Forensic Cyberpsychology Strategies for Integrating AI Ethics and Digital Citizenship into STEM Pedagogy for Adolescents in Diverse K-12 Classrooms

RAIS Journal for Social Sciences | VOL. 9, No. 2, 2025 ISSN 2574-0245 (Print) | ISSN 2574-1179 (Online) | DOI: 10.5281/zenodo.17468990 ! 254! Forensic Cyberpsychology Strategies for Integrating AI Ethics and Digital Citizenship into STEM Pedagogy for Adolescents in Diverse K-12 Classrooms ! Francis C. OHU1, Laura A. JONES2 1,2Department of Forensic Cyberpsychology, Capitol Technology University, Laurel, MD, USA 1ORCID: https://orcid.org/0009-0003-4981-8428 2ORCID: https://orcid.org/0000-0002-0299-370X Abstract: The rise of artificial intelligence (AI) in education and digital platforms has created new ethical and psychological challenges for adolescents, particularly in diverse learning environments. This study investigates how AI ethics and digital citizenship can be effectively embedded into the grades kindergarten through 12th grade (K-12), Science, Technology, Engineering, and Mathematics (STEM) curricula to support moral reasoning, identity development, and digital agency. Using a forensic cyberpsychology lens, the study analyzes adolescent behavior in response to algorithmic influence, digital surveillance, and identity shaping within AI-powered systems. A qualitative meta-synthesis of 68 peer-reviewed empirical studies published between 2024 and 2025 revealed that embedding ethical dilemmas into STEM lessons, such as bias in facial recognition or predictive policing, enhanced moral reasoning and engagement, especially among underrepresented students. Approximately 70% of included studies reported improvements in critical thinking and STEM motivation when instruction addressed psychological and justice-oriented concerns. Drawing on forensic cyberpsychology frameworks, the study highlights how validation-seeking behaviors and surveillance anxiety mimic the manipulative logic of cyber operations, positioning students as both targets and agents within digital ecosystems. The findings support the integration of culturally responsive pedagogy, digital ethics, and behavioral risk analysis in adolescent STEM education. Key implications include the design of inclusive, psychologically grounded STEM curricula and cross-sector collaboration to promote ethical awareness, digital resilience, and equitable innovation in AIdriven education. Keywords: Forensic Cyberpsychology, AI Ethics, Adolescent STEM Education, Algorithmic Bias, Digital Agency, Surveillance, Culturally Responsive Pedagogy, K-12 ! Introduction Background and Context As artificial intelligence (AI) becomes increasingly embedded in everyday life, adolescents are interacting with systems that shape their digital experiences in obscure and often inequitable ways (Lin, 2024; Zhou, 2024). Yet few classrooms in the kindergarten through 12th grade (K–12) address AI ethics or digital citizenship in a structured, critical, or culturally responsive manner (Charmaraman et al., 2024; Singh & Cheema, 2024), and this disconnect is particularly concerning given the rise of algorithmic bias, data privacy concerns, and surveillance capitalism, which underscore the urgent need to prepare students OHU & JONES: Forensic Cyberpsychology Strategies for Integrating AI Ethics and Digital Citizenship into STEM Pedagogy for Adolescents in Diverse K-12 Classrooms ! 255! not only as consumers of digital tools, but as informed, ethical participants in a society shaped by AI (Burnell et al., 2024). Adolescents are especially vulnerable to the persuasive architecture of AI-driven platforms, often seeking validation, mimicking influencer behaviors, and misunderstanding the implications of algorithmic sorting and predictive profiling (Murad, 2024; Pérez-Torres, 2024). As digital platforms increasingly mediate identity, information access, and educational opportunity, Science, Technology, Engineering, and Mathematics (STEM) education must evolve to teach students not only how AI works, but also how it affects power, equity, and autonomy (McGovern et al., 2024). Despite this growing urgency, most K–12 STEM programs continue to prioritize technical competencies such as coding, robotics, or automation without engaging students in the ethical, civic, or sociotechnical dimensions of AI (Lin & Van Brummelen, 2021; McGovern et al., 2024). This approach creates a critical gap between adolescents' digital realities and the classroom environments designed to prepare them for their futures, particularly for those from marginalized communities who are disproportionately affected by obscure or discriminatory algorithmic systems (Singh & Cheema, 2024). Furthermore, while over 58% of U.S. school districts now integrate AI-based technologies into the classroom, only 11% include instruction on AI ethics or digital citizenship, global institutions like the United Nations Educational, Scientific and Cultural Organization (UNESCO) and the Organization for Economic Co-operation and Development (OECD), have emphasized the importance of addressing AI's sociotechnical impacts, yet education systems have been slow to respond (Varsik & Vosberg, 2024). While recent advancements in forensic cyberpsychology offer promising insights into how youth interpret and respond to digital manipulation (Ohu & Jones, 2025a), there remains a lack of empirical research on how these insights can be effectively incorporated into STEM pedagogy. The study addresses this gap by investigating how AI ethics and digital citizenship can be meaningfully integrated into K-12 STEM education in culturally and economically diverse classrooms. The overarching research question guiding this study is: “How can AI ethics and digital citizenship be effectively integrated into K-12 STEM curricula in culturally diverse classrooms to foster equity, critical thinking, and ethical awareness in technologically mediated societies?” By addressing this gap, the study aims to contribute to the development of inclusive, justice-oriented curricula that prepare adolescents to critically analyze and ethically engage with the AI systems shaping their lives. Problem Statement Current K–12 STEM curricula often emphasize computational thinking, coding, and problem-solving but largely neglect the ethical, civic, and psychological dimensions of AI technologies (Lewis et al., 2024). Most STEM classrooms do not address the sociotechnical systems underpinning machine learning or the disparate impacts of automated decisionmaking on different demographic groups (Pellegrino & Stasi, 2024). The general problem is that adolescents are engaging with AI technologies without the critical literacy needed to understand their social and ethical consequences (Gu & Ericson, 2025). The specific problem is the lack of inclusive and context-sensitive STEM instruction that integrates AI ethics and digital citizenship education for diverse learners, particularly those from underrepresented or digitally vulnerable backgrounds (Adesina et al., 2024; Latuheru & Cangara, 2024). Moreover, without intentional integration of these themes, students risk becoming passive participants in technological systems that shape their identities, choices, and opportunities, with little understanding of how to question or challenge them. RAIS Journal for Social Sciences | VOL. 9, NO. 2, 2025 ! 