Personalized Learning Environment

Dynamic Difficulty Adjustment (DDA) seeks to maintain an optimal balance between challenge and player skill by adapting gameplay in response to player performance. While many adaptive systems rely on computationally intensive player modeling, lightweight approaches remain attractive due to their simplicity and real-time applicability. This study investigates whether a lightweight adaptive policy mechanism inspired by reinforcement learning can improve player engagement compared with static difficulty. A custom 2D platformer was developed in which enemy speed and spawn frequency were adjusted using predefined performance thresholds derived from player deaths, completion rates, and reaction-time variability. A counterbalanced within-subjects experiment involving 46 participants compared adaptive and static difficulty conditions while controlling for game mechanics and level design. Results showed that adaptive difficulty increased average session duration by approximately 69%, reduced retry frequency by 49%, and eliminated early session termination events observed under static difficulty. These findings suggest that even computationally lightweight adaptive systems can substantially improve engagement by maintaining a closer challenge-skill balance consistent with Flow Theory. Beyond game design, the results highlight the broader potential of adaptive challenge calibration in educational technologies, training simulations, and personalized learning environments.

The integration of machine learning (ML) into education has created a major interest due to its potential to transform student learning outcomes, career success, and skill retention. This paper examines the long-term impacts of ML-based educational interventions by synthesizing existing research and emerging trends. ML algorithms personalize learning by adapting to individual student needs, offering customized content, and providing real-time feedback. These tailored approaches aim to enhance student engagement, improve academic performance, and foster a deeper understanding of subjects. Learning through customized education plans can greatly improve your career success. Early familiarity with ML tools and data-driven decision-making processes equips students with essential competencies for the modern workforce, potentially increasing employability, job satisfaction, and career advancement. Skill retention, a critical aspect of lifelong learning, is also positively impacted. This study covers these aspects by reviewing current research and trends. It seeks to offer a complete understanding of how ML-based educational interventions affect student learning outcomes, career success, and skill retention for the longer duration. It will help teachers and policymakers use ML effectively in education, maximizing benefits while addressing potential challenges. This study employs a mixed-methods approach, combining quantitative analysis of longitudinal data with qualitative insights from case studies, to evaluate the effectiveness of ML-driven education. Long-term exposure to these interventions is associated with improved learning outcomes, as evidenced by higher test scores and better comprehension of complex concepts.

This research paper covers how to give students helpful feedback and keep them interested in online learning. We're looking at ways teachers can customize feedback for each student and make online classes more engaging. By studying different methods used by educators, we aim to understand how personalized feedback impacts student learning and motivation. Additionally, we're exploring strategies to boost student participation and involvement in virtual classrooms. Through this research, we aim to provide practical insights for teachers to enhance online teaching, creating a more interactive and pleasurable learning understandings for students in online environments.

Nos últimos anos, a Inteligência Artificial (IA) tem se consolidado como um fator transformador na educação, com aplicações que vão desde a prática docente até a organização e planejamento de aulas. Na Educação a Distância (EaD), seu uso tem se expandido, aprimorando a personalização do ensino, a análise de dados educacionais e a automação de processos pedagógicos nos Ambientes Virtuais de Aprendizagem (AVA). Essas inovações possibilitam o acompanhamento do desempenho dos estudantes e a adaptação do ensino às necessidades individuais, tornando a EaD mais dinâmica e eficiente. Apesar dos avanços, a implementação da IA na EaD enfrenta desafios que precisam ser analisados criticamente. Questões éticas, privacidade de dados e a formação docente surgem como obstáculos que podem comprometer sua adoção e eficácia. Assim, embora a IA tenha grande potencial para transformar o ensino a distância, sua aplicação deve ser acompanhada de diretrizes que assegurem uma implementação ética e pedagogicamente eficiente. Diante desse cenário, este estudo busca compreender como a IA tem sido inserida na EaD e quais são os desafios e impactos dessa implementação. Para isso, adota uma abordagem qualitativa e teórica, conforme Flick (2009), possibilitando a investigação das principais aplicações, dificuldades e perspectivas futuras da IA na EaD, com base em estudos recentes sobre seus impactos no ensino e na prática pedagógica. 20 Doutoranda e bolsista CAPES de doutorado em Educação (PPGE-UNICENTRO).

Data Science in Education is a provocative book, aligned with the most impactful trends at the interface between technology and education. Terms such as Data Science, Big Data, Data Mining, Learning Analytics, Deep Learning, Chatbots, Smart Education, Artificial Intelligence in Education, Intelligent Tutoring Systems and Data-Driven Instructional Design are explained and exemplified. The book is intended for those who work in education – whether face-to-face, distance or corporate – at the most different levels and sectors – and who need to understand the principles of extraction, interpretation and application of knowledge from the enormity and diversity of educational data.

In the contemporary era defined by rapid technological advancements and shifting educational paradigms, the integration of artificial intelligence (AI) into learning environments has emerged as a transformative force . This research article explores the role of generative AI as a catalyst for innovative collaboration in group projects among Saudi students in digital learning environments, highlighting its potential to foster creativity, engagement, and effective teamwork. The landscape of education in Saudi Arabia is evolving, particularly with the increasing emphasis on digital learning platforms. As universities and educational institutions adapt to the demands of a globalized economy, there is a pressing need to cultivate skills that prepare students for the complexities of the modern workforce . The effectiveness of group projects can often be hampered by challenges such as unequal participation, communication barriers, and varying levels of engagement among group members (Fittipaldi, 2020). Generative AI can enhance the collaborative process and empower students to engage more meaningfully with their peers and course materials. Furthermore, the application of generative AI aligns with Saudi Arabia's Vision 2030, which seeks to foster innovation, enhance the quality of education, and prepare the next generation for a technology-driven future (Saudi Vision 2030). This study aimed to investigate how generative AI can be leveraged to enhance collaborative projects among Saudi students, focusing on the benefits, challenges, and best practices associated with its implementation in digital learning environments . By examining the perceptions and experiences of students who engage with generative AI tools in group projects, this research seeks to contribute valuable insights into the evolving role of technology in education. The findings of this study shed light on providing actionable recommendations for educators and policymakers aiming to enhance student engagement and success in an increasingly digital academic landscape. Ultimately, this research endeavoured to position generative AI as a key driver of innovative collaboration, equipping Saudi students with the skills and experiences necessary to thrive in a rapidly changing world.

