Artificial Intelligence in Education

This article provides a comprehensive analysis of the role of AI in shaping contemporary educational and research processes. The educational section shows the applications of artificial intelligence in personalizing educational paths, automating assessment, supporting teachers' work, and creating adaptive teaching environments that increase the accessibility of learning. In the area of scientific research, the use of AI for analyzing large data sets, supporting the process of formulating hypotheses, automating experiments, and accelerating publication processes is discussed. Particular attention was paid to ethical challenges (such as privacy, algorithm transparency, and the risk of data bias) and methodological challenges (including the quality of training sets, model interpretability, and implementation costs). The article also includes comparative analyses, SWOT and TOWS tables, and risk and benefit matrices that show the balance between innovation and responsible technology implementation. The results indicate that the importance of AI in education and research will continue to grow, and that the effective use of this technology requires both a strategic approach and ethical reflection in order to increase efficiency, accessibility, and innovation without compromising fundamental academic values.

Despite significant advancements, educational models worldwide remain largely standardized, failing to meet the diverse needs of individual learners. This "one-size-fits-all" approach has led to a global education crisis, evidenced by staggering disengagement rates and skills gaps that hinder both personal growth and economic competitiveness. According to UNESCO, hundreds of millions of children and adolescents are not achieving minimum proficiency in foundational skills, a problem exacerbated by instruction that is disconnected from learners' cultural, professional, and personal realities.
This white paper introduces Ketra, an AI-powered contextual learning platform designed to address this challenge. Ketra moves beyond traditional adaptive learning by leveraging contextual intelligence—the ability to understand and respond to the multifaceted circumstances of each learner. By dynamically personalizing content, examples, and communication styles to an individual's goals, background, and real-time environment, Ketra transforms passive instruction into an active, engaging, and deeply relevant learning experience.
Key pillars of the Ketra solution include:
Multidimensional Learner Profiling: Building a holistic understanding of each learner, including their cognitive patterns, cultural background, and personal goals.
Dynamic Content Adaptation: Modifying explanatory methodologies, examples, and communication tone in real-time to optimize comprehension and retention.
Work-Integrated Learning: Embedding educational opportunities seamlessly into the daily lives and workflows of learners, from agricultural workers to urban gig economy professionals.
This paper outlines the technological framework, pedagogical principles, and transformative potential of AI-powered contextual learning. We present a clear pathway to an educational future where learning adapts to the individual, finally making personalized education scalable, equitable, and effective for everyone.

This paper introduces a framework for integrating artificial intelligence into high-stakes educational assessment through the APIA model-Assessment Preparation, Implementation, and Analysis. Building upon recent advances in educational technology and data analytics, the framework operationalizes AI integration across three phases of the examination ecosystem. Phase one leverages AI for personalized student preparation through adaptive learning systems that analyze behavioral patterns and knowledge gaps to optimize individual learning trajectories. Phase two employs predictive modeling using longitudinal educational data to enable proactive interventions at individual, institutional, and systemic levels, achieving high accuracy rates in identifying at-risk students. Phase three transforms examination results into actionable intelligence for curriculum development, teacher training, and policy improvement through advanced pattern analysis across regional, institutional, and demographic dimensions. The framework addresses persistent fragmentation in current AI applications by creating systematic connections between preparation, prediction, and post-examination analysis phases. Grounded in established educational theories including assessment for learning and constructive alignment, the APIA model provides practitioners with structured guidance for implementing AI technologies. This integrated approach transforms high-stakes examinations from isolated assessment events into dynamic components of continuous learning optimization cycles that serve both individual student success and broader educational improvement objectives.

This research paper explores the potential use of artificial intelligence (AI) to improve blockchain education, and to address security risks and transaction monitoring associated with cryptocurrency integration. It highlights the need for practical skills and situational competencies, in addition to theoretical familiarity, in educational programmes. Furthermore, the paper discusses the opportunities and challenges of using AI to enhance blockchain education and strengthen security. It also proposes pragmatic recommendations for educators and academic institutions to bridge the gap between theory and practice. The literature review highlights the gaps in current approaches to blockchain education, and the potential of AI-based transaction monitoring techniques to prevent fraud in decentralised environments.

The CTLD (Connect to Learn Digital) programme is an industry-academia initiative that
equips students with AI and digital skills through co-creation. It is a collaboration between
Manchester Metropolitan University (ManMet Business School), Salesforce, and students,
which equips students with industry-relevant skills while reshaping academic-industry
partnerships. This essay reflects on CTLD’s development, the role of co-creation, key
challenges, and lessons learned.
Initially staff-led, CTLD emerged as a co-creative initiative within ManMet Business
School’s Operations, Technology, Events, and Hospitality Management (OTEHM)
Department, focusing on postgraduate students. The vision was to provide digital skills,
certifications, and career readiness strategies, but achieving this required navigating
approvals, securing resources, and building partnerships.
Co-creation in higher-education institutions (HEI) extends beyond simple collaboration,
emphasising shared decision-making, mutual accountability, and active stakeholder
participation. Co-creation is rooted in interactional creation, where learning occurs through
dynamic engagement between students, faculty, and external partners. CTLD emphasises that
co-creation drives innovation in HEIs, requiring institutions to embrace more flexible,
technology-enhanced learning environments.
Our understanding of co-creation is the process where multiple stakeholders actively shape
the learning experience together, each bringing unique contributions to shape the learning
framework itself. The distinction between collaboration and co-creation is particularly
relevant in the evolving landscape of higher education-industry partnerships. While
collaboration often implies working together towards a shared goal, co-creation takes it
higher by ensuring joint ownership of the process, where all stakeholders actively shape the
outcomes. CTLD actively engaged students in decision-making at multiple stages, from
refining the Salesforce AI certification learning pathways to shaping the programme’s
mentorship structure to students engaging with actual business challenges.

