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This study presents an ethical framework for learning analytics aimed at addressing key challenges related to the collection and use of student data in higher education. Learning analytics, a powerful tool for improving student outcomes and institutional decision-making, has raised ethical concerns regarding data privacy, transparency, fairness, and equity. The proposed framework integrates four core principles: data privacy, informed consent, transparency, and fairness, ensuring that institutions use learning analytics responsibly while safeguarding student rights. A central feature of the framework is its focus on promoting equitable decision-making, minimizing bias, and preventing the reinforcement of existing inequalities in algorithmic and data-driven decisions. The framework also emphasizes the importance of continuous ethical oversight, holding institutions accountable for ethical data use and adapting practices as technology evolves. The study concludes that the framework offers a comprehensive solution to the ethical challenges in learning analytics, providing institutions with a practical guide to embedding ethical principles in data practices. Additionally, the research discusses its potential to foster fairness, equity, and transparency in decision-making processes. Future research is recommended to refine the framework and explore its application across various educational contexts, ensuring responsible and inclusive use of learning analytics.

The integration of Artificial Intelligence (AI) in higher education has led to the increasing use of AI-powered adaptive learning models that support personalized and data-driven learning. However, studies examining preservice teachers’ perceptions of these models remain limited, despite their important role in future classroom implementation. This study aims to explore preservice teachers’ perceptions of AI-powered adaptive learning in higher education, focusing on perceived usefulness, learning adaptivity, learning experience, and perceived concerns. A descriptive qualitative research design was employed involving 53 preservice teachers from various universities. Data were collected using a Likert-scale questionnaire and open-ended questions. Quantitative data were analyzed descriptively using percentage distributions, while qualitative data were examined through simple thematic analysis. The findings reveal that preservice teachers generally demonstrate positive perceptions of AIpowered adaptive learning, particularly in terms of learning effectiveness, adaptability, and engagement. Nevertheless, concerns related to over-reliance on AI, ethical issues, and data privacy were also identified. These results indicate that preservice teachers show readiness to engage with AI-supported learning, while highlighting the need for teacher education programs to promote responsible and pedagogically informed AI integration.

The development of artificial intelligence has revolutionized higher education by increasing the efficiency and personalization of learning, but it has also posed a major challenge in the form of a decline in students' critical thinking skills due to their dependence on technology. Therefore, an in-depth philosophical study through the post-positivism, critical theory, and constructivism paradigms is needed to understand its impact on cognitive and epistemological processes. This study aims to analyze how these three paradigms guide the development of critical thinking in the context of AI. It uses a descriptive-analytical method based on secondary data from academic literature such as journals and books that have been critically synthesized. The findings show that post-positivism views AI as a tentative critical realism tool for empirical verification. Critical theory critiques power relations, ideology, and the ethics of technological domination. Constructivism emphasizes the construction of social knowledge through human-centered design that supports creativity and collaboration. The positive impacts of AI include instant feedback and content adaptation, while the negative impacts include social isolation, weak digital literacy, and data privacy. Therefore, the implication is the wise use of AI with the assistance of teaching staff to strengthen reflective analysis, ethical literacy, and paradigm adjustments in accordance with the exact or social sciences to create multidimensional learning in the digital era.

This paper reviews the role of Artificial Intelligence (AI) in Islamic education within secondary schools, emphasizing both its practical uses and the ethical challenges it presents. The review looks into the current trends, tools, and the impact of AI on the learning experience, as well as its ethical implications from an Islamic perspective. The study follows a systematic literature review (SLR) approach based on the PRISMA guidelines and includes research from 2022 to 2025, sourced from platforms like Google Scholar. After a thorough selection process, 15 articles were included in the review, offering valuable insights into the technological and ethical aspects of AI in Islamic secondary education. The use of AI has notably enhanced learning outcomes in Islamic education by allowing personalized learning, boosting student engagement, and streamlining feedback mechanisms. Tools like intelligent tutoring systems and educational chatbots have been widely adopted. However, challenges around data privacy, algorithmic bias, and technology access persist. Additionally, incorporating Islamic ethical values into AI-driven educational platforms presents both opportunities and challenges. Addressing these ethical implications is vital, requiring frameworks that align with Islamic principles such as maṣlaḥa (public welfare), justice, and human dignity. Education policies and teacher training programs should concentrate on promoting the responsible use of AI, ensuring it improves educational experiences while preserving ethical and cultural integrity.

