From calculators to artificial intelligence: A multi-level framework for technology adoption and resistance in education and organisations

Michael Mncedisi Willie

doi:10.59400/cai3958

From calculators to artificial intelligence: A multi-level framework for technology adoption and resistance in education and organisations

Michael Mncedisi Willie
Council for Medical Schemes, Policy Research and Monitoring, Pretoria 0028, South Africa

Article ID: 3958

Keywords: artificial intelligence, innovation diffusion, socio-technical systems, strategic integration, technology adoption, technology resistance, workplace transformation

Abstract

Technological innovations are frequently associated with enhanced efficiency and improved decision-making; however, their initial adoption has often been characterised by notable resistance. This study examined the persistence of this phenomenon by synthesising empirical evidence from the historical adoption of calculators, spreadsheets, and statistical software, and by comparing these insights with contemporary developments in artificial intelligence (AI). The findings indicated that resistance extended beyond technical limitations and reflected a complex, multi-dimensional process shaped by individual factors such as self-efficacy, perceived usefulness, and anxieties related to skill displacement as well as organisational culture and broader systemic conditions. The evidence further demonstrated that successful adoption was contingent upon structured support mechanisms, incremental exposure to new technologies, and the clear articulation of value propositions. At the organisational level, effective leadership, adequate resource allocation, and coherent policy alignment emerged as critical enablers of sustained integration. Drawing on these insights, the study proposed a multi-level conceptual framework that integrates individual, organisational, and systemic determinants to guide future technology adoption. The framework underscores that meaningful and sustainable integration depends on coordinated, cross-level interventions rather than isolated initiatives. The framework also highlights the need for further empirical research into AI adoption in under-resourced contexts. It calls for critical engagement with the ethical implications of algorithmic decision-making in evolving socio-technical systems.

Published

2026-02-26

How to Cite

Willie, M. M. (2026). From calculators to artificial intelligence: A multi-level framework for technology adoption and resistance in education and organisations. Computing and Artificial Intelligence, 4(1). https://doi.org/10.59400/cai3958

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Vol. 4 No. 1 (2026)

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This work is licensed under a Creative Commons Attribution 4.0 International License.

References

[1]Ebrahim AQ, Van den Berg CL. The barriers to technology adoption among businesses in Cape Town's informal economy. South African Journal of Information Management. 2024; 26(1): a1872. doi: 10.4102/sajim.v26i1.1872

[2]Jr. Pimentel Singun A. Unveiling the barriers to digital transformation in higher education institutions: A systematic literature review. Discover Education. Research Square Preprint. 2024. doi: 10.21203/rs.3.rs-4970233/v1

[3]Noriega Del Valle M, Łaba K, Mayer CH. Unlocking technology acceptance among South African employees: A psychological perspective. South African Journal of Industrial Psychology. 2024; 50: a2177. doi: 10.4102/sajip.v50i0.2177

[4]Yurdunkulu A, Bulut MA, Göçen A. From academics to Aidemics: Unpacking the human–AI symbiosis in higher education. Acta Psychologica. 2025; 261: 105796. doi: 10.1016/j.actpsy.2025.105796

[5]Brandao PR. The impact of artificial intelligence on modern society. AI. 2025; 6(8): 190. doi: 10.3390/ai6080190

[6]Xiao J, Xu Z, Xiao A, et al. Overcoming barriers and seizing opportunities in the innovative adoption of next-generation digital technologies. Journal of Innovation and Knowledge. 2024; 9(4): 100622. doi: 10.1016/j.jik.2024.100622

[7]Tshabalala M, Marnewick C. Agile as an enabler towards innovation-based organisational transformations. South African Journal of Information Management. 2021; 23(1): 1–10. doi: 10.4102/sajim.v23i1.1309

[8]UNESCO. AI and the Future of Education: Disruptions, Dilemmas, and Directions. UNESCO; 2025. doi: 10.54675/KECK1261

[9]Banks SA. A Historical Analysis of Attitudes Toward the Use of Calculators in Junior High and High School Math Classrooms in the USA Since 1975 [Master's thesis]. Cedarville University; 2011.

[10]Masuadi E, Mohamud M, Almutairi M, et al. Trends in the usage of statistical software and their associated study designs in health sciences research: A bibliometric analysis. Cureus. 2021; 13(1): e12639. doi: 10.7759/cureus.12639

[11]Taber KS. Scaffolding learning: Principles for effective teaching and the design of classroom resources. In: Effective Teaching and Learning: Perspectives, Strategies and Implementation. Nova Science Publishers; 2018. pp. 1–43.