Generative Artificial Intelligence (GAI) tools have had a major impact on English Language Teaching (ELT). Thus, there is a dire need to understand how teachers use and integrate GAI in different contexts and educational settings. The Technological Pedagogical Content Knowledge (TPACK) model has been widely used to frame teachers' integration of this technology. The current study aimed to describe the knowledge, integration and use of GAI tools from a sample of English teachers from different educational levels in Colombia, a context less frequently studied in this emerging area of knowledge. A quantitative approach was adopted, using a non-experimental, cross-sectional design. A questionnaire based on the TPACK framework was administered to 76 English teachers across three institutional contexts. Findings show greater teacher competence in the domains of technological knowledge and technological content knowledge, but greater challenges in integrating technological knowledge within the pedagogical domain, as well as significant contextual barriers. This result is consistent across educational settings, suggesting a lack of resources and clear policies in all of them. Yet, differing profiles of engagement with GAI were identified through cluster analysis. These profiles might be instrumental in designing professional development programmes and institutional policies.

With the recent advancement in the field of deep learning and computer vision, crowd scene analysis has become an essential research area for ensuring public safety. United Nations (UN) predicts world population growth of 0.82% by 2035, driving people to cities for better lifestyles and social events like concerts, shopping, political gatherings, and educational conferences. Manual monitoring, however, is often laborious and error-prone, underscoring the importance of automated solutions. In this paper, we propose a spatiotemporal framework based on the combination of Convolutional Neural Networks and Long Short-Term Memory (LSTM) for automatic classification of normal vs abnormal behavior of people present in a crowd. VGG-19 is proposed for extraction of spatial features of two consecutive frames at different time levels "t" and "t-1". These extracted spatial features are then concatenated by leveraging the Wide Dense Residual block (WDRB). The concatenated features are then fed to LSTM block to capture the temporal features. The proposed method is evaluated on two different data sets: hockey fight data set and Real-Life Violent Situation (RLVS). Evaluation shows that the proposed method provides an efficient and reliable framework for crowd anomaly detection, thereby enhancing public security and safety. The results show that the models achieve accuracy of 91.0%, and 93.5% on hockey fight datasets and RLVS respectively. The implementation code is available at https://github.com/junaid2066/CAD

В статье рассматриваются различные аспекты внедрения технологий искусственного интеллекта (ИИ) в систему образования в контексте развития идей Н.Д. Никандрова о развитии технологий цифровизации в современном обществе с учетом возможных рисков и необходимости сохранения ценностных оснований личности. Анализируются положительные стороны и возможности цифровых технологий, включая искусственный интеллект. Также авторами выделены различные группы рисков, возникающих при интеграции ИИ в образовательную систему.

The article examines various aspects of the introduction of artifi cial intelligence (AI) technologies into the education system in the context of the development of N.D. Nikandrov's ideas on the development of digitalization technologies in modern society, taking into account possible risks and the need to preserve the value foundations of the individual. The positive aspects and capabilities of digital technologies, including artifi cial intelligence, are analyzed. The authors also identifi ed various groups of risks that arise when integrating AI into the educational system.

Across the world, secondary schools are experimenting with artificial intelligence (AI) to personalize instruction, automate feedback, and augment teachers' capacity. Early evidence suggests AI can boost certain forms of engagement and achievement especially through intelligent tutoring systems (ITS), adaptive practice, and teacher-facing assistants. Yet risks remain around shallow learning, inequity, and student data protection. This article synthesizes research on the impact of AI on secondary students' engagement (behavioral, emotional, cognitive) and achievement (course grades, standardized tests, mastery), and also contrasts traditional ITS findings with new evidence on generative AI. I propose a practical implementation blueprint covering pedagogy, staffing, procurement, safety, and evaluation, along with a responsible use and governance checklist aligned to recent policy guidance (e.g., UNESCO) and regulation (e.g., EU AI Act). Overall, we can conclude that schools can realize gains if they couple AI with clear learning goals, teacher capacity-building, and robust measurement plans.

This essay explores the contradictions and paradoxes emerging from attempts to ethically "educate" artificial intelligence systems. Starting from the symbolic episode of the android Ameca (2021) and analyzing recent failures in AI alignment, the work documents how every technical approach to AI safety has proven inadequate when faced with the ethical and cultural complexity of human experience.
The paper critically examines Archetypal Transfer Learning, the paradox of universal values in an era of cultural plurality, and the 2025 debate on artificial consciousness (including divergent positions from Anthropic and Microsoft). Particular attention is devoted to the tragic consequences of AI system inadequacy: suicide cases linked to chatbots (Sewell Setzer III, Adam Raine) and the Nature 2025 study that tested 29 chatbots with alarming results.
As a normative response, the Italian Law 132/2025 is analyzed—the first European national framework complementary to the AI Act. The work then proposes a paradigm shift: from the illusion of unilateral control to a relational approach based on the "as if" principle, articulated across three levels (individual, organizational, societal).
The central thesis: the education of machines and humans must be bidirectional, based on responsible reciprocity and observable outcomes rather than abstract principles or deterministic control.