The implementation of Outcome-Based Education (OBE) in higher education requires a learning approach that can support the achievement of measurable learning outcomes and student competencies. Intelligent Tutoring Systems (ITS) are adaptive and personalized artificial intelligence-based learning systems, thus potentially supporting the implementation of OBE. However, empirical findings related to the implementation and effectiveness of ITS in the context of OBE in higher education are still scattered and have not been systematically synthesized. This study aims to examine the role, characteristics, and effectiveness of ITS in supporting outcome-based education in higher education. This study employed a systematic literature review method, adhering to the PRISMA 2020 guidelines. A literature search was conducted through the Scopus database for English-language journal articles published between 2018 and 2025. A rigorous selection process resulted in 56 journal articles meeting the inclusion criteria and analyzed using a narrative synthesis approach. The study results show that ITS is generally built on core components such as a learner model, a domain model, a pedagogical model, and a tutor interface. Commonly used artificial intelligence techniques include machine learning, rule-based systems, Bayesian networks, and natural language processing. Most studies report that ITS has a positive impact on academic performance, competency mastery, and student engagement. However, further research is needed to evaluate the long-term impact and integration of ITS within the OBE framework at the institutional level.

The rapid proliferation of Generative Artificial Intelligence (GenAI) has catalyzed a paradigm shift in higher education, challenging traditional notions of pedagogical delivery and academic assessment. This research investigates the dual-faceted impact of Large Language Models (LLMs) on undergraduate students' cognitive development and the concomitant threats to academic integrity. Employing a convergent parallel mixed-methods research design, the study synthesized quantitative data from a stratified sample of 1,200 students with qualitative insights derived from semi-structured interviews with 45 senior faculty members across diverse disciplines. The findings reveal a statistically significant "Performance-Competence Gap": while students utilizing GenAI reported a marginal increase in GPA (+0.2), their performance in invigilated, AI-free examinations demonstrated a marked decline ($r =-0.58$), suggesting a high degree of "cognitive offloading" and erosion of critical synthesis skills. Furthermore, the study identifies a "Detection Gap": 68% of AI-assisted submissions bypassed standard plagiarism-detection protocols, rendering traditional integrity frameworks obsolete. Qualitative thematic analysis highlights an emerging educator burnout and a "homogenization of thought" in student prose. The study concludes by proposing the "Hybrid Intelligence Model" and the "AI-Translucency Framework," advocating process-oriented assessment over product-based grading. These results provide a critical roadmap for policymakers to integrate AI-literacy as a core competency, ensuring that the advancement of algorithmic synthesis does not come at the expense of human cognitive rigor.

The prevailing response among independent course creators to declining completion rates and rising student demand for personalization is to increase the volume of synchronous instructor presence-additional live sessions, expanded one-to-one coaching, and longer office hours. This paper argues that this response misidentifies the bottleneck. Drawing on four decades of empirical research, from Bloom's (1984) two-sigma problem to recent randomized controlled trials of artificial-intelligence (AI) tutoring (Kestin et al., 2025; De Simone et al., 2025; Wang et al., 2024), the analysis demonstrates three convergent findings. First, the personalization-scale trade-off is structurally bounded by the human time budget; one-to-one delivery cannot be the answer at population scale (Baumol & Bowen, 1966). Second, the silent dropout-the student who disengages without ever requesting help-is the dominant failure mode of online education and is driven by social cost rather than personality (Karabenick & Knapp, 1988; Ryan & Pintrich, 1997). Third, wellengineered AI implementation systems produce learning effects statistically comparable to one-to-one human tutoring (Ma et al., 2014; VanLehn, 2011) at population sizes orders of magnitude beyond synchronous capacity. The paper proposes the CursoVivo framework-embedding AI-driven personalization within existing course structures to encode the creator's professional judgment into a system that delivers individualized intervention twenty-four hours a day, without the creator's real-time presence. The framework is presented as a model proposition, not as an empirical claim, with explicit acknowledgment of contemporary counter-evidence (Bastani et al., 2024) on the conditional value of generative AI in educational contexts.

The integration of Generative Artificial Intelligence (GenAI) tools in English as a Second Language (ESL) classrooms has gained increasing attention in recent years. While these tools offer numerous advantages, such as personalized learning, automated feedback, and enhanced student engagement, their effectiveness largely depends on teachers' perceptions and willingness to integrate them into their teaching practices. This literature review examines existing research on teacher perceptions of GenAI in ESL classrooms, focusing on both the potential benefits and the challenges associated with its implementation. The review explores themes such as pedagogical applications, teacher attitudes, technological barriers, and ethical concerns. Findings suggest that while many educators recognize the potential of GenAI for improving instructional efficiency and student autonomy, concerns related to accuracy, digital literacy, and the risk of over-reliance on AI-generated content persist. Additionally, a lack of institutional support and teacher training remains a significant barrier to widespread adoption. The review highlights gaps in current research, particularly in non-Western ESL contexts, and emphasizes the need for further studies on professional development programmes and long-term impacts of the integration of GenAI in language learning.

