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Cited by 1Artificial Intelligence in EducationArtificial Intelligence in Healthcare and EducationEthics and Social Impacts of AI

Teachers' Experiences with AI in K–12 Education: Opportunities and Challenges

Open access
Güneş Metin, Kevser, Çetin, Münevver·2026·Journal of Educational Technology Systems
DOI 10.1177/00472395261415889
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Abstract

This study explores K–12 teachers’ perceptions and sense-making processes regarding artificial intelligence (AI) in education. Using a qualitative phenomenological design, data were collected through semi-structured interviews with 16 teachers from public and private schools. Findings indicate that teachers associate AI with potential benefits such as instructional efficiency, personalized learning, formative assessment and professional development, while also expressing ethical and emotional concerns related to data privacy, algorithmic bias and unequal access. Teachers primarily interpret AI through its anticipated pedagogical implications rather than through sustained classroom implementation. The results emphasize the importance of teacher agency, ethical awareness and contextual factors in shaping how AI is understood and positioned within educational practice. By foregrounding teachers’ perspectives, this study addresses the gap between the predominantly higher education–focused AI literature and the underexplored K–12 context, highlighting the need for supportive policies, equitable infrastructure and professional development initiatives that promote human-centered and ethically grounded approaches to AI in education.

Integrity signals

Registry

Crossref · 10.1177/00472395261415889

Cites

33 of 34 references verified1 citation errors · 0 not checkable

Statistics

Nothing to recompute.

Claims the paper makes

31 of 78 cited sources checked · 7 supported · 0 flagged · accuracy not yet measured

  • Premisecitation

    Infrastructural disparities, including unequal access to devices and technical support, influence how AI is perceived across schools and regions.

    “Infrastructural disparities, including unequal access to devices and technical support, further influence how AI is perceived across schools and regions (Huang et al., 2021;Qiu et al., 2020).”
    Quote in the paperNot a substantial claim1 of 2 sources checked
    • cites Huang et al., 2021 source

      The Challenges section (p. 7) identifies a digital divide and a lack of basic technology and infrastructure as barriers to AI in education, while the Impact section (p. 7) describes regional inequalities in educational resources. The paper does not establish that unequal access to devices or technical support influences how AI is perceived across schools and regions. (LLM, conf=0.85)

      Experimental — accuracy not yet measured
    • cites Qiu et al., 2020 source
  • Premisecitation

    In K-12 contexts and particularly within Türkiye, there is a lack of empirical research examining how teachers conceptualize AI, articulate expectations and concerns, and position AI within pedagogical norms.

    “In K-12 contexts and particularly within Türkiye, there is a lack of empirical research examining how teachers conceptualize AI, articulate their expectations and concerns, and position AI within existing pedagogical norms (Chiu et al., 2023;Nigam et al., 2021).”
    Quote in the paperSubstantial claim1 of 2 sources checked
    • cites Chiu et al., 2023 source
    • cites Nigam et al., 2021 source

      The Conclusion and Discussions section identifies a research gap concerning trust in AI-based versus classroom proctoring, not teachers’ views of AI. Searches for K-12 teachers’ conceptualizations, pedagogical norms, and Türkiye found no passage addressing the claimed empirical-research gap; page numbers were not available in our copy. (LLM, conf=0.74)

      Experimental — accuracy not yet measured
  • Premisecitation

    Empirical studies focusing on K-12 contexts remain limited.

    “empirical studies focusing on K-12 contexts remain limited (Chiu et al., 2023;Nigam et al., 2021;Roll & Wylie, 2016).”
    Quote in the paperSubstantial claim2 of 3 sources checked
    • cites Chiu et al., 2023 source
    • cites Nigam et al., 2021 source

      The Search Criteria section (page unavailable) describes a review of 43 papers on online proctoring but does not classify them by educational level. Searches for K-12, primary and secondary schools, and limited empirical studies found no passage assessing whether K-12 research is scarce. (LLM, conf=0.75)

      Experimental — accuracy not yet measured
    • cites Roll & Wylie, 2016 source

      The Type and Focus of Paper section reports that empirical papers increased in the journal sample, but does not break them down by K–12 context. Embedded in Context says no K–12 schools were listed as author affiliations; that does not establish that empirical studies in K–12 settings were limited. Searches for K–12 empirical studies and primary or secondary school research found no passage making the claimed comparison. (LLM, conf=0.74)

      Experimental — accuracy not yet measured
  • Premisecitation

    Ethical challenges include concerns about bias, opacity, and data privacy, particularly in contexts where student data are increasingly used for analytics and monitoring.