256! Purpose of the Study The purpose of this study is to explore how AI ethics and digital citizenship can be meaningfully integrated into K–12 STEM instruction for adolescents in culturally and economically diverse classrooms. Through the design and implementation of interdisciplinary instructional modules, the study examines student engagement with key ethical and sociotechnical concepts, including algorithmic bias and fairness, data ethics and consent, deep fakes and misinformation, surveillance and autonomy, and ethical dilemmas in emerging technologies. Employing a qualitative, meta-synthesis design, the study investigates how these modules influence students’ understanding of AI, their critical thinking about digital systems, and their capacity for ethical reasoning. The study further explores how teacher facilitation, culturally responsive pedagogy, and peer dialogue contribute to the development of students’ ethical awareness and digital agency (Min & Nelson, 2024; Nagel et al., 2023; Ozturk, 2025). Rationale, Significance, and Originality This research responds to a growing demand among educators, policymakers, and researchers for inclusive, justice-oriented STEM education that reflects the ethical complexities of life in an AI-driven society (Jones, 2024; Singh & Cheema, 2024). While many current initiatives focus on expanding access to AI tools or coding instruction, few critically examine AI as a sociotechnical system that can reinforce or challenge existing social inequalities (Ko & Kim, 2024). The originality of this study lies in its integration of AI ethics with digital citizenship within STEM contexts, its centering of the lived experiences of diverse adolescents disproportionately affected by biased or opaque AI systems, and its innovative application of forensic cyberpsychology to understand how youth interpret and respond to algorithmic manipulation and surveillance (Ahmed, 2024; Burnell et al., 2024). By contributing to emerging fields such as critical AI literacy, STEM justice, and youthcentered digital ethics education, this study not only enriches academic discourse but also addresses global calls for ethical AI governance and design. Ultimately, it seeks to prepare the next generation of technologists, data citizens, and civic leaders with the tools to think ethically, inclusively, and critically, supporting broader efforts to democratize AI education, amplify diverse student voices, and foster ethical innovation in both classrooms and society at large (Ganapathy, 2024; Pellegrino & Stasi, 2024). Literature Review This literature review aims to identify, select, and analyze relevant studies in order to explore the integration of AI ethics and digital citizenship in K–12 STEM education, with a specific focus on adolescents in culturally and economically diverse classrooms. The overarching research question guiding this review is: “How can AI ethics and digital citizenship be effectively integrated into K-12 STEM curricula in culturally diverse classrooms to foster equity, critical thinking, and ethical awareness in technologically mediated societies?" This question aligns with the broader problem statement, emphasizing the lack of inclusive, justice-oriented, and developmentally appropriate AI education in K–12 classrooms. A systematic literature search was conducted across PsycINFO, Scopus, MDPI, ERIC, and Google Scholar. Search terms included: “AI literacy and adolescents,” “algorithmic bias in STEM,” “digital citizenship in K–12 education,” “ethical reasoning in AI learning,” “culturally responsive STEM pedagogy,” and “youth data privacy and algorithmic fairness.” Peer-reviewed articles published between 2021 and 2025 were prioritized. Excluded were articles that lacked empirical rigor, were not focused on adolescent populations, or did not directly examine ethics or digital citizenship in relation to STEM or OHU & JONES: Forensic Cyberpsychology Strategies for Integrating AI Ethics and Digital Citizenship into STEM Pedagogy for Adolescents in Diverse K-12 Classrooms ! 257! AI. Out of 115 initially retrieved sources, 76 met the criteria for inclusion following title and abstract screening, followed by full-text evaluation. Overview of AI Integration in K–12 STEM Education The push to incorporate AI into K–12 education has gained momentum globally, with countries piloting national frameworks for K–12 AI literacy, and these initiatives include hands-on coding, machine learning basics, and problem-solving with intelligent systems (Lau et al., 2024). However, most existing AI curricula emphasize technical fluency over ethical or civic considerations (Lewis et al., 2024). Studies show that while 65% of students engage with AI-based platforms such as adaptive learning apps and social media algorithms, fewer than 15% receive any formal instruction on how these systems work or affect equity, privacy, or identity (Schluchter, 2024; Zhou, 2024). Moreover, Burnell et al. (2024) stated that educators often report feeling underprepared to teach AI topics, particularly those involving ethics or controversial technologies like facial recognition. Adolescent Development and Digital Citizenship Adolescence is marked by heightened identity exploration, peer sensitivity, and an emerging sense of moral reasoning making this developmental phase a critical window for digital citizenship education (Charmaraman et al., 2024). Yet adolescents frequently demonstrate poor judgment in online environments, often engaging in risky or impulsive behavior without fully understanding the digital consequences (Jungselius, 2024). Cyberpsychological studies emphasize that adolescents are susceptible to algorithmic validation loops, where likes, shares, and influencer norms guide behavior more than ethical reasoning (Ohu & Jones, 2025b; Ruan et al., 2023; Voggenreiter et al., 2023). Effective teaching of digital