Transforming Clicks into Critical Thinking: An AI-Based Media Literacy Program for Children

Transforming Clicks into Critical Thinking: An AI-Based Media Literacy Program for Children | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Transforming Clicks into Critical Thinking: An AI-Based Media Literacy Program for Children Ramazan Demir, Cüneyt Akar This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6786882/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 4 You are reading this latest preprint version Abstract This study investigates the pedagogical impact of an AI-enhanced critical media literacy program implemented with fourth-grade students in a public primary school. Using an explanatory sequential mixed-methods design, the research combined quantitative data from the Critical Media Literacy Scale for Primary Students with qualitative insights from interviews, student artifacts, and classroom observations. The 18-hour intervention followed the 5E instructional model and integrated generative AI tools such as ChatGPT and Grammarly. Aligned with the national Turkish and Social Studies curricula, the program ensured curricular coherence and contextual relevance. Quantitative findings revealed significant gains in students’ media literacy skills, particularly in reading and writing. Qualitative data highlighted developments in digital safety, source verification, online ethics, and media critique. Students also showed enhanced awareness of ethical digital behavior and reflective media use. These results underscore the potential of AI-supported instruction to foster critical thinking, digital citizenship, and media literacy in early education settings. AI-supported education Critical media literacy Digital citizenship Elementary education Mixed-methods research Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Introduction In today's digitally saturated world, media no longer merely disseminates information—it actively shapes civic engagement, self-expression, and educational innovation. Digital platforms have become central arenas for youth to explore, learn, and build their socio-political identities (Jenkins et al., 2016; Mihailidis & Thevenin, 2013). In this multifaceted landscape, media literacy emerges not as an optional skill but as a core competency, encompassing critical analysis, ethical reasoning, and participatory engagement (Livingstone et al., 2019). Nevertheless, the digital sphere presents escalating challenges, notably the rapid circulation of misinformation, algorithmic distortion, and cybersecurity risks. Notably, research by Vosoughi et al. (2018) indicates that false narratives disseminate at a rate nearly six times faster than factual content—highlighting the urgency of equipping young learners with robust evaluative and ethical media skills (Wang et al., 2021). Addressing this issue is both a pedagogical necessity and a civic responsibility: fostering young people's critical media engagement is key to nurturing informed digital citizens. Initially framed by Aufderheide (1993) and later expanded by Hobbs (2011), media literacy involves accessing, analyzing, evaluating, and producing media across contexts. However, traditional instructional methods may fall short in addressing the evolving dynamics of digital media. The integration of artificial intelligence (AI) into educational contexts introduces new possibilities for enhancing these literacies. AI supports personalized learning and facilitates critical and creative engagement with complex media forms (Grajeda et al., 2023; Tekwa et al., 2024). Cognitively, AI tools can lower extraneous load and enhance learning depth, consistent with Sweller’s (1988) Cognitive Load Theory. Tools like ChatGPT, Canva AI, and Grammarly illustrate how AI can enhance conceptual clarity, media creation, and linguistic accuracy (Kargın & Demir, 2023; Westbrook et al., 2021). When deliberately integrated, these tools become pedagogical agents rather than mere technological add-ons. This aligns with research by Aleven and Koedinger (2002), showing that embedded self-explanation prompts in intelligent tutors boost conceptual understanding and transfer. Emerging evidence also suggests that AI-driven dialogue systems foster reflective and metacognitive thinking by presenting challenging viewpoints that counteract cognitive biases (Weber et al., 2024). The theoretical foundation of this study draws from Hobbs’s (2011) five-part media literacy model—access, analysis, evaluation, creation, and reflection—stressing learner agency in meaning-making and content creation. This model is further operationalized through Jenkins’s (2009) Participatory Culture Theory, which sees learners as not just consumers but active contributors in digital spaces. The AI-enhanced media literacy initiative presented here seeks to instill this participatory ethos in primary education settings. The study also resonates with the digital citizenship framework of Mossberger et al. (2007), which emphasizes responsible and ethical online behavior. Observable behavioral shifts—such as skepticism toward unverified content, increased digital privacy awareness, and conscious screen time usage—suggest the transformative potential of AI in shaping ethical digital habits (Kotsonis & Dunne, 2024; Paltacı, 2024). These outcomes correspond with recent studies highlighting AI’s role in promoting ethical judgment, digital self-regulation, and collaborative inquiry in young learners (Lee et al., 2025). While prior studies have explored the use of Web 2.0 tools in media education (Genç, 2024; Kulaca et al., 2024), the intersection of AI integration and media literacy in primary education remains understudied. Given the rise of deepfakes, algorithmic bias, and targeted disinformation, a paradigm shift is essential—from emphasizing technical proficiency to cultivating critical thinking and ethical media engagement (UNESCO, 2021; EU Commission, 2020). Set against this backdrop, the current research examines the efficacy of an AI-integrated media literacy program tailored for elementary students. Using a mixed-methods approach, the study addresses the following questions: How does AI-assisted instruction influence the development of students’ critical media literacy? In what ways do students' interpretations of media benefits and risks evolve? What cognitive and behavioral shifts are evident in students' practices around media evaluation, digital safety, and ethical reflection? This study positions AI not as an auxiliary tool, but as an epistemic partner in cultivating critical awareness, responsibility, and civic engagement. Although situated in Turkey, the proposed model offers broader international relevance, serving as a scalable and adaptable approach for education systems navigating the complexities of a digital age. Methodology Research Design To rigorously investigate the pedagogical impact of AI-integrated critical media literacy education on elementary students, this study adopted an explanatory sequential mixed methods design, as conceptualized by Creswell (2009). This design was chosen to capitalize on the strengths of both quantitative and qualitative approaches, offering a robust framework to analyze not only statistical trends but also the nuanced experiential dimensions of learning processes. The initial phase involved a quasi-experimental pre-test–post-test control group model (Büyüköztürk et al., 2018), enabling the measurement of learning gains attributable to the intervention while ensuring comparative rigor between experimental and control cohorts. This design allowed for the isolation of treatment effects under real-world classroom conditions where random assignment was not feasible. Subsequently, a qualitative phase was conducted to provide contextual depth and interpretive richness. Data were collected via semi-structured interviews, student-generated artifacts (e.g., digital posters, drawings, slogans), and structured observation forms. These sources facilitated triangulation and deepened the interpretive validity of the findings by capturing cognitive, behavioral, and affective dimensions of student engagement. This two-phased approach was strategically employed to synthesize empirical generalizability with ecological validity—an essential consideration in educational interventions involving dynamic, learner-centered pedagogies. Participants The study was conducted with 36 fourth-grade students enrolled in a public primary school located in the Aegean region of Turkey during the 2023–2024 academic year. Participants were assigned to either the experimental group (n = 18) or the control group (n = 18), ensuring equal gender distribution (10 girls and 8 boys in each group) to control for potential gender-based differences in media engagement and technology use. The experimental group received instruction aligned with an AI-enhanced critical media literacy framework, while the control group continued with the conventional curriculum aligned with the Ministry of National Education's (MoNE) standards. Participants were selected through purposive sampling based on their availability and the school's infrastructural readiness. Efforts were made to ensure socio-demographic parity across groups, thereby mitigating confounding variables such as access to technology, parental education level, and digital literacy exposure. All research procedures adhered strictly to ethical standards for educational research involving minors. Ethical approval was obtained from the university’s institutional review board (Approval No: 2024-30, dated March 8, 2024), and formal permissions were secured from the Turkish Ministry of National Education. Informed written consent was obtained from the parents or legal guardians of all participants. Throughout the study, participants’ rights to confidentiality, anonymity, and voluntary withdrawal were upheld in accordance with international ethical guidelines. Data Collection Tools Quantitative Instruments: Critical Media Literacy Disposition Scale To quantitatively assess students’ dispositions toward critical media engagement, the study employed the Critical Media Literacy Disposition Scale for Elementary Students developed by Demir and Akar (2024). Administered both pre- and post-intervention to experimental and control groups, the instrument comprises 12 items distributed across two subdimensions: Critical Media Reading (CMR) and Critical Media Writing (CMW) . The scale adopts a 3-point Likert response format—Never (1), Sometimes (2), and Always (3)—to capture developmental variations in media literacy habits among children. Psychometric evaluation affirmed the instrument’s structural robustness. Exploratory Factor Analysis (EFA) yielded a two-factor solution accounting for 52.2% of the total variance (41.9% attributed to CMR and 10.3% to CMW). Confirmatory Factor Analysis (CFA) further validated this structure with strong model fit indices: GFI = .958, AGFI = .936, IFI = .970, CFI = .970, TLI = .920, RMSEA = .057. Reliability coefficients demonstrated high internal consistency, with Cronbach’s alpha scores of .825 for CMR, .794 for CMW, and .812 for the total scale—each exceeding the .70 threshold recommended by Nunnally and Bernstein (1994). Qualitative Instruments and Analytical Strategy Qualitative data were collected through three interrelated methods—semi-structured interviews, student-generated media artifacts, and structured classroom observations—allowing for methodological triangulation and the elicitation of rich, multi-modal insights (Creswell & Clark, 2018; Tracy, 2010). Semi-Structured Interviews Interviews with students from the experimental group sought to reveal their evolving understandings of media utility, digital risks, and ethical content engagement. The interview protocol was informed by seminal literature in media literacy education (e.g., Hobbs, 2011; Kellner & Share, 2007), and validated through expert review involving three in-service teachers and two academic researchers. Interviews were conducted with all 18 students in the experimental group, with each session lasting 10–15 minutes. Audio recordings were securely stored and anonymized before being deleted post-transcription and analysis, in full compliance with ethical protocols. Student Artifacts Throughout the intervention, students created a range of artifacts—posters, visual illustrations, multimedia slogans, and reflective written pieces—which were collected as tangible evidence of conceptual understanding and critical expression. These products served as cognitive and affective indicators of students' abilities to analyze, critique, and ethically reproduce media content (Berg & Schensul, 2016). A content analysis approach was employed to evaluate the thematic density and conceptual alignment of these artifacts with the study’s theoretical framework. Researcher Observations Structured observation forms, developed in accordance with qualitative fieldwork standards (Angrosino, 2007), were used to systematically capture classroom interactions, media tool usage, and verbal/non-verbal engagement cues. Observational memos were written after each session to document evolving competencies and participation dynamics. These field notes enriched the data corpus and supported internal validation of emergent themes. Data Analysis Quantitative Analysis Quantitative data were analyzed using IBM SPSS (v.26). Initial tests for normality based on skewness and kurtosis values confirmed the data's appropriateness for parametric analysis. A combination of paired samples t-tests (within-group comparisons) and independent samples t-tests (between-group comparisons) was conducted to determine statistically significant differences in students’ pre- and post-test scores on the CMR and CMW subscales. Significance levels were set at p < .05, with effect sizes (Cohen’s d) computed to assess the magnitude of observed changes. Qualitative Analysis Thematic analysis was guided by Braun and Clarke’s (2006) six-phase model, supplemented with constructivist grounded theory coding techniques from Strauss and Corbin (1998). This dual-analytic lens provided both structure and flexibility, capturing the depth and complexity of participants’ media literacy development. Open Coding : Data were deconstructed line-by-line to generate initial codes, revealing cognitive, emotional, and behavioral markers. Axial Coding : Relationships between codes were analyzed to construct subthemes and organize them under broader conceptual categories. Selective Coding : Core themes were synthesized to encapsulate key pedagogical impacts, including ethical reasoning, digital self-regulation, and critical reflection. Inter-Coder Reliability : To ensure coding reliability, a dual-coding process was conducted independently by the lead researcher and a collaborating educational expert. Cohen’s Kappa was calculated at .85, indicating near-perfect agreement (Landis & Koch, 1977). Triangulation and Trustworthiness : Data from interviews, artifacts, and observations were triangulated to enhance credibility. Peer debriefing with external researchers and member checks with participating teachers reinforced analytical validity. Rich, thick description (Lincoln & Guba, 1985) was employed to contextualize themes within classroom dynamics and student discourse. This integrated analytical strategy ensured not only methodological rigor but also epistemological coherence, enabling a nuanced exploration of how AI-enhanced media literacy education shapes young learners’ digital identities, critical capacities, and ethical consciousness. Implementation Instructional Design and Implementation Framework: AI-Integrated 5E Learning Model The implementation phase of this study involved an 18-hour instructional intervention designed using the 5E learning cycle model—Engage, Explore, Explain, Elaborate, Evaluate—integrated with AI-based tools to foster critical media literacy among elementary school students. The pedagogical framework was carefully aligned with the national curriculum outcomes specified in the Turkish Language (MoNE, 2018) and Social Studies (MoNE, 2019) programs. This approach not only preserved curricular relevance but also ensured instructional coherence and contextual