    “Ethical challenges include concerns about bias, opacity, and data privacy, particularly in contexts where student data are increasingly used for analytics and monitoring (Ayyoub et al., 2025;Floridi, 2019).”
    Quote in the paperSubstantial claim2 of 2 sources checked
    • cites Ayyoub et al., 2025 source

      Section 2.2 identifies privacy and bias as ethical concerns in higher education and mentions predictive analytics and identifying at-risk students. It does not discuss opacity or say that student data are increasingly used for analytics and monitoring, so those parts of the attributed claim are unsupported. Page numbers are unavailable in our copy. (LLM, conf=0.86)

      Experimental — accuracy not yet measured
    • cites Floridi, 2019 source

      Section 5 (Ethics Dumping; page not provided) raises a personal-data protection concern, and Section 6 (Ethics Shirking; page not provided) discusses bias. Section 3 (Ethics Bluewashing; page not provided) calls for transparency about ethical practices, but does not specifically address algorithmic opacity. The copy does not discuss student data, learning analytics, or monitoring, so it does not support that context. (LLM, conf=0.84)

      Experimental — accuracy not yet measured
  • Premisecitation

    Educators often report difficulty distinguishing AI from other digital technologies, complicating how they interpret its role in pedagogy.

    “Demonstrating conceptual uncertainty, educators often report difficulty distinguishing AI from other digital technologies, complicating how they interpret its role in pedagogy (Roll & Wylie, 2016).”
    Quote in the paperNot a substantial claim1 of 1 sources checked
    • cites Roll & Wylie, 2016 source

      The Embedded in Context section calls for teachers to collaborate in AIED research and for study of how technology changes pedagogy. The Technology and Setting section distinguishes types of technology used in AIED research, but neither section reports that educators have difficulty distinguishing AI from other digital technologies or interpreting its pedagogical role. Searches for those perceptions found no supporting passage in our copy; page numbers were unavailable. (LLM, conf=0.76)

      Experimental — accuracy not yet measured
  • May combine two claims. 22
  • Classified as probably not a substantial claim. 21

  • May combine two claims.

    Premisecitation

    The integration of AI into everyday teaching practice remains complex, uneven, and highly context-dependent.

    “Despite these anticipated benefits, the integration of AI into everyday teaching practice remains complex, uneven and highly context-dependent (Lameras & Arnab, 2021).”
    Quote in the paperSubstantial claim1 of 1 sources checked
    • cites Lameras & Arnab, 2021 source

      The Introduction describes teachers’ decision to use AI as complex and ill-defined. Sections 3.3 and 3.6.2 also say that AI-supported teaching must account for the activity’s context and teachers’ skills. The paper does not establish that integration into everyday teaching practice is uneven. (LLM, conf=0.83)

      Experimental — accuracy not yet measured

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Premisecitation

    AI-related technologies are frequently associated in the literature with personalization, formative assessment, learning analytics, and instructional support.

    “AI-related technologies are frequently associated in the literature with personalization, formative assessment, learning analytics, and instructional support (Akgun & Greenhow, 2022;Hrastinski et al., 2019;Murphy, 2019).”
    Quote in the paperNot a substantial claim2 of 3 sources checked
    • cites Akgun & Greenhow, 2022 source
    • cites Hrastinski et al., 2019 source

      The “Individualization” and “Artificial Intelligence in Education” sections associate AI with individualized teaching and support for teachers, including assessment. Our copy does not establish the more specific association with formative assessment or learning analytics, so it supports only part of the attributed list. (LLM, conf=0.86)

      Experimental — accuracy not yet measured
    • cites Murphy, 2019 source

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Premisecitation

    Concerns related to algorithmic bias, data privacy, and transparency are well documented, particularly in relation to vulnerable populations such as children.

