Cited by 84Artificial Intelligence in EducationArtificial Intelligence in Healthcare and EducationEducation and Technology Integration

Teaching and learning with AI: a qualitative study on K-12 teachers' use and engagement with artificial intelligence

Open access
Tripathi, Tarang, Sharma, Smriti R., Singh, Vatsala, Bhargava, Palaash, Raj, Chandraditya·2025·Frontiers in Education

Abstract

Introduction: As artificial intelligence technologies rapidly enter educational settings, teachers find themselves navigating fundamental pedagogical shifts while maintaining professional agency. Despite growing AI adoption in schools, limited research examines how teachers actually experience and shape AI integration within their classroom contexts, especially in the contexts of developing countries. This study investigates teachers' beliefs about AI's educational role, their incorporation patterns, and associated challenges and opportunities. Methods: Using semi-structured interviews with 20 teachers from two private schools in Delhi, India, the paper employed thematic analysis grounded in an AI Literacy and Ecological Teacher Agency Framework. Findings/Results: Three key findings emerged: Teachers simultaneously embrace AI's potential to enhance learning and administrative efficiency while expressing deep concerns about student over-dependence and erosion of critical thinking skills. Both teachers and students demonstrate emerging technical AI competencies, but significant gaps in critical evaluation and ethical application remain, revealing a concerning literacy imbalance. Beyond surface-level tool adoption, AI integration fundamentally reshapes teachers' professional beliefs and sense of agency, prompting ongoing renegotiation of their pedagogical identities and classroom authority. Discussion: The findings demonstrate an urgent need for teacher education that centers the teacher rather than the technology and moves beyond basic technical training to embed critical thinking and ethical reasoning into AI engagement. Parallel to this, policymakers must prioritize collaborative frameworks that position teachers as partners in AI integration design rather than passive recipients of top-down mandates. The paper concludes with a call for future research to examine AI integration across diverse educational contexts to understand how institutional and socioeconomic factors shape teachers' engagement with AI technologies.

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.3389/feduc.2025.1651217

Cites

0 citation errors · 0 not checkable

Statistics

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

  • The widespread availability of GAI tools capable of generating explanations, case analyses, and personalized learning materials challenges the conventional assumption that teachers serve as the primary source of information and guidance.

    “The widespread availability of GAI tools capable of generating explanations, case analyses, and even personalized learning materials challenges the conventional assumption that teachers serve as the primary source of information and guidance [11, 12].”

    From Expanding the Roles of Medical Educators in the Generative Artificial Intelligence Era: A Qualitative Study (2026) · cites this paper as [11]

    The Introduction and context describes generative AI providing instant tutoring, and Emerging challenges in incorporating AI reports that students compare teacher-led instruction with AI-generated content, weakening teachers’ authority. Pragmatic acceptance and future-oriented AI integration mentions personalized learning materials, but as an application teachers are exploring; the paper does not establish widespread availability of case analyses or personalized materials as the cause of that shift. (LLM, conf=0.85)

    Experimental — accuracy not yet measured

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Cites

The publisher hasn’t deposited this paper’s reference list.

Research works printed but not linked: 7

  1. Chintha, K., Shetty, S., and Ramkumar, N. (2024). Overview of Innovations in AI Education in India: School, Teachers and Students. Report shared at National Leadership Conference on AI in School Education, 19-21 January 2024, Creativity Lab, Agastya International Foundation, Andhra Pradesh. Available at: https://www.researchgate. net/publication/385696777_Overview_of_Innovations_in_AI_Education_in_India_ School_Teachers_and_Students_A_report_shared_with_participants_of_National_ Leadership_Conference_on_AI_in_School_Education (Accessed May 30, 2025).
  2. Creswell, J. W., and Poth, C. N. (2018). Qualitative Inquiry and Research Design: Choosing Among Five Approaches, 4th Edn. Thousand Oaks, CA: SAGE Publications.
  3. Hobbs, R., and Moore, D. C. (2013). Discovering Media Literacy: Teaching Digital Media and Popular Culture in Elementary School. Thousand Oaks, CA: Corwin/Sage. doi: 10.4135/9781506335445 · DOI
  4. 76727 · DOI · Paper page
  5. Luckin, R., and Holmes, W. (2016). Intelligence Unleashed: An Argument for AI in Education. London: Pearson.
  6. Nyaaba, M., and Zhai, X. (2025). Developing custom GPTs for education: bridging cultural and contextual divide in generative AI [Preprint]. doi: 10.2139/ssrn.50 74403 OECD (2025). Empowering Learners for the Age of AI: An AI Literacy Framework for Primary and Secondary Education (Review draft). Paris: OECD. Available at: https://ailiteracyframework.org/wp-content/uploads/2025/05/AILitFramework_ ReviewDraft.pdf~ft.com$\pm$15 (Accessed May 30, 2025). · DOI
  7. Sarin, M. N. (2015). Quality education for all? A case study of a New Delhi government girls' secondary school post-RTE. Policy Futures Educ. 13, 17-31. doi: 10.1177/1478210315569042 · DOI

Cited by

Sorted by citations

Authors
Tripathi, T., Sharma, S. R., Singh, V., Bhargava, P., & Raj, C.
Affiliations
  • University of California San Diego (US)
  • University of Chicago (US)
Corresponding author
Tripathi, Tarang
Venue
Frontiers in Education
Published
2025-08-14
Volume
10
ISSN
2504-284X (ISSN-L)
OpenAlex
W6964790841
Cited by
51
References
0
Citations by year
2026: 45 · 2025: 6
FWCI
53.47
Type
journal article
Language
en
Open access
Locations
  • Frontiers in Education (publishedVersion, cc-by)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Funders
University of California, San Diego
Topics
  • Physical Sciences › Computer Science › Information Systems › Artificial Intelligence in Education
  • Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
  • Social Sciences › Education › Education and Technology Integration
Keywords
artificial intelligence in education, K-12 teachers, teacher agency, AI literacy, critical thinking, ethical use of AI, teacher professional development, pedagogical identity, classroom authority, AI integration in schools, developing countries, India

About these checks

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.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review