adaptability. Given the socioeconomically disadvantaged profile of the participating school, particular attention was paid to selecting digital tools that were both pedagogically potent and technologically accessible. The suite of tools employed included ChatGPT, Canva AI, Grammarly, Mentimeter, DALL·E, Padlet, Quizizz, Microsoft Word, PowerPoint, and OneDrive. These tools were not used as standalone applications but were embedded within pedagogical tasks to promote conceptual learning, creativity, and ethical reasoning. The instructional sequence is detailed in Table 1, which maps each phase of the 5E model onto specific digital tools, instructional activities, and curriculum outcomes. This design ensured that technological integration served not as an add-on, but as a scaffold for deeper cognitive and ethical engagement with media content. Table 1. Integration of the 5E model with AI tools, instructional activities, and curriculum learning outcomes 5E Stage Duration Instructional Activities AI Tools Utilized Curricular Alignment (T/SS) Engagement 2 hours Watching curated videos, generating reflective questions via ChatGPT, empathy tasks YouTube, ChatGPT, Mentimeter, Canva Magic Write Evaluates watched/listened content (T); Evaluates media texts (T) Exploration 2 hours Analyzing media messages, generating original content Perplexity AI, Canva Magic Write, DALL·E, Synthesia, HeyGen Questions content coherence (T); Assesses source reliability (T); Compares tech use (SS); Uses tech sustainably (SS) Explanation 7 hours Conceptual lessons, Q&A with ChatGPT, collaborative note-taking ChatGPT, PowerPoint, MS Word, OneDrive Understands digital messages (T); Evaluates media texts (T); Classifies technologies (SS) Elaboration 5 hours Poster and slogan creation, peer feedback, text editing Canva AI, Grammarly, Padlet, Quizizz Interprets signs/symbols (T); Investigates innovation (SS); Uses tech ethically (SS) Evaluation 2 hours Pre/post testing, observations, self-reflection ChatGPT, Padlet, Observation Forms Assesses information reliability (T); Uses tech ethically (SS) 3.2 Pedagogical Execution and Instructional Dynamics Each phase of the 5E model was meticulously structured to progress from curiosity and inquiry to synthesis and self-assessment, thereby scaffolding students’ cognitive, ethical, and behavioral development. Engagement Phase : Students were introduced to authentic media experiences through curated YouTube videos, followed by real-time questioning via ChatGPT. They then used Mentimeter to co-construct word clouds representing their interpretations of media content. These tasks not only triggered curiosity but also activated prior knowledge and cultivated empathy—essential prerequisites for reflective media engagement. Exploration Phase : Students began to deconstruct digital texts using AI-based platforms. They employed Perplexity AI for source validation, DALL·E for image generation, and Canva Magic Write to experiment with content formulation. These activities nurtured inquiry, interpretation, and creative articulation in line with national curriculum goals emphasizing sustainability and historical awareness of technology. Explanation Phase : Key media literacy concepts were explicated through instructor-led lessons and digital presentations. ChatGPT was employed as a conversational co-tutor to address student inquiries and extend discussions. Through this dual-channel scaffolding, students engaged with topics such as media bias, source triangulation, and message construction, thus developing both cognitive acuity and technical proficiency. Elaboration Phase : Students synthesized their learning into tangible media products, including digital posters and reflective slogans. Grammarly facilitated ethical editing practices, while Padlet enabled asynchronous peer feedback. These collaborative activities emphasized message clarity, visual literacy, and intellectual property respect—skills at the intersection of media critique and civic responsibility. Evaluation Phase : The final phase integrated multiple forms of assessment: pre/post testing with validated instruments, observational field notes, and AI-assisted self-reflection exercises. ChatGPT and Padlet were leveraged to support students in metacognitive evaluations of their learning. Results indicated marked gains in source discernment, content judgment, and digital ethics. This AI-integrated 5E instructional framework transcended the conventional boundaries of media education by fusing technological fluency with civic competence. Rather than passive recipients of content, students were positioned as reflective practitioners and digital citizens. The model's alignment with curricular standards, its adaptability to low-tech settings, and its demonstrated effectiveness in shaping critical and ethical media behaviors make it a replicable and scalable educational innovation. Findings Quantitative Findings: Impact of AI-Augmented Media Literacy Education Normality Assumptions and Test Prerequisites In order to assess the suitability of parametric statistical tests, the skewness and kurtosis values for the pre-test and post-test scores were examined. Following the guidelines proposed by Tabachnick and Fidell (2013), which consider ±1 as an acceptable range for normal distribution, all variables met the assumption of normality. The skewness and kurtosis values indicate that the data approximate a normal distribution. Skewness values ranged from –0.20 to –0.47 in the pretest and from –0.26 to –0.61 in the posttest. Similarly, kurtosis values ranged between –0.68 and –0.26. These values fall within the commonly accepted threshold of ±1, as suggested by Tabachnick and Fidell (2013), supporting the assumption of normality and justifying the use of parametric tests. Baseline Equivalence of Groups An independent samples t-test was conducted to assess whether initial differences existed between the experimental and control groups prior to the intervention. Table 2 illustrates the comparability of both groups: Table 2. Baseline equivalence: pre-test comparison of experimental and control groups Subscale Group N Mean SD t p Media Reading Control 18 15.22 2.78 0.316 .756 Experimental 18 15.50 2.53 Media Writing Control 18 11.83 1.72 0.846 .405 Experimental 18 12.39 2.20 Total Media Literacy Control 18 27.06 3.47 0.650 .520 Experimental 18 27.89 4.19 No statistically significant differences were found across any of the dimensions (p > .05), confirming the initial equivalence of groups in terms of critical media literacy dispositions. Within-Group Gains in the Control Group A paired samples t-test revealed that the control group exhibited modest gains in the post-test scores. However, statistical significance was limited: Table 3. Control group pre- and post-test comparison Subscale N Pre-Test Mean SD Post-Test Mean SD t p Media Reading 18 15.22 2.78 16.61 1.75 -2.379 .029 * Media Writing 18 11.83 1.72 12.11 1.84 -0.676 .508 Total Media Literacy 18 27.06 3.47 28.72 2.91 -2.755 .014 * Although reading scores showed a statistically significant improvement (p < .05), the absence of significant gains in media writing underscores the limited effectiveness of traditional instruction in fostering multidimensional literacy growth. Effectiveness of AI-Enhanced Instruction: Experimental Group Analysis The experimental group demonstrated statistically robust gains across all subscales following the AI-supported intervention, as reflected in Table 4: Table 4. Experimental group pre- and post-test comparison Subscale N Pre-Test Mean SD Post-Test Mean SD t p Media Reading 18 15.50 2.53 19.00 2.47 -4.894 .000 ** Media Writing 18 12.39 2.20 14.06 1.43 -3.536 .003 ** Total Media Literacy 18 27.89 4.19 33.06 3.67 -5.129 .000 ** The magnitude of improvement observed in media reading (+3.50), media writing (+1.67), and overall media literacy (+5.17) indicates the multidimensional effectiveness of the AI-integrated curriculum. These outcomes affirm the capacity of AI-supported environments to catalyze critical reasoning, creative expression, and ethical awareness. Between-Group Post-Test Comparisons and Effect Size To determine the relative efficacy of the AI-based model, post-test scores of the experimental and control groups were compared. Effect size calculations using Cohen’s d underscored the practical significance of the findings: Table 5. Between-group post-test comparison and effect sizes Subscale Group N Mean (SD) t p Cohen’s d Media Reading Control 18 16.61 (1.75) -3.343 .002 ** 1.12 Experimental 18 19.00 (2.47) Media Writing Control 18 12.11 (1.84) -3.532 .001 ** 1.18 Experimental 18 14.06 (1.43) Total Media Literacy Control 18 28.72 (2.91) -3.927 .000 ** 1.31 Experimental 18 33.06 (3.67) Cohen’s d values across subscales—1.12 (Reading), 1.18 (Writing), and 1.31 (Total Literacy)—indicate large effect sizes, thereby reinforcing the transformative capacity of AI-driven pedagogy (Cohen, 1988). These findings collectively validate the research hypothesis that AI-supported media literacy education significantly enhances students’ analytical, productive, and reflective media skills. Qualitative Findings In the qualitative dimension of the study, data were gathered from semi-structured interviews, student artifacts (e.g., posters, drawings, slogans), and structured classroom observations conducted with 18 students in the experimental group. The data were analyzed thematically and categorized under four main themes: (1) Positive uses of media, (2) Awareness of negative aspects, (3) Development of critical media literacy skills, and (4) Student feedback on AI-supported instruction. The findings demonstrate that students underwent not only cognitive but also attitudinal, behavioral, and awareness-based transformations. Each theme is supported by student expressions, observed behaviors, and tangible outputs, offering a holistic insight into the pedagogical effects of integrating AI into media literacy education. The following tables present detailed thematic analyses, illustrating participant coverage, representative quotations, observed behaviors, and digital artifacts that collectively highlight the educational outcomes of AI-based media literacy training. Positive Uses of Media The analysis of student interviews, digital artifacts, and classroom observations revealed four key subthemes under the overarching category of Positive Uses of Media : Information Seeking, Communication, Entertainment, and Consumer Engagement. These subthemes collectively reflect how students utilize digital media not only for leisure but also as a multifaceted learning and communication environment. Detailed student responses, representative quotes, and observed behavioral outcomes supporting these findings are presented in Table 6. Table 6. Thematic analysis of positive uses of media with observational and digital evidence Subtheme Participants (n) Interview Data Observational Data Digital Product / Indicator Educational Contribution / CML Domain Information Seeking S1–S3, S4, S6, S8–S11, S13, S14, S16–S18 (n=14) “I look it up when I don’t know something.” (S8); “I find my sources there.” (S2) Independent searching, fact-checking without teacher intervention S17’s “curious researcher” poster; S10’s info-access drawing Fact-checking, inquiry skills, source literacy Communication S1–S4, S6–S11, S13–S15, S17–S18 (n=15) “I message my friends.” (S9); “I learn from my teacher.” (S1) Media-mediated group interactions, guided online engagement S12’s digital drawing; S15’s media screen design Digital communication ethics, safe sharing practices Entertainment S1–S4, S5–S7, S8–S14, S16–S18 (n=16) “I play games and watch funny videos.” (S5) Time management behaviors, content filtering S11’s gaming child drawing; S16’s humor cartoon Media awareness, digital self-regulation, edutainment balance Consumer Engagement S3–S7, S8–S14, S16–S17 (n=14) “I browse clothes online.” (S4) Filtering advertisements, preference for research over impulse buys S13’s shopping-themed artwork; S7’s “ads” visual product Consumer literacy, ad critique, economic awareness of messages The "Information Seeking" theme reveals students’ increased independence and critical thinking in accessing online content, indicating the early development of digital research skills. The "Communication" theme underscores the role of digital tools not only for social interaction but also for educational engagement, with a focus on ethical and safe communication practices. The "Entertainment" theme allowed students to express themselves emotionally and creatively while balancing fun and learning through media. Lastly, the "Consumer Engagement" theme highlighted students’ growing discernment in response to digital advertisements and their inclination toward critical media consumption. Collectively, the findings position students as selective, thoughtful, and responsible media users. The integration of AI tools played a pivotal role in nurturing these critical competencies. Negative Aspects of Media Within the broader theme of Negative Aspects of Media , six prominent subthemes were identified: Scams & Misinformation, Health Impacts, Time Loss, Cyberbullying, Harmful Content, and Addiction. These themes emerged consistently across student interviews, classroom observations, and digital artifacts, revealing a nuanced awareness of the risks associated with digital media use among elementary school students. Detailed student responses, representative quotes, and observed behavioral outcomes supporting these findings are presented in Table 7. Table 7. Thematic analysis of negative aspects of media and their pedagogical ımplications Subtheme Participants (n) Interview Excerpt Observational Insight Digital Product / Indicator CML Contribution Scams & Misinformation S1–S4, S5–S16, S18 (n=15) “Sometimes they show things that aren't real.” (S3) Cautious behavior toward ads and misleading content S16’s fake ad awareness poster Critical evaluation of sources, credibility awareness Health Impacts S2–S8, S10–S17 (n=15) “My eyes hurt, and my head aches after a while.” (S6) Concentration loss after screen exposure S13’s eye-health drawing Digital wellness, somatic awareness Time Loss S1, S2, S6–S8, S11, S12, S14–S16, S18 (n=11) “I lose track of time watching videos and forget homework.” (S2) Difficulty disconnecting from games, delayed class transitions S15’s “time drain” artwork Time management, digital self-regulation Cyberbullying S1–S3, S5, S7, S10, S12, S13, S15, S17–S18 (n=11) “Someone sent me mean messages, I didn’t know them.” (S13) Hesitant online behavior, passive participation S18’s “hurtful comments” illustration Empathy, digital safety, rights-conscious citizenship Harmful Content S2–S5, S7–S10, S12–S16 (n=13) “I saw something scary; it appeared in my dreams.” (S9) Distraction after disturbing content exposure S14’s “beware of monsters” themed drawing Content curation, age-appropriateness, digital hygiene Addiction S1–S3, S5, S7, S9–S11, S15–S17 (n=11) “If I play too long, I can't stop—I always want to open it.” (S5) Difficulty detaching from media, disinterest in class (S9, S15) S10’s “screen-glued kids” sketch Behavioral self-regulation, inner awareness, digital restraint The “Scams & Misinformation” theme reflects students’ ability to question the authenticity of digital content, demonstrating a critical awareness of source reliability. The “Health Impacts” and “Time Loss” themes highlight students’ growing recognition of how digital media affects their physical well-being and academic focus, emphasizing the need for time and attention management skills. Themes such as “Cyberbullying” and “Harmful Content” reveal the emotional consequences of unsafe digital environments and the urgency for protective digital literacy. Finally, the “Addiction” theme illustrates students’ evolving ability to recognize their own overuse and actively attempt to set boundaries, indicating emerging digital self-discipline. Overall, these findings show that critical media literacy is not only about analyzing content but also about cultivating resilience, safety, and agency in the digital realm. AI-supported education has proven effective in facilitating these multidimensional competencies. Contributions to Critical Media Literacy