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Artificial Intelligence systems, subjects or objects in the legal relationship?
As the first quarter of the 21st century has passed, humanity has witnessed a phenomenon that has impacted various sectors, and the legal field has been no exception. Artificial Intelligence is transforming the world as we knew it until now. The development achieved by Artificial Intelligence systems and their widespread use in all human activities have certainly benefited society by facilitating and expediting these tasks with greater efficiency than humans. However, it has also raised the question of where to place Artificial Intelligence when it interacts within civil legal relationships, with various differing viewpoints existing today. Through a bibliographic study that included the analysis of classical and modern texts, publications, articles, and other sources, it was concluded that Artificial Intelligence systems do not meet the necessary requirements to be granted legal personhood and thus acquire the corresponding rights and obligations. Instead, Artificial Intelligence belongs to the objective element of civil legal relations, as despite possessing certain intelligence and reasoning capabilities, it remains an object within legal transactions.
Copyright (c) 2026 Vania Jacomino González, Bárbara Rosa Chinea Cárdenas (Author) https://creativecommons.org/licenses/by/4.0
Artificial Intelligence and the New Civil and Family Procedure in Mexico: Challenges and Opportunities
Despite being a relatively new development, artificial intelligence is already considered one of the most important technologies in human history. Also known as AI, artificial intelligence is used every day in an increasing number of tasks. However, AI is also a controversial topic. Despite its many benefits, specialists also warn of its risks. Amid this debate, numerous projects continue to explore new and more complex uses for this technology. One of these areas is the administration of justice. From its use as a legal information search tool to its potential deployment as an artificial judge or mediator, AI undoubtedly has the potential to bring about profound changes in the justice system in the future. In this context, the present study seeks to analyze the suitability of the current Mexican legal framework both to leverage the opportunities offered by artificial intelligence and to address its challenges in the administration of justice. To this end, the case of the new civil and family justice model, which will begin upon the entry into force of the National Code of Civil and Family Procedure projected for April 2027, will be used. It is worth mentioning that the choice of civil and family procedural law is not accidental; on the contrary, the new Code stands out, among other innovations, for its special emphasis on implementing digital justice and using information and communication technologies.
National Code of Civil and Family Procedures
Copyright (c) 2026 Luisa Córdova Marín, Alfonso Calcáneo Sánchez (Author) https://creativecommons.org/licenses/by/4.0
Key criteria as guarantors of legal certainty for contracts involving artificial intelligence systems
With the increasing development of IA, as well as, with its massive use, the absence of a clear regulation on the creation, execution and exploitation of the results of IA systems determines that the lex inter partes becomes even more important, especially to adequately distribute the risks inherent to IA and the uncertainty that surrounds it. Therefore, when drafting contracts involving IA, it is necessary to propose essential criteria for an analysis of their contents in order to strengthen their legal certainty. The research carried out has a qualitative approach, using applied research with descriptive and propositional advance and also addresses the issue from the national and international perspective, in order to process the information and discuss results, using as methods the analysis of documents, the interview, and following an exhaustive analysis guide, for the case study of a contract, in which the Central University Marta Abreu de las Villas is involved. From them, the main problems encountered in this type of contract will be identified, as well as the issues that should be improved to provide greater legal certainty to the parties involved.
Copyright (c) 2026 Bárbara Rosa Chinea Cárdenas, Vania Jacomino González (Author) https://creativecommons.org/licenses/by/4.0
Interplay of AI Literacy, Readiness-Confidence, and Acceptance among Pre-Service Teachers in Philippine Higher Education: A Gender, Discipline, and Connectivity Perspective
This study explores the role of artificial intelligence (AI) in teacher education, focusing on preservice teachers’ preparedness for AI integration. It examined the levels of AI literacy, readiness-confidence, and acceptance among preservice teachers in Philippine higher education institutions, and investigated differences across gender, academic discipline, and internet connectivity. Using a cross-sectional survey design, data were collected from 384 preservice teachers through validated instruments that measured AI literacy, readiness-confidence, and acceptance. Analyses included descriptive statistics, independent samples t-tests, and correlation analysis. Findings revealed high readiness-confidence and moderate to high literacy and acceptance levels. Significant differences emerged, with male preservice teachers, STEM students, and those with reliable internet access reporting higher scores, particularly in readiness-confidence. Strong positive correlations among literacy, readiness-confidence, and acceptance underscored their interdependent relationship in shaping preparedness for AI integration. These results emphasize the need for tailored and inclusive AI education and training programs that address demographic and infrastructural disparities. Beyond equipping preservice teachers with skills, preparing them for AI adoption is about shaping the future of education by ensuring that tomorrow’s classrooms are led by educators who are competent, confident, and capable of driving innovation, equity, and progress in a rapidly evolving digital age.
Copyright (c) 2025 Keir A. Balasa (Author) https://creativecommons.org/licenses/by/4.0
Perceptions of Generative AI among Development Communication Students: Insights by Gender and Age from the Philippines
Generative artificial intelligence (GenAI) tools such as ChatGPT are increasingly used in higher education, yet students’ perceptions remain varied and may be shaped by demographic factors. This study examined the overall perceptions of Development Communication students toward generative AI and investigated whether these perceptions differ by gender and age. Using a descriptive-quantitative design, survey data were collected from 208 students and analyzed using descriptive statistics and independent samples t-tests. The results showed a neutral overall perception of generative AI (M = 3,31; SD = 0,65), indicating a balanced view of its advantages and limitations. Students positively rated AI’s 24/7 availability (M = 3,46; SD = 0,97), its ability to offer unique perspectives (M = 3,42; SD = 1,00), and teachers’ growing awareness of AI-assisted work (M = 3,63; SD = 0,82). Skepticism was evident regarding AI’s potential to replace teachers (M = 2,86; SD = 1,20). A significant gender difference emerged, with male students (M = 3,81; SD = 0,28) reporting higher perceptions than female students (M = 3,07; SD = 0,65), t(206) = 8,94; p < 0,001; d = 0,55. No significant differences were found across age groups, t(206) = –0,52; p = 0,61. Overall, the findings suggest that students recognize the usefulness of generative AI but remain cautious about its limitations and ethical implications. The observed gender disparity underscores the need for inclusive AI literacy initiatives to support equitable and responsible integration of GenAI in higher education.
