SINTA

2.5

Impact

Google Scholar

10

h-index

Indonesian Journal of Education Research (IJoER)

Open Access Journal

A Documentary Evaluation of Offline AI Integration in Teacher-in-a-Box OER Programmes for Low-Connectivity Schools

Kate Huth ORCID
Griffith University ROR
Australia
Jason Zagami
Griffith University ROR
Australia
j.zagami@griffith.edu.au
Share

  • Purpose of the study: To conduct a documentary, ex ante programme evaluation of how a bounded offline AI augmentation could be specified, governed and later tested within an offline OER teacher development programme delivered through Teacher in a Box.

    Methodology: Documentary programme evaluation using a thesis corpus. Methods: Theory of Change reconstruction, CIPP evaluation framework, directed qualitative content analysis with a scope-aligned codebook, and construct-to-instrument mapping for assessment tools. Offline AI specification is GPT4All class local LLMs plus retrieval-augmented generation (RAG) over the local Teacher in a Box OER and training repository, with governance wrappers.

    Main Findings: Offline OER effectiveness depends on integrated, in-situ teacher support rather than access alone. Offline AI is most defensible as a governed, retrieval-grounded, teacher-facing layer that improves resource discovery, supports formative assessment routines, and scaffolds assessment literacy. The study outputs measurable CIPP indicators, an evidence-to-implication warrant structure, a baseline-versus-augmentation risk and safeguard matrix, and offline-feasible instruments for formative assessment, summative task quality, character education evidence, and deeper learning portfolios.

    Novelty/Originality of this study: This study provides an audit-ready, evaluation methodology linking offline OER programmes to offline AI. It advances knowledge by separating evidence, implication, and design claims; specifying a minimum viable governance package; and producing offline-feasible assessment instruments and indicators for formative, summative, character, and deeper learning assessment under low-connectivity conditions, supporting policy and implementation decisions before field trials.

  • How to cite

    [1]
    K. Huth and J. Zagami, “A Documentary Evaluation of Offline AI Integration in Teacher-in-a-Box OER Programmes for Low-Connectivity Schools”, Ind. Jou. Edu. Rsc, vol. 7, no. 4, pp. 463–479, Aug. 2026, doi: 10.37251/ijoer.v7i4.2954.