Thematic analysis of qualitative data revealed six key domains where students demonstrated growth aligned with the core principles of critical media literacy. These categories—Digital Self-Protection & Data Privacy, Purposeful and Responsible Media Use, Safe Communication & Boundary Awareness, Critical Evaluation & Misinformation Awareness, Online Risk Awareness, and Media Ethics & Digital Citizenship—reflect a comprehensive transformation in students' cognitive, behavioral, and ethical engagement with media. Detailed student responses, representative quotes, and observed behavioral outcomes supporting these findings are presented in Table 8. Table 8. Thematic categories based on student ınterviews, participants, and contributions to critical media literacy Thematic Category Participants (n) Interview Excerpt Observational Data Digital Product / Tangible Evidence Contribution to Critical Media Literacy Digital Self-Protection & Data Privacy S2–S6, S10–S15, S17 (n=12) “We shouldn’t share personal information.” (S5) Hesitation on sharing screens, requests for teacher support (S14) S13’s “don’t share with everyone” poster Identity protection, data privacy awareness, cybersecurity Purposeful and Responsible Media Use S1, S3–S8, S10, S14–S16, S18 (n=13) “I use it for fun but try not to stay too long.” (S14) Content filtering and time management behaviors (S16) S16’s “turn it off if you don’t need it” poster Self-regulation, screen time control, critical content selection Safe Communication & Boundary Awareness S4–S8, S10–S15, S17 (n=12) “I don’t open messages from strangers.” (S10) Restrictive social behaviors online, rejecting unknown contacts S15’s “don’t talk to strangers!” drawing Boundary-setting, safe digital interactions, online discretion Critical Evaluation & Misinformation Awareness S2, S3, S6–S9, S13–S18 (n=12) “I don’t believe everything—I verify first.” (S6) Comparing sources, questioning reliability (S9) S17’s “what if it’s fake?” poster Source verification, content skepticism, misinformation literacy Online Risk Awareness S1, S3–S5, S8, S10–S15, S17 (n=12) “Some videos were scary, I closed them immediately.” (S4) Emotional discomfort (grimacing, averting eyes), quick exit (S10) S11’s “if it’s bad, leave” cartoon Risk detection, emotional boundaries, digital self-defense Media Ethics & Digital Citizenship S2–S6, S7–S8, S10–S12, S15–S16, S18 (n=13) “We shouldn’t say bad words online.” (S7); “I didn’t copy-paste.” (S10) Citing sources, respecting others’ work (S15) S18’s “cite and show respect” poster Ethical digital behavior, attribution, online responsibility The themes "Digital Self-Protection" and "Purposeful Media Use" illustrate students’ growing competencies in safeguarding their personal data and managing screen time consciously. The themes “Safe Communication” and “Online Risk Awareness” reflect students’ proactive attitudes toward maintaining boundaries and defending against harmful content. “Critical Evaluation” reveals their capacity to engage in higher-order thinking by comparing information sources and questioning credibility. Lastly, “Media Ethics and Digital Citizenship” shows that students internalized principles like respect, originality, and responsibility in digital contexts. Collectively, these findings position students not just as content consumers but as ethically aware and critically engaged digital citizens, offering a comprehensive view of the transformative potential of AI-supported media literacy education. Student Feedback on AI-Supported Media Literacy Education Student reflections gathered through semi-structured interviews, observational data, and artifacts revealed a diverse range of learning outcomes that underscore both the cognitive and affective dimensions of AI-supported media literacy education. Detailed student responses, representative quotes, and observed behavioral outcomes supporting these findings are presented in Table 9. Table 9. Student feedback on AI-supported media literacy education, themes, and contributions to critical media literacy Student Direct Quote Interpreted Theme Observed Behavioral Outcome Contribution to Critical Media Literacy S1 “This activity helped me... media has both good and bad sides.” Awareness of media’s dual nature Distinguishing positive and negative content Holistic media interpretation, multidimensional analysis S4 “The activities were fun and educational.” Joyful learning and risk awareness Alertness to fake content Recognizing threats, emotional engagement enhances retention S8 “I don’t spend as much time now. I watch out for ads.” Time control and ad literacy Time-limiting behaviors, selective attention Digital self-regulation, resistance to media manipulation S11 “I learned not to share bad info or personal data.” Digital privacy and safety Awareness of personal data protection Cyber hygiene, data privacy awareness S13 “I don’t believe everything I see online anymore.” Fact-checking behavior Cross-verifying sources, content skepticism Critical thinking, misinformation resistance S14 “It was nice... thank you.” General satisfaction and emotional bond Voluntary participation, positive attitude Openness to digital learning, pedagogical engagement S16 “I don’t talk to strangers online.” Safe communication and boundary setting Avoiding unknown contacts online Digital safety, personal boundary awareness S18 “I learned the difference between old and new media.” Media evolution and citizenship Conscious comparison of media types Historical-digital awareness within media literacy S9–S18 (Supported by journals, observations, and artifacts) Multiple outcomes and retention Awareness, ethics, content curation Behavioral transformation, sustainable media competence This table integrates students’ direct reflections with observed behavioral outcomes to offer a meaningful map of educational transformation. These statements reflect not only emotional engagement but also cognitive and behavioral development. Students exhibited enhanced time management, ad skepticism, data privacy, and source verification. Remarks like “I don’t believe everything online” indicate growth in critical thinking. Emotional investment also increased the program’s sustainability and impact. Themes such as avoiding strangers and recognizing media evolution underscore emerging digital citizenship. Overall, these findings present AI-supported media literacy education as a transformative pedagogical model that fosters digital competence and social responsibility. Discussion The findings of this study underscore the transformative potential of AI-supported media literacy education in fostering critical, ethical, and participatory digital competencies among elementary school students. The statistically significant improvements observed in both media reading and writing skills reveal that AI tools—when strategically integrated—can serve not merely as instructional aids but as catalysts for deep cognitive and ethical engagement. These findings align with earlier research emphasizing the pedagogical benefits of intelligent learning environments that provide timely feedback, adapt to learner needs, and encourage metacognitive reflection (Aleven & Koedinger, 2002 ; Weber et al., 2024; Lee et al., 2025 ). From a theoretical standpoint, the integration of Hobbs’s ( 2011 ) five-domain media literacy framework with Jenkins’s ( 2009 ) participatory culture model has proven pedagogically coherent and practically viable. Students demonstrated measurable gains across all five dimensions—access, analysis, evaluation, creation, and reflection—suggesting that the AI-enhanced curriculum facilitated a comprehensive and active engagement with media. For example, the use of ChatGPT and Perplexity AI promoted deeper source interrogation and balanced reasoning, while Canva AI and Grammarly supported students in generating their own ethical and creative media outputs. These shifts represent more than just skill acquisition; they indicate a broader epistemic transformation in how students perceive, interact with, and contribute to media ecosystems. This development supports the argument that media literacy should not be compartmentalized as a discrete subject but embedded across disciplines through interdisciplinary, values-driven pedagogy. Furthermore, the behavioral and affective transformations observed—such as critical skepticism toward misinformation, increased data privacy consciousness, and thoughtful screen-time regulation—point to the internalization of ethical digital practices. These outcomes align with contemporary discourses on digital citizenship (Mossberger et al., 2007 ) and validate the argument that early exposure to structured, reflective media education fosters responsible digital behavior. Notably, the curricular alignment with the Turkish and Social Studies programs enhanced the intervention’s relevance and scalability. By embedding media literacy goals into the existing educational framework rather than treating them as supplementary or extracurricular, the study offers a sustainable model that classroom teachers can adopt without extensive restructuring. This curricular coherence also supports broader efforts to institutionalize media literacy within national education systems—a move increasingly advocated by international organizations such as UNESCO and the European Commission. Despite these strengths, several limitations must be acknowledged. The study's sample size and geographic scope, though sufficient for preliminary insights, may limit the generalizability of the findings. Additionally, while AI tools proved effective in this context, ongoing advancements in algorithmic design necessitate continual evaluation to mitigate risks related to bias, privacy, and misinformation. Future studies might explore longitudinal impacts, cross-cultural applications, and the role of teacher training in maximizing the pedagogical efficacy of AI-enhanced media literacy education. Conclusion This study contributes to the growing body of scholarship advocating for a critical, ethical, and participatory approach to media literacy education—one that is increasingly mediated by artificial intelligence technologies. The integration of AI into the curriculum not only amplified students’ cognitive and creative capacities but also fostered meaningful behavioral and affective transformations that align with the principles of responsible digital citizenship. By embedding AI-supported media literacy instruction into national education standards, this research demonstrated a feasible and scalable pathway for educational innovation. The intervention proved effective in nurturing students' ability to access, analyze, evaluate, and produce media content with discernment and ethical awareness. These competencies are indispensable in an era characterized by misinformation, algorithmic manipulation, and digital saturation. Ultimately, the findings suggest that AI-enhanced media literacy education is not simply a pedagogical trend but an educational necessity. As digital environments continue to evolve, so too must our strategies for equipping young learners to navigate them with critical acumen and ethical integrity. This study offers a replicable framework that may inform policy, curriculum development, and future research—paving the way for an education system attuned to the complexities of 21st-century media landscapes. Recommendations In light of the findings, several pedagogical and policy-level strategies are proposed to enhance the integration and impact of AI-supported critical media literacy education: Curricular Integration Across Disciplines : Media literacy should be holistically embedded into existing curricula starting from early education, not as a stand-alone subject but as a cross-disciplinary theme encompassing information verification, ethical reasoning, and digital rights awareness (UNESCO, 2021 ). Pedagogical Utilization of AI Tools : AI platforms such as ChatGPT, Canva AI, and Grammarly should be positioned not merely as instructional technologies, but as cognitive scaffolds that foster critical inquiry, creativity, and student agency (Westbrook et al., 2021 ). Professional Development for Educators : Teacher training programs must be restructured to include modules on digital pedagogy and AI integration, equipping educators to guide students in becoming critical and ethical participants in digital media environments (OECD, 2018 ). Scenario-Based Instructional Design : Instructional content should be grounded in real-life, age-appropriate scenarios that address current digital challenges such as misinformation, cyberbullying, algorithmic bias, and media addiction, ensuring relevance and engagement. Alignment with Global Competency Standards : Future implementations should align with internationally recognized frameworks such as the ISTE Student Standards, promoting a unified vision of digital literacy, responsible media participation, and ethical content production. Limitations This study is not without its limitations. Firstly, the sample size was restricted to 36 students from a single public elementary school, limiting the generalizability of the findings. Secondly, reliance on self-reported data may have introduced response bias, particularly in qualitative reflections. Thirdly, the relatively short duration of the intervention (18 hours) may constrain the observation of long-term cognitive or behavioral transformations. Lastly, while AI tools enhanced instructional delivery, disparities in digital infrastructure and access could present challenges for scalability in under-resourced settings. Directions for Future Research To expand upon the current findings and address the noted limitations, future research is encouraged to pursue the following avenues: Longitudinal Impact Studies : Examine the sustained effects of AI-supported media literacy interventions on students’ digital behaviors, critical thinking, and civic participation over extended periods. Cross-Contextual Scalability : Explore the adaptability and effectiveness of the model across diverse educational contexts, particularly in underserved or socioeconomically disadvantaged regions. Comparative Tool-Specific Analyses : Conduct controlled studies to assess the differential pedagogical impact of specific AI tools (e.g., generative vs. evaluative platforms) on discrete domains of media literacy such as source evaluation, ethical production, and reflective thinking. International Comparative Research : Engage in multi-site, cross-national studies to assess cultural responsiveness, policy integration, and systemic barriers, thereby contributing to a globally adaptable pedagogical model. Declarations Ethical Considerations This study was conducted with the approval of the Ethics Committee of the relevant university (Approval No: 2024-30, Date: March 8, 2024). Written consent was obtained from the parents of participating students, and all procedures were carried out based on voluntary participation and informed consent. Throughout the data collection process, full adherence to the principles of confidentiality and anonymity was ensured; all identifying information was kept confidential, and audio recordings were securely deleted after analysis. Statement on AI Assistance Artificial intelligence tools (e.g., ChatGPT by OpenAI) were employed solely to support language editing, improve clarity, and ensure grammatical accuracy in the preparation of this manuscript. Acknowledgements The authors express their gratitude to the participating students, school administrators, and teachers for their collaboration and support during the implementation process. Data Availability The data and code used in this study are available from the corresponding author upon request. Conflict of Interest The authors declare no competing interests. Author Contribution Author Contributions StatementR.D. conceptualized the study, designed the AI-based media literacy program, and conducted the quantitative analysis. C.A. managed data collection, performed the qualitative analysis, and contributed to the interpretation of findings. R.D. and C.A. co-wrote the manuscript. Both authors reviewed and approved the final version of the manuscript. 