Copyright (c) 2025 Jay Rodel C. Serdenia (Author) https://creativecommons.org/licenses/by/4.0
A dignitarian approach to ai ethics: grounding normative principles in human value
Introduction: The proliferation of guidelines for artificial intelligence ethics presented a field without a firm philosophical foundation. Current documents offered a disparate collection of principles, often lacking a unified justification for their normative force. This paper confronted that deficiency by proposing a novel dignitarian framework. The objective of this research was to establish a stable and rationally defensible basis for the design, deployment, and governance of AI systems. Methods: This study employed a conceptual analysis of the Kantian philosophical tradition to define human dignity as an absolute, intrinsic value. This core concept was then formalized into a coherent axiomatic system using elementary set theory and deontic logic. The analysis was based on a critical review of foundational texts in moral philosophy and contemporary AI ethics literature. Results: A primary normative constraint emerged from this formalization: an AI system's action, a, was morally permissible only if it did not treat any person, p, merely as a means to an end. This was expressed logically as Permissible(a) → ∀p ∈ P, ¬ViolatesDignity(a, p). This principle functioned as a strict deontological limit on any goal-oriented programming. Conclusions: The proposed framework provided a stable and rationally defensible basis for the design, deployment, and governance of AI systems. It moved the conversation from a list of suggestions to a structured ethical system, contributing to the growing field of computational ethics by offering a clear, implementable, and non-negotiable constraint on AI behavior.
Copyright (c) 2025 Mohammed Zeinu Hassen (Author) https://creativecommons.org/licenses/by/4.0
The Impact of AI-Based Learning on Academic Performance
This study compellingly demonstrates the effectiveness of AI-driven personalised learning algorithms in boosting academic performance among secondary school students in Portugal. Using a rigorous quasi-experimental, non-randomised two-shot pre-test and post-test design, we engaged sixty 10th-grade students divided into two distinct groups. The experimental group experienced AI-assisted instruction through innovative platforms, including Brisk Teaching, Khanmigo, ChatGPT 4.0 Turbo, and Quizizz AI, while the control group adhered to traditional teaching methods. Both groups participated in identical pre-tests and post-tests for two essential units: Energy in the Ecosystem and Heredity and Variation. Robust statistical analyses, including paired and independent samples t-tests, revealed significantly greater learning gains in the AI-driven group compared to the control group. Moreover, we assessed the influence of key factors, including student engagement, prior knowledge, and learning preferences, using validated Likert-scale questionnaires. The results clearly indicated a strong positive correlation between AI-driven learning and enhanced student motivation and comprehension. These findings strongly support the use of AI-based personalised instruction as an effective strategy for enhancing learning outcomes in STEM education, particularly in diverse classroom settings.
Copyright (c) 2026 Maria Nascimento Cunha, Maria Leonor dos Santos Esteves, Mariana Lopes de Sá Matos, Patricia Silva Martins (Author) https://creativecommons.org/licenses/by/4.0
Artificial Intelligence for Fraud Detection and Portfolio Optimization: Insights from UK Financial Institutions
Introduction: Artificial intelligence has become an essential tool in modern financial services, particularly in the areas of fraud detection and investment management. Increasing financial crimes and the growing complexity of market environments have created a need for advanced technological solutions capable of supporting more accurate and timely decision-making in financial institutions. Objective: This study examines the role of artificial intelligence in enhancing fraud detection and portfolio optimization within financial institutions in the United Kingdom. Method: A quantitative, observational survey was conducted among one hundred and fifty banking professionals from five major financial institutions. The data collection instrument was developed by the researchers using information derived from twenty peer-reviewed academic sources and industry publications. Data were analysed using descriptive statistics to identify general patterns and tendencies in respondents’ perceptions. Results: The results show that artificial intelligence contributes meaningfully to strengthening fraud prevention mechanisms and improving investment management processes, particularly through enhanced analytical capacity and improved risk interpretation. Conclusion: The study further demonstrates that artificial intelligence supports more secure, efficient, and responsive financial operations, indicating its relevance for the future of financial services in the United Kingdom.
Copyright (c) 2025 Iyanu Emmanuel Olatunbosun, Abosede Rebecca Olatunbosun (Author) https://creativecommons.org/licenses/by/4.0
Exploring Academic Librarians’ Perception towards Artificial Intelligence in Nigerian Polytechnics
Introduction: Artificial intelligence is increasingly shaping library practices worldwide. Although Nigerian academic libraries are gradually embracing digital technologies, little is known about how librarians in polytechnic institutions interpret the integration of artificial intelligence in their work environment. Objective: The study aimed to examine the perceptions of academic librarians in Nigerian polytechnics regarding the adoption of artificial intelligence in library operations. Method: A survey design using mixed methods was employed. Quantitative data were collected from fifty academic librarians, while qualitative insights were obtained from five heads of ICT or automation units. Participants were purposefully selected from five polytechnic libraries in the South South region of Nigeria. Descriptive statistics and narrative analysis were used to analyse the data. Results: The study shows that librarians are aware of artificial intelligence and acknowledge its value in improving library services. However, concerns about job security and institutional readiness influence their attitudes toward adoption. While respondents recognise the potential of artificial intelligence to enhance user satisfaction and operational efficiency, they also highlight the need for adequate training and supportive infrastructure. Conclusion: The perceptions of librarians reflect both enthusiasm and caution toward artificial intelligence adoption. To ensure meaningful integration, institutions must invest in capacity development, strategic planning, and supportive policies. Strengthening the digital competence of librarians will be essential for sustaining their relevance and ensuring effective participation in emerging technological landscapes.