Digital transformation has significantly reshaped human resource management (HRM) through the adoption of Human Resource Information Systems (HRIS), artificial intelligence (AI), big data analytics, e-learning platforms, and remote work technologies. Although these innovations improve efficiency and decision-making, they also generate ethical challenges related to data privacy, algorithmic bias, transparency, and employee monitoring. This article examines the role of professional ethics in HRM within the context of digital transformation, highlighting both emerging challenges and potential opportunities. This study employs a conceptual research approach supported by a comprehensive literature review of scholarly works on HRM, professional ethics, and digitalization. The analysis focuses on core ethical principles such as integrity, fairness, responsibility, professionalism, and confidentiality, and evaluates their implementation in digital HR practices. The findings indicate that unethical use of digital technologies may lead to discrimination, reduced employee trust, and violations of individual rights, particularly through biased AI-based recruitment systems and opaque performance evaluation mechanisms. However, digital transformation also offers opportunities to strengthen ethical HR governance. The use of ethical data management, algorithmic audits, digital transparency, and e-learning-based ethics training can enhance accountability and fairness in HR processes. The study concludes that integrating professional ethics with digital HRM is essential for developing human-centered, sustainable, and trustworthy organizations in the digital era.

Abstract: The rapid diffusion of Generative Artificial Intelligence (AI) has intensified public debate regarding its benefits, risks, and societal implications. This study investigates public sentiment and thematic structures surrounding Generative AI by analyzing Twitter discourse as a representation of large-scale, real-time public perception. The research addresses two main problems: how public sentiment toward Generative AI is distributed and what dominant themes shape this perception. Accordingly, the objective is to map both emotional polarity and thematic narratives embedded in social media conversations. A computational mixed-methods approach was employed using a dataset of 12,470 tweets collected on 17 December 2024. Sentiment classification was conducted using a transformer-based DistilBERT model, while semantic representations were generated with Sentence-BERT. Topic modeling was performed using BERTopic, integrating HDBSCAN clustering and class-based TF-IDF to extract coherent and interpretable topics. Human-in-the-loop validation supported the interpretive robustness of topic labeling. The findings reveal that public sentiment toward Generative AI is predominantly positive (41.8%), particularly in relation to productivity enhancement, education, and creative applications. Neutral sentiment (31.4%) reflects informational discourse, while negative sentiment (26.8%) centers on ethical concerns, privacy risks, misinformation, and AI hallucinations. Seven dominant topics were identified, with clear topic–sentiment alignment showing optimism in utility-driven themes and skepticism in ethics- and risk-related discussions. In conclusion, public perception of Generative AI is dualistic—characterized by strong enthusiasm alongside persistent caution. These results provide empirical insights for AI governance, responsible innovation, and future research on socio-technical impacts of Generative AI. *    

The integration of Artificial Intelligence (AI) into Human Resource Management (HRM) is accelerating and reshaping how organizations attract, develop, manage, and retain talent. Despite abundant case examples and growing practitioner interest, academic findings remain fragmented regarding the antecedents (drivers), impediments (barriers), and organizational effects (outcomes) of AI-based HR transformation. This paper presents a PRISMA-guided systematic literature review of 112 peer-reviewed articles (2015–2025) to synthesize empirical and conceptual evidence on AI in HRM. Results identify three primary drivers: technological capability, strategic alignment, and a data-driven culture; three critical barriers: ethical concerns (bias, privacy, and transparency), skill and capability gaps, and resistance to change; and three outcome clusters: operational efficiency, enhanced employee experience, and elevated strategic HR contribution. We propose a socio-technical conceptual framework that models drivers moderated by barriers to outcomes, and we advance a research agenda focused on ethical governance, human–AI collaboration, capability measurement, and longitudinal evaluation. The review contributes to theory by integrating socio-technical and dynamic capability  perspectives and provides actionable guidance for HR leaders on responsible AI adoption.