citizenship, defined as the responsible and informed use of technology, requires facilitating critical thinking and offering contextualized examples relevant to students' lived experiences (Ko & Kim, 2024). Emerging Focus on AI Ethics in School Contexts There is growing recognition that AI literacy without ethical grounding is insufficient, and that ethical AI education involves interrogating values, fairness, consequences, and agency embedded within technical systems (Singh & Cheema, 2024). While some high school programs introduce AI coding, few address deeper issues like surveillance, racial bias in datasets, or transparency in decision-making (Adesina et al., 2024). Recent curricular models suggest that students can grapple with ethical dilemmas when presented through accessible, inquiry-based tasks, for example, students asked to audit a biased hiring algorithm demonstrated improved understanding of fairness, discrimination, and justice in machine learning (McGovern et al., 2024). However, these models remain largely experimental and lack scale. Algorithmic Bias, Data Privacy, and Equity Gaps in Adolescent AI Exposure Marginalized adolescents, including low-income youth, face disproportionate harm from algorithmic bias and surveillance technologies (Ferrara, 2024). AI systems deployed in schools for behavior monitoring or performance tracking often reinforce racialized assumptions and trigger disciplinary actions (Burnell et al., 2024; Zhou, 2024). Adolescents in under-resourced schools often lack access to rigorous digital literacy instruction, widening the AI preparedness gap (Lewis et al., 2024). Privacy violations are another concern, where data collected through AI-powered platforms may be used without consent or protection, with students unaware of how their behaviors feed commercial and behavioral profiles (Lin, 2024). RAIS Journal for Social Sciences | VOL. 9, NO. 2, 2025 ! 258! Culturally Responsive Pedagogy in Digital STEM Environments To ensure AI ethics education reaches all learners equitably, it must be delivered through culturally responsive teaching (CRT). CRT affirms students’ identities, connects learning to sociocultural contexts, and challenges dominant narratives (Dvir, 2023; Choudhary & Louis, 2024). In AI ethics, this might involve exploring how bias impacts different communities or asking students to reflect on digital justice through personal storytelling. Educators trained in culturally responsive STEM practices are more likely to use inclusive case studies, encourage dissenting perspectives, and foster critical debate (Murad R.J., 2024), yet many report lacking professional development in CRT and AI ethics simultaneously, calling for interdisciplinary teacher preparation and training (Ko & Kim, 2024). Critical Pedagogies and Ethical Reasoning in STEM Learning Ethics education benefits from critical pedagogies that engage students in debate, reflection, and social analysis, and tools such as case-based reasoning, ethics labs, and simulation games have shown promise in prompting adolescent reflection on digital dilemmas (Hani et al., 2024). One study implemented an "AI ethics challenge" in which students designed their own ethical guidelines for platform design, and results showed significant gains in moral reasoning, systems thinking, and STEM engagement, particularly among students who were previously disengaged from science (Angelini et al., 2024). These findings support the notion that integrating ethics into STEM education can deepen learning and enhance student engagement, rather than detracting from it. Identified Gaps and Research Directions Despite increasing interest, systemic integration of AI ethics and digital citizenship in adolescent STEM education is limited (Barthwal et al., 2025; Shouli et al., 2025). Most studies are small-scale, region-specific, or not longitudinal and there is also insufficient research on how adolescents from historically excluded backgrounds interpret ethical content, or how algorithmic systems shape their STEM identities over time (Bogdan et al., 2023). Future research must explore scalable, culturally attuned models of instruction, especially those that merge forensic cyberpsychology, youth agency, and equity-centered design. However, teacher development remains a key bottleneck; without training in ethical reasoning and digital justice, educators will struggle to support the next generation of responsible technologists (Boulay, 2023; Nganga et al., 2025). Methodology Research Design This study employed a qualitative meta-synthesis approach to extract, analyze, and synthesize empirical findings from recent peer-reviewed research articles examining AI ethics, digital citizenship, and STEM instruction for adolescents. A systematic metasynthesis is a methodological approach used to identify, analyze, and synthesize findings from multiple qualitative studies on a specific topic, and unlike a quantitative meta-analysis, which pools statistical data, a meta-synthesis interprets and integrates conceptual insights to build new theories, frameworks, or deeper understandings on a particular research interest. Qualitative meta-synthesis was selected for its capacity to distill conceptual themes from heterogeneous empirical sources while maintaining contextual and methodological fidelity (Adesina et al., 2024; McGovern et al., 2024). The study was guided by the overarching research question, “How can AI ethics and digital citizenship be effectively integrated into K-12 STEM curricula in culturally diverse classrooms to foster equity, critical thinking, and ethical awareness in technologically mediated societies?" Figure 1 shows the PRISMA flow diagram of the document selection process. OHU & JONES: Forensic Cyberpsychology Strategies for Integrating AI Ethics and Digital Citizenship into STEM Pedagogy for Adolescents in Diverse K-12 Classrooms 259! Figure 1. PRISMA Flow Diagram for Document Selection Data Source Selection and Inclusion Criteria A systematic literature search was conducted across Scopus, PsycINFO, ERIC, MDPI, and Google Scholar between February and May 2025. The search terms included "AI literacy and adolescents," "algorithmic bias in STEM," "ethical reasoning in digital education," "culturally responsive AI pedagogy," and "youth digital citizenship and equity." The inclusion criteria consisted of empirical, peer-reviewed articles published between 2022 and 2025, a focus on adolescents (ages 11-18), study topics intersecting AI ethics, STEM instruction, digital identity, or culturally responsive teaching, and availability in English with clearly defined