Artificial Intelligence Competence: A Crucial Skill for the Digital Citizens

The Era 5.0 represents a paradigm shift in education where artificial intelligence (AI) convergence with human-centered values necessitates fundamental reconceptualization of digital citizenship competencies. This systematic literature review examines the integration of AI technologies within digital citizenship education in Indonesian educational contexts, exploring pedagogical innovations, implementation challenges, and alignment with national development priorities. Following PRISMA 2020 guidelines, this study synthesizes empirical research on AI adoption in education, digital literacy frameworks, and citizenship education practices across Indonesian primary, secondary, and higher education institutions. Findings reveal critical insights: AI integration in Indonesian education remains in early stages with pronounced disparities between urban and rural settings; digital citizenship frameworks require contextualization within Pancasila values to foster ethical and critical digital citizens; and teacher capacity-building emerges as a fundamental prerequisite for successful implementation. The analysis identifies promising practices linking digital citizenship competencies with sustainable development goals (SDGs 4, 10, and 16), particularly through environmental education programs and problem-based learning approaches. This study proposes an integrated framework positioning AI-enhanced digital citizenship education as essential infrastructure for developing adaptive, ethically-grounded citizens capable of navigating Indonesia's increasingly complex digital society while advancing educational equity and social inclusion.

Statistical literacy has become a crucial skill in the data-driven digital era. However, significant challenges remain in enhancing a deep understanding of statistical concepts. This study systematically reviews the role of Artificial Intelligence (AI) in improving statistical literacy in statistics education. Employing a Systematic Literature Review (SLR) approach, the study follows three stages: planning, implementation, and reporting. A total of 11 main articles were selected from 173 relevant articles retrieved from the Scopus database. The analysis categorizes the AI tools used, the aspects of statistical literacy enhanced, the research methods employed, the instruments utilized, and the impact on statistics learning. The findings reveal that AI contributes to three key aspects of statistical literacy: (1) conceptual understanding, facilitated by generative language models (ChatGPT) and machine learning-based simulation tools; (2) data analysis and computational skills, enhanced through platforms such as Jupyter Notebook, CODAP, and AI-based statistical visualization applications; and (3) critical thinking and problem-solving, developed through predictive model evaluation and AI-driven data interpretation. Additionally, AI positively influences engagement and collaborative learning, particularly through interactive learning analytics-based instruction. From a methodological perspective, most studies adopt a qualitative approach (55%), followed by quantitative methods (45%), employing research instruments such as eye tracking, learning analytics analysis, and pre-post surveys. The primary respondents include university students, high school students, educators, and statistics professionals. While AI demonstrates significant potential in improving statistical literacy, challenges such as algorithmic bias, curriculum integration limitations, and ethical concerns in AI implementation require further investigation. Therefore, this study recommends developing more adaptive, moral, and effective AI-based learning strategies to strengthen statistical literacy across various educational levels and professional environments.

FOR A NEW EDUCATIONAL PARADIGM BETWEEN ARTIFICIAL INTELLIGENCE, CURRICULUM AND DIGITAL CITIZENSHIP. A FIRST REFLECTION Abstract The present research work will attempt to clarify some of the phases of the new educational paradigm in its initial state of progress. Digital, albeit with difficulty, has become the protagonist of the classrooms in our schools more in recent years. Will artificial intelligence become part of the curriculum to aim at training «new aware citizens»? The conditions seem favorable but it is necessary to create a solid team of teachers who work for the training of other teachers and that there is greater awareness of this new challenge which will bring a substantial change in our lives, without forgetting the ethical challenges that accompany the new dimension of artificial intelligence. Precisely on this point, Europe appears to be the first continent to want to regulate the spread of artificial intelligence also in the educational field.

AI literacy encompasses proficiencies and skills to use AI applications and tools, considering them critically and interpreting their context and underlying principles. While students, as digital citizens, possess the ability to harness AI for enhanced learning outcomes, there remains a potential gap in utilizing suitable AI techniques effectively within specific learning contexts. This study focused on conceptualizing artificial intelligence literacy from the perspective of distance learners. By using a quantitative approach, a survey was conducted on distance learners of teacher training programs (B. Ed and M. Ed) from AIOU, Pakistan. A randomly selected sample of 104 students was approached through an online survey. Based upon the objectives of the study, the researcher developed a questionnaire with extensive effort by focusing on the psychometric properties of the scale and sought expert opinion on it. Findings of the study indicated that most of the respondents agreed that distance education taught by AI has a high impact on students learning and it indicates a positive response towards the students. Gender differences on t-test indicated no difference in conceptualizing role of AI literacy in their education. This study indicated the overall effectiveness of AI in education. effectiveness of artificial intelligence videos in education. It is recommended that in distance learning scenario, artificial intelligence may be encouraged to be used in the learning process.