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02:43:43","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":282006,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eStudent feedback on AI-supported media literacy education\u003c/em\u003e\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-6786882/v1/e0e48d9cc84c2245bab26d44.png"},{"id":91937579,"identity":"ee868df6-4e03-474b-8ede-1301b7d0197f","added_by":"auto","created_at":"2025-09-23 02:59:44","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2754937,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6786882/v1/a2e9393a-7bef-4341-9a2d-39971ae2b161.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Transforming Clicks into Critical Thinking: An AI-Based Media Literacy Program for Children","fulltext":[{"header":"Introduction","content":"\u003cp\u003eIn today\u0026apos;s digitally saturated world, media no longer merely disseminates information\u0026mdash;it actively shapes civic engagement, self-expression, and educational innovation. Digital platforms have become central arenas for youth to explore, learn, and build their socio-political identities (Jenkins et al., 2016; Mihailidis \u0026amp; Thevenin, 2013). In this multifaceted landscape, media literacy emerges not as an optional skill but as a core competency, encompassing critical analysis, ethical reasoning, and participatory engagement (Livingstone et al., 2019).\u003c/p\u003e\n\u003cp\u003eNevertheless, the digital sphere presents escalating challenges, notably the rapid circulation of misinformation, algorithmic distortion, and cybersecurity risks. Notably, research by Vosoughi et al. (2018) indicates that false narratives disseminate at a rate nearly six times faster than factual content\u0026mdash;highlighting the urgency of equipping young learners with robust evaluative and ethical media skills (Wang et al., 2021). Addressing this issue is both a pedagogical necessity and a civic responsibility: fostering young people\u0026apos;s critical media engagement is key to nurturing informed digital citizens.\u003c/p\u003e\n\u003cp\u003eInitially framed by Aufderheide (1993) and later expanded by Hobbs (2011), media literacy involves accessing, analyzing, evaluating, and producing media across contexts. However, traditional instructional methods may fall short in addressing the evolving dynamics of digital media. The integration of artificial intelligence (AI) into educational contexts introduces new possibilities for enhancing these literacies. AI supports personalized learning and facilitates critical and creative engagement with complex media forms (Grajeda et al., 2023; Tekwa et al., 2024).\u003c/p\u003e\n\u003cp\u003eCognitively, AI tools can lower extraneous load and enhance learning depth, consistent with Sweller\u0026rsquo;s (1988) Cognitive Load Theory. Tools like ChatGPT, Canva AI, and Grammarly illustrate how AI can enhance conceptual clarity, media creation, and linguistic accuracy (Kargın \u0026amp; Demir, 2023; Westbrook et al., 2021). When deliberately integrated, these tools become pedagogical agents rather than mere technological add-ons. This aligns with research by Aleven and Koedinger (2002), showing that embedded self-explanation prompts in intelligent tutors boost conceptual understanding and transfer. Emerging evidence also suggests that AI-driven dialogue systems foster reflective and metacognitive thinking by presenting challenging viewpoints that counteract cognitive biases (Weber et al., 2024).\u003c/p\u003e\n\u003cp\u003eThe theoretical foundation of this study draws from Hobbs\u0026rsquo;s (2011) five-part media literacy model\u0026mdash;access, analysis, evaluation, creation, and reflection\u0026mdash;stressing learner agency in meaning-making and content creation. This model is further operationalized through Jenkins\u0026rsquo;s (2009) Participatory Culture Theory, which sees learners as not just consumers but active contributors in digital spaces. The AI-enhanced media literacy initiative presented here seeks to instill this participatory ethos in primary education settings.\u003c/p\u003e\n\u003cp\u003eThe study also resonates with the digital citizenship framework of Mossberger et al. (2007), which emphasizes responsible and ethical online behavior. Observable behavioral shifts\u0026mdash;such as skepticism toward unverified content, increased digital privacy awareness, and conscious screen time usage\u0026mdash;suggest the transformative potential of AI in shaping ethical digital habits (Kotsonis \u0026amp; Dunne, 2024; Paltacı, 2024). These outcomes correspond with recent studies highlighting AI\u0026rsquo;s role in promoting ethical judgment, digital self-regulation, and collaborative inquiry in young learners (Lee et al., 2025).\u003c/p\u003e\n\u003cp\u003eWhile prior studies have explored the use of Web 2.0 tools in media education (Gen\u0026ccedil;, 2024; Kulaca et al., 2024), the intersection of AI integration and media literacy in primary education remains understudied. Given the rise of deepfakes, algorithmic bias, and targeted disinformation, a paradigm shift is essential\u0026mdash;from emphasizing technical proficiency to cultivating critical thinking and ethical media engagement (UNESCO, 2021; EU Commission, 2020).\u003c/p\u003e\n\u003cp\u003eSet against this backdrop, the current research examines the efficacy of an AI-integrated media literacy program tailored for elementary students. Using a mixed-methods approach, the study addresses the following questions:\u003c/p\u003e\n\u003col\u003e\n \u003cli\u003eHow does AI-assisted instruction influence the development of students\u0026rsquo; critical media literacy?\u003c/li\u003e\n \u003cli\u003eIn what ways do students\u0026apos; interpretations of media benefits and risks evolve?\u003c/li\u003e\n \u003cli\u003eWhat cognitive and behavioral shifts are evident in students\u0026apos; practices around media evaluation, digital safety, and ethical reflection?\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eThis study positions AI not as an auxiliary tool, but as an epistemic partner in cultivating critical awareness, responsibility, and civic engagement. Although situated in Turkey, the proposed model offers broader international relevance, serving as a scalable and adaptable approach for education systems navigating the complexities of a digital age.\u003c/p\u003e"},{"header":"Methodology","content":"\u003cp\u003e\u003cstrong\u003eResearch Design\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo rigorously investigate the pedagogical impact of AI-integrated critical media literacy education on elementary students, this study adopted an explanatory sequential mixed methods design, as conceptualized by Creswell (2009). This design was chosen to capitalize on the strengths of both quantitative and qualitative approaches, offering a robust framework to analyze not only statistical trends but also the nuanced experiential dimensions of learning processes.\u003c/p\u003e\n\u003cp\u003eThe initial phase involved a quasi-experimental pre-test\u0026ndash;post-test control group model (B\u0026uuml;y\u0026uuml;k\u0026ouml;zt\u0026uuml;rk et al., 2018), enabling the measurement of learning gains attributable to the intervention while ensuring comparative rigor between experimental and control cohorts. This design allowed for the isolation of treatment effects under real-world classroom conditions where random assignment was not feasible.\u003c/p\u003e\n\u003cp\u003eSubsequently, a qualitative phase was conducted to provide contextual depth and interpretive richness. Data were collected via semi-structured interviews, student-generated artifacts (e.g., digital posters, drawings, slogans), and structured observation forms. These sources facilitated triangulation and deepened the interpretive validity of the findings by capturing cognitive, behavioral, and affective dimensions of student engagement.\u003c/p\u003e\n\u003cp\u003eThis two-phased approach was strategically employed to synthesize empirical generalizability with ecological validity\u0026mdash;an essential consideration in educational interventions involving dynamic, learner-centered pedagogies.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eParticipants\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study was conducted with 36 fourth-grade students enrolled in a public primary school located in the Aegean region of Turkey during the 2023\u0026ndash;2024 academic year. Participants were assigned to either the experimental group (n = 18) or the control group (n = 18), ensuring equal gender distribution (10 girls and 8 boys in each group) to control for potential gender-based differences in media engagement and technology use.\u003c/p\u003e\n\u003cp\u003eThe experimental group received instruction aligned with an AI-enhanced critical media literacy framework, while the control group continued with the conventional curriculum aligned with the Ministry of National Education\u0026apos;s (MoNE) standards. Participants were selected through purposive sampling based on their availability and the school\u0026apos;s infrastructural readiness. Efforts were made to ensure socio-demographic parity across groups, thereby mitigating confounding variables such as access to technology, parental education level, and digital literacy exposure.\u003c/p\u003e\n\u003cp\u003eAll research procedures adhered strictly to ethical standards for educational research involving minors. Ethical approval was obtained from the university\u0026rsquo;s institutional review board (Approval No: 2024-30, dated March 8, 2024), and formal permissions were secured from the Turkish Ministry of National Education. Informed written consent was obtained from the parents or legal guardians of all participants. Throughout the study, participants\u0026rsquo; rights to confidentiality, anonymity, and voluntary withdrawal were upheld in accordance with international ethical guidelines.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Collection Tools\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eQuantitative Instruments: Critical Media Literacy Disposition Scale\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo quantitatively assess students\u0026rsquo; dispositions toward critical media engagement, the study employed the \u003cem\u003eCritical Media Literacy Disposition Scale for Elementary Students\u003c/em\u003e developed by Demir and Akar (2024). Administered both pre- and post-intervention to experimental and control groups, the instrument comprises 12 items distributed across two subdimensions: \u003cem\u003eCritical Media Reading (CMR)\u003c/em\u003e and \u003cem\u003eCritical Media Writing (CMW)\u003c/em\u003e. The scale adopts a 3-point Likert response format\u0026mdash;Never (1), Sometimes (2), and Always (3)\u0026mdash;to capture developmental variations in media literacy habits among children.\u003c/p\u003e\n\u003cp\u003ePsychometric evaluation affirmed the instrument\u0026rsquo;s structural robustness. Exploratory Factor Analysis (EFA) yielded a two-factor solution accounting for 52.2% of the total variance (41.9% attributed to CMR and 10.3% to CMW). Confirmatory Factor Analysis (CFA) further validated this structure with strong model fit indices: GFI = .958, AGFI = .936, IFI = .970, CFI = .970, TLI = .920, RMSEA = .057. Reliability coefficients demonstrated high internal consistency, with Cronbach\u0026rsquo;s alpha scores of .825 for CMR, .794 for CMW, and .812 for the total scale\u0026mdash;each exceeding the .70 threshold recommended by Nunnally and Bernstein (1994).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eQualitative Instruments and Analytical Strategy\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eQualitative data were collected through three interrelated methods\u0026mdash;semi-structured interviews, student-generated media artifacts, and structured classroom observations\u0026mdash;allowing for methodological triangulation and the elicitation of rich, multi-modal insights (Creswell \u0026amp; Clark, 2018; Tracy, 2010).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSemi-Structured Interviews\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eInterviews with students from the experimental group sought to reveal their evolving understandings of media utility, digital risks, and ethical content engagement. The interview protocol was informed by seminal literature in media literacy education (e.g., Hobbs, 2011; Kellner \u0026amp; Share, 2007), and validated through expert review involving three in-service teachers and two academic researchers. Interviews were conducted with all 18 students in the experimental group, with each session lasting 10\u0026ndash;15 minutes. Audio recordings were securely stored and anonymized before being deleted post-transcription and analysis, in full compliance with ethical protocols.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStudent Artifacts\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThroughout the intervention, students created a range of artifacts\u0026mdash;posters, visual illustrations, multimedia slogans, and reflective written pieces\u0026mdash;which were collected as tangible evidence of conceptual understanding and critical expression. These products served as cognitive and affective indicators of students\u0026apos; abilities to analyze, critique, and ethically reproduce media content (Berg \u0026amp; Schensul, 2016). A content analysis approach was employed to evaluate the thematic density and conceptual alignment of these artifacts with the study\u0026rsquo;s theoretical framework.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResearcher Observations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eStructured observation forms, developed in accordance with qualitative fieldwork standards (Angrosino, 2007), were used to systematically capture classroom interactions, media tool usage, and verbal/non-verbal engagement cues. Observational memos were written after each session to document evolving competencies and participation dynamics. These field notes enriched the data corpus and supported internal validation of emergent themes.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eQuantitative Analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eQuantitative data were analyzed using IBM SPSS (v.26). Initial tests for normality based on skewness and kurtosis values confirmed the data\u0026apos;s appropriateness for parametric analysis. A combination of \u003cem\u003epaired samples t-tests\u003c/em\u003e (within-group comparisons) and \u003cem\u003eindependent samples t-tests\u003c/em\u003e (between-group comparisons) was conducted to determine statistically significant differences in students\u0026rsquo; pre- and post-test scores on the CMR and CMW subscales. Significance levels were set at p \u0026lt; .05, with effect sizes (Cohen\u0026rsquo;s d) computed to assess the magnitude of observed changes.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eQualitative Analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThematic analysis was guided by Braun and Clarke\u0026rsquo;s (2006) six-phase model, supplemented with constructivist grounded theory coding techniques from Strauss and Corbin (1998). This dual-analytic lens provided both structure and flexibility, capturing the depth and complexity of participants\u0026rsquo; media literacy development.\u003c/p\u003e\n\u003col start=\"1\" type=\"1\"\u003e\n \u003cli\u003e\u003cem\u003eOpen Coding\u003c/em\u003e\u003cstrong\u003e:\u003c/strong\u003e Data were deconstructed line-by-line to generate initial codes, revealing cognitive, emotional, and behavioral markers.\u003c/li\u003e\n \u003cli\u003e\u003cem\u003eAxial Coding\u003c/em\u003e\u003cstrong\u003e:\u003c/strong\u003e Relationships between codes were analyzed to construct subthemes and organize them under broader conceptual categories.\u003c/li\u003e\n \u003cli\u003e\u003cem\u003eSelective Coding\u003c/em\u003e\u003cstrong\u003e:\u003c/strong\u003e Core themes were synthesized to encapsulate key pedagogical impacts, including ethical reasoning, digital self-regulation, and critical reflection.