methods and findings. In contrast, the exclusion criteria comprised articles published prior to 2023 unless foundational, non-empirical commentary or theoretical opinion pieces, and studies with adult populations or general digital literacy without AI context. A total of 114 records were identified, with 68 articles meeting the final inclusion criteria after title and abstract screening, followed by full-text analysis. Data Extraction Process A structured data extraction matrix was used to capture the required study elements from each included article, as shown in Table 1. Table 2 highlights a visual summary of the variables extracted from selected articles on AI ethics and adolescent STEM education. The data extracted were validated through cross-checking among research assistants, and discrepancies were reconciled by consensus. Table 1. Data Extraction Matrix Variable Description Author(s) and Year For citation and recency validation Country/Context Geographical or educational setting Population Focus Adolescents (age 11–18), teachers, or instructional content Study Type Qualitative, quantitative, or mixed methods Key Themes Identified Empirical patterns relevant to AI ethics and instruction Ethical/Equity Considerations Mention of justice, inclusion, bias, privacy Records identified through database searching (n = 114) Record screened (n = 114) Full-text articles assessed for eligibility (n = 88) Studies included in Metasynthesis (n = 68) Studies included in the Qualitative analysis (n = 68) Record excluded (n = 26) Full-text articles excluded, with reasons •Lack of focus on AI (n=8) •General STEM education (n=7) •Non-adolescent population (n=5) RAIS Journal for Social Sciences | VOL. 9, NO. 2, 2025 ! 260! Table 2. Summary of Extracted Studies on AI Ethics and STEM Education For Adolescents Sample Study (Author, Year) Country Population Focus Study Type AI Ethics Topic Key Outcome/Themes McGovern et al., 2024 USA High school students Mixed Algorithmic bias Increased ethical reasoning with design critiques Burnell et al., 2024 UK Adolescents (13–17) Qualitative Social media manipulation Algorithmic awareness influenced digital autonomy Zhou, 2024 China K–12 students Quantitative TikTok recommendation algorithms Bias awareness and behavioral impact noted Singh & Cheema, 2024 Canada Educators & students Mixed Surveillance and equity in AI tools CRT strategies enhanced student trust and participation Pérez-Torres, 2024 Spain Adolescents (14–18) Qualitative Algorithmic validation seeking Engagement affected by platform-induced validation loops Ko & Kim, 2024 South Korea Teachers & female students Qualitative Digital selfpresentation Identity conflicts and gendered tech interaction Charmaraman et al., 2024 USA Diverse youth Quantitative AI influence on identity development Emphasis on digital citizenship improved self-regulation Adesina et al., 2024 Nigeria STEM teachers Mixed AI integration challenges in education Teachers underprepared for ethical integration Lewis et al., 2024 Australia High school students Qualitative Digital health & data privacy Stronger outcomes with co-designed curriculum Murad, 2024 Egypt Adolescents Qualitative Identity formation in digital context Surveillance narratives shaped moral judgment Data Analysis Data was analyzed using reflexive thematic analysis, following the six-phase method proposed by Braun & Clarke (2024). Codes were generated both inductively from article findings and deductively based on prior literature. NVivo v15 software supported coding consolidation and theme clustering, and Table 3 enumerates the thematic coding process used in the study. To ensure rigor, the research applied triangulation, audit trails, and interrater reliability checks across sampled papers. OHU & JONES: Forensic Cyberpsychology Strategies for Integrating AI Ethics and Digital Citizenship into STEM Pedagogy for Adolescents in Diverse K-12 Classrooms ! 261! Table 3. Thematic coding process Phase Activity Familiarization Systematic reading and memoing across all selected studies Initial Coding Open coding of repeated themes and pedagogical patterns Theme Generation Axial grouping of codes based on similarity and relevance Theme Review Removal of overlaps, refining boundary definitions Theme Naming Articulation of finalized interpretive categories Reporting Synthesis with representative examples and literature cross-validation ! Results and Findings The qualitative meta-synthesis of the 68 selected peer-reviewed studies revealed four overarching themes that characterize the integration of AI ethics and digital citizenship into adolescent STEM education. These themes reflect the developmental, psychological, and sociocultural complexity of ethical instruction in technology driven learning environments. As shown in Table 4, the emergent themes demonstrate that ethically grounded and culturally responsive instruction enhances adolescents’ moral reasoning, identity development, and digital agency, particularly among students from underrepresented communities (Hölscher et al., 2024; Yangambi, 2025). The findings also emphasize the role of pedagogical design in supporting algorithmic awareness, privacy consciousness, and critical engagement with sociotechnical systems Table 4. Thematic Code Analysis Matrix Table Theme Code(s) Description Representative Findings Sample Sources 1. Algorithmic Awareness & Ethical Curiosity Algorithm Bias Awareness, Curiosity about Personalization Students develop curiosity and skepticism about how algorithms shape their experiences Increased skepticism of TikTok and YouTube content; students ask how systems decide what they see McGovern et al., 2024; Zhou, 2024; PérezTorres, 2024a 2. Surveillance, Identity & Agency Surveillance Anxiety, Self-Curation for Visibility, Loss of Control Adolescents feel monitored by digital systems, impacting identity and perceived autonomy. negotiation emerges from these tensions Students feel exposed by school tech or public facial recognition, discomfort affects online behavior, express discomfort about surveillance, and feeling "watched" by AI systems Charmaraman et al., 2024; Ko & Kim, 2024; Murad, 2024 3. ValidationSeeking and Psychological Vulnerability Digital Validation Dependence, Self-Worth Tied to Likes Adolescents seek affirmation online, often compromising ethical judgment or authenticity, behaviors are shaped by a need for digital approval and attention Algorithm-driven validation loops linked to self-worth and ethical disengagement, and Repeated checking for likes, mimicking viral behavior, lowered selfworth tied to online engagement loops Ohu & Jones, 2025; Burnell et al., 2024 RAIS Journal for Social Sciences | VOL. 9, NO. 2, 2025 ! 