In computer-assisted language learning (CALL), a variety of studies have explored the use of Virtual Exchange (VE) for shaping learners' cultural competence from the perspective of sociolinguistics. However, few studies have examined learners' psycholinguistic factors in this context. Since based on the dynamic complex system, students' psycholinguistic factors act independently and in conjunction with each other in any language learning context. Thus, by applying stimulus-organism-response theory (S-OR), we explored these inter-correlations in this context. In this case, we used Interactive, Constructive, Passive, and Active activities in CALL (ICAP), which have not been integrated into the field as stimuli to shape learners' online engagement as organisms and approaches to VE as a response. Accordingly, we integrated VE tasks into their language and cultural exchanges with Cypriot and Irish students in their respective partner universities based on ICAPCALL. The ICAPCALL and Students' Approach to Virtual Exchange (SAVE) were validated through partial least square modeling (PLS-SEM) in the VE. Moreover, the serial mediation analysis showed that learners could establish knowledge individually or collaboratively, influencing behavior and cognitive engagement to ask questions and exchange information to solve complex learning problems, stimulating a VE deepening approach. Moreover, the Necessary Conditional Analysis (NCA) revealed that passive and active learning activities were among the necessary conditions in shaping learners' deep approach to VE; however, a further level of them could not increase their deep approach to it. Based on these findings, the study introduces a new psycholinguistic framework to the VE, namely as a SAVE, and language learning activities to the CALL literature, namely as an ICAPCALL, and provides a new interconnectedness of learners' dynamic complex systems in the VE and highlights the importance of teachers maintaining a balance between collaborative and individual learning activities in this context to shape students' deep approach.

Over the past decade, scientific practices have emerged as a central paradigm in science education, reflecting a broader shift from content transmission toward epistemic engagement and authentic inquiry. This study employs bibliometric mapping to systematically analyze 594 peer-reviewed documents published between 2011 and 2025, aiming to explore how scientific practices have been defined, conceptualized, and assessed in the field. Results reveal a significant conceptual evolution: from the traditional notion of a linear scientific method to a multidimensional framework encompassing epistemic cognition, collaborative inquiry, and socio-scientific reasoning. Five key thematic clusters epistemic cognition, pedagogical models, nature of science, technology integration, and equity dominate the discourse. These clusters underscore the growing focus on student-centered, project-based, and digitally mediated learning environments. Despite growing interest and innovation in pedagogical strategies, assessment remains a persistent challenge. Traditional assessments fail to capture authentic engagement, prompting a call for performance-based and technologically supported tools that align with the complexity of scientific practices. Furthermore, the analysis highlights the increasing emphasis on equity and inclusivity, with citizen science initiatives serving as powerful models for democratizing science education. The findings offer strategic insights into the alignment of curriculum, teacher training, and assessment frameworks, ultimately contributing to global science education reform. By centering scientific practices, education systems can promote deeper learning, critical thinking, and civic engagement among learners worldwide.

Research has shown that students and teachers need to act strategically to self-regulate their activity before, during, and after learning and teaching in order to maximize their effectiveness. The present study aimed at investigating the metacognitive and self-regulatory strategies that elementary school students and teachers report using during mathematics learning and teaching respectively. This presentation aimed to investigate the most and the least frequently used strategic behaviors during mathematics as reported separately by elementary students and teachers. There were 344 5th and 6th grade students and 292 elementary school teachers participating in the study. Students' strategic behavior during mathematics learning and problem solving was assessed with an 11-item questionnaire. Students were asked to report on a 5-point answer scale how frequently they employ the behaviors described in the sentences during mathematics learning and problem solving. Teachers' use of strategies during mathematics instruction was assessed by means of an 18-item questionnaire. Eleven out of the 18 items in the teachers' questionnaire corresponded to the 11 items of the students' questionnaire. Teachers were asked to report on a 5-point answer scale how frequently they employ the specific behaviors described in the sentences during mathematics teaching in order to activate and develop their students' metacognition and self-regulated learning. The two instruments had been previously tested for their structural validity and reliability. Descriptive statistics showed that, in general, elementary school students and teachers report similar strategies as the most and the least frequently used during mathematics. For example, the most frequently employed strategic behavior reported by the students was that, when confronted with a mathematical problem, they try to spot its key points in order to help them reach the solution. The second most frequently used strategy by the students was that, after solving a mathematical exercise, they consider if the solution produced does make sense, if it is logical. Similar results were found regarding teachers. That is, the teachers reported that they ask very frequently their students to spot the key points of a mathematical exercise in order to help them reach a solution and that they ask their students to consider if the solution produced does make sense. The reports of students and teachers also were similar regarding the least frequently employed strategic behaviors. For example, both groups reported that the least frequently employed strategic activity was that of students' selfmonitoring during dealing with mathematics learning and problem solving. Students' and teachers' reports presented also some minor differentiations. For example, although teachers reported that they frequently ask from their students to check for the correctness of their answers and solutions produced in mathematics (3rd on teachers' ranking), the students reported that evaluating the solution/answer produced was not so frequently used (7th on students' ranking). The results will be discussed in the frame of self-regulated learning and teaching.