Copyright (c) 2025 Bolaji David Oladokun, Magnus Osahon Igbinovia, Ebiere Diana Orubebe, Iyanu Emmanuel Olatunbosun (Author) https://creativecommons.org/licenses/by/4.0
Attitudes and perceptions toward artificial intelligence on teacher job satisfaction at a medical school in northern Peru
Objective: determine the relationship between attitude and perception of AI in teaching job satisfaction at a Faculty of Human Medicine. Materials and methods: A quantitative, non-experimental, analytical-correlational and transectional research was carried out, with a sample of 87 teachers selected through non-probabilistic sampling. Results: The results revealed that 74.7% of teachers expressed positive attitudes towards AI, while 80.5% presented positive perceptions. Regarding job satisfaction, 78.2% reported a regular level, 20.7% a high level and only 1.1% a low level. Perception towards AI showed a low but significant positive correlation with job satisfaction (Rho = 0.285, p = 0.036), while attitudes did not show a significant relationship (p = 0.264). Additionally, it was found that factors such as the contractual employment relationship influence openness towards AI. Hired teachers with less experience (1-3 years) showed a greater positive predisposition towards technology. Conclusions: It was determined that the perception towards AI of university teachers has a significant relationship with their job satisfaction. These findings underscore the importance of teacher training and institutional policies to integrate AI effectively, optimizing both job satisfaction and educational performance.
Copyright (c) 2025 Teresa Carolina Cacho Quiroz, Miguel Ángel Díaz Cabanillas, Rosa Milagros Vigo Quispe, Enzo Bazualdo Fiorini (Author) https://creativecommons.org/licenses/by/4.0
Teachers’ Perceptions of Learning From, About, and With Artificial Intelligence in Education (AIED): Implications for Ethical Practice and the Challenges of AI Use in Basic Education
Teachers’ perceptions play a key role in shaping how emerging technologies are accepted and applied in education. With artificial intelligence (AI) becoming more prominent in schools, it is important to explore how teachers view its role as a source of knowledge, a subject to be taught, and a tool for instruction. The purpose of this study was to examine teachers’ perceptions across three domains—learning from AI, learning about AI, and learning with AI—and to analyze how these areas are interrelated. A descriptive-quantitative-correlational design was employed, involving 204 public elementary teachers selected through proportionate random sampling from 22 schools in Manicahan District, Division of Zamboanga City. Results revealed that teachers expressed high perceptions of learning from AI (M = 4,36, SD = 0,58) and learning about AI (M = 4.22, SD = 0.62), while learning with AI received a very high rating (M = 4,48, SD = 0,55). The overall mean score of 4,35 (SD = 0,58) indicated generally favorable views toward AI in education. Correlation analysis further showed significant positive relationships among the three domains, with the strongest link between learning about AI and learning with AI (r = 0,546, p < 0,001). These findings suggest that as teachers deepen their knowledge of AI, they are more inclined to apply it in classroom practice, highlighting the importance of professional development that integrates both conceptual understanding and practical application of AI in teaching.
Copyright (c) 2025 Christopher Iris Francisco (Author) https://creativecommons.org/licenses/by/4.0
The impact of artificial intelligence on prostate cancer diagnosis through magnetic resonance imaging
The study examines the impact of artificial intelligence (AI) on the diagnosis of prostate cancer using magnetic resonance imaging (MRI), emphasizing its ability to overcome the limitations of traditional methods. Through a systematic review based on the PRISMA guidelines, 20 recent studies (2023-2024) employing advanced techniques such as convolutional neural networks, deep learning, and computer-aided detection systems were evaluated. Findings revealed that AI significantly enhances diagnostic accuracy, achieving areas under the curve (AUC) of up to 0.997, 99.5% sensitivity, and 99% specificity, while reducing interobserver variability and the need for invasive procedures. Additionally, limitations related to the required technological infrastructure and algorithm transparency were identified. The study concludes that AI is an essential tool in modern diagnosis, complementing traditional methods and improving the precision and efficiency of prostate cancer detection.
Copyright (c) 2024 C. Ramirez Chávez , Enzo Bazualdo Fiorini , Segundo Bueno Ordoñez (Author) https://creativecommons.org/licenses/by/4.0
Preservice Teachers and AI in Education 5.0: Examining Literacy, Anxiety, and Attitudes across Gender, Socioeconomic Status, and Training
Education 5,0 underscores the central role of artificial intelligence (AI) in reshaping teaching and learning, yet the readiness of preservice teachers to engage with these technologies remains at an early stage. This study set out to examine the levels of AI literacy, anxiety, and attitudes among preservice teachers in state universities and colleges in the Zamboanga Peninsula, taking into account gender, socioeconomic status, and training as key demographic variables. Using a descriptive-quantitative, correlational-comparative design, data were gathered from 378 respondents and analyzed through descriptive statistics, independent samples t-tests, one-way ANOVA, and Pearson correlation. Results revealed that preservice teachers demonstrated moderately high literacy (M = 3,80), moderate anxiety (M = 3,00), and generally positive attitudes (M = 3,60). Gender differences were evident, with males reporting higher literacy but lower anxiety, while females showed greater anxiety and slightly more positive attitudes. Socioeconomic status also influenced literacy and anxiety, favoring students from higher-income groups, though attitudes showed little variation. Training enhanced literacy but had negligible effects on anxiety and attitudes. Correlation analysis confirmed that higher literacy was strongly linked to lower anxiety and moderately associated with more positive attitudes, while anxiety was related to less favorable attitudes. These findings highlight the pivotal role of literacy in reducing apprehension and strengthening acceptance of AI. The study recommends embedding structured AI literacy programs in teacher education curricula, alongside targeted interventions for female and low-income students, to ensure equitable and confident readiness for AI integration in line with the goals of Education 5,0.