262! Theme Code(s) Description Representative Findings Sample Sources 4. Moral Reasoning through Ethical STEM Tasks Critical Thinking through Case Studies, Moral Dilemma Engagement, Ethical Design Awareness Instructional ethics scenarios activate higher-order thinking, and Exposure to AI dilemmas in STEM fosters student reasoning, empathy, and digital justice perspectives Students co-created ethical guidelines for app design; debated fairness of facial recognition in schools. Ethics-based tasks improved decisionmaking and prompted dialogue on fairness and justice McGovern et al., 2024; Singh & Cheema, 2024 5. Culturally Responsive Pedagogy Elevates Engagement CommunityRelevant Case Examples, Representation in Tech Ethics, Cultural Framing in Instruction Pedagogical approaches grounded in identity and community context drive deeper engagement, and Ethical lessons connected to students' lived experiences enhanced STEM motivation and dialogue Students responded most positively when ethics instruction reflected their own cultural or lived experiences Lewis et al., 2024; Dvir, 2023; Singh & Cheema, 2024 6. Forensic Cyberpsycholo gy & Adolescent Risk Profiling Self-Doubt as Risk Marker, Craving for Peer Approval, Manipulative Gratification Patterns Risk behaviors interpreted using validation syndrome, identity instability, and digital impulsivity, and Based on the VSDT model, these psychological cues signal heightened risk for cyber manipulation The VSDT framework effectively predicted psychological risk zones in adolescent online behavior. Forensic profiles align with behaviors like online deception, oversharing, and identity masking Ohu & Jones, 2025 7. Teacher Readiness and Ethical Instructional Design Lack of Ethics Integration Training, Tech Fluency Barriers, Curriculum Gaps in AI Topics Educators need support to embed ethics and AI content in ways that are rigorous and inclusive Teachers reported unfamiliarity with AI bias and discomfort facilitating conversations around justice, and Reported barriers include limited ethics training, tech fluency, and curriculum support Adesina et al., 2024; Ko & Kim, 2024 The four overarching themes identified through qualitative meta-synthesis of empirical literature on AI ethics in adolescent STEM education, are enumerated in Table 5. Each theme reflects a distinct dimension of student engagement with ethical, sociotechnical, and identity-related aspects of AI. Percentages were derived based on the frequency with which each theme was present as a primary or secondary outcome in the included studies, with some studies contributing to multiple themes. OHU & JONES: Forensic Cyberpsychology Strategies for Integrating AI Ethics and Digital Citizenship into STEM Pedagogy for Adolescents in Diverse K-12 Classrooms ! 269! dilemmas, such as facial recognition, predictive policing, and algorithmic recommendation systems, demonstrated not only a technical understanding of these mechanisms but also voiced concerns, exhibiting critical reflection, and proposed socially conscious solutions, expressing a newfound awareness that coding involves making decisions about people, not just working with numbers” as noted in the study by Jarzemsky et al., (2023). Implications for Practice The findings from this study highlight ten most actionable insights for policymakers, educators, and curriculum designers, thus: 1. Incorporating ethics into STEM learning deepens student engagement and enhances moral reasoning, particularly in topics like machine learning and AI fairness (Damio et al., 2024). 2. Curriculum that allows students to reflect on how digital systems affect their identity or community is most effective in teaching digital citizenship. 3. Adolescents require exposure to critical inquiry to develop an accurate understanding of algorithms, as algorithmic awareness is not intuitive. 4. Using culturally responsive pedagogy in AI ethics education boosts inclusion for students from underrepresented backgrounds. 5. Providing targeted professional development is essential for teacher training, as many educators feel unprepared to teach AI ethics. 6. Students value transparency and control, and ethics education increases their demand for fairness and transparency in data systems. 7. Collaborative learning enhances reflection, as group-based ethical challenges encourage peer dialogue and moral reasoning. 8. Using community-based examples works best in teaching AI ethics, as case studies rooted in local or racialized contexts resonate more than generic AI examples. 9. Incorporating ethics into STEM education promotes career identity, as students begin to envision themselves as ethical designers, coders, or innovators. 10. Intentional design is required to achieve equity, as digital ethics must be built into STEM frameworks rather than treated as optional or enrichment. Recommendations for Future Research While this study provides important insights, it also highlights several limitations and opportunities for further exploration. Future research should investigate additional variables that may influence the relationship between STEM-ethics integration and student outcomes. For instance, peer and parental digital modeling could play a significant role in shaping students' online behaviors and ethical decision-making (Hernandez et al., 2024; MoralesÁlvarez et al., 2025; Morales-Navarro, 2025). Furthermore, factors such as socioeconomic status, device access, exposure to disinformation, and algorithmic targeting can influence students' capacity to effectively engage with digital technologies, critically evaluate online information, and develop essential media literacy skills (Ohu & Jones, 2025c). Moreover, several key areas of research are needed to advance our understanding of the impact of STEM-ethics integration on student outcomes. Longitudinal studies are necessary to assess whether the positive effects of STEM-ethics integration on ethical reasoning and digital citizenship persist or evolve over time with continued exposure. Many included studies were cross-sectional, and future research should track students' ethical reasoning and identity development over time to assess lasting impact. Future studies should also employ mixedmethod designs, integrating quantitative pre/post assessments with qualitative reflections, to provide stronger evidence of moral and identity shifts. Additionally, research is needed to explore the experiences of underrepresented populations, including rural, Indigenous, and non-Western school contexts, where digital access and cultural framing may differ, and the use of real-time data and digital behavior analysis could also provide valuable insights, as RAIS Journal for Social Sciences | VOL. 9, NO. 2, 2025 ! 