    “Concerns related to algorithmic bias, data privacy, and transparency are well documented, particularly in relation to vulnerable populations such as children (Floridi, 2019;Jobin et al., 2019).”
    Quote in the paperNot a substantial claim2 of 2 sources checked
    • cites Floridi, 2019 source

      Section 5 (Ethics Dumping) describes a risk to personal-data protection when algorithms are trained under weaker safeguards, and Section 3 (Ethics Bluewashing) calls for transparency about AI practices. The paper does not document algorithmic bias or identify children or other vulnerable populations as particularly affected; its mention of bias in Section 6 concerns moral double standards, not algorithms. Page numbers are unavailable in our copy. (LLM, conf=0.86)

      Experimental — accuracy not yet measured
    • cites Jobin et al., 2019 source

      The Justice, fairness, and equity, Privacy, and Transparency sections document concerns about bias, data protection, and transparency. The Solidarity section mentions respecting vulnerable persons and groups, but the paper does not identify children or establish that these three concerns are particularly documented in relation to them. (LLM, conf=0.87)

      Experimental — accuracy not yet measured

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Interpretationcitation

    The findings resonate strongly with constructivist and sociocultural approaches, which conceptualize learning as an interactive and mediated process shaped by tools, social interaction, and professional judgment.

    “From a theoretical perspective, the findings resonate strongly with constructivist and sociocultural approaches, which conceptualize learning as an interactive and mediated process shaped by tools, social interaction, and professional judgment (Lameras & Arnab, 2021;Vygotsky, 1978).”
    Quote in the paperNot a substantial claim1 of 2 sources checked
    • cites Lameras & Arnab, 2021 source
    • cites Vygotsky, 1978

      Couldn’t be matched to a paper in Crossref, so it can’t be checked.

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Findingcitation

    Teachers noted that limited resources in disadvantaged schools could exacerbate existing inequalities, reinforcing rather than mitigating the digital divide.

    “Teachers noted that limited resources in disadvantaged schools could exacerbate existing inequalities, reinforcing rather than mitigating the digital divide (Huang et al., 2021).”
    Quote in the paperNot a substantial claim1 of 1 sources checked
    • cites Huang et al., 2021 source

      The review’s “Challenges of AI in Education” section (p. 7) warns that new technologies could exacerbate educational divisions and identifies inadequate technology and infrastructure as obstacles. It does not report teachers’ observations or specifically locate this risk in disadvantaged schools; it discusses developing countries and an algorithmic divide. (LLM, conf=0.86)

      Experimental — accuracy not yet measured

  • Classified as probably not a substantial claim.

    Interpretationcitation

    This study offers an important contribution to a literature largely focused on higher education.

    “These interpretations position teachers as active agents in digital transformation rather than passive technology users, offering an important contribution to a literature largely focused on higher education (Ayyoub et al., 2025;Khlaif et al., 2024).”
    Quote in the paperSubstantial claim1 of 2 sources checked
    • cites Ayyoub et al., 2025 source
    • cites Khlaif et al., 2024 source

  • May combine two claims.

    Interpretationown data

    This finding challenges technologically deterministic narratives and reinforces research highlighting the centrality of professional judgment in AI-supported educational environments.

    “This finding challenges technologically deterministic narratives and reinforces research highlighting the centrality of professional judgment in AI-supported educational environments (Hrastinski et al., 2019;Pedro et al., 2019;Zimmerman, 2018;Cameron, 2001).”
    Quote in the paperSubstantial claim1 of 3 sources checked
    • cites Hrastinski et al., 2019 source

      The Introduction questions who drives AI adoption and warns that commercial interests could diminish teachers’ agency. The Artificial Intelligence in Education section says AI could complement teachers and that teachers should help co-design AI-supported classroom activities. These passages support a challenge to technology-driven narratives, but they do not explicitly establish the centrality of professional judgment in AI-supported education. (LLM, conf=0.78)

      Experimental — accuracy not yet measured
    • cites Pedro et al., 2019

      Couldn’t be matched to a paper in Crossref, so it can’t be checked.