Copyright (c) 2025 Alexandhrea Hiedie Dumagay (Author) https://creativecommons.org/licenses/by/4.0
Recent Advances in Generative AI and Their Impact on Education: Exploring Self-Efficacy in Learning Environments
Abstract: The study analyzes recent advances in generative AI and examines how these technologies interact with student self-efficacy within educational settings. It discusses how innovative AI tools can enrich learning experiences and aims to outline potential roles for these tools in boosting learners’ confidence and motivation.This study emphasis on the comprehensive understanding of how GenAI technology will increase confidence levels of a student into academic work. Method: A quantitative approach was employed with a sample of 161 students from diverse disciplines. Participants completed a self-efficacy scale and answered items related to their experience with generative AI tools. Statistical analysis were conducted to explore relationships between AI engagement and self-efficacy levels. Results: The analysis's findings show a positive relationship between the application of generative AI and improved self-efficacy in pupils. The outcomes include increased confidence in learning activities and a stronger willingness to take on difficult tasks.The mean score on the Self-Efficacy (SE) scale was 3.12 , SD = 0.58, indicating that on average, the participants reported a relatively high level of self-efficacy. This suggests a sample of students who, in general, feel confident in their ability to overcome challenges and succeed academically. Regarding generative AI usage, the data showed a wide range of engagement. The mean frequency of use was 3.45, SD = 1.15, with a significant portion of students reporting that they "often" or "very often" use GenAI tools for academic purposes. The most commonly reported uses were for brainstorming ideas (78% of users), followed by drafting outlines (65%) and revising text (55%). A smaller percentage reported using AI for complex tasks such as generating code (28%) or scientific summaries (22%). Conclusions: The findings suggest that introducing generative artificial intelligence into educational programs might be beneficial to the growth of students; nevertheless, additional research is necessary. The findings of this study highlight the importance of self-efficacy as a potential mediator in learning advances offered by artificial intelligence. Future research should investigate specific uses of artificial intelligence and the ways in which these applications affect the teaching and learning processes.
Copyright (c) 2025 Sarita Peddi, Geetha Manoharan (Author) https://creativecommons.org/licenses/by/4.0
Critical and correlational analysis of the use of artificial intelligence among teachers and students in online early childhood education programmes: ethical, educational and technological challenges
Introduction: artificial intelligence is increasingly embedded in education, offering opportunities for innovation while raising concerns about ethics and academic integrity. Understanding this duality is essential to ensure that technological advances are accompanied by critical reflection and responsible use. Objective: this study examined the relationship between the use of Artificial Intelligence tools and the ethical perceptions of students and teachers in the online Early Childhood Education programme at the National University of Education in Ecuador. The growing presence of automated platforms in academic practice highlighted the need to evaluate both their functionality and their ethical implications. Methods: A cross-sectional, quantitative study with a correlational design was carried out at the National University of Education in Ecuador between January and March 2025. The sample consisted of 151 students and 25 teachers, selected intentionally. Two five-point Likert-type questionnaires were used to measure participants’ knowledge and use of AI, as well as their ethical perceptions. Statistical analyses were conducted using Spearman’s correlation coefficient in SPSS v26. Results: Positive and statistically significant correlations were identified in both groups: students (ρ = 0,489, 95% CI [0,357–0,602], p < 0,001) and teachers (ρ = 0,560, 95% CI [0,212–0,782], p < 0,001). Conclusions: The findings confirm that greater experience with AI tools is associated with stronger ethical awareness. This highlights the need to strengthen digital literacy with an ethical focus in both initial and continuing training, addressing the existing gap in formal preparation for the responsible use of AI.
Copyright (c) 2025 Paola Andrea Pérez Bravo, Derling José Mendoza Velazco , Elizeth Mayrene Flores Hinostroza , María del Cisne Ruiz Serrano (Author) https://creativecommons.org/licenses/by/4.0
Perceptions of AI Collaboration in Writing among Teacher Aspirants: An Empirical Cross-Sectional Study among Teacher Aspirants
The integration of artificial intelligence (AI) into education has generated increasing interest, particularly in its role in academic writing. While prior studies have examined students’ use of AI, limited attention has been given to teacher aspirants’ perceptions of AI collaboration with human writers across subject disciplines. Addressing this gap is crucial in preparing future educators for responsible AI integration in teaching and learning. This study aimed to determine the perceptions of English, science, and mathematics teacher aspirants toward AI collaboration with human writers in academic essay writing and to examine differences across subject disciplines. A descriptive‒quantitative design was employed, involving 90 undergraduate teacher aspirants equally distributed across the three disciplines. Stratified random sampling was used to ensure adequate representation, and data were collected through a structured questionnaire consisting of 10 items on a 5-point Likert scale with high internal reliability (α = 0,94). The data were analyzed via descriptive statistics and one-way ANOVA. The findings revealed generally positive perceptions of AI’s role in writing, particularly in generating outlines, assisting with citations, and supporting editing processes. Significant differences emerged among disciplines, with science majors expressing the most favorable perceptions (M = 4,13), followed by English (M = 3,94) and mathematics majors (M = 3,90). The study concludes that disciplinary orientation shapes openness to AI collaboration in academic writing. It is recommended that teacher education programs integrate structured training on the ethical and effective use of AI, ensuring a balance between technological assistance and the preservation of creativity and critical thinking.