With artificial intelligence (AI) becoming more present in ed- ucation globally, it is essential to consider how cultural con- texts shape teachers’ perspectives, an understanding that sup- ports more inclusive and sustainable learning systems. This study draws on the African philosophy of Ubuntu to frame our cross-cultural investigation of how children conceptu- alize AI through the lens of their teachers. We conducted semi-structured interviews with twelve middle school teach- ers in Nigeria and the United States, asking them to interpret AI-themed essays written by students. These teacher reflec- tions revealed differing educational priorities, cultural val- ues, and infrastructural realities: U.S. educators’ interpreta- tions centered on personal development and future careers, while Nigerian teachers highlighted students’ focus on fam- ily, community well-being, and practical societal challenges. Nigerian participants also pointed to the need for improved infrastructure (e.g., electricity, internet), broader AI literacy, and education policies that reflect local needs. Our findings il- lustrate how culturally grounded worldviews, such as Ubuntu, shape interpretations of AI and its role in society, and sug- gest that AI education is never culturally neutral. We argue that AI literacy initiatives must be designed not only to teach technical skills but also to support educational sustainability, defined here as inclusive, resilient, and culturally responsive learning systems capable of evolving within diverse contexts. We offer actionable recommendations for the HCI commu- nity to co-design AI education tools that foreground collec- tive well-being, foster global digital citizenship, and reduce epistemic exclusion in the development of future technolo- gies.

The article explores the potential of artificial intelligence (AI) as a key driver of public administration transformation in the context of the digitalization of social relations. It analyzes the theoretical foundations for integrating intelligent technologies into the public sector through the lens of digital governance concepts, the algorithmization of administrative processes, and institutional adaptation. The main directions of AI application in public administration are highlighted, including automation of administrative functions, big data analytics, risk forecasting, digital citizen services, and decision support in crisis situations. Special attention is paid to the Ukrainian context of AI implementation, particularly within the framework of national digital transformation strategies (e.g., “Diia.Digital State”). Promising sectors for AI integration are identified: administrative service provision, taxation, healthcare, environmental monitoring, and urban governance. The key conditions for successful AI integration are outlined, including legal and regulatory frameworks, open data infrastructure, development of digital competencies among civil servants, and cross-sectoral partnerships. At the same time, the article highlights major risks and challenges: legal uncertainty, algorithmic bias and discrimination, cybersecurity threats, insufficient digital skills, and ethical dilemmas of delegating decisions to machines. It is argued that the implementation of AI should be understood not only as a technological process but also as a conceptual transformation of public governance toward greater transparency, adaptability, and human-centeredness.

Imbellus is an assessment company that aims to test cognitive processes within the context of immersive simulation-based assessments. This paper explores our work with McKinsey & Company, a best-in-class management consulting firm, to build a simulation-based assessment that gauges applicants’ cognitive skills and abilities. Leveraging a cognitive task analysis grounded in theoretical work and practical observations of on the job activities, we defined key work activities and skills needed to complete them. We then developed scenarios that abstracted and generalized the most crucial skills. To make sense of significant telemetry data from users’ interactions with the assessment, we applied theoretically grounded expert models to guide our scoring algorithms. Our assessment draws inferences across seven major problem-solving constructs. We will present our initial findings and describe implications of our current work for the fields of artificial intelligence and assessment.

The rapid rise of artificial intelligence (AI) and digital tools in education and work has transformed learning, communication, and decision-making. While AI enhances access and personalization, it also raises ethical issues related to privacy, bias, misinformation, and academic integrity. This chapter explores how educational psychology can foster ethical decision-making, digital responsibility, and moral reasoning in the AI era. Grounded in Piaget's and Kohlberg's developmental theories and Bandura's Social Learning Theory, it connects moral growth with reflective digital practice. Through case-based learning, empathy, and critical digital literacy, it equips educators to build ethical, responsible digital citizens.

In recognition of the integration of Information and Communications Technology (ICTs) along with Artificial Intelligence (AI) into education, several educational organizations have recommended cultivating 21st-century digital skills as part of every educational curriculum. The computational thinking skill, which is at the core of 21st-century digital skills, has gained research in several fields, such as physics and psychology (Barkela et al., 2024). However, applied linguistics and computer-assisted language learning (CALL) have overlooked this crucial skill, which might stem from the lack of language teachers’ computational thinking competency. For this reason, in the present study the researchers developed a new theoretical framework, and it’s corresponding scale specifically designed for applied linguistics and CALL, namely the Language Teachers’ Computational Thinking Competency in Computer Assisted Language Learning (LTCCTCALL). Using deductive and inductive methods, the researchers developed the items, followed by a validation process that used the Rasch-Andrich rating scale model (RSM) to assess the scale’s item difficulty and validity using Exploratory Factor Analysis (EFA) and Confirmatory Factor Analysis (CFA). LTCCTCALL was validated with five factor structures that included 15 items in the Iranian EFL context, involving 273 Iranian in-service language teachers. Based on this result, the study introduced a new theoretical framework and its scale to the fields of CALL, applied linguistics, and the Common European Framework of Reference for Languages (CEFR) to lead the community’s continued growth and to prevent the community from falling behind other fields that incorporate computational thinking skills into our classrooms to have problem-solving approaches with CALL, AI, and chatbots in language class.