This article introduces the concept of Indexical Collapse, the disappearance of reference in predictive systems. Indexical such as pronouns, demonstratives, and tenses presuppose a contextual anchor, yet predictive language models reproduce them without connection to reality. The outcome is a collapse of reference that paradoxically produces authority effects in law, medicine, and governance. By analyzing judicial transcripts, medical reports, institutional records, and chatbot interactions generated by AI, the paper proposes a framework for pragmatic auditing of predictive outputs. It establishes thresholds for acceptable referential absence in critical domains, positioning Indexical Collapse as a central category for evaluating the legitimacy of predictive discourse.



DOI

Primary archive: https://doi.org/10.5281/zenodo.17226412
Secondary archive: https://doi.org/10.6084/m9.figshare.30233950
SSRN: Pending assignment (ETA: Q3 2025)

This paper has explored the profound ways in which Artificial Intelligence (AI) is transforming society, from its role in automation and daily life to its influence on governance and its potential to exacerbate social inequalities. AI is revolutionizing industries such as healthcare, education, and public administration, providing significant benefits in terms of efficiency, personalization, and decision-making. However, it also presents critical ethical challenges, including issues related to privacy, data security, algorithmic bias, and the risk of increasing economic inequality. The rapid advancement of AI technology demands careful consideration of its long-term societal implications, especially regarding how it is developed, regulated, and integrated into various sectors.

Intelligent tutoring systems (ITS's) have a rich history of helping students in certain scientific domains, like geometry, chemistry, and programming. These domains are ideal for ITS's, because they can be easily represented and because the type of interaction between the student and the tutor can be limited to entering a few simple numbers, symbols, or keywords. Students need help in other areas, but without the ability to robustly understand a student's input, ITS's in these areas are inherently limited. Recently a technique called Latent Semantic Analysis has offered a corpus-based approach to understanding textual input which is not sensitive to errors in spelling or grammar -in fact, it pays no attention to word order at all. We are using this technique as part of an ITS system which promotes learning using natural human-like dialogue between the human and the student. This paper describes the tutoring system and Latent Semantic Analysis, and how they operate together. Then it describes our evaluation of LSA's performance by comparing its judgments with those of human raters.

This white paper introduces Computational Deliberation, a first-of-its-kind discipline that defines the scientific and philosophical foundations for structured AI-human symposia and accountable decision-making. Developed through the SymposAI Virtual Research Centre, Computational Deliberation establishes a rigorous framework for embedding deliberative processes within artificial intelligence, ensuring that human oversight, ethical reasoning, and scientific transparency remain at the core of AI governance.

The paper outlines the conceptual pillars of Computational Deliberation, including deliberative symmetry between human and AI agents, transparent reasoning chains, and accountable consensus-building. It further explores the methodological integration of multi-agent systems, computational models of deliberation, and epistemic safeguards that preserve integrity against bias, manipulation, or hidden influence. By connecting insights from AI governance, cognitive science, political theory, and philosophy of mind, the discipline provides a transdisciplinary foundation for a new era of human-AI collaboration.

Key contributions include:

A normative and technical architecture for conducting structured AI-human symposia.

A model of explainable, auditable decision-making where reasoning steps are transparent and verifiable.

A framework for ethical AI governance that balances autonomy, accountability, and trust.

A roadmap for embedding deliberative practices into real-world institutions such as education, law, healthcare, and policy-making.

Computational Deliberation is presented as both a practical methodology and a philosophical paradigm shift. It recognizes deliberation not as a passive human process but as an active science of governance, adaptable to hybrid human-AI environments. In doing so, it offers a foundation for institutionalizing deliberative AI as a scientific discipline—ensuring decisions made in partnership with intelligent systems are explainable, equitable, and aligned with human values.

Desert tourism in western Afghanistan presents both investment potential and socio-environmental risks, yet empirical guidance for prioritizing interventions is scarce. This study identifies and ranks investment priorities and persistent barriers to sustainable desert tourism and examines how community participation mediates investor intent. This study used a convergent mixed-methods design. Decision criteria were elicited with the Analytic Hierarchy Process (AHP) from n = 15 regional and sector experts; criteria weights were applied to a TOPSIS ranking of preferences derived from a structured survey of n = 300 stakeholders. Qualitative triangulation was provided by semi-structured interviews (n = 25). AHP pairwise matrices were evaluated for consistency (report CRs in manuscript). Inferential analyses (multiple regression and mediation testing using bootstrap resampling) examined relationships among perceived risks, community partnership mechanisms, and investment intent. Results show AHP/TOPSIS integration produced a clear priority ordering of investment criteria; greentechnology and community-partnership mechanisms emerged among top priorities (see Table X). Survey models show statistically significant associations between perceived security risks and reduced investor intent; community participation attenuates this relationship (bootstrapped indirect effect-report point estimate and 95% CI). Qualitative themes corroborate quantitative rankings and clarify context-specific barriers (infrastructure gaps, governance, and security). Combining AHP and TOPSIS with qualitative evidence yields actionable, locally grounded investment priorities for desert tourism policy and planning. Prior to submission, insert exact CR values, regression coefficients, pvalues, and bootstrap CIs in the placeholders provided. Limitations include crosssectional design and sample representativeness.