The rapid rise of Indonesia’s digital economy is changing how people participate as citizens and how their consumer rights are protected. This study explores the legal dimensions of digital citizenship through a normative juridical review of the Consumer Protection Law, the Electronic Information and Transactions Law, and the Personal Data Protection Law. Although these laws provide basic safeguards, significant problems remain such as data privacy violations, unclear algorithmic practices, cross-border legal barriers, and deceptive online advertising. Low public legal awareness and the absence of a unified dispute-resolution system further leave consumers vulnerable to powerful global digital platforms. The study argues that stronger protection requires embedding digital citizenship principles into Indonesia’s legal framework, harmonizing national rules with international standards, enhancing platform oversight, and improving digital literacy. In conclusion, a flexible and forward-looking legal approach is crucial to making consumer protection a core part of digital citizenship and ensuring public trust and human rights in the digital era.

This study aims to analyze user sentiment toward the integration of Artificial Intelligence (AI) in online learning platforms, which are increasingly expanding in the digital era. With the growing use of AI technologies in education—such as learning chatbots, material recommendation systems, and automated assessments—it is essential to understand users’ perceptions and reactions to these implementations. The research employs sentiment analysis based on text mining using user review data collected from various online learning platforms. The analysis process includes data preprocessing, sentiment classification using machine learning algorithms, and interpretation of results based on the proportion of positive, negative, and neutral sentiments. The findings indicate that most users express positive sentiments toward AI integration, as it enhances learning efficiency and personalization. However, some users raise concerns regarding data privacy and the lack of human interaction. This study is expected to serve as a reference for educational platform developers to design AI systems that are more adaptive, transparent, and user-centered

Security in e-commerce applications is a crucial factor that significantly affects user trust. Many users often feel anxious about the confidentiality of personal data, transaction security, and the potential for misuse of information. This study is a systematic literature review (SLR) using the PRISMA model, aiming to analyze in depth the influence of security on user trust in the context of e-commerce applications. Through this review, relevant previous studies on user security and trust were identified and evaluated to provide a more comprehensive understanding. The results of the analysis show that the improvement and implementation of superior security features, including strict data protection, multi-layered authentication, and transparent and robust privacy policies, have an essential role in growing and strengthening user trust. Guaranteed security not only creates a sense of convenience during the transaction process, but it is also very effective in increasing and maintaining user loyalty to the e-commerce platform in question. Improving security can be interpreted as a strategic investment for the sustainability of digital businesses.

The rapid growth of online commerce in Indonesia has significantly transformed the way people fulfill their daily needs by providing easier, faster, and more flexible access to goods and services through digital technology. Despite these advantages, the development of e-commerce also presents serious challenges, including rising cases of online fraud, discrepancies between advertised and delivered products, failed transactions, and personal data breaches that threaten consumer privacy. These issues create imbalances in digital contractual relationships, undermining trust and legal certainty for buyers. This study aims to analyze the implementation of the principle of fairness for consumers in e-commerce practices in Indonesia, while also identifying regulatory weaknesses and existing dispute resolution mechanisms. Using a normative legal research approach supported by case studies, the study examines the effectiveness of relevant legal frameworks, particularly the Consumer Protection Law (UUPK) and the Electronic Information and Transactions Law (UU ITE). The findings reveal that although these regulations provide a legal basis for consumer protection, their implementation remains inadequate. Weak supervision of online business actors, limited accountability of platform providers in ensuring transaction security, and complex, costly compensation procedures continue to hinder consumer rights protection. These conditions highlight the urgent need to strengthen consumer protection systems that are more adaptive, efficient, and oriented toward public interest. The study emphasizes the importance of improving online dispute resolution mechanisms, enhancing transparency and responsibility of digital platforms, and expanding digital literacy among consumers. Such measures are essential to ensure that fairness in e-commerce is not only guaranteed normatively, but also effectively realized in everyday digital transactions.

This article discusses the modernity of educational instruments, along with the phenomenon of technological progress, particularly the emergence of artificial intelligence (AI), and its implications for educators in the 21st century. Rapid advances in AI have led to fundamental shifts in teaching strategies and resources, shifting from traditional models to personalized and adaptive learning environments. Through automated assessments, curriculum customization, and the implementation of virtual assistants that enhance accessibility, artificial intelligence (AI) in education aims to improve efficiency and effectiveness. However, this modernity requires educators in the 21st century to change. Teachers now play the role of facilitators, designers of learning experiences, and mentors in ethics, in addition to being providers of knowledge. Teachers need to master AI literacy and focus on teaching students the four Cs of the 21st century: communication, creativity, critical thinking, and teamwork. The integration of AI raises ethical issues related to data privacy, the digital divide, and the dangers of plagiarism, but it also offers significant potential. Therefore, the effective integration of AI technology with irreplaceable human qualities is crucial for the success of contemporary educational technology. AI is an important tool for educators in the 21st century to develop a generation of technologically literate and moral individuals.

