فصلنامه بازیابی دانش و نظام‌های معنایی

طراحی مدل توسعه سواد علم داده مبتنی بر هوش مصنوعی در دانش‌آموزان - رویکرد فراترکیب نظریه‌ساز

نوع مقاله : مقاله پژوهشی

نویسندگان

فرزانه دیمه کار حقیقی
علیرضا حیدری
پریسا یزدان دوست
بهاره خیاطی

گروه آموزشی مدیریت و برنامه‌ریزی آموزشی، دانشگاه شیراز، شیراز، ایران.

https://doi.org/10.22054/jks.2026.89092.1747
چکیده
هدف کلی از این پژوهش، ارائه مدل توسعه سواد علم داده با استفاده از هوش مصنوعی در دانش‌آموزان مبتنی بر پیشایندها، فرایندها و پیامدها با رویکرد سنتزپژوهی و روش فراترکیب نظریه‏ساز بود. براین‌اساس، واژه‌های کلیدی مانند «علم داده»، «سواد علم داده»، «سواد داده»، «هوش مصنوعی»، «هوش ماشینی»، «پشتیبانی هوشمند» و «واقعیت مجازی» در مطالعات مرتبط با موضوع در پایگاه‏های داده لاتین مانند Wiley، Science Direct، Taylor & Francis، Pro-Quest، Scopus، Emerald و فارسی مگیران، نورمگز و SID، شناسایی و پس از ارزیابی و غربالگری مقالات و پایان‏نامه‏ها، نهایتاً تعداد 77 مقاله برای استفاده در پژوهش انتخاب و مورد تحلیل قرار گرفتند. شیوه تحلیل داده‏ها بر مبنای نظریه داده‏بنیاد و شامل کدگذاری باز، محوری و انتخابی بود. یافته‌های پژوهش نشان داد که پیشایندهای توسعه سواد علم داده مبتنی بر هوش مصنوعی در دانش‌آموزان در سه کد محوری چارچوب‌های نظری و راهبردی برنامه درسی، شایستگی‌های معلم، و شایستگی‌های دانش‌آموزان و 59 کد باز دسته‌بندی می‌شوند. فرایند توسعه سواد علم داده مبتنی بر هوش مصنوعی در دانش‌آموزان نیز در هشت کد محوری از طریق 13 کد باز (ابزارها و پلتفرم‌های دیجیتال) پشتیبانی می‌گردند، همچنین پیامدهای توسعه سواد علم داده مبتنی بر هوش مصنوعی در دانش‌آموزان در سه کد محوری پیامدهای تحصیلی، فرامعرفتی و عاطفی، و اجتماعی (شهروندی و اخلاق) دسته‌بندی می‏گردند که در پانزده کد باز گسترش یافته‌اند. نتایج بیانگر آن است که توسعه سواد علم داده مبتنی بر هوش مصنوعی دانش‌آموزان را برای مشارکت آگاهانه و مسئولانه در جامعه داده‌محور توانمند به‌طوری‌که ضرورت طراحی برنامه‌های درسی داده‌محور و حمایت از توسعه حرفه‌ای معلمان برای تحقق سواد علم داده موردتأکید قرار می‏گیرد.

کلیدواژه‌ها

سواد علم داده
هوش مصنوعی
دانش آموزان
موضوعات

عنوان مقاله English

Developing a Model for AI-Driven Data Science Literacy in Students: A Meta-Synthesis Approach

نویسندگان English

Farzane Deimehkar Haghighi
alireza heidari
parisa yazdandoust
bahareh khayatti
Department of Education and Psychology, Shiraz University, Shiraz, Iran.
چکیده English