    • cites Zimmerman, 2018

      Couldn’t be matched to a paper in Crossref, so it can’t be checked.

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Interpretationcitation

    This interpretation aligns with research framing AI as a scaffold that can support personalization, inquiry, and inclusive learning when guided by pedagogical intent.

    “This interpretation aligns with research framing AI as a scaffold that can support personalization, inquiry, and inclusive learning when guided by pedagogical intent (Hrastinski et al., 2019;Murphy, 2019).”
    Quote in the paperNot a substantial claim1 of 2 sources checked
    • cites Hrastinski et al., 2019 source

      The Individualization section (page unavailable) reports participants envisioning AI-supported individualized teaching and opportunities for students who receive insufficient information at school. The Artificial Intelligence in Education section (page unavailable) discusses teachers as codesigners and students seeking and discussing information collaboratively, which supports the pedagogical-guidance and inquiry elements. The paper does not establish the broader claim that AI supports inclusive learning; these are participants’ envisioned possibilities rather than demonstrated outcomes. (LLM, conf=0.78)

      Experimental — accuracy not yet measured
    • cites Murphy, 2019 source

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Interpretationreasoning

    These reflections echo international discussions on responsible and human-centered AI in education, particularly in contexts involving children and vulnerable learners.

    “These reflections echo international discussions on responsible and humancentered AI in education, particularly in contexts involving children and vulnerable learners (Akgun & Greenhow, 2022;Jobin et al., 2019).”
    Quote in the paperNot a substantial claim2 of 2 sources checked
    • cites Akgun & Greenhow, 2022 source
    • cites Jobin et al., 2019 source

      The Discussion describes international debate over ethical AI, and the Dignity and Beneficence sections discuss human dignity and well-being. The paper does not connect those discussions to AI in education involving children or vulnerable learners; its supplementary table lists vulnerable groups and education or training as separate categories. (LLM, conf=0.85)

      Experimental — accuracy not yet measured

  • May combine two claims.

    Findingown data

    Some participants reported no or very limited experience, while others described exploratory or experimental engagement with AI-related applications.

    “While some participants reported no or very limited experience, others described exploratory or experimental engagement with AI-related applications.”
    Quote in the paperNot a substantial claim

  • May combine two claims.

    Findingcitation

    Tools such as text-to-speech and audiobooks were viewed as supporting students with disabilities and enabling more equitable access to learning opportunities.

    “Tools such as text-to-speech and audiobooks were viewed as supporting students with disabilities and enabling more equitable access to learning opportunities.”
    Quote in the paperNot a substantial claim

  • May combine two claims.

    Findingown data

    AI was interpreted as supporting personalized instruction and administrative processes.

    “AI was also interpreted as supporting personalized instruction and administrative processes.”
    Quote in the paperSubstantial claim

  • May combine two claims.

    Findingown data

    Teachers associated AI with increased interaction and collaboration, emphasizing shared responsibility between teachers and learners.

    “In addition, teachers associated AI with increased interaction and collaboration, emphasizing shared responsibility between teachers and learners.”
    Quote in the paperNot a substantial claim

  • May combine two claims.

    Findingown data

    Participants emphasized improved access to educational materials and rapid information retrieval.

    “Participants emphasized improved access to educational materials and rapid information retrieval.”
    Quote in the paperNot a substantial claim

  • May combine two claims.

    Findingown data

    Teachers frequently associated AI with access to richer instructional materials, support for projects and research, and improved time management.

    “Teachers frequently associated AI with access to richer instructional materials, support for projects and research, and improved time management.”
    Quote in the paperSubstantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Findingown data

    P16 noted that AI keeps them technologically up to date, highlighting its perceived role in lifelong learning.

    “P16 noted that AI “keeps me technologically up to date,” highlighting its perceived role in lifelong learning.”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Findingown data

    Participants highlighted perceived benefits for student learning processes, particularly in relation to attention and imagination.