The exchange of information in the digital era has become a general need for society. However, the information sent often has a public or confidential nature. Therefore, security is needed so that confidential information remains safe. Cryptography is a field of knowledge used to secure information using encryption and decryption processes. One of the cryptographic methods used is the permutation method, which changes the layout, sequence, or structure of data into a form that is difficult to understand without knowledge of the exact key. Implementing cryptography using the permutation method in Android-based applications can increase the security and privacy of user data, as well as protect sensitive information from unauthorized access. This research aims to implement permutation method cryptography in Android-based applications to protect the confidentiality or privacy of user data. By using the permutation method, the sequence of bits or characters in the data is scrambled so that it is difficult for unauthorized parties to understand. The research results show that the implementation of permutation method cryptography in Android applications can provide a higher level of security in maintaining data confidentiality. However, it is worth considering that simple permutation methods may not be secure enough to deal with more sophisticated attacks. Therefore, choosing a stronger cryptographic method needs to be considered to achieve a higher level of security. In conclusion, implementing cryptography using the permutation method in Android-based applications can increase the security and privacy of user data. The permutation method is able to randomize the sequence of bits or characters in the data so that it is difficult for unauthorized parties to understand. However, for a higher level of security, it is necessary to consider stronger cryptographic methods.

The advancement of Artificial Intelligence (AI) technology has generated significant benefits while simultaneously creating new opportunities for cyber-based sexual offenses, particularly through deepfake technology, which enables the manipulation of a person's face into pornographic content without consent. This study examines criminal liability for perpetrators of AI-based sexual harassment and legal protection for victims in deepfake pornography cases, focusing on a case involving a student at Udayana University, Bali. This research employs a normative legal method by analyzing primary, secondary, and tertiary legal materials. The findings indicate that although Indonesian law does not yet explicitly regulate deepfake technology, perpetrators can still be prosecuted under the Electronic Information and Transactions Law, Pornography Law, Sexual Violence Law, and the new Criminal Code. Criminal intent is established through the deliberate manipulation, creation, and dissemination of non- consensual sexual content. Furthermore, victims are entitled to legal protection, including psychological recovery, privacy rights, restitution, and content removal. This study highlights the urgency of developing specific regulations addressing AI-related sexual crimes, enhancing forensic digital capabilities, and strengthening victim-centered protection mechanisms. The research contributes to the legal discourse on emerging digital crimes and emphasizes the need for adaptive legal frameworks in the AI era.  

The development of the digital economy has driven major changes in consumer behavior, from traditional transactions to online interactions. These changes present both challenges and opportunities for businesses, particularly in the e-commerce sector, where consumer trust, data privacy, and digital technology adoption are important factors in decision-making. This study aims to map global trends in online consumer behavior research in the context of the digital economy using bibliometric analysis. Data was collected from the Scopus database using the keyword “online consumer behavior” for the period 2020-2025. The dataset was exported in CSV format and analyzed using VOSviewer and R Studio (Biblioshiny). The analysis included annual publication trends, identification of authors, journals, countries, as well as keyword mapping and citation networks. The results show a significant increase in publications after 2020, with China, the United States, and India as the main contributors. Keyword mapping reveals four research clusters: consumer trust and data privacy, e-commerce and digital platform adoption, consumer experience and personalization, and social commerce and social media influence. These findings confirm a growing research focus on issues of digital trust and opportunities for utilizing new technologies such as artificial intelligence and social media. This study provides a comprehensive overview of global trends in online consumer behavior and an important contribution to academics, practitioners, and policymakers.

The development of information technology in the era of the Industrial Revolution 5.0 has brought significant changes to the world of education, including guidance and counseling services in schools. The use of artificial intelligence (AI)-based chatbots is an innovation that can improve the effectiveness, accessibility, and interactivity of services for students. However, on the other hand, the use of this technology also raises challenges related to ethics, privacy, and the decline of humanistic values in counseling interactions. Based on this, this study aims to analyze the positive and negative impacts of chatbots on the effectiveness of guidance and counseling services in schools. This study uses library research. Data were collected from various sources, such as books and journals related to the impact and role of chatbot use in guidance and counseling services. The results of the study show that the use of chatbots has negative and positive impacts that are very helpful and beneficial in guidance and counseling services in the current digital era, but they only function as a supporting tool and cannot replace the role of human counselors.

