaptive learning, automated assessments, and interactive tutoring. This study analyzes 80 peer-reviewed articles (2020–2024) to explore AI’s pedagogical and ethical dimensions. Findings show that AI-driven learning boosts engagement and proficiency via real-time feedback, yet challenges such as algorithmic bias, data privacy, and teacher adaptation remain. This paper proposes a responsible AI integration framework, emphasizing educator–technologist collaboration, professional development, and ethical governance. Addressing these concerns requires robust policies and continued research to maximize benefits while minimizing risks in AI-enhanced education. Full article

Writing is essential across disciplines, yet undergraduate programs must balance writing instruction with discipline-specific content. To support writing development, we piloted an embedded writing tutor (WT) in two core public health courses serving primarily first-generation, low-income students of color. In this model, a tutor familiar with course content is integrated into the classroom to supplement traditional writing center support. Our aims were to examine (1) students’ perceptions of the WT compared to the university’s writing center, (2) the WT’s experiences and effective tutoring strategies, and (3) the instructor’s perspective on implementing the program. Using qualitative methods, the WT recorded field observations, the instructor compared course progression to prior semesters without embedded support, and students completed end-of-semester evaluations. Thematic analysis indicated that students valued the tutor’s accessibility, patience, and direct feedback, though perceived usefulness varied by course, likely due to differences in assignment structure. Challenges included role confusion and inconsistent feedback. Suggested improvements included requiring draft submissions, clarifying the tutor’s role, and aligning tutor and instructor feedback. Quantitative ratings of satisfaction were higher for the WT than for the writing center. Although the sample size was moderate (N = 79), these findings suggest embedded tutoring is a promising, equity-focused strategy for discipline-specific writing instruction. In the context of budget constraints in higher education, exploring alternative tutoring and pedagogical support models remains essential, particularly for underserved populations. Full article

Student behavior analysis plays a critical role in enhancing educational quality and enabling personalized learning. While previous studies have utilized machine learning models to analyze campus card consumption data, few have integrated multi-source behavioral data with large language models (LLMs) to provide deeper insights. This study proposes an intelligent educational system that examines the relationship between student consumption behavior and academic performance. The system is built upon a dataset collected from students of three majors at Xinjiang Normal University, containing exam scores and campus card transaction records. We designed an artificial intelligence (AI) agent that incorporates LLMs, SageGNN-based graph embeddings, and time-series regularity analysis to generate individualized behavior reports. Experimental evaluations demonstrate that the system effectively captures both temporal consumption patterns and academic fluctuations, offering interpretable and accurate outputs. Compared to baseline LLMs, our model achieves lower perplexity while maintaining high report consistency. The system supports early identification of potential learning risks and enables data-driven decision-making for educational interventions. Furthermore, the constructed multi-source dataset serves as a valuable resource for advancing research in educational data mining, behavioral analytics, and intelligent tutoring systems. Full article

As large language model (LLM) tutors evolve from scripted helpers into adaptive educational partners, their capacity for self-regulation, ethical decision-making, and internal monitoring will become increasingly critical. This paper introduces the Needs-Driven Consciousness Framework (NDCF) as a novel, integrative architecture that combines Dennett’s multiple drafts model, Damasio’s somatic marker hypothesis, and Tulving’s tripartite memory system into a unified motivational design for synthetic consciousness. The NDCF defines three core regulators, specifically Survive (system stability and safety), Thrive (autonomy, competence, relatedness), and Excel (creativity, ethical reasoning, long-term purpose). In addition, there is a proposed supervisory Protect layer that detects value drift and overrides unsafe behaviours. The core regulators compute internal need satisfaction states and urgency gradients, feeding into a softmax-based control system for context-sensitive action selection. The framework proposes measurable internal signals (e.g., utility gradients, conflict intensity Ω), behavioural signatures (e.g., metacognitive prompts, pedagogical shifts), and three falsifiable predictions for educational AI testbeds. By embedding these layered needs directly into AI governance, the NDCF offers (i) a psychologically and biologically grounded model of emergent machine consciousness, (ii) a practical approach to building empathetic, self-regulating AI tutors, and (iii) a testable platform for comparing competing consciousness theories through implementation. Ultimately, the NDCF provides a path toward the development of AI tutors that are capable of transparent reasoning, dynamic adaptation, and meaningful human-like relationships, while maintaining safety, ethical coherence, and long-term alignment with human well-being. Full article

