AI–teacher collaboration: professional capital, cultural values and identity grafting

Purpose This paper examines how teachers with varying levels of professional capital respond to artificial intelligence (AI) collaborations, aiming to inform policymakers and school leaders about how to communicate change more effectively. We synthesize theories of professional capital, cultural values and identity grafting (IG) to develop propositions regarding teachers' willingness and ability to engage in AI–teacher collaboration. Specifically, the study analyses how professional capital (human, social and decisional capital) and cultural values (power distance, uncertainty avoidance and risk-taking) interact to shape teachers' IG processes (blending, balancing, repressing and reversing). Design/methodology/approach Drawing on international literature on AI in education and IG constructs as analytical tools, we conducted a secondary analysis to reinterpret our previously published cluster analysis results on Hong Kong teachers. Insights from this secondary analysis informed the development of theoretical and practical implications for future research and practice of AI–teacher collaboration. The process involved: (1) analysing how profiles of professional capital and cultural values from the published results might respectively indicate willingness and ability to engage in AI–teacher collaboration; and (2) theorizing with IG constructs how these patterns might indicate processes that will shape teachers' responses to AI–teacher collaboration. Findings High-professional-capital teachers, being less hierarchical and more risk-taking, are most likely to regard AI as a collaborator, blending their identities during human–machine collaboration. However, in terms of values, their aversion to power distance can lead them to reverse from AI adoption. Medium-professional-capital teachers exemplify the risk-averse middle majority who tend to avoid uncertainty and reverse from novel ideas. They may eventually form collaborative relationships with AI if authoritative agencies provide guidance regarding the benefits. Low-professional-capital teachers are highly likely to become risk-takers who disregard the intended outcomes of AI–teacher collaboration and remain skeptical of reform initiatives. Research limitations/implications This concept paper used our previously published results on teacher collaboration as the grounds for projecting how research on AI–teacher collaboration employing our proposed conceptual framework might look like. As the previously published results were meant to illustrate how the model we propose might link present research on teacher collaboration with future research agendas on AI–teacher collaboration, future research could validate the propositions presented in this paper with updated mixed-methods datasets directly related to AI–teacher collaboration. Practical implications The study offers practical implications for schools to sustain meaningful AI–teacher collaboration in nuanced ways. For high-professional-capital teachers, integrating them in professional learning communities can empower their social capital to co-construct AI–teacher collaboration with other teachers. For medium-professional-capital teachers, low-stakes participation and scaffolded entry into AI use can build confidence in this new knowledge base, promote trust in ethical AI use and ensure that AI usage is coherent with existing teaching norms. For low-professional-capital teachers, trust-building through inclusive yet careful scaffolding of low-stakes leadership opportunities may foster higher engagement toward AI integration and strengthen organizational change capacity. Social implications We offer initial insights into how teachers with different levels of professional capital might respond to calls for AI–teacher collaboration. To address concerns regarding the social ramifications of AI–teacher collaboration, changemakers must go beyond the adopter–nonadopter divide to understand the cultural values that underpin teachers' identity processes when interpreting change. Ignoring these differences risks dividing the teaching profession and the sustainability of AI–teacher collaboration. Initiatives that disempower, force consensus, promote distrust and exclude those who do not appear compliant bring diverse social ramifications, and our conceptual framework provides plausible indications of how they might manifest. Originality/value This paper proposes a theoretically novel IG approach to future research on AI–teacher collaboration, channeling existing research on technological adoption toward understanding teacher identity formations that underpin their willingness and ability to collaborate with AI (cultural values and professional capital). It provides tailored recommendations for policymakers and school leaders on professional development in AI–teacher collaboration for teachers with varied levels of professional capital, considering their cultural values and how they might respond to such support, given their IGs.