Plagiarism in higher education has long been a concern, exacerbated by the
emergence of Generative Artificial Intelligence (GenAI), which has heightened worries about academic integrity. This study examined the association between GenAI use and self-reported plagiarism among university students in Ecuador. Data were collected from 4,811 students across eight universities through a validated questionnaire (α = 0.73) that explored academic and non-academic technology use. A binary logistic regression was applied, with plagiarism level as the dependent variable. The model explained 20.2% of the variance (Nagelkerke R² = 0.202) and identified key predictors: reporting the use of ChatGPT to instructors (OR = 1.223, p < 0.001) and trusting ChatGPT-generated information (OR = 1.176, p < 0.001) were positively associated with plagiarism, while perceiving that AI improves academic performance (OR = 0.945, p < 0.01) and possessing broader knowledge of AI tools (OR = 0.911, p < 0.01) were associated with lower plagiarism levels. Although the study’s cross-sectional design limits causal inference, findings highlight the importance of ethical awareness, digital literacy, and institutional policies for responsible GenAI integration. This research contributes to understanding how GenAI use interacts with academic integrity in higher education and informs evidence-based approaches to promote ethical and transparent learning practices.

The integration of generative artificial intelligence (AI) in education has prompted a critical reevaluation of the teacher's role, particularly within the empirical domain of science education in the Philippines. This thought paper argues that the science educator must evolve from a primary source of knowledge into a critical mediator to prevent the risks of cognitive offloading and the erosion of analytical reasoning. By exploring the necessity of strengthening high-order inquiry, maintaining AI as a supplemental scaffold rather than a surrogate, and exercising ethical leadership, this paper highlights how teachers can anchor scientific integrity in an era of synthetic information. It concludes that the irreplaceability of the science teacher lies in their ability to foster human-led innovation and skepticism, ensuring that AI remains a secondary tool for discovery rather than a replacement for the human intellect.

Artificial intelligence (AI) is rapidly transforming the landscape of science education, reshaping how students learn, how teachers teach, and how classrooms operate. This thought paper examines the evolving role of science teachers in an AI-driven educational environment. Drawing from global and Philippine-based studies, it explores how Artificial Intelligence enhances scientific inquiry, personalizes learning, and automates assessment, while also introducing challenges related to ethics, equity, and teacher readiness. The paper argues that science teachers remain central to a meaningful learning despite various and current technological advancements. Their roles are shifting toward becoming facilitators of inquiry, ethical mediators, designers of AI-integrated learning environments, and lifelong learners who continuously adapt to emerging technologies. The discussion highlights the need for teachers or educators to have professional development, policy support, and critical AI literacy to ensure that AI strengthens learners' science education rather than replaces human-centered science education.

Artificial Intelligence (AI) is revolutionizing inclusive education by providing personalized, accessible learning tools that address diverse student needs, particularly in science classrooms. This thought paper examines AI's transformative potential through adaptive platforms, virtual labs, and real-time feedback systems, while addressing critical challenges like algorithmic bias, digital divides, and teacher preparedness. Drawing from STEM education studies and global AI implementations, it argues for ethical frameworks, professional development, and equitable access to ensure AI amplifies inclusion rather than exclusion. Science educators must lead interventions to harness AI effectively in 21st-century classrooms, fostering scientific reasoning for all learners. Introduction Imagine a science classroom where a visually impaired student independently conducts a chemistry experiment using voice-activated AI tools, while a peer with dyslexia receives realtime text-to-speech explanations of complex molecular structures. This scenario exemplifies how artificial intelligence (AI) is reshaping inclusive education, particularly in science disciplines, by dismantling traditional barriers to learning. This thought paper argues that AI holds transformative potential for inclusive science education but demands ethical safeguards, teacher training, and equitable access to prevent exacerbating inequalities. Inclusive education ensures all learners, regardless of ability, engage meaningfully in curricula like science, where hands-on labs and abstract concepts often exclude marginalized students. As AI integrates into classrooms, it offers personalized tools aligning with 21st-century demands, yet challenges like bias and digital divides persist. Drawing from studies on problembased learning in STEM and AI applications, this paper explores AI's role, benefits, challenges, and interventions for science teachers. Background and Context Inclusive education traces its roots to the 1994 Salamanca Statement, which advocated for schooling all children in mainstream settings regardless of abilities, yet science education has historically lagged due to inaccessible labs and abstract concepts like molecular structures or relativity. The rise of AI coincides with global shifts: UNESCO reports 258 million children out of school, many with disabilities, while the Philippines' K-12 science curriculum emphasizes performance-based assessments that AI can now support through adaptive tech. In the STEM context, studies like Blancia's problem-based learning research reveal persistent gaps in scientific reasoning among diverse learners, exacerbated by traditional methods. The COVID-19 pandemic accelerated AI adoption, with tools like Google Classroom and AI tutors exposing both potential (e.g., 30% engagement gains) and pitfalls (e.g., 40% digital divide in rural areas). Current trends show AI integration in 65% of U.S. schools but only 20% in Southeast Asia, highlighting equity needs for contexts like Romblon State University classrooms.

This paper titled inclusive education in the age of artificial intelligence a paradigm shift explores how artificial intelligence is fulfilling the long overdue promise of inclusive schooling at scale. These papers explore the ways that AI is helping to move from reactive accommodation to proactive universal design, with data and multimodal sensory supports and "executive function" coaching for neurodivergent learners.

