technology-adoption
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- PaperEdArXiv (OSF Preprints)17 Jul 2026
Why AI Implementation Fails in Schools: The Human Readiness Gap
Dr NIRMALA KRISHNAN
A critical audit reveals that existing technology adoption models fail to explain recurring AI implementation failures in schools because they were built for bounded change episodes with fixed protocols, not for the open-ended, relational nature of AI adoption. Through case analysis of two failed large-scale initiatives at Los Angeles Unified School District, the paper identifies a 'Human Failure Chain' and a 'Human Readiness Gap' between typical pre-adoption assessments (funding, devices) and the organizational conditions needed for success. The paper proposes process-tracing studies to validate this causal model.
Original abstract
This paper conducts a critical audit of the theoretical models most often used to plan, justify, and explain artificial intelligence (AI) implementation in schools, and asks why none of them adequately explains the pattern of failure that recurs across technology generations. Individual-level acceptance theories (the Technology Acceptance Model; the Unified Theory of Acceptance and Use of Technology), diffusion theory, organization-level adoption frameworks (the Technology-Organization-Environment framework), sequential change-management models (Kotter's eight-step process), and implementation science (the Consolidated Framework for Implementation Research; the National Implementation Research Network's implementation-driver model) each illuminate part of the adoption process but were built for change episodes with properties AI implementation in schools does not share: a fixed protocol to be delivered with fidelity, a bounded start and end point, or a rational individual decision-maker as the unit of analysis. Using two documented, procurement-sound, well-resourced AI and technology initiatives at the same U.S. school district that nonetheless collapsed the Los Angeles Unified School District's 2013 one-to-one iPad programme and its 2024 “Ed” AI chatbot this paper traces a recurring causal sequence, termed here the Human Failure Chain, that existing models do not name. It argues that a Human Readiness Gap exists between what districts typically assess before AI adoption (funding, devices, vendor credentials, policy compliance) and the organizational and relational conditions that determine whether adoption succeeds. The paper does not claim to have tested this causal sequence statistically; it offers a critical synthesis and an illustrative case analysis and proposes the comparative and process-tracing studies that would be required to establish the Human Failure Chain as a validated causal model. Keywords: AI implementation failure; educational technology; implementation science; organizational readiness; technology adoption theory; school transformation
- PaperEdArXiv (OSF Preprints)17 Jul 2026
Introducing Human Readiness for AI in Education: A New Theoretical Framework for School Transformation
Dr NIRMALA KRISHNAN
This paper introduces the Human Readiness for AI in Education framework, which argues that the success of AI implementation in schools depends more on the readiness of people (shared purpose, professional capability, relational trust, collective judgement, adaptive growth) than on the technology itself. Synthesizing organizational readiness theory, technology-acceptance research, and sociotechnical systems, the framework positions human readiness as a multidimensional organizational capacity that determines whether AI amplifies or degrades educational practice. The paper presents core propositions, boundary conditions, and a research agenda for testing the construct.
Original abstract
Three decades of large-scale technology investment in schools from one-to-one laptop programmes to interactive whiteboards to, most recently, artificial intelligence (AI) has produced a consistent pattern: substantial capital expenditure paired with modest, uneven, or absent instructional change. Prevailing accounts of this pattern locate the explanation in the technology itself its cost, its usability, its alignment with curriculum and prescribe better tools, more training, or clearer policy as the remedy. This paper argues that the explanation lies elsewhere. Synthesizing organizational readiness theory, technology-acceptance research, and the sociotechnical-systems tradition with an emerging body of evidence on AI adoption in K–12 settings, the paper introduces Human Readiness for AI in Education as a conceptual framework in which the decisive variable in AI-implementation outcomes is not the technology adopted but the readiness of the people asked to adopt it. Human readiness is proposed as a multidimensional organizational capacity spanning shared purpose, professional capability, relational trust, collective judgement, and adaptive growth that determines whether a given technology amplifies or degrades educational practice. The paper positions this construct against established adoption theories, states its core propositions and boundary conditions, and specifies the evidence that would validate or falsify it. It closes with a research agenda through which the construct can be tested. Consistent with the norms of theory-building scholarship, this paper offers a conceptual foundation, not a validated instrument or an implementation methodology. Keywords: human readiness; artificial intelligence in education; technology adoption; educational leadership; organisational readiness for change; school transformation
- PaperEdArXiv (OSF Preprints)17 Jul 2026
Human Readiness as the Missing Variable in AI Adoption in Education
Dr NIRMALA KRISHNAN
This paper positions the construct 'Human Readiness for AI in Education' against seven established technology-adoption and organizational-change models, arguing it is a distinct organization-level construct spanning five dimensions. It specifies the discriminant-validity tests the construct must pass to avoid being deemed redundant, emphasizing the need for empirical validation.