    “Participants also highlighted perceived benefits for student learning processes, particularly in relation to attention and imagination.”
    Quote in the paperNot a substantial claim

  • May combine two claims.

    Findingown data

    Teachers raised technical and ethical concerns, including inadequate infrastructure, digital addiction, algorithmic bias, and data privacy risks.

    “Teachers also raised technical and ethical concerns, including inadequate infrastructure, digital addiction, algorithmic bias, and data privacy risks.”
    • Also stated in: § 4.4. Perceived Disadvantages and Risks of Artificial Intelligence in K-12 Education · plain
      “Participants such as P14 referred to “digital addiction,” while others emphasized ethical dilemmas related to data security (Akgun & Greenhow, 2022;Jobin et al., 2019).”
    Quote in the paperSubstantial claim

  • May combine two claims.

    Findingown data

    Teachers frequently associated AI with enhanced support in lesson delivery and material preparation, emphasizing its potential to facilitate design of creative and visually enriched instructional resources.

    “Teachers frequently associated AI with enhanced support in lesson delivery and material preparation, emphasizing its potential to facilitate the design of creative and visually enriched instructional resources.”
    Quote in the paperSubstantial claim

  • May combine two claims.

    Findingown data

    Automated grading and content preparation were perceived as reducing administrative burden and allowing teachers to focus more on pedagogical work.

    “Automated grading and content preparation were perceived as reducing administrative burden and allowing teachers to focus more on pedagogical work.”
    Quote in the paperSubstantial claim

  • May combine two claims.

    Findingown data

    Participants emphasized the role of AI in formative assessment and feedback, associated with more responsive instructional practices.

    “Participants also emphasized the role of AI in formative assessment and feedback, which they associated with more responsive instructional practices.”
    Quote in the paperNot a substantial claim

  • May combine two claims.

    Findingcitation

    Participants raised concerns regarding data privacy, algorithmic bias, fairness, and inequality.

    “Participants raised concerns regarding data privacy, algorithmic bias, fairness, and inequality, situating AI within broader debates on responsible and human-centered technology use (Akgun & Greenhow, 2022;Jobin et al., 2019;Remian, 2019).”
    Quote in the paperSubstantial claim

  • May combine two claims.

    Findingreasoning

    Teachers' interpretations were shaped by uneven access to infrastructure, varying levels of institutional support, and differing opportunities for professional learning.

    “Teachers' interpretations were shaped by uneven access to infrastructure, varying levels of institutional support, and differing opportunities for professional learning.”
    Quote in the paperSubstantial claim

  • May combine two claims.

    Findingown data

    Teachers did not frame AI as an autonomous driver of change; instead, they positioned themselves as mediators who critically evaluate, adapt, and contextualize AI-related possibilities in light of learners' needs, institutional conditions, and ethical considerations.

    “Teachers did not frame AI as an autonomous driver of change; instead, they positioned themselves as mediators who critically evaluate, adapt, and contextualize AI-related possibilities in light of learners' needs, institutional conditions, and ethical considerations.”
    Quote in the paperSubstantial claim

  • May combine two claims.

    Findingown data

    Many participants reported limited or exploratory engagement with AI, underscoring the early stage of adoption in K-12 education.

    “Many participants reported limited or exploratory engagement with AI, underscoring the early stage of adoption in K-12 education.”
    Quote in the paperSubstantial claim

  • May combine two claims.

    Study design and conductcitation

    Maximum variation sampling was used to capture a diverse range of perspectives across school types, grade levels, subject areas, and professional experience, enhancing analytical richness.

    “Maximum variation sampling was used to capture a diverse range of perspectives across school types, grade levels, subject areas, and professional experience, thereby enhancing the analytical richness of the findings (Büyüköztürk et al., 2018).”
    Quote in the paperSubstantial claim0 of 1 sources checked
    • cites Büyüköztürk et al., 2018

      Couldn’t be matched to a paper in Crossref, so it can’t be checked.

  • Classified as probably not a substantial claim. May combine two claims.

    Study design and conductcitation

    All interviews were transcribed verbatim and analyzed using thematic analysis following Braun and Clarke (2006).