\u003c/li\u003e\n \u003cli\u003e\u003cem\u003eInter-Coder Reliability\u003c/em\u003e\u003cstrong\u003e:\u003c/strong\u003e To ensure coding reliability, a dual-coding process was conducted independently by the lead researcher and a collaborating educational expert. Cohen\u0026rsquo;s Kappa was calculated at .85, indicating near-perfect agreement (Landis \u0026amp; Koch, 1977).\u003c/li\u003e\n \u003cli\u003e\u003cem\u003eTriangulation and Trustworthiness\u003c/em\u003e\u003cstrong\u003e:\u003c/strong\u003e Data from interviews, artifacts, and observations were triangulated to enhance credibility. \u003cem\u003ePeer debriefing\u003c/em\u003e with external researchers and \u003cem\u003emember checks\u003c/em\u003e with participating teachers reinforced analytical validity. Rich, thick description (Lincoln \u0026amp; Guba, 1985) was employed to contextualize themes within classroom dynamics and student discourse.\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eThis integrated analytical strategy ensured not only methodological rigor but also epistemological coherence, enabling a nuanced exploration of how AI-enhanced media literacy education shapes young learners\u0026rsquo; digital identities, critical capacities, and ethical consciousness.\u003c/p\u003e"},{"header":"Implementation","content":"\u003cp\u003e\u003cstrong\u003eInstructional Design and Implementation Framework: AI-Integrated 5E Learning Model\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe implementation phase of this study involved an 18-hour instructional intervention designed using the 5E learning cycle model\u0026mdash;Engage, Explore, Explain, Elaborate, Evaluate\u0026mdash;integrated with AI-based tools to foster critical media literacy among elementary school students. The pedagogical framework was carefully aligned with the national curriculum outcomes specified in the Turkish Language (MoNE, 2018) and Social Studies (MoNE, 2019) programs. This approach not only preserved curricular relevance but also ensured instructional coherence and contextual adaptability.\u003c/p\u003e\n\u003cp\u003eGiven the socioeconomically disadvantaged profile of the participating school, particular attention was paid to selecting digital tools that were both pedagogically potent and technologically accessible. The suite of tools employed included ChatGPT, Canva AI, Grammarly, Mentimeter, DALL\u0026middot;E, Padlet, Quizizz, Microsoft Word, PowerPoint, and OneDrive. These tools were not used as standalone applications but were embedded within pedagogical tasks to promote conceptual learning, creativity, and ethical reasoning.\u003c/p\u003e\n\u003cp\u003eThe instructional sequence is detailed in Table 1, which maps each phase of the 5E model onto specific digital tools, instructional activities, and curriculum outcomes. This design ensured that technological integration served not as an add-on, but as a scaffold for deeper cognitive and ethical engagement with media content.\u003c/p\u003e\n\u003cp\u003eTable 1. \u003cem\u003eIntegration of the 5E model with AI tools, instructional activities, and curriculum learning outcomes\u003c/em\u003e\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"605\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 13.3884%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e5E Stage\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.9091%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDuration\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25.6198%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eInstructional Activities\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20.3306%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAI Tools Utilized\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 29.7521%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCurricular Alignment (T/SS)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 13.3884%;\"\u003e\n \u003cp\u003eEngagement\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.9091%;\"\u003e\n \u003cp\u003e2 hours\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25.6198%;\"\u003e\n \u003cp\u003eWatching curated videos,\u0026nbsp;\u003c/p\u003e\n \u003cp\u003egenerating reflective questions via ChatGPT, empathy tasks\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20.3306%;\"\u003e\n \u003cp\u003eYouTube, ChatGPT, Mentimeter, Canva Magic Write\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 29.7521%;\"\u003e\n \u003cp\u003eEvaluates watched/listened content (T); Evaluates media texts (T)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 13.3884%;\"\u003e\n \u003cp\u003eExploration\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.9091%;\"\u003e\n \u003cp\u003e2 hours\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25.6198%;\"\u003e\n \u003cp\u003eAnalyzing media messages, generating original content\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20.3306%;\"\u003e\n \u003cp\u003ePerplexity AI, Canva Magic Write, DALL\u0026middot;E, Synthesia, HeyGen\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 29.7521%;\"\u003e\n \u003cp\u003eQuestions content coherence (T); Assesses source reliability (T); Compares tech use (SS); Uses tech sustainably (SS)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 13.3884%;\"\u003e\n \u003cp\u003eExplanation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.9091%;\"\u003e\n \u003cp\u003e7 hours\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25.6198%;\"\u003e\n \u003cp\u003eConceptual lessons, Q\u0026amp;A with ChatGPT, collaborative note-taking\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20.3306%;\"\u003e\n \u003cp\u003eChatGPT, PowerPoint, MS Word, OneDrive\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 29.7521%;\"\u003e\n \u003cp\u003eUnderstands digital messages (T); Evaluates media texts (T); Classifies technologies (SS)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 13.3884%;\"\u003e\n \u003cp\u003eElaboration\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.9091%;\"\u003e\n \u003cp\u003e5 hours\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25.6198%;\"\u003e\n \u003cp\u003ePoster and slogan creation, peer feedback, text editing\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20.3306%;\"\u003e\n \u003cp\u003eCanva AI, Grammarly, Padlet, Quizizz\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 29.7521%;\"\u003e\n \u003cp\u003eInterprets signs/symbols (T); Investigates innovation (SS); Uses tech ethically (SS)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 13.3884%;\"\u003e\n \u003cp\u003eEvaluation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.9091%;\"\u003e\n \u003cp\u003e2 hours\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25.6198%;\"\u003e\n \u003cp\u003ePre/post testing, observations, self-reflection\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20.3306%;\"\u003e\n \u003cp\u003eChatGPT, Padlet, Observation Forms\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 29.7521%;\"\u003e\n \u003cp\u003eAssesses information reliability (T); Uses tech ethically (SS)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003e3.2 Pedagogical Execution and Instructional Dynamics\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eEach phase of the 5E model was meticulously structured to progress from curiosity and inquiry to synthesis and self-assessment, thereby scaffolding students\u0026rsquo; cognitive, ethical, and behavioral development.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eEngagement Phase\u003c/em\u003e\u003cstrong\u003e:\u003c/strong\u003e Students were introduced to authentic media experiences through curated YouTube videos, followed by real-time questioning via ChatGPT. They then used Mentimeter to co-construct word clouds representing their interpretations of media content. These tasks not only triggered curiosity but also activated prior knowledge and cultivated empathy\u0026mdash;essential prerequisites for reflective media engagement.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eExploration Phase\u003c/em\u003e\u003cstrong\u003e:\u003c/strong\u003e Students began to deconstruct digital texts using AI-based platforms. They employed Perplexity AI for source validation, DALL\u0026middot;E for image generation, and Canva Magic Write to experiment with content formulation. These activities nurtured inquiry, interpretation, and creative articulation in line with national curriculum goals emphasizing sustainability and historical awareness of technology.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eExplanation Phase\u003c/em\u003e\u003cstrong\u003e:\u003c/strong\u003e Key media literacy concepts were explicated through instructor-led lessons and digital presentations. ChatGPT was employed as a conversational co-tutor to address student inquiries and extend discussions. Through this dual-channel scaffolding, students engaged with topics such as media bias, source triangulation, and message construction, thus developing both cognitive acuity and technical proficiency.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eElaboration Phase\u003c/em\u003e\u003cstrong\u003e:\u003c/strong\u003e Students synthesized their learning into tangible media products, including digital posters and reflective slogans. Grammarly facilitated ethical editing practices, while Padlet enabled asynchronous peer feedback. These collaborative activities emphasized message clarity, visual literacy, and intellectual property respect\u0026mdash;skills at the intersection of media critique and civic responsibility.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eEvaluation Phase\u003c/em\u003e\u003cstrong\u003e:\u003c/strong\u003e The final phase integrated multiple forms of assessment: pre/post testing with validated instruments, observational field notes, and AI-assisted self-reflection exercises. ChatGPT and Padlet were leveraged to support students in metacognitive evaluations of their learning. Results indicated marked gains in source discernment, content judgment, and digital ethics.\u003c/p\u003e\n\u003cp\u003eThis AI-integrated 5E instructional framework transcended the conventional boundaries of media education by fusing technological fluency with civic competence. Rather than passive recipients of content, students were positioned as reflective practitioners and digital citizens. The model\u0026apos;s alignment with curricular standards, its adaptability to low-tech settings, and its demonstrated effectiveness in shaping critical and ethical media behaviors make it a replicable and scalable educational innovation.\u003c/p\u003e"},{"header":"Findings","content":"\u003cp\u003e\u003cstrong\u003eQuantitative Findings: Impact of AI-Augmented Media Literacy Education\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eNormality Assumptions and Test Prerequisites\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn order to assess the suitability of parametric statistical tests, the skewness and kurtosis values for the pre-test and post-test scores were examined. Following the guidelines proposed by Tabachnick and Fidell (2013), which consider \u0026plusmn;1 as an acceptable range for normal distribution, all variables met the assumption of normality.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe skewness and kurtosis values indicate that the data approximate a normal distribution. Skewness values ranged from \u0026ndash;0.20 to \u0026ndash;0.47 in the pretest and from \u0026ndash;0.26 to \u0026ndash;0.61 in the posttest. Similarly, kurtosis values ranged between \u0026ndash;0.68 and \u0026ndash;0.26. These values fall within the commonly accepted threshold of \u0026plusmn;1, as suggested by Tabachnick and Fidell (2013), supporting the assumption of normality and justifying the use of parametric tests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eBaseline Equivalence of Groups\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAn independent samples t-test was conducted to assess whether initial differences existed between the experimental and control groups prior to the intervention. Table 2 illustrates the comparability of both groups:\u003c/p\u003e\n\u003cp\u003eTable 2.\u003cem\u003e\u0026nbsp;Baseline equivalence: pre-test comparison of experimental and control groups\u003c/em\u003e\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eSubscale\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eGroup\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eN\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eMean\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eSD\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003et\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003ep\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\"\u003e\n \u003cp\u003eMedia Reading\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eControl\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e15.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2.78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\"\u003e\n \u003cp\u003e0.316\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\"\u003e\n \u003cp\u003e.756\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eExperimental\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e15.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2.53\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\"\u003e\n \u003cp\u003eMedia Writing\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eControl\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e11.83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\"\u003e\n \u003cp\u003e0.846\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\"\u003e\n \u003cp\u003e.405\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eExperimental\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e12.39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2.20\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\"\u003e\n \u003cp\u003eTotal Media Literacy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eControl\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e27.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e3.47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\"\u003e\n \u003cp\u003e0.650\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\"\u003e\n \u003cp\u003e.520\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eExperimental\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e27.89\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e4.19\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eNo statistically significant differences were found across any of the dimensions (p \u0026gt; .05), confirming the initial equivalence of groups in terms of critical media literacy dispositions.\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eWithin-Group Gains in the Control Group\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA paired samples t-test revealed that the control group exhibited modest gains in the post-test scores. However, statistical significance was limited:\u003c/p\u003e\n\u003cp\u003eTable 3. \u003cem\u003eControl group pre- and post-test comparison\u003c/em\u003e\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eSubscale\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eN\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003ePre-Test\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eMean\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eSD\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003ePost-Test\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eMean\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eSD\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003et\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003ep\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eMedia Reading\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e15.22\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2.78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e16.61\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-2.379\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e.029 *\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eMedia Writing\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e11.83\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e12.11\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.84\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-0.676\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e.508\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eTotal Media Literacy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e27.06\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e3.47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e28.72\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2.91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-2.755\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e.014 *\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eAlthough reading scores showed a statistically significant improvement (p \u0026lt; .05), the absence of significant gains in media writing underscores the limited effectiveness of traditional instruction in fostering multidimensional literacy growth.