Artificial Intelligence (AI) and Machine Learning (ML) are
transforming educational research through the integration of
computational intelligence, data-driven analysis, and adaptive learning
systems. This chapter explores the multifaceted applications of AI and
ML in education, ranging from personalized learning environments to
predictive analytics, natural language processing, and intelligent
tutoring systems. The discussion extends to methodological and ethical
considerations, emphasising the importance of transparency, fairness,
and data privacy in the use of these technologies. It also highlights the
challenges associated with algorithmic bias, interpretability, and
scalability in educational contexts. Drawing upon contemporary
research, the chapter articulates a conceptual framework linking AI’s
computational capacity with educational theory and practice, providing
a critical perspective on how emerging technologies are reshaping
pedagogy, assessment, and policy. The concluding sections envision the
future trajectory of AI in education, underscoring the need for
interdisciplinary collaboration, ethical governance, and human-centred
design in educational AI systems.

Technology is a critical driver of national development, and the National Education Policy (NEP) 2020 strongly emphasises the integration of emerging technologies in education. As the education system moves forward with emerging technologies such as Artificial Intelligence (AI), it brings with it several physical, ethical, and methodological challenges. This paper examines the perspectives of key stakeholders on the integration of Artificial Intelligence (AI) in education. A systematic literature review was conducted, followed by thematic analysis of peer-reviewed studies focusing on students, teachers, parents, and administrators. The findings reveal approaches and concerns toward AI adoption in education.

Machine learning has become a powerful influence in education, providing new ways to evaluate students' learning. By examining large sets of data, machine learning models can discover patterns that traditional assessment methods may miss. These findings help create adaptive learning systems, predictive analytics and tailored feedback to enhance educational outcomes. The use of machine learning in schools has grown rapidly, with studies showing its ability to boost efficiency, lessen the burden on teachers and allow for immediate assessments of student development. Tools for automated assessment driven by machine learning have proven effective in simplifying evaluation tasks while ensuring reliability. However, issues like ethical dilemmas, concerns about data privacy and biases in algorithms need to be considered. Recent research highlights the need for advanced machine learning models that can handle various types of data-including behavioral, cognitive and performance indicators to offer a well-rounded perspective on student learning. These models go beyond standard testing methods, allowing for a deeper understanding of individual learning styles. In addition, the combination of psychological and psychometric methods like standardized testing and case studies with machine learning techniques is being increasingly looked at for more customized assessments. This review paper compares different machine learning models utilized in education and concludes that ongoing progress in this field is vital for creating adaptive and inclusive learning environments. By matching educational methods with the specific learning requirements and preferences of each student, machine learning has great potential to personalize education and effectively support a variety of student groups.

Generative Artificial Intelligence (GenAI) has caused a significant disruption to the higher education sector by increasing the efficiency of pedagogy, hastening research activities, and advancing automation processes. However, this rapid development has given rise to considerable "Professional Friction" driven by faculty apprehension related to job replacement, deskilling, and a threat to academic integrity. The present conceptual investigation examines the interface between GenAI technological advancement and the value of the human professor with the aim of developing a framework for harmonization. The article introduces a unique conceptual model: The Balanced Scale Model, which consists of three integral parts: AI's Push-a worldwide drive for adaptive learning and technological acceleration, Professional Friction-the devaluation of the professor's role and expertise, and Institutional Mediators-strategic initiatives to bridge the gap between GenAI and academia. The research indicates that a paradigm shift is required to transition from viewing GenAI as a replacement for the educator to viewing GenAI as a part to be synthesized with the educational community. The use of institutional mediators transforms the professor's role to a "Human Architect". This allows the professor to break free from routine and mechanical tasks to focus on high-value human activities that GenAI cannot accomplish, such as ethical mentoring, thematic research, and the curation of complex learning experiences. This model provides a strategic pathway for administrators and researchers to manage GenAI-related anxiety to ensure a balanced future where GenAI enhances and improves the "human element" of higher education.

This paper introduces AI-accelerated learning systems and presents the NeuroGenesis Learning Framework, a structured model describing how artificial intelligence enhances human learning through cognitive interaction, adaptive explanation, and memory optimization. Drawing on interdisciplinary research across cognitive science, artificial intelligence, and educational technology, the study demonstrates that human–AI collaboration improves learning speed, retention, and problem-solving performance. The findings position artificial intelligence as a core component of modern cognitive infrastructure.
OSF Project:
https://osf.io/q7b5u/overview

The adoption of Artificial Intelligence (AI) for personalized learning (PL) is rapidly transforming global higher education. However, institutional type often dictates resource allocation and technological infrastructure, potentially leading to disparate impacts on student outcomes. This study conducted a comparative analysis of the perceived impact of AI-driven personalized learning (AI/PL) on academic performance and academic engagement of undergraduates, measured as student-perceived outcomes, across Nigeria's tripartite higher education structure, namely federal, state, and private universities. Amidst persistent infrastructural and policy disparities in the region, the research sought to determine the relationship between AI utilization and student outcomes and assess whether institutional type significantly mediates this impact. A quantitative descriptive survey research design was employed using a stratified sample of 150 undergraduates (50 from each of the federal, state, and private universities) from Oyo State, Nigeria. Data were collected using a questionnaire, utilizing a 5-point Likert scale. Statistical analysis confirmed a positive and statistically significant correlation between AI utilization and both perceived academic performance (r=0.58, p < 0.01) and academic engagement (r=0.63, p < 0.01). Significantly, One-Way Analysis of Variance (ANOVA) revealed a highly significant difference in the perceived impact scores across the three institutional types for both academic performance (F=9.87, p < 0.001) and academic engagement (F=11.24, p < 0.001). Private universities reported the highest mean scores, followed by federal and then state institutions, demonstrating that the positive effect of AI/PL is heavily moderated by institutional resource availability and infrastructural reliability. The study concludes that AI/PL holds transformative potential, but its successful and equitable realization is contingent upon overcoming structural barriers, particularly inconsistent technological infrastructure in public institutions. It is recommended that national policy mandates robust ICT infrastructure standards and ethical governance frameworks, prioritizing reliable digital access in public institutions for equitable AI integration.