Copyright (c) 2025 Richelle Ann P. Penpeña (Author) https://creativecommons.org/licenses/by/4.0
ChatGPT in the Academic Sphere: Teacher Aspirants’ Perceptions of Privacy and Security Across Education Career Programs
The integration of artificial intelligence (AI) into education has raised questions about privacy, security, and ethical use, particularly with tools such as ChatGPT. While prior research has focused primarily on students’ adoption, limited attention has been given to teacher aspirants’ perceptions across education career programs, leaving a gap in understanding future educators’ readiness to engage with AI. This study aimed to determine the perceived privacy and security of ChatGPT among teacher aspirants and to examine whether significant differences exist across programs in teacher education. A descriptive–comparative quantitative design was employed, involving 150 respondents enrolled in the Bachelor in Elementary Education (BEED), Bachelor in Secondary Education (BSED), Bachelor in Special Needs Education (BSNED), Bachelor in Early Childhood Education (BECED), and Bachelor in Culture and Arts Education (BCAED) programs. Data were collected through a structured online questionnaire with 14 items on a five-point Likert scale and analyzed via descriptive statistics and one-way ANOVA. The results revealed generally positive perceptions of ChatGPT’s privacy (M = 3,44, SD = 0,84) and security (M = 3,42, SD = 0,83). However, uncertainty persisted regarding the safety of sharing personal information. No significant differences were observed across the five programs, indicating shared perceptions regardless of disciplinary background. Notably, consistent with national trends, teacher education remains dominated by female students. The study concludes that while teacher aspirants recognize ChatGPT’s benefits, concerns about data privacy and security persist. It is recommended that teacher education programs integrate AI literacy training, with emphasis on data ethics, transparency, and responsible usage, to prepare future educators as both confident and cautious technology users.
Copyright (c) 2025 Ma. Julianna Yzabel G. Ragay (Author) https://creativecommons.org/licenses/by/4.0
Epistemic Injustice in Generative AI: A Pipeline Taxonomy, Empirical Hypotheses, and Stage-Matched Governance
Introduction: generative AI systems increasingly influence whose knowledge is represented, how meaning is framed, and who benefits from information. However, these systems frequently perpetuate epistemic injustices—structural harms that compromise the credibility, intelligibility, and visibility of marginalized communities. Objective: this study aims to systematically analyze how epistemic injustices emerge across the generative AI pipeline and to propose a framework for diagnosing, testing, and mitigating these harms through targeted design and governance strategies. Method: a mutually exclusive and collectively exhaustive (MECE) taxonomy is developed to map testimonial, hermeneutical, and distributive injustices onto four development stages: data collection, model training, inference, and dissemination. Building on this framework, four theory-driven hypotheses (H1–H4) are formulated to connect design decisions to measurable epistemic harms. Two hypotheses—concerning role-calibrated explanations (H3) and opacity-induced deference (H4)—are empirically tested through a PRISMA-style meta-synthesis of 21 behavioral studies. Results: findings reveal that AI opacity significantly increases deference to system outputs (effect size d ≈ 0,46–0,58), reinforcing authority biases. In contrast, explanations aligned with stakeholder roles enhance perceived trustworthiness and fairness (d ≈ 0,40–0,84). These effects demonstrate the material impact of design choices on epistemic outcomes. Conclusions: Epistemic justice should not be treated as a post hoc ethical concern but as a designable, auditable property of AI systems. We propose stage-specific governance interventions—such as participatory data audits, semantic drift monitoring, and role-sensitive explanation regimes—to embed justice across the pipeline. This framework supports the development of more accountable, inclusive generative AI.
Copyright (c) 2025 Joffrey Baeyaert (Author) https://creativecommons.org/licenses/by/4.0
Smart Teaching in Rural Indonesia: Harnessing AI-Assisted Deep Learning for Teacher Professional Development
This study presents an innovative AI-assisted TPD framework that uses Microsoft Copilot and AI-powered lesson plan generators to fully incorporate Indonesia’s four core teacher competencies: pedagogy, professionalism, social engagement, and interpersonal development, while applying deep learning principles of joyful, meaningful, and mindful education. Using a mixed-methods approach, this study combined quantitative experimental analysis with qualitative teacher perceptions to evaluate the effectiveness of AI-assisted interventions. Results from an independent t-test showed a significant increase in post-test scores among the experimental group, with a t-value of 17,1 exceeding the critical value of 1,984 (α = 0,05, df = 98), leading to the rejection of the null hypothesis. This indicates that AI-assisted training had a meaningful and statistically significant impact on teacher development. Moreover, AI promotes community-based learning, improves institutional readiness, and encourages educators to think globally while acting locally. Despite these benefits, challenges remain, such as interpersonal disengagement, cultural and pedagogical mismatches, difficulties in curriculum adaptation, the need for prompt engineering skills, and concerns about teacher autonomy. However, the limited duration of the intervention is a constraint, suggesting that long-term engagement is necessary for sustained improvement. Policy efforts should focus on extending training periods and integrating culturally responsive AI pedagogy.