[12]Al-Haddad S, Chick N, Safi F. Teaching statistics: A technology-enhanced supportive instruction (TSI) model during the COVID-19 pandemic and beyond. Journal of Statistics and Data Science Education. 2024; 32(2): 129–142. doi: 10.1080/26939169.2024.2315939

[13]Übellacker T. Making Sense of AI Limitations: How Individual Perceptions Shape Organisational Readiness for AI Adoption. arXiv preprint. 2025. doi: 10.48550/arXiv.2502.15870

[14]Rebman CM, Booker QE, Wimmer H, et al. An industry survey of analytics spreadsheet tools adoption: Microsoft Excel vs Google Sheets. Information Systems Education Journal. 2023; 21(5).

[15]Gonçalves RA, Rodrigues SJ. Error in the technological resources used for mathematics education. Modern Sciences Journal. 2023; 12(2): 38–42.

[16]Sahin I. Detailed review of Rogers’ diffusion of innovations theory and educational technology-related studies based on Rogers’ theory. Turkish Online Journal of Educational Technology. 2006; 5(2): 14–23.

[17]Chaamwe N, Shumba L. ICT integrated learning: Using spreadsheets as tools for e-learning, a case of statistics in Microsoft Excel. International Journal of Information and Education Technology. 2016; 6(6): 435–440. doi: 10.7763/IJIET.2016.V6.728

[18]Pabilario L. Impact of technological tools on mathematics pedagogy: Data-driven insights into educators’ practices in math classrooms. Engineering Proceedings. 2025; 107(1): 5. doi: 10.3390/engproc2025107005

[19]Amaliah A, Linggi TT, Suyuthi NF. A comprehensive literature review on the factors influencing resistance to organisational change and strategies for effective management. Journal of Development Management and Economics. 2025; 3(1): 1–9. doi: 10.71435/604109

[20]Eltaiba N, Hosseini S, Okoye K. Benefits and impact of technology-enhanced learning applications in higher education in the Middle East and North Africa: A systematic review. Global Transitions. 2025; 7: 350–374. doi: 10.1016/j.glt.2025.06.004

[21]Dlamini R, Simuja C, Feldman J, et al. AI in education: Challenges, opportunities, and possibilities. Journal of Education. 2025; 98: 1–4.

[22]Abdul Hasim SN, Abu Bakar K. Challenges and impacts of technology adoption in education: A systematic literature review. International Journal of Research and Innovation in Social Science. 2025; 3(5): 7536–7548. doi: 10.47772/IJRISS.2025.903SEDU0558

[23]Rönnebeck S, Bernholt S, Ropohl M. Searching for a common ground – A literature review of empirical research on scientific inquiry activities. Studies in Science Education. 2016; 52(2): 161–197. doi: 10.1080/03057267.2016.1206351

[24]Chigbu UE, Atiku SO, Du Plessis CC. The science of literature reviews: Searching, identifying, selecting, and synthesising. Publications. 2023; 11(1): 2. doi: 10.3390/publications11010002

[25]Snyder H. Literature review as a research methodology: An overview and guidelines. Journal of Business Research. 2019; 104: 333–339.

[26]Jasti NVK, Kodali R. A literature review of empirical research methodology in lean manufacturing. International Journal of Operations and Production Management. 2014; 34(8): 1080–1122. doi: 10.1108/IJOPM-04-2012-0169

[27]Rogers EM. Diffusion of Innovations, 5th ed. Free Press; 2003.

[28]Scholkmann AB. Resistance to (digital) change: Individual, systemic and learning-related perspectives. In: Ifenthaler D, Hofhues S, Egloffstein M, et al. (editors). Digital Transformation of Learning Organisations. Springer; 2020. pp. 219–236. doi: 10.1007/978-3-030-55878-9_13

[29]Mhlongo S, Mbatha K, Ramatsetse B, et al. Challenges, opportunities, and prospects of adopting and using smart digital technologies in learning environments: An iterative review. Heliyon. 2023; 9(6): e16348. doi: 10.1016/j.heliyon.2023.e16348

[30]Sarwono J. Quantitative, Qualitative and Mixed Method Research Methodology, 1st ed. Independently published; 2022.

[31]Díaz-Arancibia J, Hochstetter-Diez J, Bustamante-Mora A, et al. Navigating digital transformation and technology adoption: A literature review from small and medium-sized enterprises in developing countries. Sustainability. 2024; 16(14): 5946. doi: 10.3390/su16145946

[32]Gill SS, Wu H, Patros P, et al. Modern computing: Vision and challenges. Telematics and Informatics Reports. 2024; 13: 100116. doi: 10.1016/j.teler.2024.100116

[33]Novianti I. An exploratory study on the challenges of AI technology in education and its practical recommendations. International Journal of Social Science Research and Review. 2025; 8(3). doi: 10.47814/ijssrr.v8i3.2481

[34]Winnberg M. The use of spreadsheet tools in assessment: an instrumented technique perspective. NOMAD Nordic Studies in Mathematics Education. 2025; 30(1): 83–102. doi: 10.7146/nomad.v30i1.152932

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