Objectives The Evolution of Literacy: Explore existing literature on how "digital literacy" is being redefined to include AI competencies in K-12 and higher education. Current Educational Landscape: Review the adoption of Generative AI (GenAI), adaptive learning platforms, and automated grading systems. Identifying Gaps: Address the lack of empirical, quantifiable data regarding how AI proficiency directly affects student performance and teacher burnout. Methods To measure the impact of AI tools in education, the following quantitative metrics and data sources are utilized: Analysis of Student Performance and Learning Efficiency Metrics: Grade improvement percentages, time-tocompletion for complex assignments, and retention rates of core concepts. Data Sources: Learning Management Systems (LMS) like Canvas or Blackboard, and standardized assessment scores. Technique: Statistical correlation between students using AI-assisted tutoring versus traditional self-study. Measuring Educator Productivity and Wellbeing. Metrics: Reduction in hours spent on administrative tasks (grading, lesson planning), and the "sentiment score" of faculty regarding workload. Data Sources: Faculty surveys, time-tracking logs, and institutional HR reports. Technique: Comparative analysis of "AI-integrated" classrooms versus "traditional" control groups.

This review critically examines the conceptual and normative framework proposed by Vendrell and Johnston (2026) for integrating Large Language Models (LLMs) into higher education to scaffold critical thinking. The paper highlights key postulates, including the non-neutrality of LLMs, risks of cognitive offloading, metacognitive disengagement, and epistemic narrowing, alongside hypotheses that structured pedagogical design can mitigate these risks and enhance epistemic agency. Although the original work lacks empirical results, it advances six essential intellectual processes and eight design principles, illustrated through classroom scenarios. Similar research from 2024-2026, such as systematic reviews on LLM integration for critical thinking and personalized learning, reveals a growing consensus on the need for human-centered frameworks, amid concerns over equity and ethical biases. Current trends indicate a shift toward hybrid human-AI pedagogies, with future directions pointing to empirical validation and scalable implementations. Algebraic interpretations model critical thinking as a multidimensional vector space, while geometric analogies represent scaffolding as a lattice structure supporting epistemic integrity. This review Mamun, SM (2026)…………………………………1 A Comprehensive Review of "Scaffolding Critical Thinking with Generative AI: Design Principles for Integrating Large Language Models in Higher Education" (Vol-I) synthesizes secondary literature, tests hypotheses through comparative analysis, and concludes with implications for educational policy and practice. Mamun, SM (2026)…………………………………2

This article, as part of the special issue "Superheroes and Villains: Engagement, Effects, and Empowerment", reflects on Master Splinter's educational approach with the Teenage Mutant Ninja Turtles. Master Splinter employed personalized educational tools (the weapons) that were not based on the disciples' predispositions, but rather on their deficits and limitations. This approach is compared with traditional visions such as the Montessorian, Gardner's multiple intelligences and Tomlinson's "differentiated classroom". The importance of addressing obstacles instead of indulging pre-existing tendencies is highlighted with reference to problem-based learning and un updated view of the Zone of Proximal Development theorized by Vytogsky.

This paper examines whether AI can replace teachers by conducting a document-based case study of Alpha School, drawing exclusively on publicly available materials. The study addresses two research questions: (RQ1) how Alpha School's AI-centered model is described to operate, and (RQ2) what potential benefits are claimed or plausibly enabled by its design. Across sources, the model is consistently portrayed as a full-day schedule in which core academics are concentrated into an approximately two-hour morning block supported by adaptive, mastery-oriented learning software and ongoing progress monitoring, followed by afternoon workshops and project-based activities framed as life skills. Adult staff ("guides") are described as focusing primarily on motivation, routines, goal-setting, supervision, and socioemotional support rather than lecture-based instruction, traditional lesson planning, or conventional grading. Interpreted through established literature on mastery learning, one-to-one tutoring, and intelligent tutoring systems, the model suggests potential advantages such as individualized pacing, rapid feedback, and a "time dividend" that reallocates instructional time toward applied projects and broader competencies. Overall, the case indicates a reallocation of instructional functions between AI and traditional teacher roles, rather than a straightforward substitution of human educators.

Continuous Decision Intelligence (CDI) represents a paradigm shift from traditional educational analytics, creating an intelligent learning support system that enables personalized, adaptive responses to language learner needs. This article explores the architectural foundations of CDI in language education, examining how event-driven data integration from learner interactions, real-time proficiency modeling, and intelligent conversational simulation collectively transform assessment data into actionable learning interventions. The transition from periodic testing to continuous proficiency monitoring dramatically compresses intervention response times while improving pedagogical effectiveness. Cloud-native implementation strategies support educational CDI through scalable deployment models that balance performance with privacy protection. The long-term implications for language education are profound, as institutions with mature CDI capabilities demonstrate superior anticipatory capabilities for learning challenge identification and personalized pathway development. By synchronizing instructional approaches with individual learning rhythms, CDI-enabled language programs maintain learner engagement through continuous adaptation rather than standardized curricula, creating equitable educational opportunities aligned with Sustainable Development Goal 4 (Quality Education).