270! future work could incorporate digital trace data such as social media usage and app interaction patterns, to correlate ethical awareness with digital behavior (Ohme et al., 2024; Sultan et al., 2023). Teacher-centered research is needed to investigate how educators interpret and implement AI ethics content, including their identity, beliefs, and barriers (Ravi et al., 2023; Shaayesteh et al., 2025). Comparative curriculum models are also essential, as there is a need for cross-national comparisons of AI ethics instruction in STEM, for instance, how ethical education differs between the United States, the United Kingdom, South Korea, and Finland. Intervention testing through controlled studies across multiple sites can help refine best practices and scalable models. Student co-design, through participatory action research involving adolescents in curriculum creation, would further democratize ethical instruction (Akhmetova et al., 2025; Geurts et al., 2024). It is also crucial to examine the intersectional impacts of identity on adolescents' interpretation and response to digital ethics, including the interactions between race, gender, and socioeconomics (Amadori et al., 2025). Finally, as AI integration expands to emerging platforms, such as virtual worlds and immersive platforms like the Metaverse, future research should explore how these affect youth ethics, identity, and agency (Shouli et al., 2025; Solyst et al., 2025). By incorporating these controls and exploring these areas, future studies can more accurately isolate the mechanisms by which ethical STEM instruction impacts student outcomes, ultimately informing the development of more effective interventions (Barthwal et al., 2025). Final Thoughts This study provides compelling evidence that integrating AI ethics and digital citizenship into adolescent STEM education is crucial. When implemented with cultural relevance and critical intent, these themes promote deeper learning, stronger engagement, and more equitable educational outcomes (Kenny & Antle, 2024). Future research should aim to expand the evidence base by conducting longitudinal and multi-site studies, controlling for socio-cultural confounds, and centering the voices of adolescents themselves in curriculum co-creation (Morales-Navarro, 2025). Several open-ended questions remain to guide future research and broaden the conversation, such as “How might emerging immersive technologies further blur ethical boundaries for youth?”, “What would AI education look like if co-led by youth from underrepresented communities?”, “Can we shift from ‘teaching ethics’ to ‘doing ethics’ through co-design, advocacy, and justice-driven innovation?” The ultimate goal of AI education is not only to teach students how AI works but also to help them critically evaluate its impact and ask questions like, “Whom does it serve, whom does it exclude, and what kind of world do we want to build with it?” This study’s findings provide a foundation for reimagining STEM education in a world increasingly governed by AI. As AI becomes ubiquitous in various aspects of life, including hiring, housing, policing, and education, it is vital that young people are not only taught how these systems work but also how to ensure they work ethically and equitably. Ongoing dialogue and collaboration are necessary to ensure that AI education prioritizes the needs, perspectives, and values of diverse youth populations (Shouli et al., 2025; Solyst et al., 2025). References Adesina, A. O., Abuka, O. A., Adesiyan, J. S., & Patra Mondal, S. (2024). Enhancing adolescent mental health outcomes through integrated technology (AI) innovations in the United States. ~ 22 ~ International Journal of Advanced Psychiatric Nursing, 6(2). https://doi.org/10.33545/26641348.2024.v6.i2a.170 Ahmed, W. (2024). Digital Terrorism: The Emerging Threat of Behavioral Manipulation in the Digital Age. Journal of Digitainability, Realism & Mastery (DREAM), 3(07). https://doi.org/10.56982/DREAM.V3I07.251 Akhmetova, A. I., Sovetkanova, D. M., Komekbayeva, L. K., Abdrakhmanov, A. E., Yessenuly, D., & Serikova, O. S. (2025). A systematic review of artificial intelligence in high school STEM education research. Eurasia Journal of Mathematics, Science and Technology Education, 21(4), em2623. https://doi.org/10.29333/ejmste/16222 OHU & JONES: Forensic Cyberpsychology Strategies for Integrating AI Ethics and Digital Citizenship into STEM Pedagogy for Adolescents in Diverse K-12 Classrooms ! 271! Amadori, A., Real, A. G., Brighi, A., & Russell, S. T. (2025). An Intersectional Perspective on Cyberbullying: Victimization Experiences Among Marginalized Youth. Journal of Adolescence. https://doi.org/10.1002/jad.12466 Angelini, F., Gini, G., Marino, C., & Van Den Eijnden, R. (2024). Social media features, perceived group norms, and adolescents’ active social media use matter for perceived friendship quality. Frontiers in Psychology, 15, 1222907. https://doi.org/10.3389/FPSYG.2024.1222907/BIBTEX Barthwal, A., Campbell, M., & Shrestha, A. (2025). Privacy Ethics Alignment in AI: A Stakeholder-Centric Based Framework for Ethcial AI. https://doi.org/10.22541/au.174221139.91360016/v1 Bogdan, T., Xie, W., Talaat, H., Mir, H., Venkataraman, B., Banfield, L. E., Georgiades, K., & Duncan, L. (2023). Longitudinal studies of child mental disorders in the general population: A systematic review of study characteristics. JCPP Advances, 3(3), e12186. https://doi.org/10.1002/JCV2.12186 Braun, V., & Clarke, V. (2024). Supporting best practice in reflexive thematic analysis reporting in Palliative Medicine: A review of published research and introduction to the Reflexive Thematic Analysis Reporting Guidelines (RTARG). Https://Doi.Org/10.1177/02692163241234800, 38(6), 608–616. https://doi.org/10.1177/02692163241234800 Burnell, K., Trekels, J., Prinstein, M. J., & Telzer, E. H. (2024). Adolescents’ Social Comparison on Social Media: Links with Momentary Self-Evaluations. Affective Science, 5(4), 295–299. https://doi.org/10.1007/S42761-024-00240-6/METRICS Charmaraman, L., Hojman, H., Auqui, J. Q., & Bilyalova, Z. (2024). Understanding Adolescent Self-esteem and Self-image Through Social Media Behaviors. Pediatric Clinics of North America, 0(0). https://doi.org/10.1016/j.pcl.2024.07.034 Damio, S. M., Zulkifli, Z. A., Kholidi, Muh. A., & Ramdhani, S. (2024). Investigating Higher Education Students’ Understanding and Engagement with the Ethical Implications of Artificial Intelligence within STEAM Education (pp. 229–254). https://doi.org/10.2991/978-94-6463-589-8_23 du Boulay, B. (2023). Artificial Intelligence in Education and Ethics. In Handbook of Open, Distance and Digital Education (pp. 93–108). Springer Nature Singapore. https://doi.org/10.1007/978-981-19-20806_6 Dvir, N. (2023). The Application of Case Studies in Digital Citizenship Education: An Empirical Study. SSRN Electronic Journal. https://doi.org/10.2139/SSRN.4532611 Ferrara, E. (2024). GenAI against humanity: nefarious applications of generative artificial intelligence and large language models. Journal of Computational Social Science, 7(1), 549–569. https://doi.org/10.1007/S42001-024-00250-1 Ganapathy, V. (2024). AI-Based Risk Assessments in Forensic Auditing: Benefits, Challenges and Future Implications. Shodh Sari-An International Multidisciplinary Journal, 03(04), 100–128. https://doi.org/10.59231/SARI7750 Geurts, E. M. A., Reijs, R. P., Leenders, H. H. M., Jansen, M. W. J., & Hoebe, C. J. P. A. (2024). Co-creation and decision-making with students about teaching and learning: a systematic literature review. Journal of Educational Change, 25(1), 103–125. https://doi.org/10.1007/s10833-023-09481-x Gu, X., & Ericson, B. J. (2025). AI Literacy in K-12 and Higher Education in the Wake of Generative AI: An Integrative Review. https://doi.org/10.1145/nnnnnnn.nnnnnnn Hani, R., Nurhasanah, L., Halim Anshor, A., & Muhidin, A. (2024). Social Media Analysis for Effective Information Dissemination and Promotions Using TOPSIS. Journal of Applied Informatics and Computing, 8(1), 146–154. https://doi.org/10.30871/JAIC.V8I1.7863 Hernandez, J. M., Ben-Joseph, E. P., Reich, S., & Charmaraman, L. (2024). Parental Monitoring of Early Adolescent Social Technology Use in the US: A Mixed-Method Study. Journal of Child and Family Studies, 33(3), 759–776. https://doi.org/10.1007/s10826-023-02734-6 Hölscher, S. I. E., Schachner, M. K., Juang, L. P., & Altoè, G. (2024). Promoting Adolescents’ Heritage Cultural Identity Development: Exploring the Role of Autonomy and Relatedness Satisfaction in School-Based Interventions. Journal of Youth and Adolescence, 53(11), 2460–2479. https://doi.org/10.1007/s10964-024-02017-3 Hooshyar, D., Šír, G., Yang, Y., Kikas, E., Hämäläinen, R., Kärkkäinen, T., Gašević, D., & Azevedo, R. (2025). Towards responsible AI for education: Hybrid human-AI to confront the Elephant in the room. https://arxiv.org/pdf/2504.16148 Jarzemsky, J., Paup, J., & Fiesler, C. (2023). “This Applies to the Real World”: Student Perspectives on Integrating Ethics into a Computer Science Assignment. SIGCSE 2023 - Proceedings of the 54th ACM Technical Symposium on Computer Science Education, 1, 374–380. https://doi.org/10.1145/3545945.3569846 Jones, L. (2024). Unveiling Human Factors: Aligning Facets of Cybersecurity Leadership, Insider Threats, and Arsonist Attributes to Reduce Cyber Risk. SocioEconomic Challenges, 8(2), 44–63. https://doi.org/10.61093/sec.8(2).44-63.2024 Jungselius, B. (2024). A scoping review of current research on social media use among children and adolescents. Discover Psychology, 4(1), 1–17. https://doi.org/10.1007/S44202-024-00226-2/TABLES/7 RAIS Journal for Social Sciences | VOL. 9, NO. 2, 2025 ! 272! Karim, B. Q., Haryanto, H., & Susanti, E. Y. (2025). AI in Education: Transforming Student Engagement for the Digital Age. Jurnal Penelitian Pendidikan IPA, 11(2), 1127–1136. https://doi.org/10.29303/jppipa.v11i2.10469 Kenny, S., & Antle, A. N. (2024). Reimagining AI: Exploring Speculative Design Workshops for Supporting BIPOC Youth Critical AI Literacies. https://doi.org/10.1145/3501712.3529717 Ko, C., & Kim, S. (2024). Adolescent Female Users’ Avatar Creation in Social Virtual Worlds: Opportunities and Challenges. Behavioral Sciences 2024, Vol. 14, Page 539, 14(7), 539. https://doi.org/10.3390/BS14070539 Latuheru, R. D., & Cangara, H. (2024). THE ROLE OF SOCIAL MEDIA ALGORITHMS IN SHAPING AND CONFLICT RESOLUTION. Journal of Digital Media Communication, 3(1), 19–27. https://doi.org/10.35760/DIMEDCOM.2024.V3I1.11681 Lau, N., Srinakarin, K., Aalfs, H., Zhao, X., & Palermo, T. M. (2024). TikTok and teen mental health: an analysis of user-generated content and engagement. Journal of Pediatric Psychology. https://doi.org/10.1093/JPEPSY/JSAE039 Lewis, C. C., Taba, M., Allen, T. B., Caldwell, P. H. Y., Skinner, S. R., Kang, M., Henderson, H., Bray, L., Borthwick, M., Collin, P., McCaffery, K., & Scott, K. M. (2024). Developing an Educational Resource Aimed at Improving Adolescent Digital Health Literacy: Using Co-Design as Research Methodology. Journal of Medical Internet Research, 26(1), e49453. https://doi.org/10.2196/49453 Lin, P., & Van Brummelen, J. (2021). Engaging Teachers to Co-Design Integrated AI Curriculum for K-12 Classrooms. Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems, 1–12. https://doi.org/10.1145/3411764.3445377 McGovern, R., Balogun-Katung, A., Artis, B., Bareham, B., Spencer, L., Alderson, H., Brown, E., Brown, J., Lingam, R., McArdle, P., Newham, J. J., Wojciechowska, A., Rankin, J., Redgate, S., Thomason, P., & Kaner, E. (2024). The Effectiveness of Preventative Interventions to Reduce Mental Health Problems in at-risk Children and Young People: A Systematic Review of Reviews. In Journal of Prevention (Vol. 45, Issue 4, pp. 651–684). Springer. https://doi.org/10.1007/s10935-024-00785-z Min, M., & Nelson, R. (2024). Exploring the Factors Influencing the Development of Teacher Agency for Culturally Responsive Teaching. International Journal on Social and Education Sciences, 6(3), 334– 350. https://doi.org/10.46328/ijonses.672 Morales-Álvarez, A., Valdés-Cuervo, A. A., Parra-Pérez, L. G., García-Vázquez, F. I., & Durón-Ramos, M. F. (2025). Adolescents’ appraisal and disclosure mediate the links between parental practices and adolescents’ digital citizenship behaviors. Humanities and Social Sciences Communications, 12(1), 133. https://doi.org/10.1057/s41599-025-04485-9 Morales-Navarro, L. (2025). Investigating Youth’s Technical and Ethical Understanding of Generative Language Models When Engaging in Construction and Deconstruction Activities. https://doi.org/10.1145/3713043.3731602 Nagel, I., Guðmundsdóttir, G. B., & Afdal, H. W. (2023). Teacher educators’ professional agency in facilitating professional digital competence. Teaching and Teacher Education, 132, 104238. https://doi.org/10.1016/j.tate.2023.104238 Nganga, L., Nydam, A., & Kambutu, J. (2025). Goal Setting for Teacher Development: Enhancing Culturally Responsive, Inclusive, and