Copyright (c) 2025 Seli Marlina Radja Leba, Tobias Nggaruaka, Ranta Butarbutar (Author) https://creativecommons.org/licenses/by/4.0
The risk of moral outsourcing: why artificial intelligence cannot and should not make our ethical decisions
Introduction: The delegation of ethical decision-making to artificial intelligence (AI), a practice termed 'moral outsourcing,' was examined. Objective: This paper critically analyzes the philosophical and social implications of moral outsourcing to AI. Method: A philosophical and theoretical analysis was conducted, synthesizing arguments from ontology, ethics, and social theory. Results: The analysis revealed three core arguments against this practice. First, an ontological gap was identified; AI systems lacked the consciousness and subjective experience necessary for genuine moral agency. Second, the study found that the rich, contextual nature of human ethics could not be successfully reduced to formal logic without mechanizing historical biases and losing essential meaning. Third, it was argued that moral outsourcing would lead to an atrophy of human moral reasoning skills and an erosion of accountability. Conclusions: It was concluded that AI should be developed as a tool to augment, not replace, human judgment, and that the final authority for ethical choice must remain a fundamentally human responsibility.
Factors Influencing ChatGPT Usage, AI Anxiety, and Learning Satisfaction: An Investigation of Teacher Aspirants’ Understanding of AI Anxiety and Ethical Concerns in Research-Based Education
Artificial intelligence (AI) has increasingly transformed education, with ChatGPT emerging as a widely used tool that supports student learning, collaboration, and research. Despite its promise, concerns remain regarding its usefulness, ethical implications, and potential for AI-related anxiety among learners. This study aimed to investigate the factors influencing ChatGPT use, AI anxiety, and learning satisfaction among preservice teachers. Specifically, it examined perceived ease of use, perceived usefulness, interaction with ChatGPT, information quality, interaction quality, collaborative learning, and learning motivation and their relationships with ChatGPT use, AI anxiety, and satisfaction. A descriptive-quantitative design was employed, utilizing survey questionnaires administered to 169 preservice teachers across five teacher education programs. The data were analyzed via descriptive statistics and Pearson correlation. The findings revealed that most constructs were rated at moderate levels, except for learning motivation, which was high, and perceived usefulness, which was weak. ChatGPT use was strongly positively correlated with learning motivation, whereas learning satisfaction was significantly related to information quality, collaborative learning, and motivation. AI anxiety was generally low but influenced how preservice teachers engaged with ChatGPT, often with caution and validation of outputs. The study concludes that while AI anxiety does not prevent ChatGPT adoption, it shapes how preservice teachers evaluate and engage with the tool. Structured training, clear guidelines, and collaborative learning opportunities are recommended to enhance perceptions of usefulness, promote responsible adoption, and strengthen learning satisfaction in teacher education.
Copyright (c) 2025 Jama Kaye B. Peluta (Author) https://creativecommons.org/licenses/by/4.0
Attitude, Anxiety, and Literacy among Teacher Aspirants’ Embrace of Artificial Intelligence: Implications for Practical and Ethical Challenges in Integrating AI in Education
Attitude, anxiety, and literacy are essential factors in determining the acceptance of artificial intelligence (AI), particularly in its integration into education. Despite their relevance, few studies have explored their mutual influence and their impact on teacher aspirants’ perspectives toward AI. This study aimed to analyze these constructs and their interrelationships through a descriptive-quantitative-correlational design. Stratified random sampling was employed to select 200 respondents from the education programs of a state university. The results indicated that teacher aspirants hold a positive attitude toward AI (M=4,19), exhibit low anxiety (M=2,44), and demonstrate very high literacy (M=6,22). Significant differences were observed in levels of anxiety, literacy, and attitudes across course programs. Furthermore, a significant interrelationship among the three constructs was established. The findings highlight the pivotal role of attitudes, anxiety, and literacy in shaping teacher aspirants’ acceptance of AI and emphasize their importance in guiding future educational integration.
Copyright (c) 2025 Niño D. Torreciba, Ericson O. Alieto (Author) https://creativecommons.org/licenses/by/4.0
Technofixing the Future in Mining Industry: Ethical Side Effects of Using AI and Big Data to Meet the SDGs
Recent advances in artificial intelligence (AI), big data, and non- geostationary satellite (NGSO; LEO/MEO) services promise faster , safer , and “ greener ” mining , but also raise ethical and governance risks . This study interrogates the technofix narrative. Objectives were to map NGSO+AI applications across the mining value chain ; assess technical , operational , environmental , and economic performance; examine governance , data rights , and justice implications ; evaluate capacity and procurement models ( with an East African lens ); and distill actionable guidance . Following a PRISMA-2020 protocol , a mixed-methods review ( database inception –12 Aug 2025) of peer- reviewed and grey literature was undertaken with duplicate screening and appraisal (JBI, RoB 2/ROBINS-I, AACODS; GRADE/ CERQual ). Over 80 empirical studies and initiatives were synthesized ; random-effects meta- analysis was used where outcomes were comparable, alongside realist narrative synthesis . NGSO connectivity reduced latency (LEO: tens of ms; MEO: ~100–200 ms) and high-revisit EO (SAR/ optical ) improved surface-change detection ; operational gains ( uptime , reporting ) were noted but with low – moderate certainty given short follow -up and sponsorship . Governance lagged capability : data ownership and portability were unclear , third-party audit access rare, and community participation uneven ; ethical risks included bias , privacy , and cultural impacts . East African pilots showed technical promise amid institutional gaps. NGSO+AI can advance SDG- aligned mining only when coupled to binding data rights , independent assurance , participatory pathways , open interfaces, and local capacity ; otherwise tools risk performative compliance rather than accountable , just outcomes .