    “All interviews were transcribed verbatim and analyzed using thematic analysis, following the systematic procedures outlined by Braun and Clarke (2006).”
    Quote in the paperSubstantial claim0 of 1 sources checked
    • cites Braun and Clarke (2006) source

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Study design and conductreasoning

    The development of interview questions was informed by a critical review of literature on AI in higher education and K-12 contexts.

    “The development of interview questions was informed by a critical review of the literature on AI in both higher education and K-12 contexts.”
    Quote in the paperNot a substantial claim

  • Classified as probably not a substantial claim.

    Study design and conductcitation

    Data were collected through semi-structured interviews, allowing participants to articulate perspectives while providing flexibility to probe emerging themes.

    “Data were collected through semi-structured interviews, allowing participants to articulate their perspectives while providing sufficient flexibility to probe emerging themes and clarify meanings (Büyüköztürk et al., 2018).”
    Quote in the paperSubstantial claim0 of 1 sources checked
    • cites Büyüköztürk et al., 2018

      Couldn’t be matched to a paper in Crossref, so it can’t be checked.

  • Classified as probably not a substantial claim.

    Interpretationown data

    The findings provide insight into how K-12 teachers in Türkiye interpret and make sense of AI within educational contexts.

    “The findings of this study provide insight into how K-12 teachers in Türkiye interpret and make sense of artificial intelligence (AI) within educational contexts.”
    Quote in the paperSubstantial claim

  • Classified as probably not a substantial claim.

    Interpretationcitation

    Rather than documenting verified classroom implementation, the results illuminate teachers' interpretive frameworks, expectations, and concerns as they engage with AI as an emerging and unevenly adopted phenomenon.

    “Rather than documenting verified classroom implementation, the results illuminate teachers' interpretive frameworks, expectations, and concerns as they engage with AI as an emerging and unevenly adopted phenomenon (Cameron, 2001;Drew & Heritage, 1992).”
    Quote in the paperSubstantial claim0 of 2 sources checked
    • cites Cameron, 2001

      Couldn’t be matched to a paper in Crossref, so it can’t be checked.

    • cites Drew & Heritage, 1992

      Couldn’t be matched to a paper in Crossref, so it can’t be checked.

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Interpretationcitation

    Teachers' views align with inclusive education and equity-oriented frameworks.

    “These views align with inclusive education and equity-oriented frameworks (Miller et al., 2018).”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites Miller et al., 2018

      Couldn’t be matched to a paper in Crossref, so it can’t be checked.

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Interpretationcitation

    These concerns echo critiques within constructivist and socio-emotional learning literature that caution against excessive automation.

    “These concerns echo critiques within constructivist and socio-emotional learning literature that caution against excessive automation (Dillenbourg, 2016).”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites Dillenbourg, 2016 source

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Interpretationcitation

    This parallels international recommendations for responsible AI use.

    “Teachers also stressed the importance of understanding ethical and legal dimensions, including data privacy and algorithmic fairness, which parallels international recommendations for responsible AI use (Hamamra, Khlaif & Mayaleh, 2025).”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites Hamamra, Khlaif & Mayaleh, 2025

      Couldn’t be matched to a paper in Crossref, so it can’t be checked.

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Study design and conductnone given

    Member checking was conducted by sharing preliminary thematic interpretations with selected participants to verify accuracy and resonance.

    “Member checking was conducted by sharing preliminary thematic interpretations with selected participants to verify accuracy and resonance with their experiences.”
    Quote in the paperNot a substantial claim

  • May combine two claims.

    Interpretationown data

    Overall, the findings indicate that while teachers recognize AI's potential to support teaching and learning, they approach its integration with caution, emphasizing the need for human-centered, ethically informed, and pedagogically grounded implementation strategies.

    “Overall, the findings indicate that while teachers recognize AI's potential to support teaching and learning, they approach its integration with caution, emphasizing the need for human-centered, ethically informed, and pedagogically grounded implementation strategies.”
    Quote in the paperSubstantial claim

  • Probably not a substantial claim: may not pass S2 (checkable).