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEffectiveness of AI-Enhanced Instruction: Experimental Group Analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe experimental group demonstrated statistically robust gains across all subscales following the AI-supported intervention, as reflected in Table 4:\u003c/p\u003e\n\u003cp\u003eTable 4. \u003cem\u003eExperimental group pre- and post-test comparison\u003c/em\u003e\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eSubscale\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eN\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003ePre-Test\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eMean\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eSD\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003ePost-Test\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eMean\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eSD\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003et\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003ep\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eMedia Reading\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e15.50\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2.53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e19.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2.47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-4.894\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e.000 **\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eMedia Writing\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e12.39\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e14.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-3.536\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e.003 **\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eTotal Media Literacy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e27.89\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e4.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e33.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e3.67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-5.129\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e.000 **\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eThe magnitude of improvement observed in media reading (+3.50), media writing (+1.67), and overall media literacy (+5.17) indicates the multidimensional effectiveness of the AI-integrated curriculum. These outcomes affirm the capacity of AI-supported environments to catalyze critical reasoning, creative expression, and ethical awareness.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eBetween-Group Post-Test Comparisons and Effect Size\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo determine the relative efficacy of the AI-based model, post-test scores of the experimental and control groups were compared. Effect size calculations using Cohen\u0026rsquo;s d underscored the practical significance of the findings:\u003c/p\u003e\n\u003cp\u003eTable 5. \u003cem\u003eBetween-group post-test comparison and effect sizes\u003c/em\u003e\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eSubscale\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eGroup\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eN\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eMean (SD)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003et\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003ep\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eCohen\u0026rsquo;s d\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eMedia Reading\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eControl\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e16.61 (1.75)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-3.343\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e.002 **\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.12\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eExperimental\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e19.00 (2.47)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eMedia Writing\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eControl\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e12.11 (1.84)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-3.532\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e.001 **\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.18\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eExperimental\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e14.06 (1.43)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eTotal Media Literacy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eControl\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e28.72 (2.91)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-3.927\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e.000 **\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.31\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eExperimental\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e33.06 (3.67)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eCohen\u0026rsquo;s d values across subscales\u0026mdash;1.12 (Reading), 1.18 (Writing), and 1.31 (Total Literacy)\u0026mdash;indicate large effect sizes, thereby reinforcing the transformative capacity of AI-driven pedagogy (Cohen, 1988). These findings collectively validate the research hypothesis that AI-supported media literacy education significantly enhances students\u0026rsquo; analytical, productive, and reflective media skills.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eQualitative Findings\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn the qualitative dimension of the study, data were gathered from semi-structured interviews, student artifacts (e.g., posters, drawings, slogans), and structured classroom observations conducted with 18 students in the experimental group. The data were analyzed thematically and categorized under four main themes: (1) Positive uses of media, (2) Awareness of negative aspects, (3) Development of critical media literacy skills, and (4) Student feedback on AI-supported instruction. The findings demonstrate that students underwent not only cognitive but also attitudinal, behavioral, and awareness-based transformations. Each theme is supported by student expressions, observed behaviors, and tangible outputs, offering a holistic insight into the pedagogical effects of integrating AI into media literacy education.\u003c/p\u003e\n\u003cp\u003eThe following tables present detailed thematic analyses, illustrating participant coverage, representative quotations, observed behaviors, and digital artifacts that collectively highlight the educational outcomes of AI-based media literacy training.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePositive Uses of Media\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe analysis of student interviews, digital artifacts, and classroom observations revealed four key subthemes under the overarching category of \u003cem\u003ePositive Uses of Media\u003c/em\u003e: Information Seeking, Communication, Entertainment, and Consumer Engagement. These subthemes collectively reflect how students utilize digital media not only for leisure but also as a multifaceted learning and communication environment.\u003c/p\u003e\n\u003cp\u003eDetailed student responses, representative quotes, and observed behavioral outcomes supporting these findings are presented in Table 6.\u003c/p\u003e\n\u003cp\u003eTable 6. \u003cem\u003eThematic analysis of positive uses of media with observational and digital evidence\u003c/em\u003e\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eSubtheme\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eParticipants (n)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eInterview Data\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eObservational Data\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eDigital Product / Indicator\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eEducational Contribution / CML Domain\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eInformation Seeking\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eS1\u0026ndash;S3, S4, S6, S8\u0026ndash;S11, S13, S14, S16\u0026ndash;S18 (n=14)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026ldquo;I look it up when I don\u0026rsquo;t know something.\u0026rdquo; (S8); \u0026ldquo;I find my sources there.\u0026rdquo; (S2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eIndependent searching, fact-checking without teacher intervention\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eS17\u0026rsquo;s \u0026ldquo;curious researcher\u0026rdquo; poster; S10\u0026rsquo;s info-access drawing\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eFact-checking, inquiry skills, source literacy\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eCommunication\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eS1\u0026ndash;S4, S6\u0026ndash;S11, S13\u0026ndash;S15, S17\u0026ndash;S18 (n=15)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026ldquo;I message my friends.\u0026rdquo; (S9); \u0026ldquo;I learn from my teacher.\u0026rdquo; (S1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eMedia-mediated group interactions, guided online engagement\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eS12\u0026rsquo;s digital drawing; S15\u0026rsquo;s media screen design\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eDigital communication ethics, safe sharing practices\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eEntertainment\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eS1\u0026ndash;S4, S5\u0026ndash;S7, S8\u0026ndash;S14, S16\u0026ndash;S18 (n=16)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026ldquo;I play games and watch funny videos.\u0026rdquo; (S5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eTime management behaviors, content filtering\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eS11\u0026rsquo;s gaming child drawing; S16\u0026rsquo;s humor cartoon\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eMedia awareness, digital self-regulation, edutainment balance\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eConsumer Engagement\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eS3\u0026ndash;S7, S8\u0026ndash;S14, S16\u0026ndash;S17 (n=14)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026ldquo;I browse clothes online.\u0026rdquo; (S4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eFiltering advertisements, preference for research over impulse buys\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eS13\u0026rsquo;s shopping-themed artwork; S7\u0026rsquo;s \u0026ldquo;ads\u0026rdquo; visual product\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eConsumer literacy, ad critique, economic awareness of messages\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eThe \u0026quot;Information Seeking\u0026quot; theme reveals students\u0026rsquo; increased independence and critical thinking in accessing online content, indicating the early development of digital research skills. The \u0026quot;Communication\u0026quot; theme underscores the role of digital tools not only for social interaction but also for educational engagement, with a focus on ethical and safe communication practices.\u003c/p\u003e\n\u003cp\u003eThe \u0026quot;Entertainment\u0026quot; theme allowed students to express themselves emotionally and creatively while balancing fun and learning through media. Lastly, the \u0026quot;Consumer Engagement\u0026quot; theme highlighted students\u0026rsquo; growing discernment in response to digital advertisements and their inclination toward critical media consumption.\u003c/p\u003e\n\u003cp\u003eCollectively, the findings position students as selective, thoughtful, and responsible media users. The integration of AI tools played a pivotal role in nurturing these critical competencies.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eNegative Aspects of Media\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWithin the broader theme of \u003cem\u003eNegative Aspects of Media\u003c/em\u003e, six prominent subthemes were identified: Scams \u0026amp; Misinformation, Health Impacts, Time Loss, Cyberbullying, Harmful Content, and Addiction. These themes emerged consistently across student interviews, classroom observations, and digital artifacts, revealing a nuanced awareness of the risks associated with digital media use among elementary school students.\u003c/p\u003e\n\u003cp\u003eDetailed student responses, representative quotes, and observed behavioral outcomes supporting these findings are presented in Table 7.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eTable 7. Thematic analysis of negative aspects of media and their pedagogical ımplications\u003c/em\u003e\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eSubtheme\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eParticipants (n)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eInterview Excerpt\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eObservational Insight\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eDigital Product / Indicator\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eCML Contribution\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eScams \u0026amp; Misinformation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eS1\u0026ndash;S4, S5\u0026ndash;S16, S18 (n=15)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026ldquo;Sometimes they show things that aren\u0026apos;t real.\u0026rdquo; (S3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eCautious behavior toward ads and misleading content\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eS16\u0026rsquo;s fake ad awareness poster\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eCritical evaluation of sources, credibility awareness\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eHealth Impacts\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eS2\u0026ndash;S8, S10\u0026ndash;S17 (n=15)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026ldquo;My eyes hurt, and my head aches after a while.\u0026rdquo; (S6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eConcentration loss after screen exposure\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eS13\u0026rsquo;s eye-health drawing\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eDigital wellness, somatic awareness\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eTime Loss\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eS1, S2, S6\u0026ndash;S8, S11, S12, S14\u0026ndash;S16, S18 (n=11)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026ldquo;I lose track of time watching videos and forget homework.\u0026rdquo; (S2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eDifficulty disconnecting from games, delayed class transitions\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eS15\u0026rsquo;s \u0026ldquo;time drain\u0026rdquo; artwork\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eTime management, digital self-regulation\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eCyberbullying\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eS1\u0026ndash;S3, S5, S7, S10, S12, S13, S15, S17\u0026ndash;S18 (n=11)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026ldquo;Someone sent me mean messages, I didn\u0026rsquo;t know them.