Reciprocal issues of artificial and human intelligence in education

With the emerging opportunities of artificial intelligence (AI), learning and teaching may be supported in situ and in real time for more efficient and valid solutions. Hence, AI has the potential to further revolutionize the integration of human and artificial intelligence and impact human and machine collaboration in learning and teaching (De Laat et al., 2020). The discourse around the utilization of AI in education shifted from being narrowly focused on automation-based tasks to augmentation of human capabilities linked to learning and teaching (Chatti et al., 2020). In this regard, in reciprocal interaction, tasks can be distributed between humans and the AI (Molenaar, 2022). As such, AI systems can analyze large datasets, including unstructured data, in real time, and can detect patterns or structures that can be used for intelligent human decision making in learning and teaching situations (Ifenthaler, 2015). For instance, recommender systems are designed to support learners in areas where the amount of data exceeds the individual’s abilities to process it and provide them with the most relevant, interesting, or useful artifacts (Verbert et al., 2012). Conventional methods, such as collaborative filtering or content-based recommender systems, assume that learners with similar preferences would favor the same things (Hemmler et al., 2022). Examples of successful adaptation include finding relevant learning content (Deschênes, 2020), entire courses (Guruge et al., 2021), or the optimal sequence of learning content and activities (Kerres & Buntins, 2020). Due to the remaining challenges of implementing meaningful AI in educational contexts, especially for more sophisticated tasks, the reciprocal collaboration of humans and AI might be a suitable approach for enhancing the capacities of both (Baker, 2016). However, understanding how AI, as a stakeholder among humans, selects and acquires data in the process of learning and knowledge creation, learns to process and forget information, and learns to share knowledge with collaborators is yet to be empirically investigated. This special issue brings together scholarly research and theory focusing on contemporary issues related to artificial and human intelligence in education and how they support students in educational settings. The contributions provide insights into how educational practice in various contexts can be augmented between AI and human stakeholders. Accordingly, this rich collection of articles critically reflects on the reciprocal issues of artificial and human intelligence in education.