    Interpretationreasoning

    The educational significance of artificial intelligence does not reside solely in its technical sophistication, but in how it is interpreted, negotiated, and enacted by teachers.

    “In sum, the educational significance of artificial intelligence does not reside solely in its technical sophistication, but in how it is interpreted, negotiated, and enacted by teachers.”
    Quote in the paperNot a substantial claim

  • Classified as probably not a substantial claim.

    Interpretationreasoning

    A key contribution of this study lies in its emphasis on teacher agency in shaping the role of AI in education.

    “A key contribution of this study lies in its emphasis on teacher agency in shaping the role of AI in education.”
    Quote in the paperSubstantial claim

  • May combine two claims.

    Premisecitation

    By focusing on K-12 teachers in Türkiye, this study addresses a notable gap in the literature, which has largely prioritized higher education contexts and technology-centered analyses.

    “By focusing on K-12 teachers in Türkiye, this study addresses a notable gap in the literature, which has largely prioritized higher education contexts and technologycentered analyses (Hamamra, Khlaif & Mayaleh, 2025;Khlaif et al., 2024).”
    Quote in the paperSubstantial claim0 of 2 sources checked
    • cites Hamamra, Khlaif & Mayaleh, 2025

      Couldn’t be matched to a paper in Crossref, so it can’t be checked.

    • cites Khlaif et al., 2024 source

  • May combine two claims.

    Interpretationreasoning

    AI in education cannot be treated as a neutral innovation; rather, it is a socio-technical phenomenon embedded in moral, cultural, and institutional contexts.

    “These reflections underscore that AI in education cannot be treated as a neutral innovation; rather, it is a socio-technical phenomenon embedded in moral, cultural, and institutional contexts.”
    Quote in the paperNot a substantial claim

  • May combine two claims.

    Interpretationreasoning

    These contextual conditions influenced not only how AI was perceived, but also the degree of confidence with which teachers engaged its potential pedagogical implications.

    “These contextual conditions influenced not only how AI was perceived, but also the degree of confidence with which teachers engaged its potential pedagogical implications.”
    Quote in the paperSubstantial claim

  • Classified as probably not a substantial claim.

    Interpretationreasoning

    Rather than offering prescriptive implementation models, the study contributes a contextually grounded understanding of AI integration as a process of professional sense-making shaped by pedagogical values, ethical reflection, and institutional realities.

    “Rather than offering prescriptive implementation models, the study contributes a contextually grounded understanding of AI integration as a process of professional sense-making shaped by pedagogical values, ethical reflection, and institutional realities.”
    Quote in the paperSubstantial claim
42 of 42

  • The paper relies on this without support.

    K-12 teachers in Türkiye often work under centralized curricula and uneven infrastructural conditions.

    “In Türkiye, K-12 teachers often work under centralized curricula and uneven infrastructural conditions, which shape how AI is understood, anticipated, and evaluated.”

  • The paper relies on this without support.

    Teachers' reflections on AI frequently intersect with concerns about creativity, emotional connection, and preservation of human interaction.

    “Teachers' reflections on AI frequently intersect with these concerns, particularly regarding creativity, emotional connection, and the preservation of human interaction.”

  • The paper relies on this without support.

    In K-12 settings, ethical dimensions are closely tied to teachers' professional responsibilities.

    “In K-12 settings, these ethical dimensions are closely tied to teachers' professional responsibilities.”

  • The paper relies on this without support.

    Teachers often conceptualize AI as a potential means of supporting instructional planning, monitoring learning progress, and responding to diverse student needs.

    “Teachers often conceptualize AI as a potential means of supporting instructional planning, monitoring learning progress, and responding to diverse student needs.”

  • The paper relies on this without support.

    The perceived pedagogical value of AI is closely linked to how it aligns with existing educational values and classroom practices.

    “Teachers' reflections suggest that the perceived pedagogical value of AI is closely linked to how it aligns with existing educational values and classroom practices.”

  • The paper relies on this without support.

    Existing studies often emphasize technological potential or policy-level considerations, leaving teachers' perspectives under-theorized.