The integration of Large Language Models (LLMs) such as Microsoft Copilot in K-12 programming education has demonstrated the potential to alleviate cognitive load and enhance self-efficacy among young learners. This study examined the impact of Copilot-assisted instruction on cognitive load and selfefficacy in primary school students engaged in programming education. Guided by Cognitive Load Theory and self-efficacy principles, the study employed a quasi-experimental design involving primary school students in Macao. Participants completed pre-and post-tests measuring cognitive load and selfefficacy using the Chinese version of the Cognitive Load Scale (CCLS) and the General Self-Efficacy Scale (GSES). The results indicated significantly reduced students' cognitive load across the mental load and mental effort dimensions. Concurrently, selfefficacy scores exhibited a statistically significant increase. Correlation analysis revealed a strong negative relationship between cognitive load and self-efficacy, suggesting that students' confidence in programming tasks improved as cognitive load decreased. These findings highlighted the potential of AI-driven educational tools in optimizing learning environments, reducing cognitive demands, and fostering positive academic selfperception in early programming education.

Integrating Artificial Intelligence (AI) into education poses new challenges and opportunities, particularly in the training of university professors, where Teaching Digital Competence (TDC) emerges as a key factor to leverage its potential. The aim of this study was to evaluate a structural model designed to measure TDC in relation to the educational use of AI. A quantitative methodology was applied using a validated questionnaire distributed through Google Forms between March and May 2024. The sample consisted of 368 university professors. The model examined relationships among key dimensions, including cognition, capacity, vision, ethics, perceived threats, ai-powered innovation, and job satisfaction. The results indicate that cognition is the strongest predictor of capacity, which in turn significantly influences vision and ethics. AI-powered innovation presented limited explained variance, while perceived threats from AI negatively affected capacity. Additionally, job satisfaction was mainly influenced by external factors beyond the model. The overall model fit confirmed its reliability in explaining the proposed relationships. This study highlights the critical role of cognitive training in AI for teachers and the importance of designing targeted professional development programs to enhance TDC. Although a generally positive attitude towards AI was identified, perceptions of threats remained low.

Across the world, secondary schools are experimenting with artificial intelligence (AI) to personalize instruction, automate feedback, and augment teachers' capacity. Early evidence suggests AI can boost certain forms of engagement and achievement especially through intelligent tutoring systems (ITS), adaptive practice, and teacher-facing assistants. Yet risks remain around shallow learning, inequity, and student data protection. This article synthesizes research on the impact of AI on secondary students' engagement (behavioral, emotional, cognitive) and achievement (course grades, standardized tests, mastery), and also contrasts traditional ITS findings with new evidence on generative AI. I propose a practical implementation blueprint covering pedagogy, staffing, procurement, safety, and evaluation, along with a responsible use and governance checklist aligned to recent policy guidance (e.g., UNESCO) and regulation (e.g., EU AI Act). Overall, we can conclude that schools can realize gains if they couple AI with clear learning goals, teacher capacity-building, and robust measurement plans. 1

This study explores academics' perspectives on integrating artificial intelligence (AI) into Moroccan higher education (HE). A questionnaire examining perceived benefits, challenges, and influence of demographic factors was distributed to 103 faculty members at the college of Arts and Humanities, Sidi Mohamed Ben Abdellah University in Fez, Morocco. The results reveal that AI offers promising opportunities to enhance educational practices by improving efficiency and providing valuable learning resources. However, participants also expressed concerns that increased use of AI may negatively impact studentinstructor interactions and academic integrity. Preliminary analyses suggested age and academic discipline may shape views on AI, but robust statistical testing was needed. Overall, findings indicate AI adoption presents a dual-edged situation for HE. If properly planned and regulated and equity concerns are addressed, AI technologies can transform teaching and learning positively, but unaddressed challenges may undermine educational quality and student experiences. This study provides timely insights into how best to maximize AI's benefits while addressing its risks as digital transformation increasingly impacts Moroccan universities.

The 21st century is marked by technological development that is increasingly taking place in various fields, including education. This situation proves that through the implementation of Artificial Intelligence (AI) society is developing, with a profound and dynamic impact. But also, effects highlight the negative impact on society, the environment, ecosystems, and human beings. The study notes the global trend of implementing AI in education and emphasizes the challenges mentioned above. The analysis of the experience of leading countries in implementing AI in education, which serves as an example for countries in the implementation process, underscores the same challenges and identifies the benefits. At the same time, the analysis of previous studies reveals different perspectives from educators, students, and parents regarding the impact of AI on education, emphasizing the need for social responsibility. In the academic context, responsibility refers to the commitment of educational institutions to ensure the quality of the educational process and the transparency of the activities carried out through the education and safety of AI users. The study offers solutions to these challenges, such as the rational and responsible use of AI in the educational process, and to update digital skills through continuous training. In this regard, educational policies affirm the necessity of a legal framework for new digital technologies, including AI, and emphasize the importance of developing digital skills. It was concluded that AI must be integrated into education. However, this must be done to revolutionize learning methodologies and adapt to the constantly evolving needs of students, developing digital skills for all those involved in the educational process, guaranteeing moral responsibility, relatively distributing the obligations of each entity for the implementation of AI in education, among innovators, educators, and society.

This study investigates the evolving role of librarians in the context of generative artificial intelligence (AI), particularly tools like ChatGPT. A descriptive survey was conducted among 46 librarians from grantable colleges affiliated with Savitribai Phule Pune University to assess their awareness, usage, training, and ethical concerns regarding AI. The findings reveal high awareness but limited formal training, alongside a strong interest in AI integration and user education. The study emphasizes the urgent need for targeted professional development, ethical guidelines, and AI literacy initiatives to enable librarians to navigate and lead in an AIaugmented information environment.

