ai-adoption
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- 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 of Human Readiness for AI in Education against seven established technology adoption models, arguing that it offers a new organization-level perspective spanning dimensions not covered by existing accounts. It outlines discriminant-validity tests to empirically distinguish Human Readiness from related constructs. The paper contributes to theory development for AI implementation in schools.
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
- PaperComputers and Education: Artificial Intelligence22 Jun 2026
Conversational AI as a catalyst for informal learning: An empirical large-scale study on LLM use in everyday learning
Nađa Terzimehić, Babette Bühler, Enkelejda Kasneci
A survey of 776 German participants found that 88% already use LLMs for informal learning, with young digitally-engaged users leading adoption. Four learner types emerged based on tasks and devices, while users showed paradoxical trust in accuracy and privacy. Implications include designing for diverse media, collaboration, and source transparency.
Original abstract
Large language models have not only captivated the public imagination but have also sparked a profound rethinking of how we learn. In the third year following the breakthrough launch of ChatGPT, everyday informal learning has been transformed as these novel tools become easily and widely available. Who is embracing LLMs for self-directed learning, and who remains hesitant? What are their reasons for adoption or avoidance? What learning patterns emerge with this novel technological landscape? We present an in-depth analysis from a large-scale survey of 776 German participants, showcasing that 88% of our respondents already incorporate LLMs into their everyday learning routines for a wide variety of (learning) tasks. Young adults among German-based, digitally engaged users are at the forefront of adopting LLMs, primarily to enhance their learning experiences independently of time and space. Four types of learners emerge across learning contexts, depending on the tasks they perform with LLMs and the devices they use to access them. Interestingly, our respondents exhibit paradoxical behaviours regarding their trust in LLMs’ accuracy and privacy protection measures. Our implications emphasize the importance of including different media types for learning, enabling collaborative learning, providing sources and meeting the needs of different types of learners and learning by design.