Original abstract
A theoretical contribution to organizational scholarship requires more than the introduction of a new label for a familiar phenomenon; it requires showing what factors an existing account omits, how those factors relate to one another, and why the resulting explanation improves on what came before (Whetten, 1989). This paper undertakes that task for Human Readiness for AI in Education, a construct introduced in a companion paper (Krishnan, 2026a) as the primary determinant of whether artificial intelligence (AI) implementation benefits a school. Rather than introducing new theory, this paper positions Human Readiness systematically against seven established accounts of technology adoption and organizational change the Technology Acceptance Model, the Unified Theory of Acceptance and Use of Technology, Diffusion of Innovations theory, Technological Pedagogical Content Knowledge, the Substitution-Augmentation-Modification-Redefinition model, the Concerns-Based Adoption Model, and Weiner's theory of organizational readiness for change comparing each on level of analysis, outcome variable, core constructs, and explanatory scope. It argues that Human Readiness is not a relabeling of any single existing construct, but a candidate organization-level construct whose five proposed dimensions span territory these accounts divide across separate literatures. Because the value of that claim rests entirely on demonstrating that Human Readiness is empirically distinct from its nearest neighbors, the paper closes by specifying, using established construct-validation methodology (Cronbach & Meehl, 1955; Campbell & Fiske, 1959; Fornell & Larcker, 1981; MacKenzie, Podsakoff, & Podsakoff, 2011), the discriminant-validity tests the construct must pass and the outcome under which it should be judged redundant and retired. Keywords: human readiness; construct validity; technology acceptance; organizational readiness for change; discriminant validity; theory development
- NewsEdSurge15 Jul 2026
What Is the AI Cheating Panic Really About?
Microsoft's third AI in Education Report reveals that daily AI use in schools lags behind initial trial, with a drop in student optimism attributed to the typical adoption curve. Training gaps exist between what leaders perceive and what teachers/students report. The cheating panic reflects long-standing academic integrity issues rather than a unique AI threat.
Original abstract
<p>Microsoft just released the third edition of its AI in Education Report, surveying more than 3,000 students, educators and leaders across six countries. Ira Apfel sat down with <a href="https://conference.iste.org/2026/program/search/detail_presenter.php?id=118445480">Pat Yongpradit</a>, general manager of global education and workforce policy at Microsoft, which sells AI tools including Copilot to schools, at <a href="https://conference.iste.org/2026/">ISTELive 26</a> in Orlando, to talk through what the data reveals about daily use, trust, training gaps, and where the policy backlash against AI in schools may be headed next.</p><h2>Pat Yongpradit on Adoption, Trust and Cheating</h2><p>Yongpradit walks through why daily AI use in schools still lags far behind overall adoption, even though nine in 10 educators, students and leaders report having tried the tools at least once. He addresses the recent drop in student optimism, framing it as a predictable dip on a familiar technology adoption curve rather than a warning sign.</p><p>The conversation also covers the wide gap between how much training school leaders believe their staff have received and what teachers and students say they have actually experienced. Yongpradit also shares his candid take on academic integrity, arguing that the panic around cheating has more to do with long-standing temptation than with any single tool.</p><p>Listen to the episode:</p><h2>Stories Mentioned in This Episode</h2><p><a href="https://www.linkedin.com/posts/patyongpradit_2026-ai-in-education-report-ugcPost-7475673037405679616-_qCk/"><u>2026 AI in Education Report</u></a> by Microsoft</p><p><strong>This Week with EdSurge</strong> is a weekly podcast from <strong>EdSurge</strong>. Subscribe to <a href="https://www.edsurge.com/newsletters">EdSurge newsletters</a> for more news and analysis on education and technology.</p>