This study discussed the dependence and attainment of SDG 4 on strategic educational planning, and efficient deployment of Artificial Intelligence in the Nigerian educational system. Specifically, it reviews extant literature on AI acceptance, usage, inclusion, empowerment, and regulation, and assesses current progress across these dimensions to identify knowledge gaps that warrant further research and inform future policy and innovation in educational planning. The attainment of Sustainable Development Goal 4 (SDG 4)-inclusive quality education and lifelong learning is expected to enhance the adoption and integration of artificial intelligence (AI) in Nigeria's educational system on the strength of strategic educational planning that promotes inclusion, equity, equality and social justice. A systematic review was conducted using Scopus-indexed sources, Web of Science, Elsevier, Google Scholar, Researchgate, and institutional repositories such as the UNESCO Institute for Educational Planning and the National Open University of Nigeria (NOUN). Both qualitative and quantitative peer-reviewed studies, spanning local and international contexts, were synthesized to address the research objectives. The findings highlight critical gaps in AI utilization, regulatory frameworks, AI literacy, and inclusive design. It recommended among others, integrated policy interventions that align AI deployment with SDG 4 targets, and the need for legislation and regulatory frameworks that will govern the deployment and utilization of AI in education to preempt violations and sanctions. This abstract, therefore, sets the stage for a comprehensive analysis that contributes to evidence-based educational planning and the responsible integration of AI in Nigerian schools, to support existing policies in educational planning in Nigeria.

Exploring how to use the Blueprint for Maryland's Future to accelerate improvement and systemic change Four Stories from Calvert County Public Schools November 2025 This Spotlight Practices Brief provides case narratives describing how four schools are organized to improve teaching and learning, focusing on collaborative teaming structures, leadership and teacher roles, and multi-tiered systems of support. The Brief intends to inform district-level planning and implementation of Blueprint policies.

The paper examines the role of computer science in contemporary education, with special attention to the integration of digital tools, algorithmic literacy and technologies based on artificial intelligence. The analysis focuses on changes occurring within the educational environment due to the development of information technologies, as well as the ways in which digital platforms and software environments influence lesson structure, content organization and student interaction with learning materials. The study includes a detailed overview of practices implemented in primary and secondary schools, specifically in Primary School "Prokosovići" and the Construction School Tuzla, where various forms of digital support have been introduced: web-based educational applications, systems for generating interactive tasks, automated quizzes, digital learning logs, simulations and platforms for monitoring student progress. The collected data encompass the structure of teaching activities, types of digital resources, frequency of use and characteristics of students' digital competencies. The methodological framework is based on a combination of analytical literature review, descriptive analysis of teaching models, categorization of digital competencies and examination of pedagogical procedures that support the development of computational thinking. Special attention is given to identifying types of software solutions used in the teaching process, analysing methods of integrating digital tools into instructional units and describing procedures through which digital resources are adapted to different subject areas. The paper also presents forms of artificial intelligence applied in the educational process, including automated content generation, assistive algorithms for producing teaching materials, adaptive learning support systems and digital models for task creation. The research elements include an overview of technical requirements, organisational conditions and pedagogical parameters that shape the process of integrating information technologies into school practice. The abstract provides a structured description of the research framework, methodological procedures and instructional models without addressing outcomes, interpretations or any evaluative or concluding statements. Its aim is to offer a comprehensive descriptive overview of the technological, pedagogical and digital contexts analysed in the study.

Artificial intelligence has emerged as a transformative force within global education, redefining the relationships between learners, educators, and the systems that facilitate knowledge acquisition. Its implementation spans adaptive learning platforms, intelligent tutoring systems, automated assessment tools, and data-driven institutional management, promising unparalleled personalization, accessibility, and efficiency. Yet, alongside these benefits arise profound ethical dilemmas related to privacy, algorithmic bias, equity, and the erosion of human agency in learning. This paper provides a comprehensive exploration of artificial intelligence in education, synthesizing existing literature, policy discussions, and theoretical frameworks. It examines the nuanced interplay between technological innovation and human-centered education, aiming to illuminate how AI can enhance learning while respecting the ethical, emotional, and societal dimensions essential to meaningful education.

Deep Learning Applications in Primary Education: A Systematic Literature Review of Emerging Trends, Challenges, and Opportunities. Objectives: Despite significant advances in healthcare and finance, Deep Learning (DL) remains underutilized in primary education, with only about 12% of AI-related studies focusing on K-6. Given the formative role of early schooling in cognitive and social development, this systematic review analyzed recent empirical studies to identify key trends, challenges, and opportunities in applying DL to primary education. Methods: Following the PRISMA 2020 guidelines, 21 studies published between 2021 and 2025 across 11 countries were analyzed using a structured coding sheet and further examined with bibliometric mapping, Microsoft Excel, and Python-based visualization tools. The review included diverse sources focusing on DL use cases across global primary education systems. Findings: The findings reveal a global increase in DL adoption, led by China and South Korea, with growing contributions from Indonesia, India, and the Philippines. Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Generative Adversarial Networks (GANs), and transformer-based models are among the most commonly applied architectures to tasks such as personalized learning, early detection of learning difficulties, assessment automation, and curriculum development. However, there are still problems in the context of ethical concerns, including data privacy, algorithmic bias, and equity of access. Technical barriers involve dataset complexity, model generalization, and resource limitations, while pedagogical issues center on aligning DL applications with developmental needs and classroom realities. Despite these obstacles, DL demonstrates significant potential to enhance personalization, foster engagement, and support holistic educational outcomes. Conclusion: This review contributes a strategic roadmap for integrating DL in primary education by balancing innovation with pedagogy and ethics. Future research should prioritize cross-disciplinary collaboration, greater geographic diversity, and improvements in scalability and interpretability to ensure DL supports equitable, future-ready learning.

In the age of artificial intelligence (AI), educational assessment is undergoing a paradigmatic shift. As institutions increasingly adopt AI-driven tools to enhance efficiency and personalization, the imperative to safeguard fairness, equity, and transparency becomes more urgent. This study investigates the transformative potential of AI in educational assessment, with particular focus on automated grading systems, bias detection algorithms, and personalized learning pathways. Through empirical analysis and ethical reflection, we demonstrate how AI can augment traditional assessment models while mitigating systemic biases. Our findings underscore the need for ethically grounded integration of AI technologies to foster a more equitable and responsive educational environment.