Copyright (c) 2025 Petros Chavula, Fredrick Kayusi (Author) https://creativecommons.org/licenses/by/4.0
The Role of ChatGPT in Academic Writing: Pedagogical and Ethical Dimensions
Introduction: Generative AI tools such as ChatGPT are reshaping academic writing in higher education, offering both opportunities for linguistic support and creativity, while raising concerns over authorship, plagiarism, and academic integrity. Objective: This study examines the pedagogical benefits and ethical challenges of ChatGPT in academic writing, exploring how it can be integrated responsibly into higher education practices. Methods: A qualitative thematic synthesis of 33 peer-reviewed studies, reports, and theoretical essays published between 2022 and 2025 was conducted. The analysis focused on two axes: pedagogical applications in writing instruction and ethical implications for academic integrity and authorship. Development: Findings indicate that ChatGPT can serve as a valuable writing coach, enhancing brainstorming, drafting, and revision processes, particularly for multilingual and novice writers. However, issues of algorithmic dependence, fabricated content, plagiarism risks, and unequal access remain significant. The study concludes that effective use of ChatGPT requires AI literacy, transparent disclosure, and equitable institutional policies. A balanced integration of AI, supported by critical pedagogy, can empower students while safeguarding academic standards.
Copyright (c) 2025 Konstantinos T. Kotsis (Author) https://creativecommons.org/licenses/by/4.0
Integrating Artificial Intelligence in Education: Advancing Personalized Learning Within Ethical Frameworks: An Overview
Introduction: Artificial Intelligence (AI) is reshaping education by facilitating adaptive, personalized, and data-informed learning experiences. Through intelligent tutoring systems, predictive analytics, and AI-powered feedback mechanisms, educators can tailor instruction to meet diverse learner needs while enhancing efficiency and engagement across both traditional and digital learning environments. Objective: this study aims to examine the effective use of artificial intelligence (AI) in education for promoting personalized learning, while upholding key ethical standards. Method: A short communication methodology was employed to synthesize recent developments in AI applications in education. Relevant literature published between 2018 and 2025 was identified through searches of databases including PubMed, Scopus, ResearchGate, and Google Scholar. Studies were selected based on criteria related to the use of AI in personalized learning, assessment, instructional design, and administrative automation. Data were thematically analyzed to evaluate the benefits, implementation strategies, and challenges associated with AI integration. Results: AI-driven tools were found to significantly enhance educational outcomes by enabling proactive (e.g., curriculum design, predictive enrollment analysis) and reactive (e.g., real-time tutoring, automated grading) engagement. Platforms like adaptive learning systems, intelligent tutoring tools, and AI-driven assessment software have enabled personalized learning experiences and eased administrative tasks. However, significant challenges persist, including concerns over data privacy, algorithmic bias, digital literacy gaps, and unequal access to technological infrastructure. Conclusion: AI technologies hold substantial promise in creating more personalized, inclusive, and effective educational ecosystems. By streamlining teaching processes and supporting learner-centered models, AI enhances both pedagogical impact and academic performance. However, ethical concerns and infrastructural barriers must be addressed to ensure responsible and sustainable adoption. Future efforts should focus on aligning AI development with equity, transparency, and accessibility in education.
Copyright (c) 2025 Sachin Gupta , Pragya Sharma, Krishna Reddy Vajrala, Ambreen Fatima , Neetu Sharma (Author) https://creativecommons.org/licenses/by/4.0
A comprehensive exploration of nongeostationary satellite systems in the mining industry: emphasizing AI , ethical considerations, and communication strategies
Non-geostationary satellite (NGSO) constellations—particularly LEO/MEO—are transforming mining by providing low-latency connectivity and taskable Earth observation to remote, infrastructure-poor sites. Objectives include mapping NGSO applications across exploration, planning, and operations; assessing AI's role in tasking, routing, and analytics; and examining governance and ESG implications, with a focus on Africa and East Africa. Methods involved a PRISMA-aligned systematic review (protocol registered) synthesising primary and secondary evidence on NGSO-enabled EO and communications in mining. A random-effects meta-analysis was planned if three or more comparable studies reported the same outcome; otherwise, a structured narrative synthesis with predefined subgroups (LEO vs MEO, EO vs backhaul, open-pit vs underground, Africa vs elsewhere) was used. Results and discussion showed that across more than 30 use cases, NGSO backhaul and EO tasking consistently reduced time to insight for pit progression, tailings surveillance, and asset tracking; simulations indicated routing improvements of approximately 10% on tree topologies and 30% on mesh networks at N=500, demonstrating tangible latency and capacity benefits for safety-critical workflows. Continuity was enhanced through multi-sensor PNT (GNSS/inertial/vision plus radio localisation ) and hierarchical link adaptation that rapidly re- parameterises under noise, weather, or interference. AI added value by improving tasking and congestion control in edge and cloud inference, though it required cascaded models, compression, and uncertainty gating to meet compute and bandwidth constraints. Governance themes—such as data protection, transparency, and community benefit—were recurring enablers of adoption. Conclusion: When combined with resilient positioning, adaptive operations, and credible ESG safeguards, NGSO combined with AI can significantly enhance mining efficiency, safety, and sustainability; priorities include standardised KPIs, transparent cost models, and long-term pilot deployments.