Introduction
The rapid convergence of data expansion and artificial intelligence (AI) is transforming scientific discovery and daily decision-making. However, focusing solely on technical specialization risks dividing society into a minority of tech creators and a passive majority of consumers. To ensure active, critical, and responsible participation, it is essential to distinguish between data science (a specialized technical field) and data literacy (a fundamental competency for all citizens).
With the rise of generative AI, data literacy serves as a prerequisite for evaluating AI tools, while AI itself acts as a medium to deepen data literacy (e.g., through interactive tools like GenAI Teachable Machine). Although schools are the primary institutional setting for fostering data literacy from an early age, a significant gap exists between strategic intent and classroom execution. This gap is driven by structural barriers, pedagogical challenges, practical issues, and emerging ethical concerns. Addressing these theoretical and practical challenges requires moving beyond fragmented approaches toward an integrated framework that balances learning with AI as a tool and learning about AI as content.
Research Questions
1. What are the antecedents of developing AI-driven data science literacy in students?
2. What are the processes involved in developing AI-driven data science literacy in students?
3. What are the outcomes associated with primary school students' data science literacy?
4. What is the relationship among the antecedents, processes, and outcomes of developing AI-driven data science literacy in students?
Literature Review
The body of literature regarding data literacy education spans four major streams, each highlighting key dimensions while leaving structural gaps in the overall framework. Research on student-centered approaches and pedagogy emphasizes early intervention using active learning frameworks, such as project-based learning and authentic datasets (Friedrich et al., 2024; Huang et al., 2023; Schreiter et al., 2024; Witte et al., 2024); however, these studies primarily address the "what" and "how" of instruction while neglecting the specific competencies required of educators. Regarding teacher competencies and professional development, studies grounded in frameworks like TPACK focus on teacher self-efficacy, pedagogical skills, and demographic influences (Cui & Zhang, 2022; Demirci et al., 2025; Gregorio et al., 2024; Hazzan & Mike, 2023; Msweli et al., 2023), yet a key limitation is the view of teachers as isolated actors that often ignores organizational culture, administrative support, and infrastructure. In terms of technological integration and AI tools, research explores GenAI tools and interactive dashboards for personalized learning (Maksimenkova et al., 2020; Nasharuddin et al., 2024), but a techno-centric bias often frames technology as a standalone solution without explicitly connecting tool utilization to meaningful pedagogical practices (Memarian & Doleck, 2024).
While existing literature provides valuable insights into individual components, it offers a fragmented perspective without a unified conceptual model to explain the dynamic interactions between these components, a gap this study addresses by synthesizing these elements into an integrated framework (Memarian & Doleck, 2024). Finally, existing evaluations of outcomes focus mainly on short-term cognitive and technical skills within higher education or STEM fields (Avila-Garzon & Bacca-Acosta, 2025; Qiao et al., 2024; Velaj et al., 2023), leaving broader, long-term outcomes such as data-informed citizenship, ethical agency, and systemic school impacts largely unexplored and highlighting the critical need for an integrated conceptual model.
Methodology
This qualitative study employed Finfgeld-Connett's (2018) seven-step meta-synthesis approach to develop an integrated model for student AI-driven data science literacy. Research objectives were first established to define the framework's antecedents, processes, and outcomes. A systematic literature search across major international and national databases (e.g., Scopus, Web of Science, ScienceDirect, Magiran, SID) yielded 167 initial records. Domain experts in curriculum studies and synthesis methodology validated the screening protocols. Applying strict criteria (peer-reviewed English studies published between 2015 and 2025 focusing on AI in data literacy instruction), 79 relevant articles were selected after screening titles, abstracts, and full texts. Data were extracted through iterative analysis, categorizing primary concepts into open, axial, and selective codes. Rigor was established using descriptive, interpretive, theoretical, and pragmatic validity criteria. Finally, qualitative content analysis synthesized the findings into a coherent conceptual framework.
Results
The meta-synthesis yielded a structured model mapping the antecedents, processes, and outcomes of AI-driven data science literacy in students. First, antecedents (59 open, 18 axial, and 3 selective codes) encompass curricular frameworks prioritizing early education (K–5), teacher tripartite competencies (data, technological, and pedagogical knowledge), and student prerequisites (foundational computational literacy, prompting skills, and critical curiosity). Second, instructional processes rely on specialized AI tools that scaffold learning and democratize data science: intelligent tutors (DaTu, ChatGPT) for self-regulated guidance, explainable AI (SHAP, LIME) for algorithmic transparency, automated machine learning (AutoML, TPOT) to lower technical syntax barriers, and infrastructure tools (MLflow, Docker) for lifecycle management. Third, outcomes (15 open codes) span academic gains (data cleaning, visualization, computational thinking), metacognitive/affective growth (knowledge transfer, data self-efficacy) , and social/civic dimensions (data-informed citizenship, privacy awareness, and ethical decision-making).
Discussion
This study synthesized multi-level empirical evidence to establish an integrated model for developing AI-driven data science literacy in school ecosystems. Navigating away from purely technical skill acquisition, the proposed architecture establishes that data literacy in the AI era requires a fundamental paradigm shift from passive tool usage to critical digital wisdom (Schüller, 2022; UNESCO, 2024).
At the macro-structural level, the findings align with Chiu (2025) and Gaertig-Daugs and Schmid (2025) by asserting that data literacy must be strategically prioritized starting from early primary education (K–5). Rather than overloading existing curricula, the model integrates data concepts cross-curricularly into STEM subjects using plug-and-play gamification and unplugged computational activities. Furthermore, the framework extends traditional TPACK models into five core dimensions (content, pedagogy, technology, data, and attitude), positioning teachers as clinical data leaders capable of navigating algorithmic modeling, interpreting learning analytics, and personalizing instruction (Mandinach & Jimerson, 2021; Msweli et al., 2023). At the student level, prerequisites transition from standard digital literacy to critical data agency, leveraging algorithmic questioning to mitigate uncritical technological consumption (Schönbrodt & Podworny, 2025).
Process-wise, generative AI and automated machine learning platforms (e.g., AutoML, ChatGPT, DaTu) act as cognitive scaffolds. By automating repetitive scripting and data cleaning tasks, these tools democratize data science, allowing students to shift focus toward higher-order problem formulation, pattern recognition, and evidence-based decision-making (Doleck et al., 2024; Wang et al., 2019). Simultaneously, integrating explainable AI tools (SHAP, LIME) ensures that algorithmic ethics, transparency, and bias awareness remain central to student inquiry (Wu, 2025).
Process-wise, generative AI and automated machine learning platforms (e.g., AutoML, ChatGPT, DaTu) act as cognitive scaffolds. By automating repetitive scripting and data cleaning tasks, these tools democratize data science, allowing students to shift focus toward high-order problem formulation, pattern recognition, and evidence-based decision-making (Doleck et al., 2024; Wang et al., 2019). Simultaneously, integrating explainable AI tools (SHAP, LIME) ensures that algorithmic ethics, transparency, and bias awareness remain central to student inquiry (Wu, 2025).
Conclusion
Ultimately, the interplay between antecedents and AI-driven processes yields holistic outcomes spanning academic competence, metacognitive self-efficacy, and data-informed citizenship. Practically, policy implementations should adopt age-tailored tools such as Google Teachable Machine in primary grades, local offline LLMs in lower secondary, and automated platforms like Julius AI in upper secondary, supported by revised teacher preparation curricula in pedagogical universities.

کلیدواژه‌ها English

Data Science Literacy
Artificial Intelligence
Students
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