This paper explores how changing digital literacy practices in educational contexts require that we continually revisit conceptualisations of digital literacy education. We begin by analysing the positions taken by stakeholders who contribute to digital literacy discourses in Australia, exploring how competing interests produce effects which manifest in ways that differently consecrate social and cultural practice in the digital age. We advocate the need for pedagogic frameworks that support digital literacy education. Existing approaches tend to privilege the operationalisation of digital technology. By contrast, teaching is needed which focusses on meaning-making and creating. However, the ‘datafication of everyday life’ (Barassi, 2018, p.170) has included extraordinary interventions into schooling that have significant implications for teachers and students. We argue that preparing young people for digital citizenship must include a focus on critical digital literacies that are responsive to contemporary digital forces (e.g. platformatisation, artificial intelligence, edu-apps, algorithms) as well as those digital technologies that are yet to make their way into formal schooling.

This study examines how secondary-school students recognize and relate to artificial intelligence (AI) and the meanings they attribute to it in their everyday lives. Using a quantitative, descriptive, cross-sectional design, we explore the subjectivities of a purposive sample of 576 students from both public and private schools. The analysis focuses on students’ use of AI across seven dimensions: teaching–learning, ethics and morality, emotions, personal development, cognition and creativity, social construction, and recreation and entertainment. Data were collected with an ad hoc instrument that showed high internal consistency (α = 0.89). The findings reveal a variety of positions, including emotional ambivalence, ethical uncertainty, autonomous learning, and diverse perceptions of the creative, social, and educational effects of AI. Although students express openness and interest in technology, they also voice doubts about its integration into school practices and its influence on critical thinking and personal initiative. The results underscore the need for educational policies and school practices aligned with Education 5.0 that foster critical, ethical, and humanistic uses of technology. This research contributes to understanding the configuration of digital youth subjectivities and provides a broad framework for designing meaningful, contextualized, and inclusive pedagogical strategies that develop digital citizenship.

This study explores the application of Large Language Models (LLMs), specifically a BERT-based architecture, coupled with predictive analytics for proactive hate speech and violence identification and forecasting in social media comment moderation. The research addresses the critical need to shift from reactive to proactive content moderation strategies in order to enhance digital safety and foster inclusivity. Using a dataset of 44,000 public comments from Facebook that are unfiltered and generalized, the methodology includes data collection through the Facebook Graph API, preprocessing phases, and model training using a balanced dataset to improve detection accuracy. The study highlights the importance of ethical deployment in artificial intelligence, noting the role of predictive analytics in identifying patterns and signals that indicate the existence of harmful content before its widespread circulation. From this, the results show that a balanced dataset helped the model to achieve strong performance metrics: accuracy of 82.63%, precision of 82.5%, recall of 82.88%, and F1 score of 82.69%. Results like this have shown the promise that advanced AI technologies hold when integrated into content moderation to successfully handle and pre-empt online hate speech and violence. This research helps to contribute to the larger discussion on ethical AI, responsible digital citizenship, and safer online communities through the promotion of proactive moderation systems.

This article explores the growing dependency on artificial intelligence in education and introduces the concept of the “automation abyss”, a new digital divide separating learners who retain agency from those who rely on AI-mediated experiences. Drawing on classroom observations, research on the Budding Botanist Paradox, and studies such as the Penn AI tutoring trial, the author argues that automation is not just improving efficiency but eroding core learning processes like struggle, inquiry, and human connection. The rise of tools like DeepSeek and the broader AI land rush reflect a transformation in the control of educational infrastructure, echoing historical shifts like the railroad boom. Through examples from coursework and digital citizenship training, the article highlights a concerning feedback loop: teachers, students, and edtech companies increasingly use AI to replace, rather than support, authentic learning. To address this, the author proposes a framework centered on human checkpoints, critical reflection on automation, and self-awareness in technology use. Ultimately, the article calls for a shift toward “augmented pedagogy” that enhances rather than automates the learning experience. Without deliberate design to preserve struggle and inquiry, education risks losing its human core in the face of accelerating AI development.

How can the efficiency of volunteers be improved in performing bushcare in the limited amount of time able to be spent caring for each location every month [1]? Bushcare is a volunteer activity with a high difficulty curve for volunteers just starting out as the crucial skill of distinguishing the native plants from the harmful invasive species only comes with experience and memorization [2]. The lack of ability to distinguish targeted plants will greatly reduce the efficiency of the volunteers as they work through the limited amount of time they have at each location each month while also discouraging newly joined volunteers from continuing this activity. To assist newly joined volunteers, the majority of each would likely be from a younger demographic with a digital app that could help the user distinguish the species of plant, making it easier for them to start familiarizing themselves with both the native and invasive species in their area [3]. The user could simply have to take a picture of the plant they wish to identify and the software would use its image recognition algorithm trained with a database of different species of plants to identify the type of plant and whether it needs to be removed. At the same time, more experienced volunteers could continue to use this app, identifying errors in the app’s identification to make it more reliable.

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