Systemic change is far more difficult than piecemeal reforms because it requires far more interrelated and interdependent sets of changes to occur. Systemic change has two potential areas of failure: 1) the change process may never reach full implementation of the new system, and 2) the new system (vision) may not be well designed. Therefore, a body of knowledge about systemic change in education must address both sides of the coin: 1) the vision and 2) the process (action) by which the vision is created and implemented. Based on an extensive review of the literature about the educational needs and tools of a digital, post-industrial society, we have identified 25 principles organized under six core ideas that inform the creation of a vision to better meet students' and communities' needs. These principles are highly systemically interdependent but incompatible with most features of teacher-centered schools, thus requiring paradigm change. Systems thinking is crucial designing a school system based on these principles. 1. Competency-based education A. Competency-based student progress B. Competency-based student assessment C. Competency-based learning targets D. Competency-based student records 2. Learner-centered instruction E. Learning by doing (project-, problem-, inquiry-, task-, and maker-based learning) F. Instructional support just-in-time during projects G. Personalized learning (personalized goals, projects, instructional support, assessment, and reflection) H. Collaborative learning (in both team-based and individual projects) 3. Restructured curriculum I. Focused on relevance to students' current and future lives J. Whole-child education K. Balance of "universal" content and cultivation of individual strengths/talents L. Sound progressions in content 4. New roles M. Teacher as guide on the side N. Student as self-directed learner O. Parent as partner P. Technology as tool for students to serve four functions: planning, instruction, assessment, and recordkeeping for student learning. 5. A nurturing culture Q. Cultivation of strong and caring relationships

Higher education often underestimates the value of data in decision-making, resulting in a lack of use of big data generated from elearning (electronic learning) activities to improve the quality of online courses and redesign teaching and learning experiences. Big data play a pivotal role in improving the quality of content and resources, accessibility, automated assessment, and understanding the impact of elearning courses on student engagement and participation. The aim of this study was to explore students' engagement with online learning environments to improve learners' personalized learning experience. The research tools used were the business analytics platform "Pyramid" as a tool for analyzing student interactions within e-learning environments, and the e-learning system "Blackboard" as a tool for collecting interaction data within the e-learning environment. The data were collected through learner engagement online in the learning management system of Blackboard. A descriptive analysis approach was used to analyze the data. A total of 20,000 students from a Saudi university undertook one of the main courses during the academic year 2022-2023. Using the convenience sampling method, which is a type of nonprobability sampling strategy that allows the selection of a study sample that can be easily reached during the course of the study, the study sample of 2,600 students was selected. The results revealed significant variations in interaction rates within the Blackboard e-learning environment, along with distinct fluctuations in the duration of learner engagement on the platform. Among the four daily time periods, the morning consistently emerged as the peak time for educational activity. Mobile application access in the morning demonstrated the highest level of engagement with the course elements and content. The interaction rates gradually declined throughout the day, reaching their lowest point in the afternoon. The results also revealed that students have a high level of academic achievement. Some learning strategies have been suggested to improve students’ participation rates during low-activity periods. This study
highlights the importance of using learning analytics to provide
exploratory insights into learner activity and develop data-driven
strategies to meet individual learner needs and improve personalized
learning experiences.

Educational AI systems often do not employ proper sophistication techniques to enhance learner interactions, organize their contextual knowledge or even deliver personalized feedback. To address this gap, this paper seeks to reform the way ChatGPT supports learners by employing fuzzy memory retention and thematic clustering. To achieve this, three modules have been developed: (a) the Fuzzy Memory Module which models human memory retention using time decay fuzzy weights to assign relevance to user interactions, (b) the Schema Manager which then organizes these prioritized interactions into thematic clusters for structured contextual representation, and (c) the Response Generator which uses the output of the other two modules to provide feedback to ChatGPT by synthesizing personalized responses. The synergy of these three modules is a novel approach to intelligent and AI tutoring that enhances the output of ChatGPT to learners for a more personalized learning experience. The system was evaluated by 120 undergraduate students in the course of Java programming, and the results are very promising, showing memory retrieval accuracy, schema relevance and personalized response quality. The results also show the system outperforms traditional methods in delivering adaptive and contextually enriched educational feedback.

The rapid integration of Artificial Intelligence (AI) into virtual learning environments has transformed digital education, yet its influence on student engagement and learning outcomes remains underexplored. This study examines the effectiveness of AI tools in enhancing behavioral, emotional, and cognitive engagement, strengthen academic performance, and shape ethical considerations in virtual classrooms. Using a survey design, data were collected from 150 students and 10 lecturers at the FCT College of Education, Zuba. Findings reveal that AI significantly enhances engagement through personalized learning pathways, adaptive feedback, timely instructional support, and increased motivation. Students also reported improved understanding of complex concepts, faster task completion, and enhanced knowledge retention. Despite these benefits, concerns persist regarding data privacy, fairness, transparency of AI processes, and inequalities in access to digital technologies. The study concludes that AI offers substantial potential for enriching virtual learning but requires robust ethical frameworks, compliance with data protection regulations, and inclusive policies that ensure effective, fair and sustainable implementation.