    More Citation Formats

  • 4
    Abstract views
    5
    Downloads

    Metrics — Badges

    1. K. Huth, “Maximising resource usefulness in donated materials for student achievement and teacher education,” Ph.D. dissertation, Griffith University, 2026, unpublished.
    2. L. Hosman, C. Walsh, M. Pérez Comisso, and J. Sidman, “Building online skills in off-line realities: The SolarSPELL Initiative (Solar Powered Educational Learning Library),” First Monday, vol. 25, no. 7, 2020, doi: 10.5210/fm.v25i7.10839.
    3. UNESCO, Recommendation on Open Educational Resources (OER). Paris, France: UNESCO, 2019.
    4. J. Hilton III, “Open educational resources and college textbook choices: A review of research on efficacy and perceptions,” Educational Technology Research and Development, vol. 64, pp. 573–590, 2016, doi: 10.1007/s11423-016-9434-9.
    5. Teacher in a Box, “What is Teacher in a Box? The technical stuff,” Teacher in a Box, accessed Mar. 2, 2026.
    6. D. Wiley and J. L. Hilton III, “Defining OER-enabled pedagogy,” International Review of Research in Open and Distributed Learning, vol. 19, no. 4, 2018, doi: 10.19173/irrodl.v19i4.3601.
    7. UNESCO, “UNESCO’s digital initiatives promoting linguistic diversity in Indonesia,” Feb. 1, 2024.
    8. UNESCO, “Launch of the 2023 UNESCO GEM Report: Transforming education with technology in Southeast Asia,” Feb. 28, 2025.
    9. Kementerian Pendidikan, Kebudayaan, Riset, dan Teknologi, “Perpanjangan jangka waktu pendaftaran Implementasi Kurikulum Merdeka secara mandiri tahun ajaran 2023/2024,” Sistem Informasi Kurikulum Nasional, 2023.
    10. UNESCO, “Unleashing innovation: Embracing digital transformation in education in Indonesia,” Sep. 9, 2024.
    11. Teacher in a Box, “Educational resources,” accessed Mar. 24, 2026.
    12. Teacher in a Box, “Our story,” accessed Mar. 24, 2026.
    13. D. L. Stufflebeam and C. L. Coryn, Evaluation Theory, Models, and Applications, 2nd ed. San Francisco, CA, USA: Jossey-Bass, 2014.
    14. P. Black and D. Wiliam, “Assessment and classroom learning,” Assessment in Education: Principles, Policy & Practice, vol. 5, no. 1, pp. 7–74, 1998, doi: 10.1080/0969595980050102.
    15. D. Boud and E. Molloy, “Rethinking models of feedback for learning: The challenge of design,” Assessment & Evaluation in Higher Education, vol. 38, no. 6, pp. 698–712, 2013, doi: 10.1080/02602938.2012.691462.
    16. M. Heritage, Formative Assessment: Making It Happen in the Classroom. Thousand Oaks, CA, USA: Corwin, 2010.
    17. W. J. Popham, “Assessment literacy for teachers: Faddish or fundamental?,” Theory Into Practice, vol. 48, no. 1, pp. 4–11, 2009, doi: 10.1080/00405840802577536.
    18. C. DeLuca, D. LaPointe-McEwan, and U. Luhanga, “Teacher assessment literacy: A three-dimensional model,” Teaching and Teacher Education, vol. 84, pp. 128–138, 2019, doi: 10.1016/j.tate.2019.05.003.
    19. G. P. Wiggins, Educative Assessment: Designing Assessments to Inform and Improve Student Performance. San Francisco, CA, USA: Jossey-Bass, 1998.
    20. Z. Rohmah, H. Hamamah, E. Junining, A. Ilma, and L. A. Rochastuti, “Schools’ support in the implementation of the Emancipated Curriculum in secondary schools in Indonesia,” Cogent Education, vol. 11, no. 1, Art. no. 2300182, 2024, doi: 10.1080/2331186X.2023.2300182.
    21. T. Lickona, Educating for Character: How Our Schools Can Teach Respect and Responsibility. New York, NY, USA: Bantam, 1991.
    22. M. Kristjánsson, Aristotle, Emotions, and Education. Aldershot, U.K.: Ashgate, 2007.
    23. M. W. Berkowitz and M. C. Bier, “What works in character education,” Journal of Research in Character Education, vol. 5, no. 1, pp. 29–48, 2007.
    24. Y. Anand et al., “GPT4All: An ecosystem of open source compressed language models,” in Proc. 3rd Workshop on Natural Language Processing and Open Source Software (NLP-OSS), 2023, pp. 59–64, doi: 10.18653/v1/2023.nlposs-1.7.
    25. P. Lewis et al., “Retrieval-augmented generation for knowledge-intensive NLP tasks,” arXiv preprint arXiv:2005.11401, 2020, doi: 10.48550/arXiv.2005.11401.
    26. UNESCO, Guidance for Generative AI in Education and Research. Paris, France: UNESCO, 2023, doi: 10.54675/EWZM9535.
    27. A. Tlili, B. Shehata, M. A. Adarkwah, A. Bozkurt, D. T. Hickey, R. Huang, and B. Agyemang, “What if the devil is my guardian angel: ChatGPT as a case study of using chatbots in education,” Smart Learning Environments, vol. 10, Art. no. 15, 2023, doi: 10.1186/s40561-023-00237-x.
    28. OECD, OECD Digital Education Outlook 2023: Towards an Effective Digital Education Ecosystem. Paris, France: OECD Publishing, 2023, doi: 10.1787/c74f03de-en.
    29. N. Selwyn, Should Robots Replace Teachers? AI and the Future of Education. Cambridge, U.K.: Polity Press, 2019.
    30. W. Holmes, K. Porayska-Pomsta, K. Holstein, et al., “Ethics of AI in education: Towards a community-wide framework,” International Journal of Artificial Intelligence in Education, vol. 32, pp. 504–526, 2022, doi: 10.1007/s40593-021-00239-1.
    31. V. González-Calatayud, P. Prendes-Espinosa, and R. Roig-Vila, “Artificial intelligence for student assessment: A systematic review,” Applied Sciences, vol. 11, no. 12, Art. no. 5467, 2021, doi: 10.3390/app11125467.
    32. H.-F. Hsieh and S. E. Shannon, “Three approaches to qualitative content analysis,” Qualitative Health Research, vol. 15, no. 9, pp. 1277–1288, 2005, doi: 10.1177/1049732305276687.
    33. D. B. Yarbrough, L. M. Shulha, R. K. Hopson, and F. A. Caruthers, The Program Evaluation Standards: A Guide for Evaluators and Evaluation Users, 3rd ed. Thousand Oaks, CA, USA: Corwin Press, 2010.
    34. T. Langenfeld, “Internet-based proctored assessment: Security and fairness issues,” Educational Measurement: Issues and Practice, vol. 39, no. 3, pp. 24–27, 2020, doi: 10.1111/emip.12359.
    35. D. T. K. Ng, J. K. L. Leung, S. K. W. Chu, and M. S. Qiao, “Conceptualizing AI literacy: An exploratory review,” Computers and Education: Artificial Intelligence, vol. 2, Art. no. 100041, 2021, doi: 10.1016/j.caeai.2021.100041.
    36. H. Crompton and D. Burke, “Artificial intelligence in higher education: The state of the field,” International Journal of Educational Technology in Higher Education, vol. 20, Art. no. 22, 2023, doi: 10.1186/s41239-023-00392-8.
    37. R. Cotton, P. A. Cotton, and J. R. Shipway, “Chatting and cheating: Ensuring academic integrity in the era of ChatGPT,” Innovations in Education and Teaching International, vol. 61, no. 2, pp. 228–239, 2024, doi: 10.1080/14703297.2023.2190148.
    38. J. Roe, M. Perkins, and Y. Tregubova, “The EAP-AIAS: Adapting the AI Assessment Scale for English for Academic Purposes,” TESOL Journal, vol. 17, no. 2, Art. no. e70122, 2026, doi: 10.1002/tesj.70122.
    39. M. Q. Patton, Qualitative Research & Evaluation Methods: Integrating Theory and Practice, 4th ed. Thousand Oaks, CA, USA: SAGE, 2015.
    40. T. Nazaretsky, M. Ariely, M. Cukurova, and G. Alexandron, “Teachers’ trust in AI-powered educational technology and a professional development program to improve it,” British Journal of Educational Technology, vol. 53, no. 4, pp. 914–931, 2022, doi: 10.1111/bjet.13232.
    41. M. Mangal and Z. A. Pardos, “Implementing equitable and intersectionality-aware ML in education: A practical guide,” British Journal of Educational Technology, vol. 55, no. 5, pp. 2003–2038, 2024, doi: 10.1111/bjet.13484.
    42. A. Barrett and A. Pack, “Not quite eye to A.I.: Student and teacher perspectives on the use of generative artificial intelligence in the writing process,” International Journal of Educational Technology in Higher Education, vol. 20, Art. no. 59, 2023, doi: 10.1186/s41239-023-00427-0.