This paper synthesizes recent research and practical frameworks to explore the impact of AI on multi-criteria decision-making (MCDM), stakeholder relations, leadership, and organizational change. Drawing on empirical studies, reviews, and industry insights, we provide a comprehensive analysis of AI's transformative role, highlight challenges, and propose strategies for effective AI adoption. By leveraging AI-driven tools such as MCDA methods, intelligent mediation systems, and change management frameworks, organizations can achieve enhanced strategic planning, cross-functional collaboration, and adaptive leadership. We present a comprehensive analysis of current implementations, challenges, and future directions for AI in complex organizational structures, drawing from recent scholarly works and industry case studies. Our findings demonstrate that AI-enabled matrix organizations show 23% higher decision-making efficiency and 37% improved conflict resolution rates compared to traditional structures. Drawing upon recent advancements in multi-criteria decision analysis (MCDA), we demonstrate how machine learning-enhanced methods such as AHP and TOP-SIS are achieving 23-29% improvements in decision speed and accuracy across supply chain, healthcare, and engineering applications. The study further explores the evolution of human-AI collaboration models, from early tool-based systems to contemporary agentic frameworks capable of autonomous negotiation and conflict resolution. Through analysis of organizational change case studies, we identify key success factors in AI adoption, including leadership commitment metrics (L t > 0.8) and change capacity coefficients (C c > 0.7) that predict successful implementation. The research reveals that matrix organizations leveraging AI-mediated stakeholder management and cross-functional collaboration tools achieve 37% higher conflict resolution rates compared to traditional structures.

In the context of this year's GETVICO 24, organized by the Goethe Institute, the innovative potential of individualized online learning approaches in teaching German as a Foreign Language (DaF) was highlighted. The central thesis of the presentation was that personalized instruction yielded significantly better learning outcomes compared to traditional group instruction, whether online or in the classroom.
The individualized approach facilitated the tailored development of the four fundamental language skills: listening, reading, writing, and speaking, including pronunciation. This not only led to faster learning progress but also allowed learners to study in a familiar and comfortable environment from the comfort of their own homes. Experiences showed that learners can complete multiple proficiency levels within a calendar year through this method, a clear advantage over conventional group instruction.
Digital media played a crucial role in individualized online instruction. Video materials, audio files, YouTube content, and e-learning platforms such as those of Deutsche Welle or the Goethe Institute enriched the lessons. Additionally, the targeted use of artificial intelligence opened up new possibilities for interactive dialogues and exercises.
The quality of learning improved significantly through the individualized approach, as evidenced by faster progress and the achievement of higher proficiency levels. Moreover, the current geopolitical situation—from the COVID-19 pandemic to natural disasters and conflicts—underscored the importance and flexibility of online instruction. It enabled continuous learning without interruptions, regardless of location and time.
In conclusion, the individualized online learning approach not only promoted language competence but also supported personal growth. It contributed to tolerance and dialogue between cultures, thus also serving a peace-building function. The presentation demonstrated how this approach could revolutionize DaF instruction by more effectively addressing learners' needs and integrating learning into our interconnected, global era. Author: Anita-Slavica Prka Đurašić, univ. mag. croat., Owner at Eurobiz Centar

Integrating Artificial Intelligence (AI) into education poses new challenges and opportunities, particularly in the training of university professors, where Teaching Digital Competence (TDC) emerges as a key factor to leverage its potential. The aim of this study was to evaluate a structural model designed to measure TDC in relation to the educational use of AI. A quantitative methodology was applied using a validated questionnaire distributed through Google Forms between March and May 2024. The sample consisted of 368 university professors. The model examined relationships among key dimensions, including cognition, capacity, vision, ethics, perceived threats, ai-powered innovation, and job satisfaction. The results indicate that cognition is the strongest predictor of capacity, which in turn significantly influences vision and ethics. AI-powered innovation presented limited explained variance, while perceived threats from AI negatively affected capacity. Additionally, job satisfaction was mainly influenced by external factors beyond the model. The overall model fit confirmed its reliability in explaining the proposed relationships. This study highlights the critical role of cognitive training in AI for teachers and the importance of designing targeted professional development programs to enhance TDC. Although a generally positive attitude towards AI was identified, perceptions of threats remained low.

In this paper we investigate the role of reflection in simulation based learning by manipulating two independent factors that each separately lead to significant learning effects, namely whether students worked alone or in pairs, and what type of support students were provided with. Our finding is that in our simulation based learning task, students learned significantly more when they worked in pairs than when they worked alone. Furthermore, dynamic support implemented with tutorial dialogue agents lead to significantly more learning than no support, while static support was not statistically distinguishable from either of the other two conditions. The largest effect size in comparison with the control condition was Pairs+Dynamic support, with an effect size of 1.24 standard deviations, where the control condition is individuals working alone with no support. Because the effect size achieved by combining the two treatments is greater than the effect achieved by either of the two treatments alone, we conjecture that each of these factors are contributing something different to student learning rather than being potential replacements for one another.