The rapid development of artificial intelligence (AI) and digital technologies has created transformative opportunities for addressing complex challenges in science, education, healthcare, and sustainable development. The aim of this study is to examine the practical applications of AI and digital platforms in enhancing operational efficiency, knowledge dissemination, personalized learning, medical diagnostics, and environmental monitoring, while also contributing to ethical, social, and ecological outcomes. The material for this research consists of documented case studies, pilot projects, and published reports covering AI-driven predictive analytics, adaptive digital learning platforms, automated diagnostic systems, IoT-enabled monitoring networks, and cloud-based data management solutions. Methods applied include comparative analysis of interdisciplinary studies, assessment of technological performance metrics, and evaluation of real-world impacts on productivity, accuracy, and accessibility. Particular attention is given to implementations that integrate environmental sustainability, human-centered design, and data-driven decision-making. The major results demonstrate that AI algorithms significantly improve predictive capabilities across multiple domains. In medical practice, AI-driven image analysis and diagnostic tools accelerate patient assessment, optimize treatment planning, and support telemedicine initiatives. In education, adaptive platforms deliver personalized content, provide real-time feedback, and enable virtual classrooms that expand access to quality learning. In scientific research, AI enhances big data analytics, facilitates predictive modeling, and supports collaboration among global institutions, increasing reproducibility and innovation. Additionally, IoT sensors and cloud infrastructures enable continuous monitoring of environmental indicators, energy consumption, and public health parameters, promoting evidence-based interventions and sustainable resource management. These practical implementations not only enhance operational efficiency but also foster interdisciplinary knowledge transfer, innovation, and responsible technological adoption. Challenges remain, including algorithmic bias, data privacy, infrastructure limitations, and the need for regulatory and ethical frameworks to guide deployment. In conclusion, artificial intelligence and digital technologies serve as powerful tools for advancing science, education, healthcare, and environmental sustainability. Their applications produce measurable improvements in performance, accuracy, and accessibility, while responsible integration ensures ethical, social, and ecological benefits. By combining advanced technological solutions with sustainable and human-centered approaches, these innovations have the potential to transform professional practice, educational systems, healthcare delivery, and environmental stewardship, ultimately contributing to a more resilient, informed, and sustainable society.

Science education is being taken in a new direction by the blistering technology in the 21 st century.
Artificial Intelligence (AI) and cyber security are two major agents of this change. They both
influence current teaching techniques and learning approaches, as well as accomplishments. AI
provides customized education,-based evaluation, and simulated tests. Digital learning spaces and the
sensitive data in these places are safeguarded by cyber security. The paper will discuss the
intersection of AI and cyber security in science education, highlights the benefits, challenges, and
future of the topic, and considers the policy, curriculum development, and teacher-training
implications.
Keywords: Science education, Artificial Intelligence, Cyber security, Digital learning, Educational

The modern enterprise is drowning in data, with an estimated 80% being unstructured (text, audio, video, sensor streams). Traditional Business Intelligence (BI) and basic Predictive Analytics have proven insufficient to harness this complexity. They answer "What happened?" (Descriptive) and "What will happen?" (Predictive) based primarily on structured, historical data. Cognitive Analytics marks the beginning of the "Third Era of Computing" [4], where systems move beyond simple calculation to mimic human cognitive processes: learning, reasoning, understanding context, and interacting naturally. Cognitive analytics is an intelligent technology that synthesizes Artificial Intelligence (AI), Deep Learning (DL), Natural Language Processing (NLP), and Probabilistic Modeling to transform raw, messy, and multimodal data into actionable, human-like insights [1]. The goal is not merely to automate decisions, but to augment human expertise with transparent, evidence-based recommendations, particularly in high-stakes environments.

The paper deals with an interrelation between social and cognitive phenomena associated with transition of the society to the post-industrial epoch. Ideas triggered our study came from two different fields: sociology and epistemology. From the sociological point, we consider the Post-industrial society as a virtual society, where artificial interfaces more and more separate individuals from the real social environment. In our study, we consider three trends: Virtualization, Personalization and Ubiquitous Learning as the main trends of the ...

Artificial Intelligence (AI) has emerged as a transformative force in science education. This paper presents a comprehensive systematic and analytical review of AI applications in science learning, focusing on conceptual understanding, critical thinking, adaptive learning, and inquiry-based approaches. A systematic literature review methodology was applied to studies published between 2020 and 2025 from Elsevier, Springer, IEEE, and Taylor & Francis. Findings indicate that AI enhances visualization of complex scientific phenomena, supports personalized instruction, and strengthens scientific reasoning. However, challenges such as reduced hands-on experimentation, ethical concerns, teacher readiness, and digital inequality remain. This paper provides practical recommendations for educators and policymakers and offers future research directions for integrating AI in science education.

Artificial Intelligence (AI) has significantly transformed higher education in recent years by enhancing teaching methods and influencing student learning outcomes. This study investigates the effects of AI on student motivation and cognitive skills, addressing both the benefits and challenges of AI integration. The research adopts a mixed-methods approach, combining a comprehensive literature review with a questionnaire-based survey of 60 university students from diverse academic disciplines. The results indicate that AI tools, such as intelligent tutoring systems and learning analytics, increase student engagement, motivation, and problem-solving abilities. However, concerns regarding over-reliance on AI reduced creativity, and ethical issues such as privacy and fairness persist. This study highlights the potential of AI to foster personalized learning while emphasizing the need for responsible AI implementation to address its limitations.

The flipped classroom model has gained significant attention in educational research, particularly in higher education, but its impact on K-12 settings, especially in Malaysia, remains understudied. This study investigates the effect of the flipped classroom on middle school students' academic performance in an English Language Arts course, explores teachers' attitudes towards this instructional model, and identifies the challenges associated with its implementation. Using a mixed-methods approach, data were collected from 184 Year 2 students and three English teachers at an International Baccalaureate Middle Years Programme (IB MYP) school in Malaysia. The findings reveal no significant difference in academic performance between the control and experimental groups, suggesting that the flipped classroom may not significantly enhance academic achievement in this context. However, teachers generally hold positive perceptions of the model, particularly regarding its impact on their role as facilitators and its potential to promote student engagement and active learning. Challenges such as student preparedness, time demands on teachers, and technological issues were also identified. The study concludes with recommendations for future research and practical implications for educators and school leaders considering the adoption of the flipped classroom model.