Copyright (c) 2025 Fredrick Kayusi, Petros Chavula (Author) https://creativecommons.org/licenses/by/4.0
AI in Anesthesia: Enhancing Safety Through Ethical Integration and Human Supervision -A Narrative Review
Introduction: Anesthesia has evolved from enormously from mysterious sleep to intelligent, personalized care and as Artificial intelligence (AI) has led to transformation of healthcare, its applications in anesthesia too has proven of late that how technology and human compassion working together can be smoother, personalized, safer and precise surgical journeys for patients. The AI is field of computer science which use various algorithms and computational resources enabling the machines to simulate human intelligence in its thought process and actions. This revolutionary technology is changing the traditional anesthesia practices of patient risk stratification, anesthesia delivery system, patient peri-operative monitoring, individualized anesthesia plans and image analysis by an automated process. However, the application of AI in anesthesia comes with its own unique limitations of limited data and its quality, lack of guidelines for ethical use, legal concerns and socioeconomic concerns. Therefore, in this review we searched human in loop approach of the hybrid models, as the future of anesthesia will likely to be shaped by a combination of artificial intelligence and human expertise rather than one replacing other. Methods: We searched PubMed, google scholar and Cochrane database search 2019-2024. The evaluation of the retrieved articles was done for effectiveness as well as limitations of AI and deep learning in the field of anesthesia. Results: The AI will enhance precision, safety and efficiency but human touch of anesthesiologist and ethical use of it will remain essential for decision making, handling complications and providing personalized empathic care. Nonetheless, its effectiveness is influenced by data quality, algorithm generalizability and the absence of standardized ethical frameworks. Conclusion: The future of anaesthesia will likely combine AI’s analytical power with anaesthetist’s expertise, ethical judgement and empathy. While AI can improve outcomes and efficiency, the human touch remains essential for decision making, complication management and personalised care.
Copyright (c) 2025 Pooja Gurnal , Lokesh Rana (Author) https://creativecommons.org/licenses/by/4.0
Human-in-the-Loop Models for Ethical AI Grading: Combining AI Speed with Human Ethical Oversight
The adoption of AI-powered grading systems in academic institutions promised improved efficiency, consistency, and scalability. However, these benefits introduced ethical challenges, including algorithmic bias, contextual insensitivity, and reduced transparency, particularly in high-stakes assessments. To address these concerns, the chapter presented a Human-in-the-Loop (HITL) grading framework that integrated AI-generated recommendations with human oversight. The model consisted of four layers: (i) pre-grading configuration with customizable rubrics and model calibration; (ii) preliminary scoring using transformer-based language models; (iii) human validation and contextual adjustment of AI outputs; and (iv) transparent feedback supported by dual-logged audit trails. A case study was conducted at a mid-sized university, where the framework was applied to 800 undergraduate essays. As a result of this implementation, the faculty validated 87 % of the AI-generated scores with only minor adjustments, while 13 % required overrides due to misinterpretations involving creative expression, linguistic nuance, or cultural context. The grading time was reduced by 40 %, and student satisfaction improved due to transparent assessment and educator involvement. These findings demonstrate that the HITL model has the potential to balance automation with ethical oversight, promoting fairer evaluations and preserving academic integrity. It enhanced faculty agency, ensured equity across diverse student populations, and built trust through explainable AI tools such as SHAP and LIME. The chapter concluded by proposing policy guidelines, technical integrations, and communication strategies, while advocating for future applications in multimodal grading and open-source ethical assessment platforms.
Copyright (c) 2025 Muthu Selvam, Rubén González Vallejo (Author) https://creativecommons.org/licenses/by/4.0
Child Sexual Abuse Material facilitated by Artificial Intelligence
The present study aimed to analyze how artificial intelligence can facilitate the production and distribution of child sexual abuse material. The scope of the study was descriptive, adopting a qualitative approach, and the research design was non-experimental cross- sectional. The selected sample comprised documents, agreements, laws and books related to the subject matter; the sampling method was non-probabilistic and qualitative data collection instruments were used in the conduct of the study. The results demonstrated the development of Artificial Intelligence (AI) in the criminal sphere, as well as the lack of efficient legislation at both national and international level, even though initiatives addressing these matters have been explored in both contexts, along with an analysis of how these crimes operate. In conclusion, the paper highlighted an increase in the prevalence of crime, emphasizing the complexities of combating it without a clear and robust regulatory framework. This study also underscored the risks posed by social platforms lacking appropriate restrictions. Mutual collaboration among governments, organizations, companies and society is key to address these crimes, and the implementation of preventive strategies is fundamental to mitigate risks and safeguard minors in digital environments. The findings presented in this paper emphasize the need for a regulatory framework that addresses child sexual abuse material preventively and reactively.
Copyright (c) 2024 Antonella Petrini, María Belén Gauna (Author) https://creativecommons.org/licenses/by/4.0
Training in the Field of Artificial Intelligence: Challenges and Opportunities in Health Science Education
Introduction: Artificial intelligence in higher education teacher training has become an imperative that challenges teachers to update themselves to be able to use this tool to generate knowledge for their students. Method: A systematic review was conducted in databases such as PUBMED, Cochrane Central Library, CINAHL, and SCIENCE DIRECT, using keywords like "health sciences," "artificial intelligence," and "teaching" during the period 2018-2024, following the PRISMA checklist. Results: Studies predominantly originated from Europe, Asia, the Middle East, and the USA, especially involving nursing and medical students. The most notable opportunities of AI include the relationship between ethics and technology, the training of academic tutors, and its contribution to professional education, clinical case management, literature searches, and student learning. However, challenges were identified, such as uncertainty about employability, limited funding for training, and the acquisition of licenses. Additionally, there is a need to promote responsible use of AI to avoid diminishing empathy and humanization in patient care. Conclusion: AI is a valuable ally in health sciences education, but ethical considerations and teacher training must be taken into account for responsible use, as well as fostering critical and reflective thinking in its implementation.
Copyright (c) 2024 Fernanda Lilibeth Ormeño Rivera, Michelle Antonella Sánchez Cedeño (Author) https://creativecommons.org/licenses/by/4.0