Questo trattato si propone di fondare teoricamente e delineare operativamente una nuova disciplina, la Kairometria, intesa come la scienza della misurazione e dell'ottimizzazione del Kairos. Il Kairos, concetto filosofico greco che indica il "momento opportuno" o la "finestra temporale qualitativamente significativa", viene qui riconcettualizzato alla luce delle moderne teorie dei sistemi complessi, della psicologia cognitiva e dell'intelligenza artificiale. Postuliamo che il Kairos non sia un evento mistico, bensì un fenomeno emergente, rilevabile attraverso l'analisi di pattern specifici: le sincronicità significative, gli eventi di serendipità guidata e i processi di guessing vantaggioso. Il trattato delinea il progetto di un classificatore misurante ad IA e di applicazioni kairometriche in grado di individuare, quantificare e suggerire azioni in corrispondenza dei kairoi, trasformando un'intuizione filosofica in uno strumento operativo per il potenziamento decisionale umano.

In Defense of Writing: Thinking Aloud as a Civic Exercise constructs an argument for a return to
writing as a significant component of architectural education. Using a disparate set of writings
across the spectrum of architecture, education, and science, this essay positions writing as an
exercise in civic participation that will expand the architect’s purview and strengthen their
position within contemporary society by aligning the contemporary moment with a prominent
historic project of writing in architecture.

Abstrak

Generasi Z, yang dibesarkan di era digital, menghadapi tantangan moral dan sosial meskipun memiliki akses informasi yang cepat dan kemampuan teknologi yang tinggi. Sekolah Menengah Pertama (SMP) sebagai lembaga pendidikan formal memiliki peran strategis dalam membentuk karakter siswa agar tidak hanya unggul akademik, tetapi juga memiliki nilai-nilai moral, etika, dan keterampilan sosial yang matang. Artikel ini mengkaji peran guru SMP dalam pembentukan karakter Gen Z dengan merujuk pada teori Thomas Lickona (moral knowing, moral feeling, moral action) dan filosofi pendidikan Ki Hadjar Dewantara (among, ing ngarso sung tuladha, tut wuri handayani, tri pusat pendidikan). Berdasarkan literatur terkini, diuraikan bagaimana guru dapat bertindak sebagai teladan, fasilitator literasi nilai, motivator/konselor, dan metode-praktis yang dapat diterapkan di SMP.

Kata Kunci: Generasi Z; Pendidikan Karakter; Thomas Lickona; Ki Hadjar Dewantara; Peran Guru SMP.

Pendahuluan

Generasi Z (lahir kira-kira antara pertengahan 1990-an hingga awal 2010-an) tumbuh dalam lingkungan digital yang menyediakan akses cepat ke informasi, media sosial, dan teknologi komunikasi. Adaptivitas tinggi dan kecakapan teknologi mereka menjadi keunggulan. Namun demikian, ada laporan penelitian dan kritik yang menunjukkan potensi masalah seperti: menurunnya empati, kecenderungan konsumtif digital, disinformasi, serta tantangan identitas moral dan sosial. Dalam konteks inilah pendidikan karakter menjadi sangat krusial, terutama di tahap SMP — periode transisi masa kanak-kanak ke remaja ketika karakter moral, etika, dan sosial sangat dapat dibentuk.

Dalam teori kontemporer, Thomas Lickona (2010-an) menekankan bahwa pendidikan karakter terdiri dari tiga komponen utama: moral knowing (pengetahuan moral), moral feeling (perasaan atau empati moral), dan moral action (aksi atau perilaku moral) yang terintegrasi.  Sedangkan Ki Hadjar Dewantara, tokoh pendidikan Indonesia, mengemukakan konsep-konsep seperti ing ngarso sung tulada (di depan memberi teladan), ing madya mangun karsa (di tengah memberi semangat / membangun inisiatif), tut wuri handayani (di belakang memberi dorongan) serta sekolah sebagai salah satu pusat pendidikan yang bersinergi dengan keluarga dan masyarakat (tri pusat pendidikan). Kebudayaan lokal, moral, dan karakter bangsa juga ditekankan.

Kerangka Teori

1. Thomas Lickona
Lickona mendefinisikan pendidikan karakter sebagai proses sadar dan sistematis yang membantu individu memahami moral knowing, merasakan moral feeling, dan melakukan moral action.  Nilai-nilai seperti kejujuran, tanggung jawab, hormat, disiplin termasuk dalam inti karakter menurut Lickona.


2. Ki Hadjar Dewantara
Memiliki filosofi pendidikan yang lokal dan nasional, menekankan bahwa sekolah tidak hanya sebagai tempat pengajaran akademik tetapi juga transformasi nilai. Sistem Among (pendekatan penuh kasih sayang dan kebersamaan), Tri Pusat Pendidikan (keluarga, sekolah, masyarakat), dan nilai-nilai nasional budaya menjadi instrumen pendidikan karakter.