This study investigates the impact of ethical norms in the application of artificial intelligence (AI) on personalized learning satisfaction among college students in Guizhou. The research focuses on how ethical aspects, such as data privacy, algorithmic fairness, and transparency, affect students' experiences with AI-driven educational tools. It underscores the importance of user control, ethics training, and system transparency in building trust and promoting active engagement in AI-powered learning environments. Findings from a sample of 388 college students reveal that when students are informed about the ethical considerations behind AI technologies and have control over their data, they exhibit higher satisfaction with personalized learning experiences. Furthermore, the study highlights that the integration of ethical principles into AI applications leads to a more supportive and trustworthy educational environment, improving overall learning outcomes. This research emphasizes that ethical AI practices are essential for fostering a positive and productive learning experience in the context of higher education.

The rapid growth of financial technology (fintech) Peer-to-Peer Lending (P2PL) in Indonesia has created both opportunities and challenges, particularly regarding debt collection practices by third parties (debt collectors). Such practices often result in violations of consumer rights, including intimidation, harassment, and breaches of privacy. To address these issues, the Financial Services Authority of Indonesia (OJK) issued Regulation No. 22 of 2023 on Consumer and Public Protection in the Financial Services Sector, which establishes legal standards for protecting fintech P2PL consumers. This study aims to analyze the regulation of debt collection by third parties under OJK Regulation No. 22/2023, assess the forms of legal protection for consumers, and identify the obligations of fintech P2PL providers in managing collection practices. The research applies a normative juridical method with statutory and conceptual approaches. The findings indicate that OJK Regulation No. 22/2023 provides consumer protection through preventive mechanisms (mandatory transparency, prohibition of intimidation, and regulation of third-party involvement) and repressive mechanisms (complaint handling and administrative sanctions). However, challenges remain in implementation, such as low consumer literacy, outsourced collection practices, and weak on-site supervision. Therefore, stronger regulation, tighter controls by providers, and collaboration among regulators, law enforcement, and fintech associations are required to ensure optimal consumer protection.

This Community Service Program (PkM) arises from the significant challenges faced by Vocational High School (SMK) students in adapting to the rapid progression of the digital era and the widespread use of artificial intelligence (AI). In an environment flooded with online information, many students tend to accept and share digital content without adequately verifying its validity, leaving them susceptible to misinformation, deceptive media, and inaccurate data. Ethical risks linked to AI misuse have also surfaced, including plagiarism through automated text-generation tools, excessive reliance on chatbots without ethical consideration, and potential privacy breaches due to limited awareness of technological vulnerabilities. To respond to these issues, the PkM program adopts an educational strategy focused on critical digital literacy training and mentoring tailored for SMK students. The initiative highlights the importance of critical thinking in evaluating and validating digital information, while simultaneously promoting ethical responsibility in using AI. Through workshops, case-based simulations, group discussions, and practical content analysis, students learn to recognize digital manipulation and reflect on the social implications of AI. Expected outcomes include increased ethical digital awareness, the development of a sustainable training module, academic publications, and long-term collaboration between schools and universities, ultimately preparing students to be competent, ethical, and responsible digital citizens.

This study presents a Systematic Literature Review on Artificial Intelligence (AI) and Natural Language Processing (NLP) applications for customer support automation and digital service optimization. The review follows the PRISMA framework to ensure methodological rigor and transparency, focusing on literature published between 2020 and 2025 from the Scopus database. The findings reveal that AI-driven technologies, including Machine Learning, Deep Learning, and Large Language Models, have significantly improved efficiency, response time, and customer satisfaction in customer support and digital service. Common NLP applications include sentiment analysis, ticket classification, and automated response generation. Among these, hybrid and transformer-based models demonstrate superior accuracy and contextual understanding compared to traditional algorithms. However, several challenges persist, including data quality limitations, privacy and security concerns, algorithmic bias, and linguistic ambiguities such as sarcasm and negation. Moreover, issues related to trust and ethical adoption continue to influence user acceptance of AI systems. This review provides a comprehensive synthesis of current methodologies, trends, and research gaps, offering insights for future studies to develop explainable, secure, and human-centered AI systems that enhance the sustainability and transparency of digital customer support services.