Sensor-Based to Interaction-Based AI Models in Education: The JOINclusion Case Study

The integration of Artificial Intelligence (AI) in education is reshaping traditional learning environments by enabling personalized learning experiences, enhancing accessibility, and providing data-driven insights into student engagement and performance. However, the rise of AI technologies in educational settings brings complex ethical challenges, including concerns over data privacy, transparency, and equity. This paper explores the dual impact of AI on education, examining both its transformative potential and the ethical issues it presents. It discusses the role of AI models that analyse how students use the learning tools as an alternative to sensor-based approaches, which can address privacy concerns while still offering adaptive, personalized support. Furthermore, this study evaluates frameworks for ethical AI implementation, emphasizing transparency, inclusivity, and trustworthiness to support responsible AI deployment in education. By addressing key ethical considerations and regulatory standards, this paper proposes a path toward data-driven tools that enhance educational outcomes without compromising student autonomy or privacy. The findings underscore the importance of aligning AI innovations with educational values, suggesting that an ethically-centred approach to AI can promote trust and effectiveness in diverse learning contexts.