Publication
*Bingyi Han, Simon Coghlan, George Buchanan, Dana McKay (*work conducted prior to joining the group)
International Journal of Human-Computer Studies · 2026
@inproceedings{han2026ethical,
title = {Ethical gaps and power dynamics in decision-making about AI adoption: The case of AI for monitoring student learning in education},
author = {*Bingyi Han and Simon Coghlan and George Buchanan and Dana McKay (*work conducted prior to joining the group)},
booktitle = {International Journal of Human-Computer Studies},
year = {2026},
doi = {10.1016/j.ijhcs.2026.103810},
}
Adopting AI in multi-stakeholder contexts often involves complex ethical challenges, particularly when decision-makers are not directly impacted by the technology. Ethical challenges arise not only from the design of the AI technology but also from social factors in its development and deployment which may harm disempowered stakeholders. This paper examines how stakeholder dynamics in AI adoption may help to create or mitigate such harm, using a case study of AI-based student monitoring in K-12 education. These systems analyse students’ emotions, concentration, and classroom performance to inform teaching. We conducted 30 semi-structured interviews with key stakeholders: parents, school representatives, and developers. Our findings identify three key social factors that may create ethical problems in decision-making: limited representation of students’ perspectives during design and adoption; developers’ compromises and pragmatism under organisational pressure; and school governors’ limited technological literacy and failure of ‘due diligence’. These factors arise from power imbalances related to knowledge and authority that may weaken accountability mechanisms for governing AI in Education (AIEd) adoption. We propose addressing these imbalances through improving the AI ethics literacy of key stakeholders to ensure effective oversight and informed decision-making. This work contributes an empirical understanding of how real-world power relations shape ethical vulnerabilities in AIEd adoption. It extends HCI and AI ethics research by revealing how stakeholder asymmetries can marginalise affected users and constrain the practical application of ethical design principles. These insights offer guidance for more inclusive and accountable AI governance and provide insights relevant to other multi-stakeholder AI contexts.