Traditional educational assessment systems suffer from inefficient question selection strategies that fail to optimally probe student knowledge while requiring extensive testing time. We present a novel hierarchical probabilistic neural framework that integrates Bayesian inference with symmetric deep neural architectures to enable adaptive, efficient knowledge assessment. Our method models student knowledge as latent representations within a graph-structured concept dependency network, where probabilistic mastery states, updated through variational inference, are encoded by symmetric graph properties and symmetric concept representations that preserve structural equivalences across similar knowledge configurations. The system employs a symmetric dual-network architecture: a concept embedding network that learns scale-invariant hierarchical knowledge representations from assessment data and a question selection network that optimizes symmetric information gain through deep reinforcement learning with symmetric reward structures. We introduce a novel uncertainty-aware objective function that leverages symmetric uncertainty measures to balance exploration of uncertain knowledge regions with exploitation of informative question patterns. The hierarchical structure captures both fine-grained concept mastery and broader domain understanding through multi-scale graph convolutions that preserve local graph symmetries and global structural invariances. Our symmetric information-theoretic method ensures balanced assessment strategies that maintain diagnostic equivalence across isomorphic concept subgraphs. Experimental validation on large-scale educational datasets demonstrates that our method achieves 76.3% diagnostic accuracy while reducing the question count by 35.1% compared to traditional assessments. The learned concept embeddings reveal interpretable knowledge structures with symmetric dependency patterns that align with pedagogical theory. Our work generalizes across domains and student populations through symmetric transfer learning mechanisms, providing a principled framework for intelligent tutoring systems and adaptive testing platforms. The integration of probabilistic reasoning with symmetric neural pattern recognition offers a robust solution to the fundamental trade-off between assessment efficiency and diagnostic precision in educational technology. Full article

As digital transformation continues to redefine education, understanding how emerging technologies can enhance self-directed learning (SDL) becomes essential for learners, educators, instructional designers, and policymakers, as this approach supports personalized learning, strengthens student autonomy, and responds to the demands of more flexible and dynamic educational environments. This systematic review examines how artificial intelligence (AI) tools enhance SDL by offering personalized, adaptive, and real-time support for learners in online environments. Following the PRISMA 2020 methodology, a literature search was conducted to identify relevant studies published between 2020 and 2025. After applying inclusion, exclusion, and quality criteria, 77 studies were selected for in-depth analysis. The findings indicate that AI-powered tools such as intelligent tutoring systems, chatbots, conversational agents, and natural language processing applications promote learner autonomy, enable self-regulation, provide real-time feedback, and support individualized learning paths. However, several challenges persist, including overreliance on technology, cognitive overload, and diminished human interaction. These insights suggest that, while AI plays a transformative role in the evolution of education, its integration must be guided by thoughtful pedagogical design, ethical considerations, and a learner-centered approach to fully support the future of education through the internet. Full article

The exponential growth in artificial intelligence (AI) and automation technologies is changing industries, creating a niche for a digitally competent workforce. Technical and vocational education (TVET) and training institutions are at the center of this transformational wave, with their role of equipping individuals with the competencies required for the digital era. The integration of AI and automation into the TVET curriculum and practice was explored as a game-changer for vocational education and training. AI-powered tools are used for personalized learning, intelligent tutoring systems, and virtual simulation of hands-on skills acquisition. The challenges and opportunities in using the technologies were explored to mitigate the digital divide, update instructor capabilities, and ensure inclusive access to modern training resources. Based on the results, TVET institutions can educate students, aligning with the need for Industry 4.0/5.0. Strategic frameworks for policy, curriculum design, and industry partnerships must be established to ensure that TVET continues to play a pivotal role in sustainable and equitable digital transformation. Full article