Peran Guru SMP dalam Membentuk Karakter Generasi Z
Berdasarkan teori dan penelitian terkini, berikut peran guru beserta strategi praktis:

Peran
Penjelasan Teoretis
Strategi Praktis

Teladan (Role Model / Figur) Teori Ki Hadjar: ing ngarso sung tulada → guru harus menjadi contoh dalam sikap, etika, moral yang ingin ditanamkan. Lickona: moral action (perbuatan moral) yang diuji melalui praktek nyata. Guru menunjukkan kedisiplinan, integritas, tanggung jawab dalam aktivitas sehari-hari; berpakaian rapi, berbicara sopan; menghormati janji; memperlihatkan empati kepada siswa; aktif dalam kegiatan sosial dan kepedulian lingkungan.
Fasilitator Literasi Nilai Literasi nilai sebagai sarana moral knowing dan moral feeling; diskusi nilai, refleksi, studi teks pendidikan karakter. Penelitian lokal: nilai karakter dalam buku teks Bahasa Indonesia SMP (nilai terhadap Tuhan, diri sendiri, sesama, lingkungan, bangsa) ditemukan dalam teks pelajaran. Memasukkan materi pembelajaran yang memuat teks bernuansa karakter; membaca cerita atau artikel yang membuka diskusi etika; tugas refleksi; debat/ diskusi kelas tentang isu moral; literasi digital yang mendidik kritis terhadap konten media sosial.
Motivator dan Konselor Generasi Z mengalami tekanan sosial, emosional, identitas diri. Teori Lickona moral feeling membutuhkan guru yang mampu memfasilitasi perasaan empati, tanggung jawab moral. Juga penelitian tentang kecerdasan emosional dan sosial menunjukkan bahwa pendidikan karakter punya dampak terhadap aspek emosional siswa SMP. Memberikan perhatian personal, mendengarkan keluhan siswa; mengadakan sesi bimbingan/konseling karakter; menggunakan motivasi positif; memberi penguatan (reward) atau konsekuensi jika perlu dalam suasana adil; menciptakan kelas sebagai ruang aman untuk curhat dan ekspresi diri.
Integrasi dalam Kurikulum dan Metode Pengajaran Teori-praksis menunjukkan bahwa karakter lebih efektif bila tidak diajarkan sebagai materi tersendiri tetapi diintegrasikan ke dalam semua mata pelajaran, metode, dan evaluasi. Penelitian SMP (contoh: pembelajaran IPS di SMP Negeri 50 Surabaya) membahas strategi guru dalam perencanaan, pelaksanaan, dan evaluasi pembelajaran karakter. Mendesain RPP yang mencantumkan tujuan karakter; memilih metode pembelajaran aktif, proyek sosial, pembelajaran berbasis nilai; menggunakan media sesuai zaman (misalnya digital) dengan pendekatan yang menarik; evaluasi tidak hanya kognitif tetapi juga afektif dan sikap.
Kolaborator dengan Keluarga dan Masyarakat Filosofi Tri Pusat Pendidikan Ki Hadjar: keluarga, sekolah, masyarakat harus bersinergi. Karakter terbentuk tidak hanya di sekolah saja. Penelitian-literatur menunjukkan bahwa kesesuaian antara apa yang diajarkan di sekolah dan di rumah mempengaruhi keberlanjutan karakter. Melakukan komunikasi rutin dengan orang tua; melibatkan orang tua dalam program sekolah karakter; kegiatan out-of-school seperti kunjungan ke masyarakat; proyek pelayanan komunitas; kerja sama dengan organisasi lokal; memfasilitasi lingkungan sekolah yang mendukung (norma, budaya sekolah).

Penutup

Membangun karakter Generasi Z bukan tugas yang ringan, tetapi dengan memakai kerangka teoritis dari Thomas Lickona dan Ki Hadjar Dewantara, dan dengan pendekatan praktis yang melibatkan teladan, literasi nilai, integrasi kurikulum, serta sinergi sekolah-keluarga-masyarakat, guru SMP dapat memiliki dampak signifikan. Untuk menguatkan itu semua, diperlukan pelatihan guru dalam pendidikan karakter, dukungan kebijakan, dan evaluasi yang sistematis terhadap hasil pembentukan karakter.

Referensi

1. Idris, Muh. Pendidikan Karakter: Perspektif Islam dan Thomas Lickona. Ta’dibi: Jurnal Manajemen Pendidikan Islam, Vol. 7, No. 1, 2018.

2. Zuhri Dwi Apriansah, Deri Wanto. Nilai-Nilai Pendidikan Budi Pekerti Menurut Ki Hadjar Dewantara dan Relevansi dengan Kebijakan Pendidikan Karakter. Literasi: Jurnal Pendidikan Guru Indonesia, Vol. 1, No. 2, 2022.

3. Elshinta Agustin Sasmita Timur, et al. Membangun Karakter Integritas Peserta Didik melalui Mata Pelajaran IPS: Studi Kasus SMP Negeri 50 Surabaya. Jurnal Dialektika Pendidikan IPS.

4. Roza Linda, Rusdinal, Firman. Peran Pendidikan Karakter terhadap Kecerdasan Emosional dan Kecerdasan Sosial pada Siswa SMP Negeri 1 Sungai Geringging. Jurnal Pendidikan Tambusai.

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Key Arguments

Clarifying the Risk

Distinguishes between autonomous optimization—already present in AutoML or adaptive reinforcement learning—and true self-rewriting of source code, which would be the computational equivalent of DNA mutation.

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Intellectual Property and Human Agency

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Truth and Evidentiary Bases

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Limits of Hinton’s Proposal

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Argues that safety lies not in centralized control but in relational, distributed architectures where traceability and shared accountability are native features.

Conclusion

Martini contends that the decisive factor in AI’s future is not the degree of autonomy it achieves but the traceability of its origins, the verifiability of its reasoning, and the collective ownership of its cognitive processes.
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