Multi-stakeholder Perspective on Responsible Artificial Intelligence and Acceptability in Education
<|reference_start|>Multi-stakeholder Perspective on Responsible Artificial Intelligence and Acceptability in Education: This study investigates the acceptability of different artificial intelligence (AI) applications in education from a multi-stakeholder perspective, including students, teachers, and parents. Acknowledging the transformative potential of AI in education, it addresses concerns related to data privacy, AI agency, transparency, explainability and the ethical deployment of AI. Through a vignette methodology, participants were presented with four scenarios where AI's agency, transparency, explainability, and privacy were manipulated. After each scenario, participants completed a survey that captured their perceptions of AI's global utility, individual usefulness, justice, confidence, risk, and intention to use each scenario's AI if available. The data collection comprising a final sample of 1198 multi-stakeholder participants was distributed through a partner institution and social media campaigns and focused on individual responses to four AI use cases. A mediation analysis of the data indicated that acceptance and trust in AI varies significantly across stakeholder groups. We found that the key mediators between high and low levels of AI's agency, transparency, and explainability, as well as the intention to use the different educational AI, included perceived global utility, justice, and confidence. The study highlights that the acceptance of AI in education is a nuanced and multifaceted issue that requires careful consideration of specific AI applications and their characteristics, in addition to the diverse stakeholders' perceptions.<|reference_end|>
@article{karran2024multi-stakeholder,
    title={Multi-stakeholder Perspective on Responsible Artificial Intelligence and
  Acceptability in Education},
    author={A.J. Karran, P. Charland, J-T. Martineau, A. Ortiz de Guinea Lopez de
  Arana, AM. Lesage, S. Senecal, P-M. Leger},
    journal={arXiv preprint arXiv:2402.15027},
    year={2024},
    archivePrefix={arXiv},
    eprint={2402.15027},
    primaryClass={cs.CY cs.AI cs.HC}
}