Social Justice Pedagogy. Education Sciences, 15(3), 264. https://doi.org/10.3390/educsci15030264 Noh, D. J., Fields, D. A., Morales-Navarro, L., Cabrera-Sutch, A., Kafai, Y. B., & Metaxa, D. (2025). Youth as Advisors in Participatory Design: Situating Teens’ Expertise in Everyday Algorithm Auditing with Teachers and Researchers. https://doi.org/10.1145/3713043.3728849 Ohu, F. C., & Jones, L. A. (2025a). AI-driven forensic cyberpsychology intervention strategies for social media platform and school managers to mitigate cyber fraud at-risk adolescents. Scientia Moralitas Conference Proceedings, February 20-21, 2025, 114-131. http://dx.doi.org/10.5281/zenodo.15075890 Ohu, F. C., & Jones, L. A. (2025b). The intersection of cyberwarfare, social media, and adolescent self-esteem: A forensic cyberpsychology analysis. Proceedings of the 20th International Conference on Cyber Warfare and Security (ICCWS 2025), March 28 – 29, 2025, 20(1) 332–344. https://doi.org/10.34190/iccws.20.1.3375 Ohu, F. C., & Jones, L. A. (2025c). An examination of digital validation-seeking behaviors in adolescents as precursors to romance scamming. Scientia Moralitas Conference Proceedings, February 20-21, 2025, 10-29 https://doi.org/10.5281/zenodo.14911844 Ohu, F. C., & Jones, L. A. (2025d). Validation syndrome: The root of deception and developmental predictors of dark triad traits in adolescents for forensic and developmental psychology. International Educational Research, 8(2), 67–86. https://doi.org/10.30560/ier.v8n2p67 Ohme, J., Araujo, T., Boeschoten, L., Freelon, D., Ram, N., Reeves, B. B., & Robinson, T. N. (2024). Digital Trace Data Collection for Social Media Effects Research: APIs, Data Donation, and (Screen) Tracking. Communication Methods and Measures, 18(2), 124–141. https://doi.org/10.1080/19312458.2023.2181319 OHU & JONES: Forensic Cyberpsychology Strategies for Integrating AI Ethics and Digital Citizenship into STEM Pedagogy for Adolescents in Diverse K-12 Classrooms ! 273! Ozturk, N. (2025). Integrating ethical knowledge into teacher education: A framework for moral formation. Journal of Moral Education, 1–16. https://doi.org/10.1080/03057240.2025.2487586 Pellegrino, A., & Stasi, A. (2024). A bibliometric analysis of the impact of media manipulation on adolescent mental health: Policy recommendations for algorithmic transparency. Online Journal of Communication and Media Technologies, 14(4), e202453. https://doi.org/10.30935/OJCMT/15143 Pérez-Torres, V. (2024). Social media: a digital social mirror for identity development during adolescence. Current Psychology, 43(26), 22170–22180. https://doi.org/10.1007/s12144-024-05980-z Pranjal Choudhary, & Lavina Louis. (2024). Impact of Social Media on Individuals’ Self-esteem. IJFMR - International Journal For Multidisciplinary Research, 6(4). https://doi.org/10.36948/IJFMR.2024.V06I04.24859 Ravi, P., Broski, A., Stump, G., Abelson, H., Klopfer, E., & Breazeal, C. (2023). Understanding Teacher Perspectives and Experiences after Deployment of AI Literacy Curriculum in Middle-school Classrooms. https://doi.org/10.21125/iceri.2023.1716 RJ Murad, H. (2024). Investigating The Impact of Digital Technology on Adolescent Identity Formation on Selected Students in SAIS: A Psychological Approach. International Journal of Innovative Science and Research Technology (IJISRT), 2726–2736. https://doi.org/10.38124/IJISRT/IJISRT24APR1823 Ruan, Q.-N., Shen, G.-H., Yang, J.-S., & Yan, W.-J. (2023). The interplay of self-acceptance, social comparison and attributional style in adolescent mental health: cross-sectional study. BJPsych Open, 9(6), e202. https://doi.org/10.1192/BJO.2023.594 Samo Varsik, & Lydia Vosberg. (2024). The potential impact of Artificial Intelligence on equity and inclusion in education. https://doi.org/10.1787/15df715b-en Schluchter, T. (2024). Investigating User Perceptions of Mental Health Content on TikTok: A Comprehensive Exploration. Conference on Human Factors in Computing Systems - Proceedings. https://doi.org/10.1145/3613905.3647964 Shaayesteh, M. T., Esfahani, S. M., & Mohit, H. (2025). AI Identity, Empowerment, and Mindfulness in Mitigating Unethical AI Use. Shouli, A., Barthwal, A., Campbell, M., & Shrestha, A. K. (2025). Ethical AI for Young Digital Citizens: A Call to Action on Privacy Governance. Shrestha, A. K., Barthwal, A., Campbell, M., Shouli, A., Syed, S., Joshi, S., & Vassileva, J. (2024). Navigating AI to Unpack Youth Privacy Concerns: An In-Depth Exploration and Systematic Review. https://doi.org/doi.org/10.48550/arXiv.2412.16369 Singh, B., & Cheema, Dr. S. S. (2024). Psychology in Cybersecurity: Unveiling the Human Dimensions of Digital Resilience. International Journal of Advanced Networking and Applications, 16(01), 6281– 6290. https://doi.org/10.35444/IJANA.2024.16107 Solyst, J., Peng, C., Deng, W. H., Pratapa, P., Hammer, J., Ogan, A., Hong, J., & Eslami, M. (2025). Investigating Youth AI Auditing. Proceedings of Make Sure to Enter the Correct Conference Title from Your Rights Confirmation Emai (Conference Acronym ’XX), 1. https://doi.org/10.1145/3715275.3732142 Sultan, M., Scholz, C., & van den Bos, W. (2023). Leaving traces behind: Using social media digital trace data to study adolescent wellbeing. Computers in Human Behavior Reports, 10, 100281. https://doi.org/10.1016/J.CHBR.2023.100281 Voggenreiter, A., Brandt, S., Putterer, F., Frings, A., & Pfeffer, J. (2023). The Role of Likes: How Online Feedback Impacts Users’ Mental Health. Proceedings of the 16th ACM Web Science Conference, WebSci 2024, 302–310. https://doi.org/10.1145/3614419.3643995 West, M., Rice, S., & Vella-Brodrick, D. (2024). Adolescent Social Media Use through a Self-Determination Theory Lens: A Systematic Scoping Review. International Journal of Environmental Research and Public Health, 21(7). https://doi.org/10.3390/IJERPH21070862 Williams, R., Ali, S., Devasia, N., DiPaola, D., Hong, J., Kaputsos, S. P., Jordan, B., & Breazeal, C. (2023). AI + Ethics Curricula for Middle School Youth: Lessons Learned from Three Project-Based Curricula. International Journal of Artificial Intelligence in Education, 33(2), 325–383. https://doi.org/10.1007/s40593-022-00298-y Yangambi, M. W. (2025). Culturally Relevant Teaching in the Digital Age: Student-Centered and Personalized Learning in Developing Countries. Creative Education, 16(01), 71–89. https://doi.org/10.4236/ce.2025.161005 Zhou, R. (2024). Understanding the Impact of TikTok’s Recommendation Algorithm on User Engagement. International Journal of Computer Science and Information Technology, 3(2), 201–208. https://doi.org/10.62051/IJCSIT.V3N2.24