\u0026rdquo; (S13)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eHesitant online behavior, passive participation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eS18\u0026rsquo;s \u0026ldquo;hurtful comments\u0026rdquo; illustration\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eEmpathy, digital safety, rights-conscious citizenship\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eHarmful Content\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eS2\u0026ndash;S5, S7\u0026ndash;S10, S12\u0026ndash;S16 (n=13)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026ldquo;I saw something scary; it appeared in my dreams.\u0026rdquo; (S9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eDistraction after disturbing content exposure\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eS14\u0026rsquo;s \u0026ldquo;beware of monsters\u0026rdquo; themed drawing\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eContent curation, age-appropriateness, digital hygiene\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eAddiction\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eS1\u0026ndash;S3, S5, S7, S9\u0026ndash;S11, S15\u0026ndash;S17 (n=11)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026ldquo;If I play too long, I can\u0026apos;t stop\u0026mdash;I always want to open it.\u0026rdquo; (S5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eDifficulty detaching from media, disinterest in class (S9, S15)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eS10\u0026rsquo;s \u0026ldquo;screen-glued kids\u0026rdquo; sketch\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eBehavioral self-regulation, inner awareness, digital restraint\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eThe \u0026ldquo;Scams \u0026amp; Misinformation\u0026rdquo; theme reflects students\u0026rsquo; ability to question the authenticity of digital content, demonstrating a critical awareness of source reliability. The \u0026ldquo;Health Impacts\u0026rdquo; and \u0026ldquo;Time Loss\u0026rdquo; themes highlight students\u0026rsquo; growing recognition of how digital media affects their physical well-being and academic focus, emphasizing the need for time and attention management skills.\u003c/p\u003e\n\u003cp\u003eThemes such as \u0026ldquo;Cyberbullying\u0026rdquo; and \u0026ldquo;Harmful Content\u0026rdquo; reveal the emotional consequences of unsafe digital environments and the urgency for protective digital literacy. Finally, the \u0026ldquo;Addiction\u0026rdquo; theme illustrates students\u0026rsquo; evolving ability to recognize their own overuse and actively attempt to set boundaries, indicating emerging digital self-discipline.\u003c/p\u003e\n\u003cp\u003eOverall, these findings show that critical media literacy is not only about analyzing content but also about cultivating resilience, safety, and agency in the digital realm. AI-supported education has proven effective in facilitating these multidimensional competencies.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eContributions to Critical Media Literacy\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThematic analysis of qualitative data revealed six key domains where students demonstrated growth aligned with the core principles of critical media literacy. These categories\u0026mdash;Digital Self-Protection \u0026amp; Data Privacy, Purposeful and Responsible Media Use, Safe Communication \u0026amp; Boundary Awareness, Critical Evaluation \u0026amp; Misinformation Awareness, Online Risk Awareness, and Media Ethics \u0026amp; Digital Citizenship\u0026mdash;reflect a comprehensive transformation in students\u0026apos; cognitive, behavioral, and ethical engagement with media.\u003c/p\u003e\n\u003cp\u003eDetailed student responses, representative quotes, and observed behavioral outcomes supporting these findings are presented in Table 8.\u003c/p\u003e\n\u003cp\u003eTable 8. \u003cem\u003eThematic categories based on student ınterviews, participants, and contributions to critical media literacy\u003c/em\u003e\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eThematic Category\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eParticipants (n)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eInterview Excerpt\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eObservational Data\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eDigital Product / Tangible Evidence\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eContribution to Critical Media Literacy\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eDigital Self-Protection \u0026amp; Data Privacy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eS2\u0026ndash;S6, S10\u0026ndash;S15, S17 (n=12)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026ldquo;We shouldn\u0026rsquo;t share personal information.\u0026rdquo; (S5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eHesitation on sharing screens, requests for teacher support (S14)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eS13\u0026rsquo;s \u0026ldquo;don\u0026rsquo;t share with everyone\u0026rdquo; poster\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eIdentity protection, data privacy awareness, cybersecurity\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003ePurposeful and Responsible Media Use\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eS1, S3\u0026ndash;S8, S10, S14\u0026ndash;S16, S18 (n=13)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026ldquo;I use it for fun but try not to stay too long.\u0026rdquo; (S14)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eContent filtering and time management behaviors (S16)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eS16\u0026rsquo;s \u0026ldquo;turn it off if you don\u0026rsquo;t need it\u0026rdquo; poster\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eSelf-regulation, screen time control, critical content selection\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eSafe Communication \u0026amp; Boundary Awareness\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eS4\u0026ndash;S8, S10\u0026ndash;S15, S17 (n=12)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026ldquo;I don\u0026rsquo;t open messages from strangers.\u0026rdquo; (S10)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eRestrictive social behaviors online, rejecting unknown contacts\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eS15\u0026rsquo;s \u0026ldquo;don\u0026rsquo;t talk to strangers!\u0026rdquo; drawing\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eBoundary-setting, safe digital interactions, online discretion\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eCritical Evaluation \u0026amp; Misinformation Awareness\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eS2, S3, S6\u0026ndash;S9, S13\u0026ndash;S18 (n=12)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026ldquo;I don\u0026rsquo;t believe everything\u0026mdash;I verify first.\u0026rdquo; (S6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eComparing sources, questioning reliability (S9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eS17\u0026rsquo;s \u0026ldquo;what if it\u0026rsquo;s fake?\u0026rdquo; poster\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eSource verification, content skepticism, misinformation literacy\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eOnline Risk Awareness\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eS1, S3\u0026ndash;S5, S8, S10\u0026ndash;S15, S17 (n=12)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026ldquo;Some videos were scary, I closed them immediately.\u0026rdquo; (S4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eEmotional discomfort (grimacing, averting eyes), quick exit (S10)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eS11\u0026rsquo;s \u0026ldquo;if it\u0026rsquo;s bad, leave\u0026rdquo; cartoon\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eRisk detection, emotional boundaries, digital self-defense\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eMedia Ethics \u0026amp; Digital Citizenship\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eS2\u0026ndash;S6, S7\u0026ndash;S8, S10\u0026ndash;S12, S15\u0026ndash;S16, S18 (n=13)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026ldquo;We shouldn\u0026rsquo;t say bad words online.\u0026rdquo; (S7); \u0026ldquo;I didn\u0026rsquo;t copy-paste.\u0026rdquo; (S10)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eCiting sources, respecting others\u0026rsquo; work (S15)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eS18\u0026rsquo;s \u0026ldquo;cite and show respect\u0026rdquo; poster\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eEthical digital behavior, attribution, online responsibility\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eThe themes \u0026quot;Digital Self-Protection\u0026quot; and \u0026quot;Purposeful Media Use\u0026quot; illustrate students\u0026rsquo; growing competencies in safeguarding their personal data and managing screen time consciously. The themes \u0026ldquo;Safe Communication\u0026rdquo; and \u0026ldquo;Online Risk Awareness\u0026rdquo; reflect students\u0026rsquo; proactive attitudes toward maintaining boundaries and defending against harmful content.\u003c/p\u003e\n\u003cp\u003e\u0026ldquo;Critical Evaluation\u0026rdquo; reveals their capacity to engage in higher-order thinking by comparing information sources and questioning credibility. Lastly, \u0026ldquo;Media Ethics and Digital Citizenship\u0026rdquo; shows that students internalized principles like respect, originality, and responsibility in digital contexts. Collectively, these findings position students not just as content consumers but as ethically aware and critically engaged digital citizens, offering a comprehensive view of the transformative potential of AI-supported media literacy education.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStudent Feedback on AI-Supported Media Literacy Education\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eStudent reflections gathered through semi-structured interviews, observational data, and artifacts revealed a diverse range of learning outcomes that underscore both the cognitive and affective dimensions of AI-supported media literacy education.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eDetailed student responses, representative quotes, and observed behavioral outcomes supporting these findings are presented in Table 9.\u003c/p\u003e\n\u003cp\u003eTable 9. \u003cem\u003eStudent feedback on AI-supported media literacy education, themes, and contributions to critical media literacy\u003c/em\u003e\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eStudent\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eDirect Quote\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eInterpreted Theme\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eObserved Behavioral Outcome\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eContribution to Critical Media Literacy\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eS1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026ldquo;This activity helped me... media has both good and bad sides.\u0026rdquo;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eAwareness of media\u0026rsquo;s dual nature\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eDistinguishing positive and negative content\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eHolistic media interpretation, multidimensional analysis\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eS4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026ldquo;The activities were fun and educational.\u0026rdquo;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eJoyful learning and risk awareness\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eAlertness to fake content\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eRecognizing threats, emotional engagement enhances retention\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eS8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026ldquo;I don\u0026rsquo;t spend as much time now. I watch out for ads.\u0026rdquo;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eTime control and ad literacy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eTime-limiting behaviors, selective attention\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eDigital self-regulation, resistance to media manipulation\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eS11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026ldquo;I learned not to share bad info or personal data.\u0026rdquo;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eDigital privacy and safety\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eAwareness of personal data protection\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eCyber hygiene, data privacy awareness\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eS13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026ldquo;I don\u0026rsquo;t believe everything I see online anymore.\u0026rdquo;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eFact-checking behavior\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eCross-verifying sources, content skepticism\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eCritical thinking, misinformation resistance\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eS14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026ldquo;It was nice... thank you.\u0026rdquo;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eGeneral satisfaction and emotional bond\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eVoluntary participation, positive attitude\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eOpenness to digital learning, pedagogical engagement\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eS16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026ldquo;I don\u0026rsquo;t talk to strangers online.\u0026rdquo;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eSafe communication and boundary setting\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eAvoiding unknown contacts online\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eDigital safety, personal boundary awareness\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eS18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026ldquo;I learned the difference between old and new media.\u0026rdquo;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eMedia evolution and citizenship\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eConscious comparison of media types\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eHistorical-digital awareness within media literacy\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eS9\u0026ndash;S18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e(Supported by journals, observations, and artifacts)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eMultiple outcomes and retention\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eAwareness, ethics, content curation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eBehavioral transformation, sustainable media competence\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eThis table integrates students\u0026rsquo; direct reflections with observed behavioral outcomes to offer a meaningful map of educational transformation. These statements reflect not only emotional engagement but also cognitive and behavioral development.\u003c/p\u003e\n\u003cp\u003eStudents exhibited enhanced time management, ad skepticism, data privacy, and source verification. Remarks like \u0026ldquo;I don\u0026rsquo;t believe everything online\u0026rdquo; indicate growth in critical thinking. Emotional investment also increased the program\u0026rsquo;s sustainability and impact. Themes such as avoiding strangers and recognizing media evolution underscore emerging digital citizenship. Overall, these findings present AI-supported media literacy education as a transformative pedagogical model that fosters digital competence and social responsibility.