    “Existing studies often emphasize technological potential or policy-level considerations, leaving teachers' perspectives under-theorized.”
6 of 6

Cites

Flagged first, then by citations

Research works printed but not linked: 16

  1. Aiken R. M., Epstein R. G. (2000). Ethical guidelines for AI in education: Starting a conversation. International Journal of Artificial Intelligence in Education, 11(2), 163–176.
  2. Bulger M. (2016). Personalised learning: The conversations we’re not having. Data and Society, 22(1), 1–29. https://www.datasociety.net/pubs/ecl/PersonalizedLearning_primer_2016.pdf.
  3. Büyüköztürk Ş, Kılıç Çakmak E., Akgün ÖE, Karadeniz Ş, Demirel F. (2018). Scientific research methods. Pegem Academy.
  4. Cameron D. (2001). Working with spoken discourse. Sage.
  5. Creswell J. W., Poth C. N. (2016). Qualitative inquiry and research design: Choosing among five approaches. Sage publications.
  6. Drew P., Heritage J. (1992). Analysing talk at work: An introduction. In Drew P., Heritage J. (Eds.), Talk at work (pp. 3–65). Cambridge University Press.
  7. Elias M. J., Zins J. E., Weissberg R. P. (2000). Promoting social and emotional learning: Guidelines for educators. Adolescence, 35(137), 221.
  8. Mcmurtrie B. (2018). How artificial intelligence is changing teaching. The Chronicle of Higher Education, 1–7.
  9. Miller F. A., Katz J. H., Gans R. (2018). The OD imperative to add inclusion to the algorithms of artificial intelligence. OD Practitioner, 50(1), 8. https://static1.squarespace.com/static/56b3ef5a20c647ed98996880/t/62a76b4fcad9d5469ab68577/1655139152111/AI2%2BArticle%2BODP.pdf.
  10. Pedro F. Subosa M. Rivas A. Valverde P. (2019). Artificial intelligence in education: Challenges and opportunities for sustainable development. https://hdl.handle.net/20.500.12799/6533.
  11. Remian D. (2019). Augmenting education: Ethical considerations for incorporating artificial intelligence in education (Unpublished master’s thesis). University of Massachusetts. https://scholarworks.umb.edu/instruction_capstone/52.
  12. Russell S. J., Norvig P. (2016). Artificial intelligence: A modern approach. Pearson.
  13. Seldon A., Abidoye O. (2018). The fourth education revolution: Will artificial intelligence liberate or infantilise humanity. Legend Press Ltd.
  14. Tegmark M. (2018). Life 3.0: Being human in the age of artificial intelligence. Allen Lane.
  15. Vygotsky L. S. (1978). Mind in society: The development of higher psychological processes. Harvard University Press.
  16. Zimmerman M. (2018). Teaching AI: Exploring new frontiers for learning. International Society for Technology in Education.

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Authors
Güneş Metin, K., & Çetin, M.
Affiliations
  • Marmara University
  • Marmara University (TR)
ORCID
Venue
Journal of Educational Technology Systems
Publisher
SAGE Publications
Published
2026-02-09
Volume
54
Issue
4
Pages
711-732
ISSN
  • 0047-2395 (ISSN-L)
  • 1541-3810
DOI
10.1177/00472395261415889
OpenAlex
W7128443570
Semantic Scholar
ef72c8f0a97ab875774b3c2502a93f853d19ebbe
Cited by
0
References
33
FWCI
0.00
Type
journal article
Language
en
Open access
Licence
journals.sagepub.com/page/policies/text-and-data-mining-license (tdm)
Locations
Journal of Educational Technology Systems (publishedVersion)
Topics
  • Physical Sciences › Computer Science › Information Systems › Artificial Intelligence in Education
  • Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
  • Social Sciences › Safety Research › Ethics and Social Impacts of AI
Keywords
artificial intelligence in education, K-12 teachers, teacher perceptions, sensemaking, personalized learning, formative assessment, teacher professional development, data privacy, algorithmic bias, educational equity, teacher agency, moral consciousness, human-centered AI

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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

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Classified by jev-1.13-20260917 (gold-guideline-v0.2) on 2026-10-11.

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