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe findings of this study underscore the transformative potential of AI-supported media literacy education in fostering critical, ethical, and participatory digital competencies among elementary school students. The statistically significant improvements observed in both media reading and writing skills reveal that AI tools\u0026mdash;when strategically integrated\u0026mdash;can serve not merely as instructional aids but as catalysts for deep cognitive and ethical engagement. These findings align with earlier research emphasizing the pedagogical benefits of intelligent learning environments that provide timely feedback, adapt to learner needs, and encourage metacognitive reflection (Aleven \u0026amp; Koedinger, \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2002\u003c/span\u003e; Weber et al., 2024; Lee et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2025\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eFrom a theoretical standpoint, the integration of Hobbs\u0026rsquo;s (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2011\u003c/span\u003e) five-domain media literacy framework with Jenkins\u0026rsquo;s (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2009\u003c/span\u003e) participatory culture model has proven pedagogically coherent and practically viable. Students demonstrated measurable gains across all five dimensions\u0026mdash;access, analysis, evaluation, creation, and reflection\u0026mdash;suggesting that the AI-enhanced curriculum facilitated a comprehensive and active engagement with media. For example, the use of ChatGPT and Perplexity AI promoted deeper source interrogation and balanced reasoning, while Canva AI and Grammarly supported students in generating their own ethical and creative media outputs.\u003c/p\u003e\u003cp\u003eThese shifts represent more than just skill acquisition; they indicate a broader epistemic transformation in how students perceive, interact with, and contribute to media ecosystems. This development supports the argument that media literacy should not be compartmentalized as a discrete subject but embedded across disciplines through interdisciplinary, values-driven pedagogy.\u003c/p\u003e\u003cp\u003eFurthermore, the behavioral and affective transformations observed\u0026mdash;such as critical skepticism toward misinformation, increased data privacy consciousness, and thoughtful screen-time regulation\u0026mdash;point to the internalization of ethical digital practices. These outcomes align with contemporary discourses on digital citizenship (Mossberger et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2007\u003c/span\u003e) and validate the argument that early exposure to structured, reflective media education fosters responsible digital behavior.\u003c/p\u003e\u003cp\u003eNotably, the curricular alignment with the Turkish and Social Studies programs enhanced the intervention\u0026rsquo;s relevance and scalability. By embedding media literacy goals into the existing educational framework rather than treating them as supplementary or extracurricular, the study offers a sustainable model that classroom teachers can adopt without extensive restructuring. This curricular coherence also supports broader efforts to institutionalize media literacy within national education systems\u0026mdash;a move increasingly advocated by international organizations such as UNESCO and the European Commission.\u003c/p\u003e\u003cp\u003eDespite these strengths, several limitations must be acknowledged. The study's sample size and geographic scope, though sufficient for preliminary insights, may limit the generalizability of the findings. Additionally, while AI tools proved effective in this context, ongoing advancements in algorithmic design necessitate continual evaluation to mitigate risks related to bias, privacy, and misinformation. Future studies might explore longitudinal impacts, cross-cultural applications, and the role of teacher training in maximizing the pedagogical efficacy of AI-enhanced media literacy education.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis study contributes to the growing body of scholarship advocating for a critical, ethical, and participatory approach to media literacy education\u0026mdash;one that is increasingly mediated by artificial intelligence technologies. The integration of AI into the curriculum not only amplified students\u0026rsquo; cognitive and creative capacities but also fostered meaningful behavioral and affective transformations that align with the principles of responsible digital citizenship.\u003c/p\u003e\u003cp\u003eBy embedding AI-supported media literacy instruction into national education standards, this research demonstrated a feasible and scalable pathway for educational innovation. The intervention proved effective in nurturing students' ability to access, analyze, evaluate, and produce media content with discernment and ethical awareness. These competencies are indispensable in an era characterized by misinformation, algorithmic manipulation, and digital saturation.\u003c/p\u003e\u003cp\u003eUltimately, the findings suggest that AI-enhanced media literacy education is not simply a pedagogical trend but an educational necessity. As digital environments continue to evolve, so too must our strategies for equipping young learners to navigate them with critical acumen and ethical integrity. This study offers a replicable framework that may inform policy, curriculum development, and future research\u0026mdash;paving the way for an education system attuned to the complexities of 21st-century media landscapes.\u003c/p\u003e\u003cdiv id=\"Sec33\" class=\"Section2\"\u003e\u003ch2\u003eRecommendations\u003c/h2\u003e\u003cp\u003eIn light of the findings, several pedagogical and policy-level strategies are proposed to enhance the integration and impact of AI-supported critical media literacy education:\u003c/p\u003e\u003cp\u003e\u003col\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003e\u003cem\u003eCurricular Integration Across Disciplines\u003c/em\u003e: Media literacy should be holistically embedded into existing curricula starting from early education, not as a stand-alone subject but as a cross-disciplinary theme encompassing information verification, ethical reasoning, and digital rights awareness (UNESCO, \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003e\u003cem\u003ePedagogical Utilization of AI Tools\u003c/em\u003e: AI platforms such as ChatGPT, Canva AI, and Grammarly should be positioned not merely as instructional technologies, but as cognitive scaffolds that foster critical inquiry, creativity, and student agency (Westbrook et al., \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003e\u003cem\u003eProfessional Development for Educators\u003c/em\u003e: Teacher training programs must be restructured to include modules on digital pedagogy and AI integration, equipping educators to guide students in becoming critical and ethical participants in digital media environments (OECD, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2018\u003c/span\u003e).\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003e\u003cem\u003eScenario-Based Instructional Design\u003c/em\u003e: Instructional content should be grounded in real-life, age-appropriate scenarios that address current digital challenges such as misinformation, cyberbullying, algorithmic bias, and media addiction, ensuring relevance and engagement.\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003e\u003cem\u003eAlignment with Global Competency Standards\u003c/em\u003e: Future implementations should align with internationally recognized frameworks such as the ISTE Student Standards, promoting a unified vision of digital literacy, responsible media participation, and ethical content production.\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003c/ol\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec34\" class=\"Section2\"\u003e\u003ch2\u003eLimitations\u003c/h2\u003e\u003cp\u003eThis study is not without its limitations. Firstly, the sample size was restricted to 36 students from a single public elementary school, limiting the generalizability of the findings. Secondly, reliance on self-reported data may have introduced response bias, particularly in qualitative reflections. Thirdly, the relatively short duration of the intervention (18 hours) may constrain the observation of long-term cognitive or behavioral transformations. Lastly, while AI tools enhanced instructional delivery, disparities in digital infrastructure and access could present challenges for scalability in under-resourced settings.\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eDirections for Future Research\u003c/h3\u003e\n\u003cp\u003eTo expand upon the current findings and address the noted limitations, future research is encouraged to pursue the following avenues:\u003c/p\u003e\u003cp\u003e\u003col\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003e\u003cem\u003eLongitudinal Impact Studies\u003c/em\u003e: Examine the sustained effects of AI-supported media literacy interventions on students\u0026rsquo; digital behaviors, critical thinking, and civic participation over extended periods.\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003e\u003cem\u003eCross-Contextual Scalability\u003c/em\u003e: Explore the adaptability and effectiveness of the model across diverse educational contexts, particularly in underserved or socioeconomically disadvantaged regions.\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003e\u003cem\u003eComparative Tool-Specific Analyses\u003c/em\u003e: Conduct controlled studies to assess the differential pedagogical impact of specific AI tools (e.g., generative vs. evaluative platforms) on discrete domains of media literacy such as source evaluation, ethical production, and reflective thinking.\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003e\u003cem\u003eInternational Comparative Research\u003c/em\u003e: Engage in multi-site, cross-national studies to assess cultural responsiveness, policy integration, and systemic barriers, thereby contributing to a globally adaptable pedagogical model.\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003c/ol\u003e\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthical Considerations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was conducted with the approval of the Ethics Committee of the relevant university (Approval No: 2024-30, Date: March 8, 2024). Written consent was obtained from the parents of participating students, and all procedures were carried out based on voluntary participation and informed consent. Throughout the data collection process, full adherence to the principles of confidentiality and anonymity was ensured; all identifying information was kept confidential, and audio recordings were securely deleted after analysis.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStatement on AI Assistance\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eArtificial intelligence tools (e.g., ChatGPT by OpenAI) were employed solely to support language editing, improve clarity, and ensure grammatical accuracy in the preparation of this manuscript.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors express their gratitude to the participating students, school administrators, and teachers for their collaboration and support during the implementation process.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data and code used in this study are available from the corresponding author upon request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of Interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eAuthor Contributions StatementR.D. conceptualized the study, designed the AI-based media literacy program, and conducted the quantitative analysis. C.A. managed data collection, performed the qualitative analysis, and contributed to the interpretation of findings. R.D. and C.A. co-wrote the manuscript. Both authors reviewed and approved the final version of the manuscript.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAleven, V., \u0026amp; Koedinger, K. R. (2002). An effective metacognitive strategy: Learning by doing and explaining with a computer-based Cognitive Tutor. \u003cem\u003eCognitive Science, 26\u003c/em\u003e(2), 147\u0026ndash;179.\u003c/li\u003e\n\u003cli\u003eAngrosino, M. (2007). \u003cem\u003eDoing ethnographic and observational research\u003c/em\u003e. SAGE Publications.\u003c/li\u003e\n\u003cli\u003eArslan, H. (2014). \u003cem\u003eEleştirel Medya Okuryazarlığı Kapsamında \u0026Ccedil;ocuk Odaklı Haber ve Programlar \u0026Uuml;zerine Bir Değerlendirme\u003c/em\u003e. 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Striatal dopamine synthesis capacity reflects smartphone social activity. \u003cem\u003eiScience\u003c/em\u003e, 24(5), 1\u0026ndash;8. https://doi.org/10.1016/j.isci.2021.102497\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"international-journal-of-artificial-intelligence-in-education","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"aied","sideBox":"Learn more about [International Journal of Artificial Intelligence in Education](http://link.springer.com/journal/40593)","snPcode":"40593","submissionUrl":"https://submission.nature.com/new-submission/40593/3","title":"International Journal of Artificial Intelligence in Education","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"AI-supported education, Critical media literacy, Digital citizenship, Elementary education, Mixed-methods research","lastPublishedDoi":"10.21203/rs.3.rs-6786882/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6786882/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis study investigates the pedagogical impact of an AI-enhanced critical media literacy program implemented with fourth-grade students in a public primary school. Using an explanatory sequential mixed-methods design, the research combined quantitative data from the Critical Media Literacy Scale for Primary Students with qualitative insights from interviews, student artifacts, and classroom observations. The 18-hour intervention followed the 5E instructional model and integrated generative AI tools such as ChatGPT and Grammarly. Aligned with the national Turkish and Social Studies curricula, the program ensured curricular coherence and contextual relevance. Quantitative findings revealed significant gains in students\u0026rsquo; media literacy skills, particularly in reading and writing. Qualitative data highlighted developments in digital safety, source verification, online ethics, and media critique. Students also showed enhanced awareness of ethical digital behavior and reflective media use. These results underscore the potential of AI-supported instruction to foster critical thinking, digital citizenship, and media literacy in early education settings.\u003c/p\u003e","manuscriptTitle":"Transforming Clicks into Critical Thinking: An AI-Based Media Literacy Program for Children","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-09-23 02:43:38","doi":"10.21203/rs.3.rs-6786882/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-08-15T23:17:43+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-06-05T13:58:20+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-06-05T13:57:16+00:00","index":"","fulltext":""},{"type":"submitted","content":"International Journal of Artificial Intelligence in Education","date":"2025-05-30T18:04:17+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"international-journal-of-artificial-intelligence-in-education","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"aied","sideBox":"Learn more about [International Journal of Artificial Intelligence in Education](http://link.springer.com/journal/40593)","snPcode":"40593","submissionUrl":"https://submission.nature.com/new-submission/40593/3","title":"International Journal of Artificial Intelligence in Education","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"1ee6a341-6380-4031-992f-894bd4532f18","owner":[],"postedDate":"September 23rd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-02-10T23:09:00+00:00","versionOfRecord":[],"versionCreatedAt":"2025-09-23 02:43:38","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6786882","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6786882","identity":"rs-6786882","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}